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Issue s in Informing Science and Information Technology Volume 6, 2009

Towards a Guide for Novice Researchers on
Research Methodology:

Review and Proposed Methods

Timothy J. Ellis and Yair Levy
Nova Southeastern University

Graduate School of Computer and Information Sciences
Fort Lauderdale, Florida, USA

[email protected], [email protected]

Abstract
The novice researcher, such as the graduate student, can be overwhelmed by the intricacies of the
research methods employed in conducting a scholarly inquiry. As both a consumer and producer
of research, it is essential to have a firm grasp on just what is entailed in producing legitimate,
valid results and conclusions. The very large and growing number of diverse research approaches
in current practice exacerbates this problem. The goal of this review is to provide the novice re-
searcher with a starting point in becoming a more informed consumer and producer of research.
Toward addressing this goa l, a new system for deriving a proposed study type is deve loped. The
PLD model inc ludes the three common drivers for selection of study type : research-worthy prob-
lem (P), valid quality peer-reviewed literature (L), and data (D). The discussion inc ludes a review
of some common research types and concludes with definitions, discussions, and examples of
various fundamentals of research methods such as: a) forming research questions and hypotheses;
b) acknowledging assumptions, limitations, and delimitations; and c) establishing re liability and
validity.

Ke ywords: Research methodology, reliability, va lidity, research questions, problem directed re-
search

Introduction
The novice researcher, such as the graduate student, can be overwhelmed by the intricacies of the
research methods employed in conducting a scholarly inquiry (Leedy & Ormrod, 2005). As both
a consumer and producer of research, it is essential to have a firm grasp on just what is entailed in
produc ing legitimate, valid results and conclusions. The very large and growing number of di-
verse research approaches in current practice exacerbates this problem (Mertler & Vannatta,

2001). The goal of this review is to pro-
vide the novice researcher with a start-
ing point in becoming a more informed
consumer and producer of research in
the form of a lexicon of terms and an
analysis of the underlying constructs
that apply to scholarly enquiry, regard-
less of the specific methods employed.

Scholarly research is, to a very great
extent, characterized by the type of

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Guide for Novice Researchers on Research Methodology

324

study conducted and, by extension, the specific methods employed in conducting that type of
study (Creswell, 2005, p. 61). Novice researchers, however, often mistakenly think that, since
studies are known by how they are conducted, the research process starts with deciding upon just
what type of study to conduct. On the contrary, the type of study one conducts is based upon three
related issues: the problem driving the study, the body of knowledge, and the nature of the data
available to the researcher.

As discussed elsewhere, scholarly research starts with the identification of a tightly focused, lit-
erature supported problem (Ellis & Levy, 2008). The research-worthy problem serves as the point
of departure for the research. The nature of the research problem and the doma in from which it is
drawn serves as a limiting factor on the type of research that can be conducted. Nunamaker,
Chen, and Purdin (1991) noted that “It is clear that some research domains are sufficiently nar-
row that they allow the use of only limited methodologies” (p. 91). The problem also serves as
the guidance system for the study in that the research is, in essence, an attempt to, in some man-
ner, develop at least a partial solution to the research problem. The best design cannot provide
meaning to research and answer the question ‘Why was the study conducted,’ if there is not the
anchor of a clearly identified research problem.

The body of knowledge serves as the foundation upon which the study is built (Levy & Ellis,
2006). The literature also serves to channel the research, in that it indicates the type of study or
studies that are appropriate based upon the nature of the problem driving the study. Likewise, the
literature provides clear guidance on the specific methods to be followed in conducting a study of
a given type. Although origina lity is of great value in scholarly work, it is usually not rewarded
when applied to the research methods. Ignoring the wisdom contained in the existing body of
knowledge can cause the novice researcher, at the least, a great deal of added work establishing
the validity of the study.

From an entirely practical perspective, the nature of the data available to the researcher serves as
a final filter in determining the type of study to conduct. The type of data available should be
considered a necessary, but certainly not sufficient, consideration for selecting research methods.
The data should never supersede the necessity of a research-worthy problem serving as the anchor
and the existing body of knowledge serving as the foundation for the research. The absence of the
ability to gather the necessary data can, however, certainly make a study based upon research
methods directly driven by a well-conceived problem and supported by current literature com-
plete ly futile. Every solid research study must use data in order to validate the proposed theory.
As a result, novice researchers should understand the centrality of access to data for their study
success. Access to data refers to the ability of the researcher to actually collect the desired data
for the study. Without access to data, it is impossible for a researcher to make any meaningful
conclusions on the phenomena. Novice researchers should be aware that their access to data will
also imply what type of methodology they will be using and what type of research, eventually
they will conduct. Figure 1 illustrates the interaction among the research-worthy problem (P), the
existing body of knowledge documented in the peer-reviewed literature (L), and the data avail-
able to the researcher (D). The research-worthy problem (P) serves as the input to the process of
selecting the appropriate type of research to conduct; the valid peer-reviewed literature (L) is the
key funnel that limits the range of applicable research approaches, based on the body of knowl-
edge; the data (D) available to the researcher serves as the final filter used to identify the specific
study type.

The balance of this paper explores the constructs underlying scholarly research in two aspects.
The first section examines some of the types of studies most commonly used in information sys-
tems research. The second section explores vital considerations for research methods that apply
across all study types.

Ellis & Levy

325

Study type

Data available

Valid peer-reviewed
literature

Research-worthy
problem

Figure 1. The PLD Mode l for De riving Study Type

Types of Research
There are a number of different ways to distinguish among types of research. The type of data
available is certainly one vita l aspect (Gay, Mills, & Airasian, 2006; Leedy & Ormrod, 2005);
different research approaches are appropriate for quantitative data – precise, numeric data derived
from a reduced variable – than for qualitative data – complex, multidimensional data derived
from a natural setting. Of equal importance is the nature of the problem be ing addressed by the
research (Isaac & Michael, 1981). Some problems, for example, are relative ly new and require

Table 1: Ke y Categories of Research

Approach Mos t common type of data Stage of proble m Cate gories of
The ory

Experimental Quantitative Evaluation Testing or
revising

Causal-comparative Quantitative Evaluation Testing or
revising

Historical Quantitative or Qualitative Description Testing or
revising

Developmenta l Quantitative and qualitative Description Building or
revising

Correlational

Quantitative Description Testing

Case study Qualitative Exploration Building or
revising

Grounded theory

Qualitative Exploration Building

Ethnography

Qualitative Descriptive Building

Action research Quantitative and qualitative Applied exploration Building or
revising

Guide for Novice Researchers on Research Methodology

326

exploratory types of research, while more mature problems might better be addressed by descrip-
tive or evaluative (hypothesis testing) approaches (Sekaran, 2003). Table 1 presents an overview
of how the research approaches most commonly used in information systems (IS) are categorized.
The subsections following Table 1 briefly explore each of these major research approaches. In
general, research studies can be classified into three categories: theory building, theory testing,
and theory revising. Theory building refers to research studies that aim at building a theory where
no prior solid theory existed to expla in phenomena or specific scenario. Theory testing refers to
research studies that aim at validating (i.e. testing) existing theories in new context. Theory revis-
ing refers to research studies that aim at revising an existing theory.

Experimental
The essence of experimental research is determining if a cause-effect relationship exists between
one factor or set of factors – the independent variable(s) – and a second factor or set of factors –
the dependent variable(s) (Cook & Campbell, 1979). In an experiment, the researcher takes con-
trol of and manipulates the independent variable, usua lly by randomly assigning partic ipants to
two or more different groups that receive different treatments or implementations of the inde-
pendent variable. The experimenter measures and compares the performance of the participants
on the dependent variable to determine if changes in the independent variables are very like ly to
cause similar changes in performance on the dependent variable. In medical settings, this type of
research is very common. However, in many research fields it is somewhat difficult to control all
the variables in the experiments, especially when dealing with research area that is related to or-
ganizations and institutions. For that reason, the use of experiments in IS it is somewhat limited,
and a less restrictive type of experiments is used. Such type is called quasi-experiment (Cook &
Campbell, 1979). Similar to experiments, in quasi-experiments, the research is attempting to de-
termining if a cause-effect relationship exists between one factor or set of factors – the independ-
ent variable(s) – and a second factor or set of factors – the dependent variable(s). However in
quasi-experiments, the researcher is unable to control a ll the variables in the experimentation, but
most variables are controlled.

An example of an experimental study would be research into which of two methods of inputting
text in a personal digita l assistant, soft-key or handwriting recognition, is more accurate. The in-
dependent variable would be method of text input. The dependent variable might be a count of
the number of entry errors, and the comparison based on the mean of the group using the soft-key
method with the mean of the group that used handwriting recognition input. An example of ex-
perimenta l research can be found at Cockburn, Savage, and Wallace (2005).

Causal-Comparative
As with experimental studies, causal-comparative research focuses on determining if a cause-
effect relationship exists between one factor of set of factors – the independent variable(s) – and a
second factor or set of factors – the dependent variable(s). Unlike an experiment, the researcher
does not take control of and manipulate the independent variable in causal-comparative research
but rather observes, measures, and compares the performance on the dependent variable or vari-
ables of subjects in naturally-occurring groupings based on the independent variable.

An example of a causal-comparative study would be research into the impact monetary bonuses
had on knowledge sharing behavior as exhibited by contributions to a company knowledge bank.
The independent variable would be “monetary bonus,” and it might have two levels (i.e. “yes”
and “no”). The dependent variable might be a count of the number of contributions, and the com-
parison based upon an examination of the mean number of knowledge-base contributions made
per employee in companies that provided a monetary bonus versus the mean number of contribu-
tions made per employee in companies that did not provide a bonus. Since the researcher did not

Ellis & Levy

327

assign companies to the “bonus” or “no bonus” categories, this study would be causal compara-
tive , not experimental. An example of causal-comparative research can be found at Becerra-
Fernandez, Zanakis, and Walczak (2002) who deve loped a knowledge discovery technique using
neural network mode ling to classify a country’s investing risk based on a variety of independent
variables.

Case Study
A case study is “an empirica l inquiry that investigates a contemporary phenomenon within its real
life context using multiple sources of evidence” (Noor, 2008, p. 1602). The evidence used in a
case study is typically qualitative in nature and focuses on developing an in-depth rather than
broad, generalizable understanding. Case studies can be used to explore, describe, or explain phe-
nomena by an exhaustive study within its natural setting (Yin, 1984). An example of a case study
can be found in the study by Ramim and Levy (2006) who described the issues related to the im-
pact of an insider’s attack combined with novice management on the survivability of an e-
learning systems of a small university.

Historical
Historical research utilizes interpretation of qua litative data to explain the causes of change
through time. This type of research is based upon the recognition of a historical problem or the
identification of a need for certain historical knowledge and generally enta ils gathering as much
relevant information about the problem or topic as possible. The research usually begins with the
formation of a hypothesis that tentatively explains a suspected relationship between two or more
historical factors and proceeds to a rigorous collection and organization of usua lly qua litative
evidence. The verification of the authentic ity and validity of such evidence, together with its se-
lection, organization, and analysis forms the basis for this type of research. An example of his-
torical research can be found in the study by Grant and Grant (2008) who conducted a study to
test the hypothesis that a new generation in knowledge management was emerging.

Correlational
The primary focus of the correlationa l type of research is to determine the presence and degree of
a relationship between two factors. Although correlationa l studies are in a superficia l way similar
to causal-comparative research – both types of study focus on analyzing quantitative data to de-
termine if a relationship exists between two variables – the difference between the two cannot be
ignored. Unlike causal-comparative research, in correlationa l studies, there is no attempt to de-
termine if a cause-effect relationship exists (variable x causes changes in variable y). The goal for
correlational studies is to determine if a predictive relationship exists (knowing the value of vari-
able x allows one to predict the value of variable y). At a practical level, there is, therefore, no
distinction made between independent and dependent variables in correlationa l research.

An example of a simple correlationa l study would be research into the relationship between age
and willingness to make e-commerce purchases. The two variables of interest would be age and
number of e-commerce purchases made over a given period of time. The comparison would be
based upon an examination of age of each subject in the study and the number of e-commerce
purchases made by that subject. Since the researcher did not control either of the variables or at-
tempt to determine if age caused changes in purchases, just if age could be used to predict behav-
ior, the study would be correlationa l, not experimental or causal-comparative. An example of cor-
relational research can be found in Cohen and Ellis (2003).

Guide for Novice Researchers on Research Methodology

328

Developmental
Developmenta l research attempts to answer the question: How can researchers build a ‘thing’ to
address the problem? It is especially applicable when there is not an adequate solution to even test
for efficacy in addressing the problem and presupposes that researchers don’t even know how to
go about building a solution that can be tested. Developmenta l research generally entails three
major elements:

• Establishing and validating criteria the product must meet

• Following a formalized, accepted process for developing the product

• Subjecting the product to a formalized, accepted process to determine if it satis-
fies the criteria.

An example of developmental research would be Ellis and Hafner (2006) that detailed the devel-
opment of an asynchronous environment for project-based collaborative learning experiences.
Developmenta l research is distinguished from product development by: a focus on complex, in-
novative solutions that have few, if any, accepted design and development princ iples; a compre-
hensive grounding in the literature and theory; empirical testing of product’s practicality and ef-
fectiveness; as well as thorough documentation, analysis, and reflection on processes and out-
comes (van den Akker, Branch, Gustafson, Nieveen, & Plomp, 2000).

Grounded Theory
Grounded Theory is defined as “a systematic, qua litative procedure used to generate theory that
expla ins, at a broad conceptual level, a process, an action, or interaction about substantive topic”
(Creswell, 2005, p. 396). Grounded theory is used when theories currently documented in the lit-
erature fail to adequately expla in the phenomena observed (Leedy & Ormrod, 2005). In such
cases, revisions for existing theory may not be valid as the fundamental assumptions behind such
theories may be flawed given the context or data at hand. Table 2 outlines the three key types of
grounded theory design. According to Creswell, “choosing among the three approaches requires
several considerations” (p. 403). He noted that such considerations depend on the key emphasis
of the study such as: Is the aim of the study to follow given procedures? Is the aim of the aim of
the study to follow predetermined categories? What is the position of the researcher? An example
of Grounded Theory in the context of information systems inc ludes the study by Oliver, Why-
mark, and Romm (2005). Oliver et al. used Grounded Theory to deve lop a conceptual mode l on
enterprise-resources planning (ERP) systems adoption based on the various types of organiza-
tional justifications and reported motives.

Table 2: Types of Grounde d Theory Des ign (Cres we ll, 2005)

Type of Grounde d Theory
Des ign

De finition

Systematic Design “emphasizes the use of data analysis steps of open, axia l, and
selective coding, and the development of a logic paradigm or a
visua l picture of the theory generated” (Creswell, 2005, p. 397)

Emerging Design “letting the theory emerge from the data rather than using spe-
cific, preset categories” (Creswell, 2005, p. 401)

Constructivist Design “focus is on the meanings ascribed by partic ipants in a
study…more interested in the views, values, beliefs, feelings,
assumptions, and ideologies of individuals than in gathering
facts and describing acts” (Creswell, 2005, p. 402)

Ellis & Levy

329

Ethnography
The study of ethnography aims at “a particular person, program, or event in considerable depth.
In an ethnography, the researcher looks at an entire group – more specifica lly, a group that shares
a common culture – in depth” (Leedy & Ormrod, 2005, p. 151). According to Creswell (2005),
ethnographic research deals with an in-depth qua litative investigation of a group that share a
common culture. He indicated that ethnography is best used to explain various issues within a
group of individuals that have been together for a considerable length of time and have, therefore,
developed a common culture. Ethnographic research also provides a chronological collection of
events related to a group of individuals sharing a common culture. Beynon-Davies (1997) out-
lined the use of ethnographic research in the context of system development. He noted that for IS
researchers, ethnographic research may provide value in the area of IS development, specifically
in the process of capturing tacit knowledge during the system development life cycle (SDLC)
(Beynon-Davies). Crabtree (2003) noted that “ethnography is an approach that is increasing inter-
est to the designers of collaborative computing systems. Rejecting the use of theoretical frame-
works and insisting instead on a rigorously descriptive mode of research” (p. ix). However, criti-
cism for Crabtree’s advocacy of ethnography in information systems research was also voiced
(Alexander, 2003).

Action Research
Action research is defined as “a type of research that focuses on finding a solution to a local prob-
lem in a local setting” (Leedy & Ormrod, 2005, p. 114). Action research is unique in the approach
as the researcher himse lf or herself are part of the practitioners group that face the actual problem
the research is trying to address(Creswell, 2005). Additionally, the aim of action research is to
investigate a localized and practical problem. According to DeLuca, Gallivan, and Kock (2008),
there are five key steps in action research including: a) Diagnosing the problem; b) Planning the
action; c) Taking the action; d) Eva luating the results; and e) Specifying lessons learned for the
next cycle. During the course of all give steps of the action research, “researchers and practitio-
ners collaborate during each step” (DeLuca et al., p. 49).

Fundamentals of Research Methods
For each study type there is an accepted methodology documented in texts (Gay et al., 2006;
Isaac & Michael, 1981; Leedy & Ormrod, 2005; Yin, 1984) and exemplified in the literature
(Levy & Ellis, 2006). As a first step in establishing the value of a proposed study, the novice re-
searcher is well advised to close ly follow the template for the study type contained in the text and
mode l her or his research methods after similar studies reported in the literature. Regardless of the
type of study being conducted, there are a number of important factors that must be accommo-
dated in an effective description of the research methods. In brief, the description must provide a
detailed, step-by-step description of how the study will be conducted, answering the vital “who,
what, where, when, why, and how” questions.

1. What is going to be done

2. Who is going to do each thing to be done

3. How will each thing to be done be accomplished

4. When, and in what order, will the things to be accomplished actually be done

5. Where will those things be done

6. Why – supported by the literature – for the answers to the What, Who, How,
When, and Where

Guide for Novice Researchers on Research Methodology

330

A properly developed description of the research methods would allow the reader to actually con-
duct the study being proposed based upon the processes outlined. Included among those processes
are: forming research questions and hypotheses; identifying assumptions, limitations, and de limi-
tations; as well as establishing reliability and validity.

Form Research Questions and Hypotheses

Research questions
Research questions are the essence of most research conducted and acts as the guiding plan for
the investigation (Mertler & Vannatta, 2001). In general, research questions are “specific ques-
tions that researchers seek to answer” (Creswell, 2005, p. 117). According to Maxwell (2005),
“research questions state what you want to learn” (p. 69). A research investigation may have one
or more research questions regardless of the specific type of the research including qua litative,
quantitative, and mixed method types of research. Most quality peer-reviewed studies will have a
specific section that highlights the research questions investigated. In most other published work
that don’t have a specific section that highlights it, the research questions will appear either at the
end of the problem statement or right after the literature review section. Maxwell suggested that a
good research question is one that will point the researcher to the information that will lead
him/her to understand what he/she set forth to investigate. According to Ellis and Levy (2008),
“in order for the research to be at all meaningful, there has to be an identifiable connection be-
tween the answers to the research questions and the research problem inspiring the study” (p. 20).
However, research questions shouldn’t be created in a vacuum, but be strongly influenced by
quality literature is suggesting about the phenomena (Berg, 1998). Moreover, the exact wording
used to note the research questions is vital as the accuracy and appropriateness of the research
question determine the methodology to be used (Mertler & Vannatta, 2001).

The nature of the research questions will be dependent on the type of study being conducted.
Studies based on quantitative data will generally be driven by research questions that are formu-
lated on the confirmatory and predictive nature, while studies based on qualitative data will be
more like ly driven by research questions that are formulated on the exploratory and interpretive
nature.

Examples of quantitative research questions in the context of information systems inc lude :

– To what extent does users’ perceived usefulness increases the odds of their e-commerce
usage?

– Do computer self-efficacy and computer anxiety have a significant difference for males
and females when using e-learning systems?

– What are the contributions of users’ systems trust, deterrent severity, and motivation to
their misuse of biometrics technology?

– To what degree do team communication and team cohesiveness predict productivity of
system development by virtual teams?

Examples of qualitative research questions in the context of information systems include:

– How does training help the implementation success of enterprise-wide information sys-
tems?

– Why do user involvement and user resistance help in the systems’ requirement gathering
process?
What are the systems characteristics that are valuable to users when using e-learning
systems?

– How do e-commerce users define information privacy?

Ellis & Levy

331

Hypotheses
One must keep in mind that “research questions are not the same as research hypotheses”
(Maxwell, 2005, p. 69). In general, a hypothesis can be defined as a “logical supposition, a rea-
sonable guess, an educated conjecture” about some aspect of daily life (Leedy & Ormrod, 2005,
p. 6). In scholarly research, however, hypotheses are more than ‘educated guesses.’ A research
hypothesis is a “prediction or conjecture about the outcome of a relationship among attributes or
characteristics” (Creswell, 2005, p. 117). By convention, research is conservative and assumes
the absence of a relationship among the attributes under consideration; hypotheses, therefore, are
expressed in null terms. For example, if a study were to examine the impact interactive multime-
dia animations have on the average amount of a purchase at an e-commerce site, the hypothesis
would be stated: The average amount of purchase on an e-commerce site enhanced with interac-
tive animations will not be different that the average amount of purchase on the same e-
commerce site that is not enhanced with interactive animations. Not all types of research entail
establishing and testing hypotheses. Research methods based upon quantitative data commonly
test hypotheses; studies based upon qualitative data, on the other hand, explore propositions
(Maxwell).

Unlink hypotheses, propositions do predict a directiona lity for the results. If, for example, one
were to examine consumer reaction to interactive animation on an e-commerce site, one might
investigate the proposition that: Consumers will express a greater feeling of engagement and sat-
isfaction when visiting e-commerce sites enhanced with interactive animations than similar sites
that lack the enhancement.

Acknowledge Assumptions, Limitations, and Delimitations
For any given research investigation there are underlying assumptions, limitations, and de limita-
tions (Creswell, 2005). According to Leedy and Ormrod (2005), assumptions, limitations, and
delimitations are critical components of a viable research proposal; without these considerations
clearly articulated, evaluators may raise some valid questions regarding the credibility of the pro-
posal. The following three sub-sections provide definition and examples for each term.

Assumptions
Assumptions serve as the basic foundation of any proposed research (Leedy & Ormrod, 2005)
and constitute “what the researcher takes for granted. But taking things for granted may cause
much misunderstanding. What [researchers] may tacitly assume, others may never have consid-
ered” (Leedy & Ormrod, p. 62). Moreover, assumptions can be viewed as something the re-
searcher accepts as true without a concrete proof. Essentially, there is no research study without a
basic set of assumptions (Berg, 1998). According to Williams and Colomb (2003), identifying the
assumptions behind a given research proposal is one of the hardest issues to address, especially
for novice researchers. Such difficulties emerge due to the fact that by nature “we all take our
deepest beliefs for granted, rarely questioning them from someone else’s point of view”
(Williams & Colomb, p. 200). It is important for novice researchers to learn how to explic itly
document the ir assumptions in order to ensure that they are aware of those things taken as givens,
rather than trying to hide or smear them from the reader. Explicitly documenting the research as-
sumptions may help reduce misunderstanding and resistance to a proposed research as it demon-
strates that the research proposal has been thoroughly considered (Leedy & Ormrod, 2005).

To identify the assumption behind a proposa l, the researcher must ask himself the following ques-
tion: “what do I believe that my readers must also believe (but may not) before they will think
that my reasons are relevant to my claims?” (Williams & Colomb, p. 200).

Guide for Novice Researchers on Research Methodology

332

Examples of assumptions researchers make include :

– Participants in the study will make a sincere effort to complete the assigned tasks

– The students participating in the Internet-based course have a basic familiarity with the
personal computer and the use of the World Wide Web.

Limitations
Every study has a set of limitations (Leedy & Ormrod, 2005), or “potential weaknesses or prob-
lems with the study identified by the researcher” (Creswell, 2005, p. 198). A limitation is an un-
controllable threat to the internal va lidity of a study. As described in greater detail below, internal
validity refers to the likelihood that the results of the study actually mean what the researcher in-
dicates they mean. Explic itly stating the research limitations is vita l in order to allow other re-
searchers to replicate the study or expand on a study (Creswell, 2005). Additionally, by explicitly
stating the limitations of the research, a researcher can help other researchers “judge to what ex-
tent the findings can or cannot be generalized to other people and situations” (Creswell, 2005, p.
198).

Examples of limitations researchers may have:

– All subjects in the study will be volunteers who may withdraw from the study at any
time. The participants who finish the study might not, therefore, be truly representative of
the population.

– The members of the expert panel that will validate the proficiency survey instrument will
be drawn from the faculty of … and may not truly represent universally accepted expert
opinion.

Delimitations
Delimitations refer to “what the researcher is not going to do” (Leedy & Ormrod, 2005). In schol-
arly research, the goals of the research outlines what the researcher intends to do; without the de-
limitations, the reader will have difficulties in understanding the boundaries of the research. In
order to constrain the scope of the study and make it more manageable, researchers should outline
in the de limitations – the factors, constructs, and/or variables – that were intentiona lly left out of
the study. Delimitations impact the external validity or generalizability of the results of the study.
Examples of delimitations inc lude :

– Participation in the study was delimited to only males aged 25-45 who had made a pur-
chase via the internet within the past 12 months; generalization to other age groups or
females may not be warranted.

– This study examined attrition rates in MBA programs offered in continuing education de-
partments of public colleges and universities; generalization to other educationa l pro-
grams or similar programs offered in private institutions may not be warranted.

Establish Reliability and Validity
Every study must address threats to validity and reliability (Leedy & Ormrod, 2005). Although
the concepts of validity and reliability originally started in quantitative research approaches, in
recent years validity and reliability are being addressed in qualitative and mixed-methods ap-
proaches as well (Berg, 1998; Maxwell, 2005). According to Leedy and Ormrod (2005), “the va-
lidity and reliability of your measurement instruments influences the extent to which you can
learn something about the phenomenon you are studying…and the extent to which you can draw
meaningful conclusions from your data” (p. 31). The following two sections define and outline

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the key types of validity and reliability re lated to common research investigation. Establishing an
approach following published methods to address validity and reliability issues in a research pro-
posal may drastically increase the overall acceptance of the research proposal.

Reliability
Reliability is defined as “the consistency with which a measuring instrument yie lds a certain re-
sults when the entity be ing measured hasn’t changed” (Leedy & Ormrod, 2005, p. 31). According
to Straub (1989), researchers should try to answer the following question in an attempt to address
reliability; “do measures show stability across the unit of observation? That is, could measure-
ment error be so high as to discredit the findings?” (p. 150). Reliability can be established in four
different ways: equivalency, stability, inter-rater, and interna l consistency (Carmines & Zeller,
1991).

Equivale ncy re liability. Equivalency reliability is concerned with how close ly measurements
taken with one instrument match those taken with a second instrument under similar conditions.
Equiva lency is often used to certify the reliability of a new measurement instrument or procedure
by comparing the results of using that instrument with those obta ined by using established in-
struments or processes. Equiva lency is usua lly established through the use of a statistical correla-
tion (Pearson’s r for linear correlation or Eta for non-linear correlation).

Stability re liability. Stability reliability – also know as test, re-test reliability – is concerned with
how consistent results of measuring with a given instrument or process are over time. Stability is
based on the assumption that, absent some identifiable explanation, the measurement should pro-
duce the same results today as last month and will produce the same results next month. Stability,
like equiva lency, is usually established through the use of a statistical correlation (Pearson’s r for
linear correlation or Eta for non-linear correlation).

Inte r-rate r re liability. Inter-rater reliability focuses on the extent agreement in the results of two
or more individuals using the same measurement instrument or process. As with stability and
equivalency, inter-rater reliability is usua lly established through the use of a statistical correlation
(Pearson’s r for linear correlation or Eta for non-linear correlation).

Inte rnal cons iste ncy. Unlike the previous methods of establishing reliability which were con-
cerned with comparing the results of using an instrument or process with some external standard
(another instrument, the same instrument over time, or the same instrument used by different
people), internal consistence focuses on the level of agreement among the various parts of the
instrument or process in assessing the characteristic being measured. In a 20-question survey
measuring attitude toward knowledge sharing, for example , if the survey is interna lly consistent,
there will be a strong correlation the responses on all 20 questions. Internal consistency is also
measured by statistical correlation, but with the Cronbach α in place of Pearson r.

Validity
Validity refers to a researchers’ ability to “draw meaningful and justifiable inferences from scores
about a sample or population” (Creswell, 2005, p. 600). There are various types of validity asso-
ciated with scholarly research (Cook & Campbell, 1979). Validity of an instrument refers to “the
extent to which the instrument measures what it is supposed to measure” (Leedy & Ormrod,
2005, p. 31). Thus, researchers when designing the ir study, must ask themselves “how might you
be wrong?” (Maxwell, 2005, p. 105). Additionally, the validity of a study “depends on the rela-
tionship of your conclusions to reality” (Maxwell, 2005, p. 105). This section will define and out-
line the key validity issues. The two most common validity issues are internal validity and exter-
nal validity.

Guide for Novice Researchers on Research Methodology

334

Inte rnal validity. Internal validity refers to the “extent to which its design and the data that it
yie lds allow the researcher to draw accurate conclusions about cause-and-effect and other rela-
tionships within the data” (Leedy & Ormrod, 2005, pp. 103-104). According to Straub (1989),
researchers should try to answer the following question in an attempt to address internal va lidity;
“are there untested rival hypotheses for the observed effects?” (p. 150). Generally, establishing
interna l validity requires examining one or more of the following: face validity, criterion va lidity,
construct validity, content validity, or statistical conclusion va lidity.

Face Validity. Face validity is based upon appearance; does the instrument or process seem to
pass the test for reasonableness. Face validity is never sufficient by itself, but an informa l assess-
ment of how well the study appears to be designed is often the first step in establishing its va lid-
ity.

Crite rion Re lated Validity. Also known as instrumental va lidity, criterion related validity is
based upon the premise that processes and instruments used in a study are valid if they paralle l
similar those used previous, validated research. In order to establish criterion re lated validity it is
necessary to draw strong parallels between as many particulars of the validated study – popula-
tion, c ircumstances, instruments used, methods followed – as possible.

Cons truct Validity. Construct validity “is in essence operational issue. It asks whether the meas-
ures chosen are true constructs describing the event or merely artifacts of the methodology itself”
(Straub, 1989, p. 150). According to Straub, researchers should try to answer the following ques-
tion in an attempt to address construct validity; “do measures show stability across methodology?
That is, are the data a reflection of true scores or artifacts of the kind of instrument chosen?” (p.
150).

Conte nt Validity. In survey-based research, the term content validity refers to “the degree to
which items in an instrument reflect the content universe to which the instrument will be general-
ized” (Boudreau, Gefen, & Straub, 2001, p. 5). According to Straub (1989), researchers should
try to answer the following question in an attempt to address content validity; “are instrument
measures drown from all possible measures of the properties under investigation?” (p. 150).

Statis tical Conclus ion Validity. Statistical conc lusion va lidity refers to the “assessment of the
mathematical relationships between variables and the like lihood that this mathematical assess-
ment provides a correct picture of the covariation …(Type I and Type II error)” (Straub, 1989, p.
152). According to Straub, researchers should try to answer the following question in an attempt
to address statistical conc lusion va lidity; “do the variables demonstrate relationships not expla in-
able by chance or some other standard of comparison?” (p. 150).

Exte rnal validity. External validity refers to the “extent to which its results apply to situations
beyond the study itself…the extent to which the conclusions drawn can be generalized to other
contexts” (Leedy & Ormrod, 2005, p. 105). Additionally, external validity addresses the “gener-
alizability of sample results to the population of interest, across different measures, persons, set-
tings, or times. External validity is important to demonstrate that research results are applicable in
natural settings, as contrasted with classroom, laboratory, or survey-response settings” (King &
He, 2005, p. 882).

Summary
One of the major challenges facing the novice researcher is matching the research she or he pro-
poses with a research method that is appropriate and will be accepted by the scholarly commu-
nity. The material presented in this paper is certainly not intended to be the ending point in the
process of establishing the research methods for a given study. The novice researcher is encour-

Ellis & Levy

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aged, even expected to augment this material by referring to one or more of the texts and research
examples cited.

This paper does present a foundation upon which such a decision can be based on:

1. Developing the PLD, a model for selection of research approach based upon the
problem driving the study, the body of knowledge documented in peer-reviewed lit-
erature, and the data available to the researcher;

2. Identifying, in brief, several of the research approaches commonly used in informa-
tion systems studies;

3. Exploring several of the important terms and constructs that apply to scholarly re-
search, regardless of the specific approach selected.

References
Ale xander, I. (2003). Designing collaborative systems. A practica l guide to ethnography. European Journal

of In formation Systems, 12(3), 247-249.

Becerra-Fe rnandez, I., Zanakis, S. H., & Walc za k, S. (2002). Knowledge discovery techniques for predict-
ing country investment risk. Computers & Industrial Engineering, 43(4), 787-800.

Berg, B. L. (1998). Qualitative research methods for the social sciences (3rd ed.). Boston, MA: Allyn &
Bacon.

Beynon-Davies, P. (1997). Ethnography and information systems development: Ethnography of, for and
within is development. Information and Software Technology, 39(8), 531-540.

Boudreau, M.-C., Gefen, D., & Straub, D. W. (2001). Validation in information systems research: A state-
of-the-art assessment. MIS Quarterly, 25(1), 1-16.

Cockburn, A., Savage, J., & Wallace, A. (2005). Tuning and testing scrolling interfaces that automatically
zoo m. Proceeding of the Computer-Human Interaction 2005 Conference, Portland, Oregon, pp. 71-80.

Cohen, M. S., & Ellis, T. J. (2003). Predictors of success: A longitudinal study of threaded discussion fo-
rums. Proceeding of the Frontiers in Education Conference, Boulder, Colorado, pp. T3F-14–T13F-18.

Cook, T. D., & Ca mpbell, D. T. (1979). Quasi-experimentation: Design & analysis issues from field set-
tings. Boston, MA: Houghton Mifflin Co mpany.

Crabtree, A. (2003). Designing collaborative systems. A practical guide to ethnography. Berlin: Springer-
Verlag.

Creswe ll, J. W. (2005). Educational research: Planning, conducting, and evaluating quantitative and
qualitative research (2nd ed.). Upper Saddle River, NJ: Pearson.

De Luca, D., Ga llivan, M. J., & Kock, N. (2008). Furthering information systems action research: A post-
positivist synthesis of four dia lectics. Journal of the Association for Information Systems, 9(2), 48-72.

Ellis, T. J., & Ha fner, W. (2006). A co mmunicat ion environment for asynchronous collaborative lea rning.
Proceeding of the 37th Hawaii International Conference on System Sciences, Big Island, Hawa ii, pp.
3a-3a.

Ellis, T. J., & Levy, Y. (2008). A fra mework of proble m-based research: A guide for novice researchers on
the development of a research-worthy proble m. In forming Science: The International Journal of an
Emerging Transdiscipline, 11, 17-33. Retrieved fro m http://inform.nu/Artic les/Vol11/ISJv 11p 017-
033Ellis486.pdf

Gay, L. R., Mills, G. E., & Airasian, P. (2006). Educational research: Competencies for analysis and ap-
plications (8th ed.). Upper Saddle River, NJ: Pearson.

Guide for Novice Researchers on Research Methodology

336

Grant, K. A., & Grant, C. T. (2008). Developing a mode l of ne xt generation knowledge manage ment. Is-
sues in Informing Science and Information Technology, 5, 571– 590. Retrieved fro m
http://proceedings.informingscience.org/InSITE2008/IISITv5p571-590Grant532.pdf

Isaac, S., & M ichael, W. B. (1981). Handbook in research and evaluation. San Diego, CA: EdITS publish-
ers.

King, W. R., & He, J. (2005). Externa l validity in IS survey research. Communications of the Association
for In formation Systems, 16, 880-894.

Leedy, P. D., & Ormrod, J. E. (2005). Practical research: Planning and design (8th ed.). Upper Saddle
River, NJ: Prentice Hall.

Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature rev iew in support of
informat ion systems research. Informing Science: The International Journal of an Emerging Transdis-
cipline, 9, 181-212. Retrieved fro m http://inform.nu/Artic les/Vol9/ V9p181-212Levy99.pdf

Maxwe ll, J. A. (2005). Qualitative research design: An interactive approach (2nd ed.). Thousand Okas,
CA: Sage Publication.

Mertler, C. A., & Vannatta, R. A. (2001). Advanced and multivariate statistical methods: Practical appli-
cation and interpretation. Los Angeles, CA: Pyrcza k Publishing.

Noor, K. (2008). Case study: A strategic research methodology. American Journal of Applied Sciences,
5(11), 1602-1604.

Nunama ker, J. F., Chen, M., & Purdin, T. D. M. (1991). Systems development in information systems re-
search. Journal of Management Information Systems, 7(3), 89-106.

Oliver, D., Why mark, G., & Ro mm, C. (2005). Researching ERP adoption: An internet-based grounded
theory approach. Online Information Review, 29(6), 585-604.

Ra mim, M. M ., & Levy, Y. (2006). Securing e-lea rning systems: A case of insider cyber attacks and novice
IT manage ment in a s ma ll university. Journal of Cases on Information Technology, 8(4), 24-35.

Sekaran, U. (2003). Research methods for business (4th ed.). Hoboken, NJ: John Wiley & Sons.

Straub, D. W. (1989). Va lidating instruments in MIS research. MIS Quarterly, 13(2), 147-170.

van den Akker, J., Branch, R. M., Gustafson, K., Nieveen, N., & Plo mp, T. (2000). Design approaches and
tools in education. Norwell, MA: Kluwer Academic Publishers.

Willia ms, J. M ., & Colo mb, G. G. (2003). The craft o f argument (2nd ed.). Ne w Yo rk: Long man Publish-
ers.

Yin, R. K. (1984). Case study research: Design and methods. Ne wbury Park, CA : Sage Publicat ion.

Biographies
Dr. Timothy Ellis obtained a B.S. degree in History from Bradley
University, an M.A. in Rehabilitation Counseling from Southern Illinois
University, a C.A.G.S. in Rehabilitation Administration from North-
eastern University, and a Ph.D. in Computing Technology in Education
from Nova Southeastern University. He joined NSU as Assistant Pro-
fessor in 1999 and currently teaches computer technology courses at
both the Masters and Ph.D. level in the School of Computer and Infor-
mation Sciences. Prior to joining NSU, he was on the faculty at Fisher
College in the Computer Technology department and, prior to that, was
a Systems Engineer for Tandy Business Products. His research interests
inc lude: multimedia, distance education, and adult learning. He has
published in several technical and educational journals inc luding

Catalyst, Journa l of Instructional Delivery Systems, and Journal of Instructiona l Multimedia and

Ellis & Levy

337

Hypermedia. His email address is [email protected] His main website is located at
http://www.scis.nova.edu/~ellist/

Dr. Yair Levy is an associate professor at the Graduate School of
Computer and Information Sc iences at Nova Southeastern University.
During the mid to late 1990s, he assisted NASA to develop e-learning
systems. He earned his Bachelor’s degree in Aerospace Engineering
from the Technion (Israel Institute of Technology). He received his
MBA with MIS concentration and Ph.D. in Management Information
Systems from Florida Internationa l University. His current research
interests inc lude cognitive value of IS, of online learning systems, ef-
fectiveness of IS, and cognitive aspects of IS. Dr. Levy is the author of
the book “Assessing the Value of e-Learning systems.” His research
publications appear in the IS journals, conference proceedings, invited
book chapters, and encyclopedias. Additionally, he chaired and co-

chaired multiple sessions/tracks in recognized conferences. Currently, Dr. Levy is serving as the
Editor-in-Chief of the Internationa l Journal of Doctoral Studies (IJDS). Additionally, he is serv-
ing as an associate editor for the Internationa l Journa l of Web-based Learning and Teaching
Technologies (IJWLTT). Moreover, he is serving as a member of editoria l review or advisory
board of several refereed journals. Additionally, Dr. Levy has been serving as a referee research
reviewer for numerous nationa l and international scientific journa ls, conference proceedings, as
well as MIS and Information Security textbooks. He is also a frequent speaker at national and
international meetings on MIS and online learning topics. To find out more about Dr. Levy,
please visit his site : http://sc is.nova.edu/~levyy/

Issue s in Informing Science and Information Technology Volume 6, 2009

Towards a Guide for Novice Researchers on
Research Methodology:

Review and Proposed Methods

Timothy J. Ellis and Yair Levy
Nova Southeastern University

Graduate School of Computer and Information Sciences
Fort Lauderdale, Florida, USA

[email protected], [email protected]

Abstract
The novice researcher, such as the graduate student, can be overwhelmed by the intricacies of the
research methods employed in conducting a scholarly inquiry. As both a consumer and producer
of research, it is essential to have a firm grasp on just what is entailed in producing legitimate,
valid results and conclusions. The very large and growing number of diverse research approaches
in current practice exacerbates this problem. The goal of this review is to provide the novice re-
searcher with a starting point in becoming a more informed consumer and producer of research.
Toward addressing this goa l, a new system for deriving a proposed study type is deve loped. The
PLD model inc ludes the three common drivers for selection of study type : research-worthy prob-
lem (P), valid quality peer-reviewed literature (L), and data (D). The discussion inc ludes a review
of some common research types and concludes with definitions, discussions, and examples of
various fundamentals of research methods such as: a) forming research questions and hypotheses;
b) acknowledging assumptions, limitations, and delimitations; and c) establishing re liability and
validity.

Ke ywords: Research methodology, reliability, va lidity, research questions, problem directed re-
search

Introduction
The novice researcher, such as the graduate student, can be overwhelmed by the intricacies of the
research methods employed in conducting a scholarly inquiry (Leedy & Ormrod, 2005). As both
a consumer and producer of research, it is essential to have a firm grasp on just what is entailed in
produc ing legitimate, valid results and conclusions. The very large and growing number of di-
verse research approaches in current practice exacerbates this problem (Mertler & Vannatta,

2001). The goal of this review is to pro-
vide the novice researcher with a start-
ing point in becoming a more informed
consumer and producer of research in
the form of a lexicon of terms and an
analysis of the underlying constructs
that apply to scholarly enquiry, regard-
less of the specific methods employed.

Scholarly research is, to a very great
extent, characterized by the type of

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Guide for Novice Researchers on Research Methodology

324

study conducted and, by extension, the specific methods employed in conducting that type of
study (Creswell, 2005, p. 61). Novice researchers, however, often mistakenly think that, since
studies are known by how they are conducted, the research process starts with deciding upon just
what type of study to conduct. On the contrary, the type of study one conducts is based upon three
related issues: the problem driving the study, the body of knowledge, and the nature of the data
available to the researcher.

As discussed elsewhere, scholarly research starts with the identification of a tightly focused, lit-
erature supported problem (Ellis & Levy, 2008). The research-worthy problem serves as the point
of departure for the research. The nature of the research problem and the doma in from which it is
drawn serves as a limiting factor on the type of research that can be conducted. Nunamaker,
Chen, and Purdin (1991) noted that “It is clear that some research domains are sufficiently nar-
row that they allow the use of only limited methodologies” (p. 91). The problem also serves as
the guidance system for the study in that the research is, in essence, an attempt to, in some man-
ner, develop at least a partial solution to the research problem. The best design cannot provide
meaning to research and answer the question ‘Why was the study conducted,’ if there is not the
anchor of a clearly identified research problem.

The body of knowledge serves as the foundation upon which the study is built (Levy & Ellis,
2006). The literature also serves to channel the research, in that it indicates the type of study or
studies that are appropriate based upon the nature of the problem driving the study. Likewise, the
literature provides clear guidance on the specific methods to be followed in conducting a study of
a given type. Although origina lity is of great value in scholarly work, it is usually not rewarded
when applied to the research methods. Ignoring the wisdom contained in the existing body of
knowledge can cause the novice researcher, at the least, a great deal of added work establishing
the validity of the study.

From an entirely practical perspective, the nature of the data available to the researcher serves as
a final filter in determining the type of study to conduct. The type of data available should be
considered a necessary, but certainly not sufficient, consideration for selecting research methods.
The data should never supersede the necessity of a research-worthy problem serving as the anchor
and the existing body of knowledge serving as the foundation for the research. The absence of the
ability to gather the necessary data can, however, certainly make a study based upon research
methods directly driven by a well-conceived problem and supported by current literature com-
plete ly futile. Every solid research study must use data in order to validate the proposed theory.
As a result, novice researchers should understand the centrality of access to data for their study
success. Access to data refers to the ability of the researcher to actually collect the desired data
for the study. Without access to data, it is impossible for a researcher to make any meaningful
conclusions on the phenomena. Novice researchers should be aware that their access to data will
also imply what type of methodology they will be using and what type of research, eventually
they will conduct. Figure 1 illustrates the interaction among the research-worthy problem (P), the
existing body of knowledge documented in the peer-reviewed literature (L), and the data avail-
able to the researcher (D). The research-worthy problem (P) serves as the input to the process of
selecting the appropriate type of research to conduct; the valid peer-reviewed literature (L) is the
key funnel that limits the range of applicable research approaches, based on the body of knowl-
edge; the data (D) available to the researcher serves as the final filter used to identify the specific
study type.

The balance of this paper explores the constructs underlying scholarly research in two aspects.
The first section examines some of the types of studies most commonly used in information sys-
tems research. The second section explores vital considerations for research methods that apply
across all study types.

Ellis & Levy

325

Study type

Data available

Valid peer-reviewed
literature

Research-worthy
problem

Figure 1. The PLD Mode l for De riving Study Type

Types of Research
There are a number of different ways to distinguish among types of research. The type of data
available is certainly one vita l aspect (Gay, Mills, & Airasian, 2006; Leedy & Ormrod, 2005);
different research approaches are appropriate for quantitative data – precise, numeric data derived
from a reduced variable – than for qualitative data – complex, multidimensional data derived
from a natural setting. Of equal importance is the nature of the problem be ing addressed by the
research (Isaac & Michael, 1981). Some problems, for example, are relative ly new and require

Table 1: Ke y Categories of Research

Approach Mos t common type of data Stage of proble m Cate gories of
The ory

Experimental Quantitative Evaluation Testing or
revising

Causal-comparative Quantitative Evaluation Testing or
revising

Historical Quantitative or Qualitative Description Testing or
revising

Developmenta l Quantitative and qualitative Description Building or
revising

Correlational

Quantitative Description Testing

Case study Qualitative Exploration Building or
revising

Grounded theory

Qualitative Exploration Building

Ethnography

Qualitative Descriptive Building

Action research Quantitative and qualitative Applied exploration Building or
revising

Guide for Novice Researchers on Research Methodology

326

exploratory types of research, while more mature problems might better be addressed by descrip-
tive or evaluative (hypothesis testing) approaches (Sekaran, 2003). Table 1 presents an overview
of how the research approaches most commonly used in information systems (IS) are categorized.
The subsections following Table 1 briefly explore each of these major research approaches. In
general, research studies can be classified into three categories: theory building, theory testing,
and theory revising. Theory building refers to research studies that aim at building a theory where
no prior solid theory existed to expla in phenomena or specific scenario. Theory testing refers to
research studies that aim at validating (i.e. testing) existing theories in new context. Theory revis-
ing refers to research studies that aim at revising an existing theory.

Experimental
The essence of experimental research is determining if a cause-effect relationship exists between
one factor or set of factors – the independent variable(s) – and a second factor or set of factors –
the dependent variable(s) (Cook & Campbell, 1979). In an experiment, the researcher takes con-
trol of and manipulates the independent variable, usua lly by randomly assigning partic ipants to
two or more different groups that receive different treatments or implementations of the inde-
pendent variable. The experimenter measures and compares the performance of the participants
on the dependent variable to determine if changes in the independent variables are very like ly to
cause similar changes in performance on the dependent variable. In medical settings, this type of
research is very common. However, in many research fields it is somewhat difficult to control all
the variables in the experiments, especially when dealing with research area that is related to or-
ganizations and institutions. For that reason, the use of experiments in IS it is somewhat limited,
and a less restrictive type of experiments is used. Such type is called quasi-experiment (Cook &
Campbell, 1979). Similar to experiments, in quasi-experiments, the research is attempting to de-
termining if a cause-effect relationship exists between one factor or set of factors – the independ-
ent variable(s) – and a second factor or set of factors – the dependent variable(s). However in
quasi-experiments, the researcher is unable to control a ll the variables in the experimentation, but
most variables are controlled.

An example of an experimental study would be research into which of two methods of inputting
text in a personal digita l assistant, soft-key or handwriting recognition, is more accurate. The in-
dependent variable would be method of text input. The dependent variable might be a count of
the number of entry errors, and the comparison based on the mean of the group using the soft-key
method with the mean of the group that used handwriting recognition input. An example of ex-
perimenta l research can be found at Cockburn, Savage, and Wallace (2005).

Causal-Comparative
As with experimental studies, causal-comparative research focuses on determining if a cause-
effect relationship exists between one factor of set of factors – the independent variable(s) – and a
second factor or set of factors – the dependent variable(s). Unlike an experiment, the researcher
does not take control of and manipulate the independent variable in causal-comparative research
but rather observes, measures, and compares the performance on the dependent variable or vari-
ables of subjects in naturally-occurring groupings based on the independent variable.

An example of a causal-comparative study would be research into the impact monetary bonuses
had on knowledge sharing behavior as exhibited by contributions to a company knowledge bank.
The independent variable would be “monetary bonus,” and it might have two levels (i.e. “yes”
and “no”). The dependent variable might be a count of the number of contributions, and the com-
parison based upon an examination of the mean number of knowledge-base contributions made
per employee in companies that provided a monetary bonus versus the mean number of contribu-
tions made per employee in companies that did not provide a bonus. Since the researcher did not

Ellis & Levy

327

assign companies to the “bonus” or “no bonus” categories, this study would be causal compara-
tive , not experimental. An example of causal-comparative research can be found at Becerra-
Fernandez, Zanakis, and Walczak (2002) who deve loped a knowledge discovery technique using
neural network mode ling to classify a country’s investing risk based on a variety of independent
variables.

Case Study
A case study is “an empirica l inquiry that investigates a contemporary phenomenon within its real
life context using multiple sources of evidence” (Noor, 2008, p. 1602). The evidence used in a
case study is typically qualitative in nature and focuses on developing an in-depth rather than
broad, generalizable understanding. Case studies can be used to explore, describe, or explain phe-
nomena by an exhaustive study within its natural setting (Yin, 1984). An example of a case study
can be found in the study by Ramim and Levy (2006) who described the issues related to the im-
pact of an insider’s attack combined with novice management on the survivability of an e-
learning systems of a small university.

Historical
Historical research utilizes interpretation of qua litative data to explain the causes of change
through time. This type of research is based upon the recognition of a historical problem or the
identification of a need for certain historical knowledge and generally enta ils gathering as much
relevant information about the problem or topic as possible. The research usually begins with the
formation of a hypothesis that tentatively explains a suspected relationship between two or more
historical factors and proceeds to a rigorous collection and organization of usua lly qua litative
evidence. The verification of the authentic ity and validity of such evidence, together with its se-
lection, organization, and analysis forms the basis for this type of research. An example of his-
torical research can be found in the study by Grant and Grant (2008) who conducted a study to
test the hypothesis that a new generation in knowledge management was emerging.

Correlational
The primary focus of the correlationa l type of research is to determine the presence and degree of
a relationship between two factors. Although correlationa l studies are in a superficia l way similar
to causal-comparative research – both types of study focus on analyzing quantitative data to de-
termine if a relationship exists between two variables – the difference between the two cannot be
ignored. Unlike causal-comparative research, in correlationa l studies, there is no attempt to de-
termine if a cause-effect relationship exists (variable x causes changes in variable y). The goal for
correlational studies is to determine if a predictive relationship exists (knowing the value of vari-
able x allows one to predict the value of variable y). At a practical level, there is, therefore, no
distinction made between independent and dependent variables in correlationa l research.

An example of a simple correlationa l study would be research into the relationship between age
and willingness to make e-commerce purchases. The two variables of interest would be age and
number of e-commerce purchases made over a given period of time. The comparison would be
based upon an examination of age of each subject in the study and the number of e-commerce
purchases made by that subject. Since the researcher did not control either of the variables or at-
tempt to determine if age caused changes in purchases, just if age could be used to predict behav-
ior, the study would be correlationa l, not experimental or causal-comparative. An example of cor-
relational research can be found in Cohen and Ellis (2003).

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Developmental
Developmenta l research attempts to answer the question: How can researchers build a ‘thing’ to
address the problem? It is especially applicable when there is not an adequate solution to even test
for efficacy in addressing the problem and presupposes that researchers don’t even know how to
go about building a solution that can be tested. Developmenta l research generally entails three
major elements:

• Establishing and validating criteria the product must meet

• Following a formalized, accepted process for developing the product

• Subjecting the product to a formalized, accepted process to determine if it satis-
fies the criteria.

An example of developmental research would be Ellis and Hafner (2006) that detailed the devel-
opment of an asynchronous environment for project-based collaborative learning experiences.
Developmenta l research is distinguished from product development by: a focus on complex, in-
novative solutions that have few, if any, accepted design and development princ iples; a compre-
hensive grounding in the literature and theory; empirical testing of product’s practicality and ef-
fectiveness; as well as thorough documentation, analysis, and reflection on processes and out-
comes (van den Akker, Branch, Gustafson, Nieveen, & Plomp, 2000).

Grounded Theory
Grounded Theory is defined as “a systematic, qua litative procedure used to generate theory that
expla ins, at a broad conceptual level, a process, an action, or interaction about substantive topic”
(Creswell, 2005, p. 396). Grounded theory is used when theories currently documented in the lit-
erature fail to adequately expla in the phenomena observed (Leedy & Ormrod, 2005). In such
cases, revisions for existing theory may not be valid as the fundamental assumptions behind such
theories may be flawed given the context or data at hand. Table 2 outlines the three key types of
grounded theory design. According to Creswell, “choosing among the three approaches requires
several considerations” (p. 403). He noted that such considerations depend on the key emphasis
of the study such as: Is the aim of the study to follow given procedures? Is the aim of the aim of
the study to follow predetermined categories? What is the position of the researcher? An example
of Grounded Theory in the context of information systems inc ludes the study by Oliver, Why-
mark, and Romm (2005). Oliver et al. used Grounded Theory to deve lop a conceptual mode l on
enterprise-resources planning (ERP) systems adoption based on the various types of organiza-
tional justifications and reported motives.

Table 2: Types of Grounde d Theory Des ign (Cres we ll, 2005)

Type of Grounde d Theory
Des ign

De finition

Systematic Design “emphasizes the use of data analysis steps of open, axia l, and
selective coding, and the development of a logic paradigm or a
visua l picture of the theory generated” (Creswell, 2005, p. 397)

Emerging Design “letting the theory emerge from the data rather than using spe-
cific, preset categories” (Creswell, 2005, p. 401)

Constructivist Design “focus is on the meanings ascribed by partic ipants in a
study…more interested in the views, values, beliefs, feelings,
assumptions, and ideologies of individuals than in gathering
facts and describing acts” (Creswell, 2005, p. 402)

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Ethnography
The study of ethnography aims at “a particular person, program, or event in considerable depth.
In an ethnography, the researcher looks at an entire group – more specifica lly, a group that shares
a common culture – in depth” (Leedy & Ormrod, 2005, p. 151). According to Creswell (2005),
ethnographic research deals with an in-depth qua litative investigation of a group that share a
common culture. He indicated that ethnography is best used to explain various issues within a
group of individuals that have been together for a considerable length of time and have, therefore,
developed a common culture. Ethnographic research also provides a chronological collection of
events related to a group of individuals sharing a common culture. Beynon-Davies (1997) out-
lined the use of ethnographic research in the context of system development. He noted that for IS
researchers, ethnographic research may provide value in the area of IS development, specifically
in the process of capturing tacit knowledge during the system development life cycle (SDLC)
(Beynon-Davies). Crabtree (2003) noted that “ethnography is an approach that is increasing inter-
est to the designers of collaborative computing systems. Rejecting the use of theoretical frame-
works and insisting instead on a rigorously descriptive mode of research” (p. ix). However, criti-
cism for Crabtree’s advocacy of ethnography in information systems research was also voiced
(Alexander, 2003).

Action Research
Action research is defined as “a type of research that focuses on finding a solution to a local prob-
lem in a local setting” (Leedy & Ormrod, 2005, p. 114). Action research is unique in the approach
as the researcher himse lf or herself are part of the practitioners group that face the actual problem
the research is trying to address(Creswell, 2005). Additionally, the aim of action research is to
investigate a localized and practical problem. According to DeLuca, Gallivan, and Kock (2008),
there are five key steps in action research including: a) Diagnosing the problem; b) Planning the
action; c) Taking the action; d) Eva luating the results; and e) Specifying lessons learned for the
next cycle. During the course of all give steps of the action research, “researchers and practitio-
ners collaborate during each step” (DeLuca et al., p. 49).

Fundamentals of Research Methods
For each study type there is an accepted methodology documented in texts (Gay et al., 2006;
Isaac & Michael, 1981; Leedy & Ormrod, 2005; Yin, 1984) and exemplified in the literature
(Levy & Ellis, 2006). As a first step in establishing the value of a proposed study, the novice re-
searcher is well advised to close ly follow the template for the study type contained in the text and
mode l her or his research methods after similar studies reported in the literature. Regardless of the
type of study being conducted, there are a number of important factors that must be accommo-
dated in an effective description of the research methods. In brief, the description must provide a
detailed, step-by-step description of how the study will be conducted, answering the vital “who,
what, where, when, why, and how” questions.

1. What is going to be done

2. Who is going to do each thing to be done

3. How will each thing to be done be accomplished

4. When, and in what order, will the things to be accomplished actually be done

5. Where will those things be done

6. Why – supported by the literature – for the answers to the What, Who, How,
When, and Where

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330

A properly developed description of the research methods would allow the reader to actually con-
duct the study being proposed based upon the processes outlined. Included among those processes
are: forming research questions and hypotheses; identifying assumptions, limitations, and de limi-
tations; as well as establishing reliability and validity.

Form Research Questions and Hypotheses

Research questions
Research questions are the essence of most research conducted and acts as the guiding plan for
the investigation (Mertler & Vannatta, 2001). In general, research questions are “specific ques-
tions that researchers seek to answer” (Creswell, 2005, p. 117). According to Maxwell (2005),
“research questions state what you want to learn” (p. 69). A research investigation may have one
or more research questions regardless of the specific type of the research including qua litative,
quantitative, and mixed method types of research. Most quality peer-reviewed studies will have a
specific section that highlights the research questions investigated. In most other published work
that don’t have a specific section that highlights it, the research questions will appear either at the
end of the problem statement or right after the literature review section. Maxwell suggested that a
good research question is one that will point the researcher to the information that will lead
him/her to understand what he/she set forth to investigate. According to Ellis and Levy (2008),
“in order for the research to be at all meaningful, there has to be an identifiable connection be-
tween the answers to the research questions and the research problem inspiring the study” (p. 20).
However, research questions shouldn’t be created in a vacuum, but be strongly influenced by
quality literature is suggesting about the phenomena (Berg, 1998). Moreover, the exact wording
used to note the research questions is vital as the accuracy and appropriateness of the research
question determine the methodology to be used (Mertler & Vannatta, 2001).

The nature of the research questions will be dependent on the type of study being conducted.
Studies based on quantitative data will generally be driven by research questions that are formu-
lated on the confirmatory and predictive nature, while studies based on qualitative data will be
more like ly driven by research questions that are formulated on the exploratory and interpretive
nature.

Examples of quantitative research questions in the context of information systems inc lude :

– To what extent does users’ perceived usefulness increases the odds of their e-commerce
usage?

– Do computer self-efficacy and computer anxiety have a significant difference for males
and females when using e-learning systems?

– What are the contributions of users’ systems trust, deterrent severity, and motivation to
their misuse of biometrics technology?

– To what degree do team communication and team cohesiveness predict productivity of
system development by virtual teams?

Examples of qualitative research questions in the context of information systems include:

– How does training help the implementation success of enterprise-wide information sys-
tems?

– Why do user involvement and user resistance help in the systems’ requirement gathering
process?
What are the systems characteristics that are valuable to users when using e-learning
systems?

– How do e-commerce users define information privacy?

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Hypotheses
One must keep in mind that “research questions are not the same as research hypotheses”
(Maxwell, 2005, p. 69). In general, a hypothesis can be defined as a “logical supposition, a rea-
sonable guess, an educated conjecture” about some aspect of daily life (Leedy & Ormrod, 2005,
p. 6). In scholarly research, however, hypotheses are more than ‘educated guesses.’ A research
hypothesis is a “prediction or conjecture about the outcome of a relationship among attributes or
characteristics” (Creswell, 2005, p. 117). By convention, research is conservative and assumes
the absence of a relationship among the attributes under consideration; hypotheses, therefore, are
expressed in null terms. For example, if a study were to examine the impact interactive multime-
dia animations have on the average amount of a purchase at an e-commerce site, the hypothesis
would be stated: The average amount of purchase on an e-commerce site enhanced with interac-
tive animations will not be different that the average amount of purchase on the same e-
commerce site that is not enhanced with interactive animations. Not all types of research entail
establishing and testing hypotheses. Research methods based upon quantitative data commonly
test hypotheses; studies based upon qualitative data, on the other hand, explore propositions
(Maxwell).

Unlink hypotheses, propositions do predict a directiona lity for the results. If, for example, one
were to examine consumer reaction to interactive animation on an e-commerce site, one might
investigate the proposition that: Consumers will express a greater feeling of engagement and sat-
isfaction when visiting e-commerce sites enhanced with interactive animations than similar sites
that lack the enhancement.

Acknowledge Assumptions, Limitations, and Delimitations
For any given research investigation there are underlying assumptions, limitations, and de limita-
tions (Creswell, 2005). According to Leedy and Ormrod (2005), assumptions, limitations, and
delimitations are critical components of a viable research proposal; without these considerations
clearly articulated, evaluators may raise some valid questions regarding the credibility of the pro-
posal. The following three sub-sections provide definition and examples for each term.

Assumptions
Assumptions serve as the basic foundation of any proposed research (Leedy & Ormrod, 2005)
and constitute “what the researcher takes for granted. But taking things for granted may cause
much misunderstanding. What [researchers] may tacitly assume, others may never have consid-
ered” (Leedy & Ormrod, p. 62). Moreover, assumptions can be viewed as something the re-
searcher accepts as true without a concrete proof. Essentially, there is no research study without a
basic set of assumptions (Berg, 1998). According to Williams and Colomb (2003), identifying the
assumptions behind a given research proposal is one of the hardest issues to address, especially
for novice researchers. Such difficulties emerge due to the fact that by nature “we all take our
deepest beliefs for granted, rarely questioning them from someone else’s point of view”
(Williams & Colomb, p. 200). It is important for novice researchers to learn how to explic itly
document the ir assumptions in order to ensure that they are aware of those things taken as givens,
rather than trying to hide or smear them from the reader. Explicitly documenting the research as-
sumptions may help reduce misunderstanding and resistance to a proposed research as it demon-
strates that the research proposal has been thoroughly considered (Leedy & Ormrod, 2005).

To identify the assumption behind a proposa l, the researcher must ask himself the following ques-
tion: “what do I believe that my readers must also believe (but may not) before they will think
that my reasons are relevant to my claims?” (Williams & Colomb, p. 200).

Guide for Novice Researchers on Research Methodology

332

Examples of assumptions researchers make include :

– Participants in the study will make a sincere effort to complete the assigned tasks

– The students participating in the Internet-based course have a basic familiarity with the
personal computer and the use of the World Wide Web.

Limitations
Every study has a set of limitations (Leedy & Ormrod, 2005), or “potential weaknesses or prob-
lems with the study identified by the researcher” (Creswell, 2005, p. 198). A limitation is an un-
controllable threat to the internal va lidity of a study. As described in greater detail below, internal
validity refers to the likelihood that the results of the study actually mean what the researcher in-
dicates they mean. Explic itly stating the research limitations is vita l in order to allow other re-
searchers to replicate the study or expand on a study (Creswell, 2005). Additionally, by explicitly
stating the limitations of the research, a researcher can help other researchers “judge to what ex-
tent the findings can or cannot be generalized to other people and situations” (Creswell, 2005, p.
198).

Examples of limitations researchers may have:

– All subjects in the study will be volunteers who may withdraw from the study at any
time. The participants who finish the study might not, therefore, be truly representative of
the population.

– The members of the expert panel that will validate the proficiency survey instrument will
be drawn from the faculty of … and may not truly represent universally accepted expert
opinion.

Delimitations
Delimitations refer to “what the researcher is not going to do” (Leedy & Ormrod, 2005). In schol-
arly research, the goals of the research outlines what the researcher intends to do; without the de-
limitations, the reader will have difficulties in understanding the boundaries of the research. In
order to constrain the scope of the study and make it more manageable, researchers should outline
in the de limitations – the factors, constructs, and/or variables – that were intentiona lly left out of
the study. Delimitations impact the external validity or generalizability of the results of the study.
Examples of delimitations inc lude :

– Participation in the study was delimited to only males aged 25-45 who had made a pur-
chase via the internet within the past 12 months; generalization to other age groups or
females may not be warranted.

– This study examined attrition rates in MBA programs offered in continuing education de-
partments of public colleges and universities; generalization to other educationa l pro-
grams or similar programs offered in private institutions may not be warranted.

Establish Reliability and Validity
Every study must address threats to validity and reliability (Leedy & Ormrod, 2005). Although
the concepts of validity and reliability originally started in quantitative research approaches, in
recent years validity and reliability are being addressed in qualitative and mixed-methods ap-
proaches as well (Berg, 1998; Maxwell, 2005). According to Leedy and Ormrod (2005), “the va-
lidity and reliability of your measurement instruments influences the extent to which you can
learn something about the phenomenon you are studying…and the extent to which you can draw
meaningful conclusions from your data” (p. 31). The following two sections define and outline

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the key types of validity and reliability re lated to common research investigation. Establishing an
approach following published methods to address validity and reliability issues in a research pro-
posal may drastically increase the overall acceptance of the research proposal.

Reliability
Reliability is defined as “the consistency with which a measuring instrument yie lds a certain re-
sults when the entity be ing measured hasn’t changed” (Leedy & Ormrod, 2005, p. 31). According
to Straub (1989), researchers should try to answer the following question in an attempt to address
reliability; “do measures show stability across the unit of observation? That is, could measure-
ment error be so high as to discredit the findings?” (p. 150). Reliability can be established in four
different ways: equivalency, stability, inter-rater, and interna l consistency (Carmines & Zeller,
1991).

Equivale ncy re liability. Equivalency reliability is concerned with how close ly measurements
taken with one instrument match those taken with a second instrument under similar conditions.
Equiva lency is often used to certify the reliability of a new measurement instrument or procedure
by comparing the results of using that instrument with those obta ined by using established in-
struments or processes. Equiva lency is usua lly established through the use of a statistical correla-
tion (Pearson’s r for linear correlation or Eta for non-linear correlation).

Stability re liability. Stability reliability – also know as test, re-test reliability – is concerned with
how consistent results of measuring with a given instrument or process are over time. Stability is
based on the assumption that, absent some identifiable explanation, the measurement should pro-
duce the same results today as last month and will produce the same results next month. Stability,
like equiva lency, is usually established through the use of a statistical correlation (Pearson’s r for
linear correlation or Eta for non-linear correlation).

Inte r-rate r re liability. Inter-rater reliability focuses on the extent agreement in the results of two
or more individuals using the same measurement instrument or process. As with stability and
equivalency, inter-rater reliability is usua lly established through the use of a statistical correlation
(Pearson’s r for linear correlation or Eta for non-linear correlation).

Inte rnal cons iste ncy. Unlike the previous methods of establishing reliability which were con-
cerned with comparing the results of using an instrument or process with some external standard
(another instrument, the same instrument over time, or the same instrument used by different
people), internal consistence focuses on the level of agreement among the various parts of the
instrument or process in assessing the characteristic being measured. In a 20-question survey
measuring attitude toward knowledge sharing, for example , if the survey is interna lly consistent,
there will be a strong correlation the responses on all 20 questions. Internal consistency is also
measured by statistical correlation, but with the Cronbach α in place of Pearson r.

Validity
Validity refers to a researchers’ ability to “draw meaningful and justifiable inferences from scores
about a sample or population” (Creswell, 2005, p. 600). There are various types of validity asso-
ciated with scholarly research (Cook & Campbell, 1979). Validity of an instrument refers to “the
extent to which the instrument measures what it is supposed to measure” (Leedy & Ormrod,
2005, p. 31). Thus, researchers when designing the ir study, must ask themselves “how might you
be wrong?” (Maxwell, 2005, p. 105). Additionally, the validity of a study “depends on the rela-
tionship of your conclusions to reality” (Maxwell, 2005, p. 105). This section will define and out-
line the key validity issues. The two most common validity issues are internal validity and exter-
nal validity.

Guide for Novice Researchers on Research Methodology

334

Inte rnal validity. Internal validity refers to the “extent to which its design and the data that it
yie lds allow the researcher to draw accurate conclusions about cause-and-effect and other rela-
tionships within the data” (Leedy & Ormrod, 2005, pp. 103-104). According to Straub (1989),
researchers should try to answer the following question in an attempt to address internal va lidity;
“are there untested rival hypotheses for the observed effects?” (p. 150). Generally, establishing
interna l validity requires examining one or more of the following: face validity, criterion va lidity,
construct validity, content validity, or statistical conclusion va lidity.

Face Validity. Face validity is based upon appearance; does the instrument or process seem to
pass the test for reasonableness. Face validity is never sufficient by itself, but an informa l assess-
ment of how well the study appears to be designed is often the first step in establishing its va lid-
ity.

Crite rion Re lated Validity. Also known as instrumental va lidity, criterion related validity is
based upon the premise that processes and instruments used in a study are valid if they paralle l
similar those used previous, validated research. In order to establish criterion re lated validity it is
necessary to draw strong parallels between as many particulars of the validated study – popula-
tion, c ircumstances, instruments used, methods followed – as possible.

Cons truct Validity. Construct validity “is in essence operational issue. It asks whether the meas-
ures chosen are true constructs describing the event or merely artifacts of the methodology itself”
(Straub, 1989, p. 150). According to Straub, researchers should try to answer the following ques-
tion in an attempt to address construct validity; “do measures show stability across methodology?
That is, are the data a reflection of true scores or artifacts of the kind of instrument chosen?” (p.
150).

Conte nt Validity. In survey-based research, the term content validity refers to “the degree to
which items in an instrument reflect the content universe to which the instrument will be general-
ized” (Boudreau, Gefen, & Straub, 2001, p. 5). According to Straub (1989), researchers should
try to answer the following question in an attempt to address content validity; “are instrument
measures drown from all possible measures of the properties under investigation?” (p. 150).

Statis tical Conclus ion Validity. Statistical conc lusion va lidity refers to the “assessment of the
mathematical relationships between variables and the like lihood that this mathematical assess-
ment provides a correct picture of the covariation …(Type I and Type II error)” (Straub, 1989, p.
152). According to Straub, researchers should try to answer the following question in an attempt
to address statistical conc lusion va lidity; “do the variables demonstrate relationships not expla in-
able by chance or some other standard of comparison?” (p. 150).

Exte rnal validity. External validity refers to the “extent to which its results apply to situations
beyond the study itself…the extent to which the conclusions drawn can be generalized to other
contexts” (Leedy & Ormrod, 2005, p. 105). Additionally, external validity addresses the “gener-
alizability of sample results to the population of interest, across different measures, persons, set-
tings, or times. External validity is important to demonstrate that research results are applicable in
natural settings, as contrasted with classroom, laboratory, or survey-response settings” (King &
He, 2005, p. 882).

Summary
One of the major challenges facing the novice researcher is matching the research she or he pro-
poses with a research method that is appropriate and will be accepted by the scholarly commu-
nity. The material presented in this paper is certainly not intended to be the ending point in the
process of establishing the research methods for a given study. The novice researcher is encour-

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aged, even expected to augment this material by referring to one or more of the texts and research
examples cited.

This paper does present a foundation upon which such a decision can be based on:

1. Developing the PLD, a model for selection of research approach based upon the
problem driving the study, the body of knowledge documented in peer-reviewed lit-
erature, and the data available to the researcher;

2. Identifying, in brief, several of the research approaches commonly used in informa-
tion systems studies;

3. Exploring several of the important terms and constructs that apply to scholarly re-
search, regardless of the specific approach selected.

References
Ale xander, I. (2003). Designing collaborative systems. A practica l guide to ethnography. European Journal

of In formation Systems, 12(3), 247-249.

Becerra-Fe rnandez, I., Zanakis, S. H., & Walc za k, S. (2002). Knowledge discovery techniques for predict-
ing country investment risk. Computers & Industrial Engineering, 43(4), 787-800.

Berg, B. L. (1998). Qualitative research methods for the social sciences (3rd ed.). Boston, MA: Allyn &
Bacon.

Beynon-Davies, P. (1997). Ethnography and information systems development: Ethnography of, for and
within is development. Information and Software Technology, 39(8), 531-540.

Boudreau, M.-C., Gefen, D., & Straub, D. W. (2001). Validation in information systems research: A state-
of-the-art assessment. MIS Quarterly, 25(1), 1-16.

Cockburn, A., Savage, J., & Wallace, A. (2005). Tuning and testing scrolling interfaces that automatically
zoo m. Proceeding of the Computer-Human Interaction 2005 Conference, Portland, Oregon, pp. 71-80.

Cohen, M. S., & Ellis, T. J. (2003). Predictors of success: A longitudinal study of threaded discussion fo-
rums. Proceeding of the Frontiers in Education Conference, Boulder, Colorado, pp. T3F-14–T13F-18.

Cook, T. D., & Ca mpbell, D. T. (1979). Quasi-experimentation: Design & analysis issues from field set-
tings. Boston, MA: Houghton Mifflin Co mpany.

Crabtree, A. (2003). Designing collaborative systems. A practical guide to ethnography. Berlin: Springer-
Verlag.

Creswe ll, J. W. (2005). Educational research: Planning, conducting, and evaluating quantitative and
qualitative research (2nd ed.). Upper Saddle River, NJ: Pearson.

De Luca, D., Ga llivan, M. J., & Kock, N. (2008). Furthering information systems action research: A post-
positivist synthesis of four dia lectics. Journal of the Association for Information Systems, 9(2), 48-72.

Ellis, T. J., & Ha fner, W. (2006). A co mmunicat ion environment for asynchronous collaborative lea rning.
Proceeding of the 37th Hawaii International Conference on System Sciences, Big Island, Hawa ii, pp.
3a-3a.

Ellis, T. J., & Levy, Y. (2008). A fra mework of proble m-based research: A guide for novice researchers on
the development of a research-worthy proble m. In forming Science: The International Journal of an
Emerging Transdiscipline, 11, 17-33. Retrieved fro m http://inform.nu/Artic les/Vol11/ISJv 11p 017-
033Ellis486.pdf

Gay, L. R., Mills, G. E., & Airasian, P. (2006). Educational research: Competencies for analysis and ap-
plications (8th ed.). Upper Saddle River, NJ: Pearson.

Guide for Novice Researchers on Research Methodology

336

Grant, K. A., & Grant, C. T. (2008). Developing a mode l of ne xt generation knowledge manage ment. Is-
sues in Informing Science and Information Technology, 5, 571– 590. Retrieved fro m
http://proceedings.informingscience.org/InSITE2008/IISITv5p571-590Grant532.pdf

Isaac, S., & M ichael, W. B. (1981). Handbook in research and evaluation. San Diego, CA: EdITS publish-
ers.

King, W. R., & He, J. (2005). Externa l validity in IS survey research. Communications of the Association
for In formation Systems, 16, 880-894.

Leedy, P. D., & Ormrod, J. E. (2005). Practical research: Planning and design (8th ed.). Upper Saddle
River, NJ: Prentice Hall.

Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature rev iew in support of
informat ion systems research. Informing Science: The International Journal of an Emerging Transdis-
cipline, 9, 181-212. Retrieved fro m http://inform.nu/Artic les/Vol9/ V9p181-212Levy99.pdf

Maxwe ll, J. A. (2005). Qualitative research design: An interactive approach (2nd ed.). Thousand Okas,
CA: Sage Publication.

Mertler, C. A., & Vannatta, R. A. (2001). Advanced and multivariate statistical methods: Practical appli-
cation and interpretation. Los Angeles, CA: Pyrcza k Publishing.

Noor, K. (2008). Case study: A strategic research methodology. American Journal of Applied Sciences,
5(11), 1602-1604.

Nunama ker, J. F., Chen, M., & Purdin, T. D. M. (1991). Systems development in information systems re-
search. Journal of Management Information Systems, 7(3), 89-106.

Oliver, D., Why mark, G., & Ro mm, C. (2005). Researching ERP adoption: An internet-based grounded
theory approach. Online Information Review, 29(6), 585-604.

Ra mim, M. M ., & Levy, Y. (2006). Securing e-lea rning systems: A case of insider cyber attacks and novice
IT manage ment in a s ma ll university. Journal of Cases on Information Technology, 8(4), 24-35.

Sekaran, U. (2003). Research methods for business (4th ed.). Hoboken, NJ: John Wiley & Sons.

Straub, D. W. (1989). Va lidating instruments in MIS research. MIS Quarterly, 13(2), 147-170.

van den Akker, J., Branch, R. M., Gustafson, K., Nieveen, N., & Plo mp, T. (2000). Design approaches and
tools in education. Norwell, MA: Kluwer Academic Publishers.

Willia ms, J. M ., & Colo mb, G. G. (2003). The craft o f argument (2nd ed.). Ne w Yo rk: Long man Publish-
ers.

Yin, R. K. (1984). Case study research: Design and methods. Ne wbury Park, CA : Sage Publicat ion.

Biographies
Dr. Timothy Ellis obtained a B.S. degree in History from Bradley
University, an M.A. in Rehabilitation Counseling from Southern Illinois
University, a C.A.G.S. in Rehabilitation Administration from North-
eastern University, and a Ph.D. in Computing Technology in Education
from Nova Southeastern University. He joined NSU as Assistant Pro-
fessor in 1999 and currently teaches computer technology courses at
both the Masters and Ph.D. level in the School of Computer and Infor-
mation Sciences. Prior to joining NSU, he was on the faculty at Fisher
College in the Computer Technology department and, prior to that, was
a Systems Engineer for Tandy Business Products. His research interests
inc lude: multimedia, distance education, and adult learning. He has
published in several technical and educational journals inc luding

Catalyst, Journa l of Instructional Delivery Systems, and Journal of Instructiona l Multimedia and

Ellis & Levy

337

Hypermedia. His email address is [email protected] His main website is located at
http://www.scis.nova.edu/~ellist/

Dr. Yair Levy is an associate professor at the Graduate School of
Computer and Information Sc iences at Nova Southeastern University.
During the mid to late 1990s, he assisted NASA to develop e-learning
systems. He earned his Bachelor’s degree in Aerospace Engineering
from the Technion (Israel Institute of Technology). He received his
MBA with MIS concentration and Ph.D. in Management Information
Systems from Florida Internationa l University. His current research
interests inc lude cognitive value of IS, of online learning systems, ef-
fectiveness of IS, and cognitive aspects of IS. Dr. Levy is the author of
the book “Assessing the Value of e-Learning systems.” His research
publications appear in the IS journals, conference proceedings, invited
book chapters, and encyclopedias. Additionally, he chaired and co-

chaired multiple sessions/tracks in recognized conferences. Currently, Dr. Levy is serving as the
Editor-in-Chief of the Internationa l Journal of Doctoral Studies (IJDS). Additionally, he is serv-
ing as an associate editor for the Internationa l Journa l of Web-based Learning and Teaching
Technologies (IJWLTT). Moreover, he is serving as a member of editoria l review or advisory
board of several refereed journals. Additionally, Dr. Levy has been serving as a referee research
reviewer for numerous nationa l and international scientific journa ls, conference proceedings, as
well as MIS and Information Security textbooks. He is also a frequent speaker at national and
international meetings on MIS and online learning topics. To find out more about Dr. Levy,
please visit his site : http://sc is.nova.edu/~levyy/

Chapter 3 – Evaluation Rubric

Criteria Does Not Meet 0.01 points Meets/NA 1 point

Introductory Remarks The section is missing; or some topic areas are not included

in the Introduction or are not explained clearly.

The chapter outline is not provided and/or is unclear.

The reader is adequately oriented to the topic areas

covered. An outline of the logical flow of the chapter is

presented.

All major themes/concepts are introduced.

Research Methodology and

Design

There is a lack of alignment among the chosen research

method and design and the study’s problem, purpose, and

research questions.

There is a lack of justification and alternate choices for

methods.

For Qualitative Studies: Lacks clear discussion of the

study phenomenon, boundaries of case(s), and/or

constructs explored.

Describes how the research method and design are aligned

with the study problem, purpose, and research questions.

Uses scholarly support to describe how the design choice

is consistent with the research method, and alternate

choices are discussed.

For Qualitative Studies: Describes the study

phenomenon, boundaries of case(s) and/or constructs

explored.

Population and Sample Lacks a description of the sample, demographics, and the

representation of the sample to the broader population.

There is little to no description of the inclusion/exclusion

criteria used to select the participants (sample) of the study.

For Quantitative Studies: A power analysis is not

described and appropriately cited.

Provides a description of the target population and the

relation to the larger population.

Inclusion/exclusion criteria for selecting participants

(sample) of the study are noted.

For Quantitative Studies: Power analysis is

described and appropriately cited.

Materials/

Instrumentation

Lacks a description of the instruments associated with the

chosen research method and design used. Details missing

regarding instrument origin, reliability, and validity.

For Quantitative Studies: (e.g., tests or surveys). Lacks

explanation of any permission needed to use the

instrument(s) and cites properly. Instrument permissions are

missing in appendices

For Qualitative Studies: (e.g., observation

checklists/protocols, interview or focus group discussion

Handbooks). Did not clearly explain the process for

conducting an expert review of instruments (e.g., provides

justification of reviewers being credible – reviewers may

include, but not limited to NCU dissertation team members,

professional colleagues, peers, or non-research participants

representative of the greater population); and/or did not

clearly explain use of a field test if practicing the

administration of the instruments is warranted.

For Pilot Study: Does not clearly explain the procedure for

conducting a pilot study (did not conduct pilot) if using a

self-created instrument (e.g., survey questionnaire); does not

include explanation of a field test if practicing the

administration of the instruments is warranted.

Provides a description of the instruments associated with

the chosen research method and design used. Includes

information regarding instrument origin, reliability, and

validity.

For Quantitative Studies: (e.g., tests or surveys).

Includes any permission needed to use the instrument(s)

and cites properly.

For Qualitative Studies: (e.g., observation

checklists/protocols, interview or focus group discussion

Handbooks). Describes process for conducting an expert

review of instruments (e.g., provides justification of

reviewers being credible – reviewers may include, but not

limited to NCU dissertation team members, professional

colleagues, peers, or non-research participants

representative of the greater population); describes use of

a field test if practicing the administration of the

instruments is warranted.

For Pilot Study: Explains the procedure for conducting a

pilot study (requires IRB approval for pilot) if using a

self-created instrument (e.g., survey questionnaire);

explains use of a field test if practicing the administration

of the instruments is warranted.

Operational Definitions

of Variables

(Quantitative Studies

Only)

Discussion of the study variables examined is lacking

information and/or is unclear.

Describes study variables in terms of being measurable

and/or observable.

(Reviewer – mark Meets/NA for Qualitative studies)

Procedures Procedures are not clear or replicable. Steps are missing;

recruitment, selection, and informed consent are not

established. IRB ethical practices are missing or unclear.

Describes the procedures to conduct the study in enough

detail to practically replicate the study, including

participant recruitment and notification, and informed

consent. IRB ethical practices are noted.

Data Collection and

Analysis

Does not clearly provide a description of the data and the

processes to collect data. Lack of alignment between the

data collected and the research questions and/or hypotheses

of the study.

For Quantitative Studies: Does not clearly provide the data

analysis processes including, but not limited to, clearly

describing the statistical tests performed and the

purpose/outcome, coding of data linked to each RQ, the

software used (e.g., SPSS, Qualtrics).

For Qualitative Studies: Does not clearly identify the

coding process of data linked to RQs; does not clearly

describe the transcription of data, the software used for

textual analysis (e.g., Nivo, DeDoose), and does not justify

manual analysis by researcher. There is missing or unclear

explanation of the use of a member check to validate data

collected.

Provides a description of the data collected and the

processes used in gathering the data. Explains alignment

between the data collected and the research questions

and/or hypotheses of the study.

For Quantitative Studies: Includes the data analysis

processes including, but not limited to, describing the

statistical tests performed and the purpose/outcome,

coding of data linked to each RQ, the software used (e.g.,

SPSS, Qualtrics).

For Qualitative Studies: Identifies the coding process of

data linked to RQs. Describes the transcription of data,

the software used for textual analysis (e.g., Nivo,

DeDoose), and describes manual analysis by researcher.

Describes the use of a member check to validate data

collected.

Assumptions/Limitations/

Delimitations

Does not clearly outline the

assumptions/limitations/delimitations (or has missing

components) inherent to the choice of method and design.

For Quantitative Studies: Does not include or lacks key

elements such as, but not limited to, threats to internal and

external validity, credibility, and generalizability.

For Qualitative Studies: Does not include or lacks key

elements such as, but not limited to threats to credibility,

trustworthiness, and transferability.

Outlines the assumptions/limitations/delimitations to the

choice of method and design.

For Quantitative Studies: Includes key elements such as,

but not limited to, threats to internal and external validity,

credibility, and generalizability.

For Qualitative Studies: Includes key elements such as,

but not limited to threats to credibility, trustworthiness,

and transferability.

Ethical Assurances Lacks discussion of compliance with the standards to

conduct research as appropriate to the proposed research

design and is not aligned to IRB requirements.

Describes compliance with the standards to conduct

research as appropriate to the proposed research design

and aligned to IRB requirements.

Summary Chapter does not conclude with a summary of key points

from the Chapter – elements are missing, incomplete,

and/new information is presented.

Chapter concludes with an organized summary of key

points discussed/presented in the Chapter.

Dissertation Chapter 3: Research Method

• Introduction (no subheading)

• Research Methodology and Design

• Population and Sample

• Materials or Instrumentation

• Operational Definitions of Variables (quantitative only)

• Study Procedures

• Data Collection and Analysis

• Assumptions

• Limitations

• Delimitations

• Ethical Assurances

• Summary

Introduction
• Briefly reintroduces the problem and purpose of the study

• Briefly describes the overall intent and components of the chapter

• Leads into the description of the research methodology and design

• Does not add any new or related information

Research Methodology and Design
• Describes the research methodology (i.e., quantitative, qualitative, mixed) and

corresponding specific design

• Elaborates on and defends how the chosen research methodology and design are
appropriate to accurately address the study’s problem, purpose, and research
questions

• Identifies alternative methodologies and designs indicating why they are less
appropriate to accurately address the study’s problem, purpose, and research
questions

Research Methodology and Design
• A quantitative methodology involves the use of variables to measure an effect or

a relationship

• Employs variables to establish cause-and-effect
• Independent/predictor variable is used as an employed effect to be measured

• Dependent/outcome variable is used to measure the employed effect

• Indicates the effect of the independent/predictor variable onto the
dependent/outcome variable

• With the use of descriptive and inferential statistics, a statistically significant/non-
significant effect or relationship involving two or more variables can be established

• Can be used to determine the size of the effect or to what degree/extent the
dependent/outcome variable was changed by the independent/predictor variable

• Can be used to predict future outcomes

• Use what is/are the effect(s) of….. on…..

• Do not use “effect” in qualitative research

Research Methodology and Design
• A quantitative methodology involves the use of variables to measure an effect or

a relationship

• Employs variables to establish non-casual relationships
• Can be used to establish a statistically significant/non-significant relationship involving

two variables

• Can be used to only determine the strength and direction of the relationship

• Control variables are held constant throughout the experiment to eliminate/reduce
their potential effect

• Extraneous/confounding/intervening variables hide/alter the true effect of the
intended independent/predictor variable in the experiment, negatively impacting the
true results

Research Methodology and Design
• Quantitative Methodology – involves descriptive and inferential statistical analyses to test

a set of hypotheses for significance

• Instruments used to derive numerical data

• Correlation – attempts to determine to what extent two variables are related

• Regression – attempts to predict the value of one variable from another variable when a
causal relationship exists between the two variables

• Multiple Regression – attempts to predict the value of a one variable based on the value
of two or more other variables.

• Group Comparison – attempts to determine differences between two or more groups
based on the measured effect of an independent variable on to a dependent variable
• Experimental: randomization selection and assignment of participants

• Quasi-Experimental: randomization does not occur

• Non-Experimental: causal comparative/ex post facto (data are obtained from pre-formed
groups without independent variable manipulation, already occurred)

Research Methodology and Design
• Qualitative Methodology – an in-depth exploration to gain a greater understanding; can

also utilize an analysis of numerical data employing descriptive statistics but does not
employ inferential statistical analyses to address a set of hypotheses

• Surveys, individual interviews, focus group interviews, anecdotal records, and
observations are used to derive numerical and/or non-numerical descriptive information

• Phenomenology – an in-depth exploration to gain a deeper understanding of individuals’
perceptions and lived experiences regarding some phenomenon from their perspective

• Ethnography – an in-depth exploration to gain a deeper understanding of a particular
culture or some facet of it from the perspective of the culture’s members

• Grounded Theory – an in-depth exploration to gain a deeper understanding for the
development of a theory to advance, refine, and expand a body of knowledge or ground
a theory in the context of the phenomenon under study

Research Methodology and Design
• Narrative – an in-depth exploration to gain a deeper understanding of individuals’

meanings they assign to their particular experiences, typically derived from one
or a small number of participants involving rich and free-ranging discourse

• Case Study – an in-depth exploration to gain a greater understanding of a single
individual, small group, event, place, phenomenon, or other type of circumstance
from the participant’s perspective utilizing multiple or triangulated data sources;
allows key characteristics, meanings, themes, trends, and implications to be
clarified for application and prediction

Population and Sample

• Population – the entirety from the sample is drawn involving people, objects, events, etc.

• Sample – the smaller more manageable subset representative of the larger population

• Describe the population including the estimated size and characteristics and why it is
appropriate to be used to address the problem, purpose, and research questions

• Describe the sample including how it was derived and why it is appropriate to be used to
address the problem, purpose, and research questions

• Describe the sampling procedure involving how participants will be recruited
• Random and convenience sampling are the most common

Population and Sample

• The purpose of a smaller sample is to develop an inference/conclusion about the
larger population

• In qualitative research, the smaller sample must be representative or
characteristic of the larger population for accuracy in the transferability of the
sample results to the population; the sample size is based on the type of
qualitative design and when data saturation occurs

• In quantitative research, the smaller sample must be representative or
characteristic of the larger population for accuracy in the generalizability of the
sample results to the population

Population and Sample

• A sample size is based on a power analysis to determine the smallest sample size suitable
to detect an effect at the desired level of significance

• A power analysis yields statistical power, ensuring the probability of a statistical test
accurately rejects the null hypothesis

• The higher the statistical power, the lower the probability of making a Type II error (false
negative)

• A type I error occurs when the null hypothesis is rejected but it is actually true (false-
positive) …the intervention really didn’t work… but concluded it did…

• A type II error occurs when the null hypothesis is failed to be rejected but it is actually
false (false negative) …the intervention really did work…but concluded it did not…

• Null Hypothesis: non-significant outcome – failed to be rejected (never accepted) or
rejected

• Alternative Hypothesis: significant outcome – accepted

Materials and Instrumentation

• Describe existing materials and/or instruments used to collect data, including
evidence of reliability and validity

• Note evidence of permission to use existing materials and/or instruments

• Describe any needed field or pilot testing of materials and/or instruments
including evidence of reliability and validity

Operational Definition of Variables

• For the quantitative method only, describe each variable and how it was used in
the study (e.g., independent/dependent, predictor/outcome)

• Describe how each instrument was used to measure each variable

• Describe the level of measurement for each variable (e.g., nominal, ordinal,
interval, ratio) and potential scores for each variable
• Nominal – variables are named or labeled in no specific order, used for classification

with no mathematical value, often used in surveys (e.g., gender)

• Ordinal – variables are used to depict a specific order or ranking, used to determine
order/degree of satisfaction or agreement (e.g., strongly disagree to strongly agree)

• Interval – labels, orders/ranks, combined with an equal interval between each value
(e.g., temperature)

• Ratio – interval data with a natural zero (e.g., weight)

Study Procedures

• Provide a detailed description (recipe) of how the study was precisely conducted

• Describe the exact steps followed to collect data, addressing what data, how it
was collected, when it was collected, where it was collected, and from whom it
was collected within sufficient detail for the study to be replicated with as much
consistency as humanly possible

Data Collection and Procedures

• Describe the collected data followed by the strategies used to analyze the
collected data (e.g., coding, statistical analyses, software)

• For quantitative designs, describe the analysis used to test each hypothesis,
provide evidence the statistical test chosen was appropriate to assess the
hypotheses and the collected data met the assumptions of the statistical tests
(e.g., tests for normality)
• Parametric – collected data resembles a normal distribution (bell-shaped curve),

greater statistical power, more likely to reveal a significant effect when one truly
exists

• Non-Parametric – collected data do not rely on any distribution shape (distribution-
free)

• For qualitative designs, describe how the collected data were processed and
analyzed, including any triangulation efforts, and explain the role of the
researcher during data collection and analysis

Assumptions, Limitations, Delimitations

• Assumptions are beliefs about the study considered to be true but without supportive evidence
• For example: study participants answered all questions honestly without fear of repercussions or what

the researcher may be perceived as desirable

• Assumptions are beyond the researcher’s control

• Limitations are the inherent weaknesses/flaws within the given research design

• For example: insufficient sample size, study time constraints, limited access to data

• Limitations are beyond the researcher’s control

• Delimitations are boundaries established by the researcher to define and limit the scope of the
study, often to support the feasibility of conducting the study
• For example: utilizing students in grade 10 to evaluate a reading achievement improvement strategy

among high school at-risk students, rather than utilizing a sample of high school students in grades 9
though 12

• Delimitations are within the researcher’s control

• Assumptions, limitations, and delimitations can be threats to the accuracy or trustworthiness in
quantitative and qualitative research results

Poll: Assess Your Understanding

Which one of the following portrays the most accurate representation of an
assumption, limitation, and delimitation:

A. Data saturation was unable to be achieved due to the time constraints of the
student’s dissertation completion course timeline.

B. Second grade student participants responded accurately when asked about how
they learn best in school.

C. Participants were only recruited through the use of a Facebook request.

Poll: Assess Your Understanding

A. Data saturation was unable to be achieved due to the time constraints of the
student’s dissertation completion course timeline. Limitation

B. Second grade student participants responded accurately when asked about how
they learn best in school. Assumption

A. Participants were only recruited through the use of a Facebook request.
Delimitation

Poll: Assess Your Understanding

Which one of the following portrays the most accurate representation of an
assumption, limitation, and delimitation:

A. The sample located in one part of a region will be representative of the entire
population in this region.

B. Instrumentation involved only Likert scale responses.

C. Several participants withdrew prior to study completion.

Poll: Assess Your Understanding

A. The sample located in one part of a region will be representative of the entire
population in this region. Assumption

B. Instrumentation involved only Likert scale responses. Delimitation

C. Several participants withdrew prior to study completion. Limitation

Qualitative and Quantitative Trustworthiness

Criteria for Determining Quantitative
Trustworthiness in Research Results
• Internal Validity – Results accurately

represented what the study intended to
address

• External Validity – Results can be
generalized/transferred/applied from the
representative smaller sample to the
larger population

• Reliability – Results are consistently the
same/similar if the study was repeated

• Objectivity – Results were objectively
realized as much as humanly possible

Criteria for Determining Qualitative
Trustworthiness in Research Results

• Credibility – Results are
credible/believable from the perspective
of the participant

• Transferability – Results can be
generalized/transferred/applied to other
related contexts/settings

• Dependability – Results are dependable or
could be repeated

• Confirmability – Results can be
confirmed/corroborated by others

Ethical Assurances

• Confirm the study received approval from NCU’s IRB prior to data collection

• Describe how confidentiality/anonymity was enforced

• Identify how the collected data will be securely stored

• Present strategies used to prevent researcher biases and experiences from
influencing the collection and analysis of the data/findings

Summary

• Summarize major points without providing new information

• Logically lead into the next chapter

Study Feasibility
Is the study specialization related, is it doable, is it needed and will it

add to the research knowledge base?

STUDY FEASIBILITY –
CONSIDERATIONS

Assess whether your study CAN be conducted practicality
• Population

• Accessibility

• Time

• Factors for success

Obtainable, accessible, timely, actionable, reasonable, manageable

Determine objectively and rationally (you may be very passionate
about your overall topic – remember this is a learning process)

FEASIBILITY

Who? How long? What skills?

• Population Identification

• Vulnerable populations

• Population knowledge &
experience

• Access for recruitment

• Recruitment process

• Quantity of participants

• Personal timelines

• Scope & duration

• Scope creep

• Weekly commitment

• Constraining time
factors

• Research methods

• Research design

• Data analysis

• Library research

• Tutoring services

• Editing services

IRB Tip!
Reminder: Students cannot recruit people they already
have a relationship with either personal or professional.

VULNERABILITY

There are two important types of vulnerability:

• “Decisional impairment, whereby potential subjects lack the capacity to make autonomous
decisions in their own interest, perhaps as a result of undue influence/inducement

• Situational/positional vulnerability, whereby potential participants may be subjected to coercion”

[Adam Mrdjenovich, Ph.D, 2016]

IRB Tip!
If you are considering these populations, red lights should go
off and you should dig deeper and talk through the research
with IRB. It’s better to discuss early then go through many IRB
revisions.

VULNERABILITY

Vulnerable under federal regulations:

• Pregnant women, fetuses, and neonates

• Children

• Prisoners

Other vulnerable populations:

• Racial Minorities

• Institutionalized

• Physically Handicapped

Continued:

• Students

• Employees

• Patients

• Educationally/Economically Disadvantaged

• Impaired decision making (mentally ill/
dementia/traumatic brain injury)

• Illiterate or low fluency in language of study

IRB Tip!
If you are considering these populations, red lights should go
off and you should dig deeper and talk through the research
with IRB. It’s better to discuss early then go through many
IRB revisions.

Exploring the Relationship between the Knowledge Quality of an Organization’s Knowledge

Management System, knowledge worker productivity, and employee satisfaction

Dissertation Manuscript

Submitted to Northcentral University

School of Business

in Partial Fulfillment of the

Requirements for the Degree of

DOCTOR OF BUSINESS ADMINISTRATION

by

LAURALY DUBOIS

La Jolla California

June 2021

Approval Page

By

Approved by the Doctoral Committee:

Dissertation Chair: INSERT NAME Degree Held Date

Committee Member: INSERT NAME Degree Held Date

Committee Member: INSERT NAME Degree Held Date

10/04/2021 | 06:45:52 MSTPh.D.

Robert Davis

Leila Sopko

Ph.D., MBA 10/01/2021 | 06:01:14 MST

10/03/2021 | 08:46:21 MSTPh.D.

Garrett Smiley

LAURALY DUBOIS

Exploring the Relationship between the Knowledge Quality of an Organization’s Knowledge Management
System, knowledge worker productivity, and employee satisfaction

Abstract

The problem addressed by this study was that there is often great difficulty encountered in trying

to retrieve knowledge assets about events in the past required for strategic decision-making

without an effective, in-place Knowledge Management System (KMS) (Oladejo & Arinola,

2019). The purpose of this quantitative, correlational study was to explore the relationship

between the knowledge quality of an organization’s KMS, knowledge worker productivity, and

employee satisfaction for software industry organizations in California. The Jennex and Olfman,

Knowledge Management (KM) Success Model served as the basis of the framework resulting in

knowledge quality as the independent variable and knowledge worker productivity and employee

satisfaction as the dependent variables (Jennex & Olfman, 2006). Data collected from 154

participant surveys guided answers to the research questions. A Spearman correlation analysis

between knowledge quality and knowledge worker productivity was assessed for the first

research question. A significant positive correlation was observed (rs = 0.94, p < .001, 95% CI

[0.92, 0.96]). This correlation indicates that as knowledge quality increases, knowledge worker

productivity tends to increase. A Spearman correlation analysis between KMS knowledge quality

and employee satisfaction was assessed for the second research question. A significant positive

correlation was observed (rs = 0.93, p < .001, 95% CI [0.91, 0.95]). This correlation indicates

that as knowledge quality increases, employee satisfaction tends to increase. The most significant

implication from this study was the unexpected strength in the correlation coefficient for each

research question. This study contributed to the Knowledge Management research community

due to the failure of organizations to implement a successful KMS in the workplace. Further

research to include updated Knowledge Management performance indicators may be helpful to

organizations in several industries worldwide.

Acknowledgments

I would like to give thanks and praise to my Lord Jesus Christ for giving me the strength

to do all things! I cannot proclaim enough love and appreciation for my wonderful husband, Bob

as my motivator and cheerleader during this entire journey. I am thankful to my boys Erik, Brian,

and Roby for encouraging me during the rough times. I want to leave a legacy to my

grandchildren Lily, Noah, Elijah, Gideon, Gabriella, and Josiah that if Nana can do it, so can

you. Many famly members also supported me through the years and I love you all.

Hey Mom, I did it!

I would like to give a word of gratitude to my chair, Dr. Garrett Smiley for his guidance

and support that kept me going through the challenging moments. I appreciate very much Dr.

Robert Davis and Dr. Leila Sopko for serving as my Northcentral University dissertation

committee members and the feedback throughout this process.

Table of Contents

Chapter 1: Introduction ……………………………………………………………………………………………………. 1

Statement of the Problem ……………………………………………………………………………………………. 3
Purpose of the Study ………………………………………………………………………………………………….. 4
Theoretical Framework ………………………………………………………………………………………………. 5
Nature of the Study ……………………………………………………………………………………………………. 8
Research Questions ……………………………………………………………………………………………………. 9
Hypotheses ……………………………………………………………………………………………………………….. 9
Significance of the Study ……………………………………………………………………………………………. 9
Definitions of Key Terms …………………………………………………………………………………………. 10
Summary ………………………………………………………………………………………………………………… 12

Chapter 2: Literature Review ………………………………………………………………………………………….. 15

Theoretical Framework …………………………………………………………………………………………….. 20
Knowledge Worker ………………………………………………………………………………………………….. 28
Knowledge Management ………………………………………………………………………………………….. 32
Knowledge Management System ………………………………………………………………………………. 43
Knowledge Worker Productivity ……………………………………………………………………………….. 55
Employee Satisfaction ……………………………………………………………………………………………… 58
Summary ………………………………………………………………………………………………………………… 60

Chapter 3: Research Method …………………………………………………………………………………………… 63

Research Methodology and Design ……………………………………………………………………………. 65
Population and Sample …………………………………………………………………………………………….. 68
Instrumentation ……………………………………………………………………………………………………….. 69
Operational Definitions of Variables ………………………………………………………………………….. 70
Study Procedures …………………………………………………………………………………………………….. 74
Data Analysis ………………………………………………………………………………………………………….. 75
Assumptions ……………………………………………………………………………………………………………. 77
Limitations ……………………………………………………………………………………………………………… 78
Delimitations …………………………………………………………………………………………………………… 78
Ethical Assurances …………………………………………………………………………………………………… 79
Summary ………………………………………………………………………………………………………………… 80

Chapter 4: Findings ……………………………………………………………………………………………………….. 81

Validity and Reliability of the Data ……………………………………………………………………………. 83
Results ……………………………………………………………………………………………………………………. 87
Evaluation of the Findings ………………………………………………………………………………………… 91
Summary ………………………………………………………………………………………………………………… 93

Chapter 5: Implications, Recommendations, and Conclusions ……………………………………………. 95

Implications…………………………………………………………………………………………………………….. 96

Recommendations for Practice ………………………………………………………………………………… 103
Recommendations for Future Research …………………………………………………………………….. 104
Conclusions …………………………………………………………………………………………………………… 105

References ………………………………………………………………………………………………………………….. 107

Appendices …………………………………………………………………………………………………………………. 131

Appendix A ………………………………………………………………………………………………………………… 132

Appendix B ………………………………………………………………………………………………………………… 133

Appendix C ………………………………………………………………………………………………………………… 134

Appendix D ………………………………………………………………………………………………………………… 140

Appendix E ………………………………………………………………………………………………………………… 141

Appendix F…………………………………………………………………………………………………………………. 146

List of Tables

Table 1 Research Study Variables …………………………………………………………………………………. 140
Table 2 Shapiro-Wilk Test Results for all Study Variables Test for Normality ………………………. 87
Table 3 Summary of Descriptive Statistics ……………………………………………………………………….. 89
Table 4 KMS Success Survey Participants by Gender………………………………………………………. 141
Table 5 KMS Success Survey Participants by Age …………………………………………………………… 141
Table 6 KMS Success Survey Years Employed ………………………………………………………………… 142
Table 7 KMS Success Survey Years of KMS Usage ………………………………………………………….. 143
Table 8 KMS Success Survey Education Level ………………………………………………………………… 143
Table 9 KMS Success Survey Employment Position …………………………………………………………. 144
Table 10 KMS Success Survey Industry Employed …………………………………………………………… 145
Table 11 Spearman Correlation Result: KMS KQ and KWP ………………………………………………. 90
Table 12 Spearman Correlation Results: KMS Knowledge Quality and Employee Satisfaction . 91

List of Figures
Figure 1 Scatterplot of KMS KQ and KWP ………………………………………………………………………. 86
Figure 2 Scatterplot of KMS KQ and employee satisfaction ……………………………………………….. 86
Figure 3 G*Power Statistics Analysis…………………………………………………………………………….. 132
Figure 4 Halawi’s (2005) KMS survey permission request/approval ………………………………….. 133
Figure 5 Halawi KMS Survey Questions (2005) ………………………………………………………………. 134
Figure 6 IRB Approval Letter ……………………………………………………………………………………….. 146

1

Chapter 1: Introduction

Harnessing the power of organizational knowledge through Knowledge Management

(KM) activities complemented by an efficient Knowledge Management System (KMS) support

the utilization of knowledge assets to meet strategic objectives aimed to gain and maintain a

competitive advantage (Oladejo & Arinola, 2019). KM activities encompass the accumulation,

retrieval, distribution, storage, sharing, and application of learned knowledge (Al-Emran et al.,

2018; Shujahat et al., 2019). The internal and external learned knowledge leads to valuable

knowledge assets during the transformation of tacit information such as undocumented and

implicit knowledge into documented, explicit information for future consumption (Andrawina,

Soesanto, Pradana, & Ramadhan, 2018; Putra & Putro, 2017). Over twenty years ago,

Knowledge Management’s rapid growth facilitated the need to leverage these knowledge assets

within a KMS, acting as the mechanism to promote management capabilities for organizational

knowledge (Orenga-Roglá & Chalmeta, 2019). KMS appeared out of a specific information

technology system to support knowledge-centric practices to manage organizational learned

knowledge (Orenga-Roglá & Chalmeta, 2019; Wilson & Campbell, 2016).

Knowledge workers utilize an organization’s KMS to store and retrieve knowledge,

improve knowledge sharing, and access knowledge sources promoting Knowledge Management

capabilities (Levallet & Chan, 2018; Orenga-Roglá & Chalmeta, 2019; Surawski, 2019; Wang &

Yang, 2016; Xiaojun, 2017; Zhang & Venkatesh, 2017). Knowledge workers within an

organization represent employees assigned to a classified business position performing tasks

requiring a specific skill set to be productive when performing the assigned job role (Surawski,

2019). When knowledge workers take part in KM activities, these actions contribute to

knowledge assets within the KMS supporting future knowledge work (Shrafat, 2018). The

2

intended flow of knowledge from daily knowledge exchange events between knowledge

workers, the KMS, and organizational management lay the foundation for business leaders to

augment strategic decision-making (Alaarj et al., 2016; Buenechea-Elberdin et al., 2018). An

effective KMS to support KM activities is critical for capturing, retrieving, storing, sharing, and

applying organizational knowledge assets (Al-Emran et al., 2018; Shujahat et al., 2019). The

successful implementation of the organization’s KMS lays the framework for KM activities to

generate knowledge assets to foster future decision-making capabilities (Orenga-Roglá &

Chalmeta, 2019; Putra & Putro, 2017). De Freitas and Yáber (2018) describe three factors

required for the successful implementation of an organization’s KMS, including stakeholders,

technology, and organizational constructs. The need to measure the successful KM activities

within a KMS of an organization resulted in the arrival of the Jennex and Olfman KM Success

Model (Jennex, 2017; Jennex & Olfman, 2006; Karlinsky-Shichor & Zviran, 2016). In this study,

the implementation of the KMS as a tool to support KM activities spotlights the technical aspect

of the KMS from the vantage point of the knowledge worker’s use of the KMS.

The Jennex and Olfman KM Success Model named six performance indicators for

measuring the implementation of an organization’s KMS to support KM activities (Jennex, 2017;

Jennex & Olfman, 2006). Six high-level categories serving as performance indicators include

knowledge quality, system quality, service quality, intent to use/perceived benefit, use/user

employee satisfaction, and net system benefits (Jennex, 2017; Jennex & Olfman, 2006). The

implementation of the KMS affects the knowledge quality component determining the accuracy

and timeliness when knowledge workers retrieve the stored knowledge assets within the correct

context to perform job tasks (Wang & Yang, 2016). The ability to retrieve accurate, timely, and

contextual knowledge assets when knowledge workers query the KMS enables value-added

3

benefits supporting knowledge worker productivity (Kianto, Shujahat, Hussain, Nawaz, & Ali,

2019; Shujahat et al., 2019). The KMS knowledge quality affects knowledge workers’

productivity, enabling value-added activities, and increased business performance (Drucker,

1999; Iazzolino & Laise, 2018).

Scholars report business leaders fail to implement an effective KMS empowering KM

activity necessary to promote knowledge worker productivity (Jennex, 2017; Karlinsky-Shichor

& Zviran, 2016; Sutanto, Liu, Grigore, & Lemmik, 2018; Vanian, 2016; Xiaojun, 2017). The

failure to enable knowledge worker productivity during KM activities influences business

et al., 2019). The

relationship between the knowledge quality of an organization’s Knowledge Management

System, knowledge worker productivity, and employee satisfaction forms the basis of this study.

Statement of the Problem

The problem addressed by this study was that there is often great difficulty encountered

in trying to retrieve knowledge assets about events in the past required for strategic decision-

making without an effective, in-place Knowledge Management System (KMS) (Oladejo &

Arinola, 2019). Knowledge Management (KM) is challenging to implement and requires

exploration and improvement in its’ continued application and development (Putra & Putro,

2017). Additional difficulties associated with the lack of an effective KMS include knowledge

asset unavailability, improper knowledge asset documentation, excessive time consumption

associated with searching for knowledge assets, decision-making overhead, and duplication of

effort (Oladejo & Arinola, 2019). A substantial number of Knowledge Management System

(KMS) implementations have not achieved their intended outcomes, such as employee

performance and employee satisfaction (Zhang & Venkatesh, 2017).

4

Researchers continue to seek additional perspectives on factors preventing knowledge

workers from retrieving expected benefits from an organization’s KMS (Iazzolino & Laise, 2018;

Karlinsky-Shichor & Zviran, 2016; Shujahat et al., 2019; Zaim et al., 2019). This problem is

significant as knowledge systems have infiltrated every aspect of the business process requiring

an organization’s capability to implement and successfully use a KMS (Nusantara, Gayatri, &

Suhartana, 2018). According to Vanian (2016), the continued loss of millions of dollars flows

from businesses’ failure to implement an efficient KMS to support knowledge worker

productivity that requires further research. When organizations fail to implement a successful

KMS, KM strategies depending on the use of knowledge assets for knowledge worker

productivity and employee satisfaction also fail (De Freitas & Yáber, 2018; Demirsoy &

Petersen, 2018; Putra & Putro, 2017; Xiaojun, 2017).

Purpose of the Study

The purpose of this quantitative, correlational study was to explore the relationship

between the knowledge quality of an organization’s KMS, the knowledge worker productivity,

and employee satisfaction for software industry organizations in California. This study is

relevant and contributes to the Knowledge Management research community as millions of

dollars in losses from unsuccessful KMS implementations fail to satisfy expected benefits in

knowledge assets to support business performance (Fakhrulnizam et al., 2018; Levallet & Chan,

2018; Nusantara et al., 2018; Vanian, 2016). Multiple challenges remain toward achieving KM

capabilities and the successful implementation of an organization’s KMS to benefit the use of

knowledge assets for knowledge worker productivity and employee satisfaction (De Freitas &

Yáber, 2018; Demirsoy & Petersen, 2018; Putra & Putro, 2017; Xiaojun, 2017).

5

The researcher conducted this study with an online survey as the research instrument and

gathered data from knowledge workers employed in software industry firms in California. The

independent variable, KMS knowledge quality construct, contains dimensions of the KM

strategy/process, richness, and linkages originating from the Jennex and Olfman KM Success

Model, providing the framework in the theoretical context of performance indicators (Jennex,

2017; Jennex & Olfman, 2006). Knowledge worker productivity and employee satisfaction

within the context of KMS usage represent the dependent variables (Jennex, 2017; Jennex &

Olfman, 2006). Halawi granted permission in writing to extract KMS survey questions to

operationalize the variables within the online survey using a 7-point Likert scale (Halawi, 2005).

An encrypted Microsoft Excel program served as the tool for storing and analyzing the survey

data using IBM SPSS Statistics version 26. G*Power 3.1.9.4 version software produced the

output for a priori power analysis (medium effect size = .0625, error = .05, power = .95,

predictors = 1) resulting in 153 as a required sample size in participants displayed in Appendix A

Figure 3.

Theoretical Framework

Initially, the design of only one information system designated to support management

processes and decision-making considered a reasonable cost for the organization (Carlson, 1969;

Ermine, 2005). At this time, information systems theory (IST) offered connections between

computational logic and the technology used to process data for supplying information known

only as the information system (IS) (Lerner, 2004). The rapid progression of emerging

technologies shifted the business processes needs spawning the separation of information

systems based on the purpose of the system, including Management Information Systems (MIS),

Decision Support Systems (DSS), and Expert Systems (ES) (Devece Carañana et al., 2016;

6

Medakovic & Maric, 2018; Mentzas, 1994). MIS supplied management with the capability to

analyze the business information for the organization related to technology management and the

management of technology use (Devece Carañana et al., 2016). DSS diverged from the primary

information system as a mechanism to supply graphical or logical data analysis for semi-

structured business problems in support of strategic decision-making and support (Medakovic &

Maric, 2018; Mentzas, 1994). ES enabled the gathering and organizing of organizational learned

knowledge toward specific technology applications for all management levels (Medakovic &

Maric, 2018; Mentzas, 1994). The split of an all-encompassing information system into distinct

organizational support systems set up the framework for systems used worldwide.

Answering the call to the evolution of innovative information systems, the DeLone and

McLean Information System (IS) Success Model supplied organizations the context to measure

the performance indicators of their various information systems (DeLone & McLean, 1992;

DeLone & McLean, 2003; DeLone & McLean, 2004). This model provided the approach in

measuring dimensions of information quality, system quality, service quality, system use and

usage intentions, user employee satisfaction, and net system benefits (Liu, Olfman, & Ryan,

2005; Zuama, Hudin, Puspitasari, Hermaliani, & Riana, 2017). The emergence of the KMS

became popular due to the infusion of knowledge-centric practices to manage knowledge assets

giving birth to the Knowledge Management System (Alavi & Leidner, 2001; Wu & Wang, 2006;

Zhang & Venkatesh, 2017). The need to measure Knowledge Management’s success resulted in

the introduction of the Jennex and Olfman KM Success Model to find performance indicators

while using the organization’s Knowledge Management Systems (Jennex, 2017; Jennex &

Olfman, 2006). The transformation of the DeLone and McLean Information System (IS)

Success Model into the Jennex and Olfman KM Success Model brought the Knowledge

7

Management System constructs within an organization’s information system for insertion of

the Knowledge Management processes.

The KM Success Model maintained the six similar categories as the DeLone and McLean

IS Success Model transforming only the measurement components as needed to accommodate

the specific measurement needs of the KMS (DeLone & McLean, 1992; DeLone & McLean,

2003; DeLone & McLean, 2004; Jennex, 2017; Jennex & Olfman, 2006). The knowledge quality

dimension within an organization’s KMS described in the KM Success Model guided the basis of

the theoretical framework in this study (Jennex, 2017; Jennex & Olfman, 2006). The dimensions

of KMS knowledge quality as an independent variable operationalize into three components

defined as KM strategy/process, richness, and linkages as measurements of success within the

KMS (Jennex, 2017; Jennex & Olfman, 2006; Liu et al., 2008). Numerous researchers have

studied how the implementation and maintenance of an organization’s KMS determine the

knowledge workers’ ability to retrieve accurate and timely organizational stored knowledge

(Andrawina et al., 2018; De Freitas & Yáber, 2018; Ferolito, 2015; Xiaojun, 2017; Zhang &

Venkatesh, 2017). The role of the knowledge worker and the outcome of knowledge worker

productivity and employee satisfaction undoubtedly continue to evolve in reaction to future KMS

features and capabilities to address KMS knowledge quality challenges in the workplace

(Fakhrulnizam et al., 2018; Jabar & Alnatsha, 2014). As businesses seek to increase knowledge

worker productivity, this demand for the successful KMS implementation deems an improved

usage of an organization’s KMS (Levallet & Chan, 2018). Therefore, examining the relationship

between the knowledge quality of an organization’s KMS, knowledge worker productivity, and

employee satisfaction assists researchers and business leaders in identifying potential barriers in

the implementation strategies of the organization’s KMS.

8

Nature of the Study

This study is a quantitative, correlational study to explore the relationship between the

knowledge quality of an organization’s KMS, knowledge worker productivity, and employee

satisfaction within the software industry in California. The quantitative research method is

appropriate and applied in this study to explore the relationship between the independent and

dependent variables based on reliable data collection methods and instruments for interpreting

the data analysis to present unbiased results (Hancock et al., 2010). The correlational research

design in this study statistically determined the relationship between the designated study

variables with online survey data (Hancock et al., 2010). The quantitative, correlational study

supported the study’s problem statement, purpose, and research questions as reflected in the

operationalized variables and statistical tests based on the relationship between the variables

(Field, 2013).

Probability sampling to collect data from a random sample of employees with the desired

characteristics evaluated the potential relationship between the variables (O’Dwyer & Bernauer,

2013). The target sample size included at least 153 qualified knowledge worker participants. The

online survey questions were available on the Qualtrics web platform, presenting a 7-point Likert

scale for each question grounded on Halawi’s KMS Success survey (Halawi, 2005). The survey

questions supplied the basis of measurement in the relationship between the designated study

variables after analyzing the collected data from knowledge workers in their natural environment

(O’Dwyer & Bernauer, 2013). IBM SPSS and Microsoft Excel tools analyzed the collected data

with statistical tests to determine if the rejection of the null hypothesis was necessary (Field,

2013).

9

Research Questions

The list of research questions applicable in this survey research method supports the

quantitative method. The research questions support the goal of this study to examine the

relationship between the knowledge quality of an organization’s Knowledge Management

System, knowledge worker productivity, and employee satisfaction. These research questions

form the basis for the research method and design, reflecting the statement of the problem and

purpose of the study. Each research question corresponds with the hypothesis statements.

RQ1. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

RQ2. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction?

Hypotheses

H10. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and knowledge worker productivity.

H1a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and knowledge worker productivity.

H20. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and employee satisfaction.

H2a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and employee satisfaction.

Significance of the Study

The continued failure to manage knowledge assets costs businesses millions of dollars in

10

(IDC) identify the various attempts to harness the knowledge assets by implementing KMS

online systems that have not provided the desired productivity result (Ferolito, 2015; Vanian,

2016). A global attempt to address the lack of standards for implementing KMS resulted in

creating ISO 30401:2018 to support Knowledge Management standards within organizations

(“ISO 30401:2018,” 2018). Researchers continue to seek additional perspectives to identify other

contingency factors preventing knowledge workers from retrieving expected benefits from an

organization’s KMS (Iazzolino & Laise, 2018; Karlinsky-Shichor & Zviran, 2016; Shujahat et

al., 2019; Zaim et al., 2019). This problem is significant as knowledge systems have infiltrated

every aspect of the business process, necessitating the implementation and successful usage of a

KMS (Nusantara et al., 2018). According to Vanian (2016), the continued loss of millions of

dollars from the failure of businesses to implement an efficient KMS to support knowledge

worker productivity requires further research.

Definitions of Key Terms

Data

Data is a fact collected or communicated without a specific meaning until analyzed and

transitioned into information (Becerra-Fernandez et al., 2008).

Employee Satisfaction

Employee satisfaction is one of the components of the KMS knowledge quality

independent variable in showing a successful experience based on the actual use of the KMS

from each use.

Explicit Knowledge

Explicit knowledge is represented in a codified such as words or numbers and easily

shared in any format (Becerra-Fernandez et al., 2008).

11

Information

Information is the transformation of data representing a specific set of values (Becerra-

Fernandez et al., 2008).

KM Strategy/Process

KM Strategy/Process is one of the three components of the knowledge quality

performance indicator is based on the specific actions of the knowledge users to locate the

knowledge asset within the KMS and the process for the knowledge strategy when using the

system (Jennex, 2017; Jennex & Olfman, 2006).

Knowledge

Knowledge is the transformation of information into facts capable of making decisions

and enabling responses (Becerra-Fernandez et al., 2008).

Knowledge Management

KM is the management of knowledge collected within the organization within an

organization for strategic decision-making (Becerra-Fernandez et al., 2008).

Knowledge Work

Knowledge work is the action of creating and using knowledge to perform tasks required

to generate output required for an organization’s products and services (Shujahat et al., 2019).

Knowledge Worker

A knowledge worker is an employee within an organization assigned to a classified

business position performing tasks requiring a specific skill set to perform the job role

(Surawski, 2019).

12

Knowledge Worker Productivity

The productivity of knowledge workers demands the timely completion of the intellectual

task delivering a quality service or product in an efficient manner while exhibiting innovative

methods (Shujahat et al., 2019).

Linkage

Linkage is one of the components of the KMS knowledge quality independent variable

describing the internal mappings of code to provide the results from the search query entered by

the knowledge worker (Jennex, 2017; Jennex & Olfman, 2006; Levallet & Chan, 2018).

Richness

Richness is one of the components of the KMS knowledge quality independent variable

indicating the accuracy and timeliness of the knowledge retrieved from the KMS, and the

applicable context expected by the user of the KMS (Jennex, 2017; Jennex & Olfman, 2006).

Tacit Knowledge

Tacit knowledge of an intangible nature requiring interpretation for contextual

understanding (Becerra-Fernandez et al., 2008).

Summary

The failure of businesses to implement a successful KMS causes them to lose millions of

dollars annually from reduced knowledge worker productivity (Ferolito, 2015; “ISO

30401:2018,” 2018 et al., 2019). International organizations and academic institutions

call for further research to identify factors contributing to the lack of KMS success (Iazzolino &

Laise, 2018; Karlinsky-Shichor & Zviran, 2016; Shujahat et al., 2019; Vanian, 2016; Zaim et al.,

2019). The researcher used the quantitative, correlational research method and design to explore

the relationship between the knowledge quality of an organization’s Knowledge Management

13

System, knowledge worker productivity, and employee satisfaction. The Jennex and Olfman KM

Success Model support the theoretical framework representing the KMS containing knowledge-

centric practices to provide knowledge assets utilized to accomplish an organizational business

purpose (Alavi & Leidner, 2001; Ermine, 2005; Jennex, 2017; Jennex & Olfman, 2006; Wu &

Wang, 2006).

The list of research questions applicable in this correlational design supports the

quantitative method exploring if a relationship exists between the knowledge quality dimension

within the KMS, knowledge worker productivity, and employee satisfaction. This problem is

significant as knowledge systems have infiltrated every aspect of the business process requiring

an organization’s capability to implement and successfully use a KMS (Nusantara et al., 2018).

The continued loss of millions of dollars from the failure of businesses to implement an efficient

KMS to support knowledge worker productivity requires further research (Iazzolino & Laise,

2018; Karlinsky-Shichor & Zviran, 2016; Shujahat et al., 2019; Zaim et al., 2019).

In Chapter 2, a synthesized review studied the relationship between the quality of a

KMS, knowledge worker productivity, and employee satisfaction and examined the historical

and current research for each major theme. The domains contributing to the knowledge of this

topic included knowledge worker (KW), Knowledge Management (KM), Knowledge

Management System (KMS), knowledge worker productivity (KWP), and employee satisfaction.

In chapter 3, descriptions of the research design and method supported the identified population

and sample participant selection. The planned instrumentation for preparing the data collection

and analysis of variables led to the assumptions, limitations, delimitations, and ethical assurances

applicable in this study. In chapter 4, the findings and evaluation from data analysis allowed the

researcher’s presentation summary of the research questions and hypothesis results. In chapter 5,

14

the final implications, recommendations, and conclusions will serve as the basis for this

researcher to present recommendations for future researchers in this domain. The researcher will

present this study to contribute to the body of knowledge contributing to the failure of

organizations to implement a successful KMS to support knowledge worker productivity and

employee satisfaction in the workplace.

15

Chapter 2: Literature Review

Business managers continue to fail in the successful implementation of the organization’s

Knowledge Management System (KMS), causing millions of dollars in annual losses from lack

of knowledge worker productivity (Ferolito, 2015; Levallet & Chan, 2018; Vla

This correlational study examines whether a relationship exists between knowledge worker

productivity and employee satisfaction with the knowledge quality of the organization’s KMS

(Jennex, 2017; Jennex & Olfman, 2006; Liu et al., 2008). The research questions align with the

problem and purpose statements in this study. The researcher used the research questions as a

guide to support the problem statement and purpose statement. The researcher used the first

research question to guide the data collection and analysis and evaluated if a relationship existed

between the knowledge quality of an organization’s KMS and knowledge worker productivity.

Next, the evaluation of employee satisfaction during the usage of the organization’s KMS

supported the second research question.

Organizations implement a KMS expecting a return on investment through improved

performance, quality, and productivity (Fakhrulnizam et al., 2018; Gunadham &

Thammakoranonta, 2019; Jahmani, Fadiya, Abubakar, & Elrehail, 2018). Yet, businesses

continue to report a loss in worker productivity despite efforts in using the KMS to manage

knowledge assets ISO 30401

offered standards and guidance in an attempt to assist organizations across the globe to

implement a successful KMS (Byrne, 2019; Corney, 2018; “ISO 30401:2018,” 2018). Scholars

are asking for continued research to gain additional insight into unknown factors related to

knowledge worker productivity while using the organization’s KMS (Iazzolino & Laise, 2018;

Karlinsky-Shichor & Zviran, 2016; Shujahat et al., 2019; Zaim et al., 2019).

16

Knowledge worker productivity begins by harnessing the power of knowledge assets

within the KMS to retrieve explicit knowledge (Ali et al., 2016; Wilson & Campbell, 2016;

Yuqing Yan & Zhang, 2019). The knowledge worker must have tacit knowledge of the task

subject allowing the worker to seek unknown knowledge assets within the KMS. The knowledge

worker interacts with the KMS applying tacit knowledge to begin searching for the stored,

explicit knowledge within the KMS. Due to the knowledge worker’s tacit knowledge of the

subject, the knowledge worker understands if the retrieved results from the KMS displaying the

explicit knowledge offers the knowledge assets expected. When the KMS search results do not

return the expected explicit knowledge assets, the knowledge worker loses productivity resulting

in financial losses for the organization over the long

Researchers have found that knowledge worker productivity while using the KMS is impacted

by the ability to retrieve knowledge and locate explicit knowledge assets as intended (Andrawina

et al., 2018; Zaim & Tarim, 2019).

There is a lack of research studies investigating knowledge worker productivity with the

specific knowledge quality of the KMS. Researchers with similar studies exploring connections

between the components of the KMS and knowledge worker productivity give attention to the

social and cultural relationships and not the KMS itself (Kianto et al., 2019; Iazzolino & Laise,

2018; Shujahat et al., 2019). Overarching research in the literature reveals there are many

knowledge sharing barriers in the workplace while using the KMS from a social perspective that

produces a hindrance to knowledge worker performance (Alattas & Kang, 2016; AlShamsi, &

Ajmal, 2018; Caruso, 2017; Eltayeb & Kadoda, 2017; Ghodsian, Khanifar, Yazdani, & Dorrani,

2017; Muqadas, Rehman, Aslam, & Ur-Rahman, 2017). Knowledge sharing as a construct does

not apply to this research study due to the social nature connection to knowledge worker

17

productivity. This researcher seeks to generate theoretical constructs toward examining potential

relationships between the dimensions of knowledge quality and employee satisfaction within the

KMS and not from a social aspect.

Knowledge quality as a component of the Jennex and Olfman KM Success Model is

relevant to the success of the knowledge worker’s productivity based on the context of the

explicit knowledge provided by the KMS (Jennex, 2017). The knowledge quality component as

one of six performance indicators of the KMS is operationalized into knowledge quality

constructs from the Jennex and Olfman KM Success Model (2017) to identify potential

relationships between knowledge worker productivity and employee satisfaction in this study.

When knowledge worker productivity becomes linked to financial outcomes, the timeliness of

the results returned by the KMS search query provides a measured value-added based on time

wasted or gained by the knowledge worker to perform their job tasks from the results (Iazzolino

& Laise, 2018). When the search query provides no results or results without the correct context,

the knowledge worker must perform another search query or search for the knowledge assets

using another tool or method (Jennex, 2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al.,

2018; Zhang, 2017).

Organizations’ consideration of time spent without results could equate to a loss in

potential earned revenue (Levallet & Chan, 2018). The problem faced by organizations using a

KMS realizing financial losses due to knowledge worker productivity presents a problem to the

a review of the literature to examine the relationship between KMS knowledge quality,

knowledge worker productivity, and employee satisfaction. The documentation and search

18

strategy used to accumulate scholarly and peer-review literature relevant to this study completes

the introduction.

A review of the literature included scholarly books, scholarly and peer-reviewed articles

in journals, empirical research, dissertations, and industry-focused Internet publications forming

the foundation of this study. A review in the origins of information systems later split into

various functions such as the KMS to harness knowledge assets and learning to form the

foundation for one dimension of the system. Further review to show the constructs of knowledge

worker productivity connections to KMS yielded the framework to investigate the relationship

between the knowledge quality of an organization’s KMS, knowledge worker productivity, and

employee satisfaction outcomes. The components of KMS knowledge quality as a performance

indicator incorporate KM strategy/process, richness, and linkages within the KMS as facets of

knowledge quality framed the search criteria for this study. Multiple databases accessed through

Northcentral University’s online library, including ProQuest, Sage, EBSCO, and Gale articles

enabled the literature basis for this study. Various searches from scholarly, primary resources

over the past four years using a combination of keywords included Information System Theory,

KMS, Knowledge Management System, information system, DeLone and McLean IS Success

Model, Jennex and Olfman KM Success Model, KM process, KM strategy, Knowledge

Management, KM model, knowledge worker, knowledge worker productivity, knowledge

sharing, information system theory, employee satisfaction, job satisfaction, knowledge theory,

organizational theory, and system quality.

In the next section, a review of the theories for the theoretical frameworks of this study

formulated the applicable knowledge themes based on the research. A brief review of multiple

seminal foundational theories evolving into the current model supported the basis of this study

19

beginning with Information Systems Theory (IST) (Langefors, 1977; Lerner, 2004). IST

supported the infancy of information systems when differing definitions between data and

information caused controversy among scholars and business leaders. The birth of separate

information systems to address separate business needs entreated performance indicators of

success resulting in the DeLone and McLean Information Systems (IS) Success Model (DeLone

& McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004; Liu et al., 2005; Zuama

et al., 2017). The rise in the digital management of knowledge assets resulted in Knowledge

Management Systems disrupting the information system arena resulting in the creation of the

Jennex and Olfman KM Success Model (Jennex, 2017; Jennex & Olfman, 2006). This

progression in specific technology systems to address a specific business need to be functioned

as a segway for each model to supply performance indicators of success for each system. The

need to finish standards and governance of Knowledge Management Systems has not progressed

since the offering of the Jennex and Olfman KM Success Model. Therefore, the context of

knowledge quality as a component of the KMS originates from the Jennex and Olfman KM

Success Model’s definition of knowledge quality, including the constructs. A review of these

models gives the content of the theoretical framework section to support the remaining theme

domains.

The review of each subcategory within each theme domain reveals a historical

perspective of past applications relevant to this research study. Next, current implications

applicable to the study topic became known based on existing research. Research findings on

each domain, including opposing views in the literature, allow a multi-faceted view of each

theme. Evidence of research in each theme forms the basis of this chapter in exploring if a

relationship exists between the knowledge quality of an organization’s KMS, knowledge worker

20

productivity, and employee satisfaction while using the KMS. The relevant knowledge themes

within this study form the major sections, including knowledge workers, Knowledge

Management, Knowledge Management Systems, knowledge worker productivity, and employee

satisfaction. Finally, the summary section of this chapter reiterates key concepts, reviews gaps in

the literature, and establishes the research and design methodology as a segway to chapter 3.

Theoretical Framework

The brief overview of seminal theories supported the selected theoretical framework as

the foundation aligning the purpose of this study, the problem statement, research questions,

and hypothesis. Next, an in-depth description of each framework applicable to this study

examined significant components. The next section describes theories within the management

of engineering and technology discipline and theories applicable to the study topic. Finally, an

introduction to the themes supporting the purpose of this study was included as a prelude to

the remaining Literature Review section and closed with a review of chapter two in the

summary section.

The historical evolution of the selected theoretical framework portrayed the natural

progression within the technology industry concerning the systematic management of

knowledge within an organization. Initially, one information system could support all facets

of the business needs due to the limited processing capabilities (Medakovic & Maric, 2018).

The grandfather of this framework developed by Borje Langefors originated from the

disagreement in definition and purpose between data and information within technological

systems progressed into Information Systems Theory (IST) (Langefors, 1977; Lerner, 2004).

Multiple information systems sprang quickly to life fostering the need for a framework

measuring information system performance met by the DeLone and McLean IS Success

21

Model (DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004; Liu

et al., 2005; Zuama et al., 2017). Innovative technology later manifested the need to clarify

the definition, purpose, and value between information and knowledge within the business

units. The Jennex and Olfman KM Success Model repurposed the DeLone and McLean IS

Success Model performance indicators to address the flood of knowledge becoming readily

available throughout the organization derived from new innovative technology (Jennex, 2017;

Jennex & Olfman, 2006). The Jennex and Olfman KM Success Model support the purpose of

this study aligning the specific system component of knowledge quality within an

organization’s KMS to determine if a relationship exists based on the measurement of the

system component to the outcome of knowledge worker productivity and employee

satisfaction.

Information Systems Theory (IST)

The seminal foundational framework of the study originates from the information

systems theory (IST), historically addressing the underlying computational logic and the

technology used to process data for providing information known only as of the information

systems (Langefors, 1977; Lerner, 2004). Langefors (1977) developed the Information Systems

Theory to address the lack of theoretical framework distinguishing information as a separate

construct from data. During this era, the terms data and information had become synonymous

leading Langefors (1977) to develop the Information systems theory to identify the specific

output of information through multiple, distinct technology systems. Volkova and Chernyi

(2018) describe applications to Information Systems Theory within current systems to magnify

theoretical implications in an efficient information flow throughout the workplace, creating a

new cultural existence (Volkova & Chernyi, 2018). The emergence of the Management

22

Information Systems (MIS), Decision Support Systems (DSS), and Expert Systems (ES) now

served distinct functional purposes within the organization (Devece Carañana et al., 2016;

Medakovic & Maric, 2018; Mentzas, 1994). A standard method to measure the performance

indicators of these various information systems did not exist until DeLone and McLean (2006)

surveyed the literature to identify six main components.

DeLone and McLean IS Success Model

As these innovative technology systems gained popularity within the business arena, the

DeLone and McLean IS Success Model provided a framework for measuring individual

information systems success (DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone &

McLean, 2004). Regardless of the purpose of the organization’s information system, the high-

level categories to measure performance indicators included information quality, system quality,

service quality, system use and usage intentions, user employee satisfaction, and net system

benefits (DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004; Liu et

al., 2005; Zuama et al., 2017). The first component of the DeLone and McLean IS Success

Model offered information quality as a performance indicator serving as the foundation of the

system’s capabilities such as the storage of information and capability to deliver the stored

information (DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004).

DeLone and McLean describe system quality as the second component acting as an indirect

capability of the system based on the benefits during usage of the system (DeLone & McLean,

2004). The third component, service quality, represents the user of the system’s initial intent to

use the system and monitor user employee satisfaction (2004). Next, DeLone and McLean

(2004) outline the system use/usage intentions as the fourth component are dependent on the

previous experience from using the same system and user allowing indication of future intent to

23

use the system based on the previous quality of results received from the system. DeLone and

McLean (2004) present user employee satisfaction as the fifth component focusing on the result

of the user’s experience upon completion of using the system. The final component of the

DeLone and McLean IS Success Model (2004) called net system benefits offers a combined

overall performance indicator of the information system based on the perceived value as a direct

result of the previous components’ performance indicator outcomes. Upon the evolution of

leveraging information into knowledge, quality as a performance indicator would necessitate a

performance indicator for the quality of knowledge addressed within in the offering of the

Jennex and Olfman KM Success Model (Jennex & Olfman, 2006).

Jennex and Olfman KM Success Model

Upon the birth of the KMS as an individual system encapsulating knowledge assets, the

Jennex and Olfman KM Success Model transformed the DeLone and McLean IS Success Model

(2004) to address the additional knowledge component of the organization’s Knowledge

Management System (Jennex, 2017; Jennex & Olfman, 2006). The six high-level performance

indicator categories within the Jennex and Olfman KM Success Model remained similar to the

DeLone and McLean IS Success Model (2004) except for replacing information quality with

knowledge quality specific to the KMS (Jennex, 2017; Jennex & Olfman, 2006). However,

Jennex and Olfman (2006) determined the underlying components of each performance indicator

category in the KM Success Model specifically support the needs of the KMS. The six high-level

categories comprising knowledge-specific performance indicators include knowledge quality,

system quality, service quality, intent to use/perceived benefit, use/user employee satisfaction,

and net system benefits (Jennex, 2017; Jennex & Olfman, 2006).

24

Knowledge quality as the first component of the Jennex and Olfman KM Success Model

transforms the DeLone and McLean IS Success Model’s first component of information quality

based on the differentiation of the knowledge system needs (Jennex, 2017; Jennex & Olfman,

2006). Jennex and Olfman (2006) incorporated subcategories within the knowledge quality

component containing three performance indicators: knowledge strategy/process, richness, and

linkages. KM strategy/process as a component of the knowledge quality performance indicator

focuses on the user’s specific actions and the process for the knowledge strategy when using the

system, according to Jennex and Olfman (2006). Richness, as the next performance indicator

within knowledge quality and one of the KMS knowledge quality components of this study,

indicates the accuracy and timeliness of the knowledge retrieved from the KMS, as well as the

applicable context expected by the user of the KMS (Jennex, 2017; Jennex & Olfman, 2006).

The third indicator of knowledge quality offered by Jennex and Olfman (2006) is the linkage

supplying the internal mappings of code to provide the results from the search query entered by

the user (Levallet & Chan, 2018).

The second component of the model includes system quality ascribing three performance

indicators as technological resources, the form of KMS, and levels of KMS tying performance

indicators to the organization’s specific KMS technology frameworks (Jennex, 2017; Jennex &

Olfman, 2006). Next, service quality as the third component describes management support, user

KM service quality, and information system KM service quality as indicators of performance

from the organization’s management and governance perspective (Jennex, 2017; Jennex &

Olfman, 2006). System use/usage intentions represent the fourth component in the Jennex and

Olfman KM Success Model (2006). These intentions become dependent on the previous

experience using the same system and user, allowing indication of future intent to use the system

25

based on the previous quality of results received from the system (2006). Similar to the DeLone

and McLean IS Success Model, Jennex and Olfman (2006) describe use/user employee

satisfaction as the fifth component of a performance indicator referencing a successful

experience based on the actual use of the KMS and employee satisfaction from each use. The

final component of the Jennex and Olfman KM Success Model (2006), similar to the DeLone

and McLean IS Success Model (2004), is called net system benefits as an indicator of the KMS

based on perceptions of value.

Frameworks out of Scope

A review of the literature found information-based theories out of scope, including the

Technology Acceptance Model (TAM) and Task Technology Fit Theory (TFF). The Technology

Acceptance Model (TAM) focuses on the user perspective from a social facet during interaction

with the technology system and not the system component (Nugroho & Hanifah, 2018). The

Task-Technology Fit (TTF) theory incorporates system components within the theoretical

constructs yet bases the outcome measurement on the relationship from the social perspective

(Wipawayangkool & Teng, 2016). These two frameworks do not align with the purpose of this

study based on the emphasis from the user perspective and not from the knowledge quality

component of the system. Organizational theories in the cited research denote the framework

evident in the collective behaviors, attitudes, and norms (Hamdoun et al., 2018; Mousavizadeh et

al., 2015; Nuñez et al., 2016; Ping-Ju Wu et al., 2015). Dynamic capabilities theory incorporates

the actions an organization must take to integrate competencies within business practices to

ensure competencies within the market, including knowledge-sharing processes (Meng et al.,

2014). Resource-based view within the literature further proposes an approach in supporting

business performance when knowledge sharing behaviors align with business processes (Meng et

26

al., 2014; Oyemomi, Liu, Neaga, Chen, & Nakpodia, 2018). The resource-based view provides

the framework of an organization’s capability to strategically manage the resources on an implicit

and explicit level to achieve a competitive advantage and often correlate with the organizational

culture comprised of the behaviors, and attitudes of the collective group within the organization

(Mousavizadeh et al., 2015; Nuñez Ramírez et al., 2016; Oyemomi et al., 2018; Ping-Ju Wu et

al., 2015). These organizational theories lean toward the social-cultural constructs which do not

align with the purpose of this study.

Relevant Knowledge Domains

Knowledge workers, Knowledge Management, Knowledge Management Systems,

knowledge worker productivity, and employee satisfaction comprise the five major themes

encircling subdomains forming the context of this research. A review of the literature provides

the basis for the theoretical framework included in each subdomain. The introduction of a

knowledge worker as a significant theme throughout the literature review introduces the

construct of knowledge work, knowledge workers in the technology industries, and the 21st-

century role of knowledge workers (Drucker, 1999; Iazzolino & Laise, 2018; Karlinsky-Shichor

& Zviran, 2016; Moussa et al., 2017; Shrafat, 2018; Shujahat et al., 2019; Surawski, 2019;

Turriago-Hoyos et al., 2016; Zhang, 2017; Zaim et al., 2019). The topic of Knowledge

Management within the literature includes the role of KM in an information technology industry,

the business leader’s use of KM, and KM utilization within the KMS (Banerjee et al., 2017;

Iazzolino & Laise, 2018; Karlinsky-Shichor & Zviran, 2016; Martinez-Conesa et al., 2017;

Shrafat, 2018; Shujahat et al., 2019; Zaim et al., 2019). KMS as the third major theme includes

the digital management of knowledge assets, implementation strategies of the KMS, and the

components of the KMS and the measurement of the components (Karlinsky-Shichor & Zviran,

27

2016; Nusantara et al., 2018; Shrafat, 2018; Shujahat et al., 2019; Surawski, 2019; Zhang, 2017;

Zaim et al., 2019). Knowledge worker productivity is the fourth theme in this study incorporates

research exploring the financial costs related to knowledge worker productivity using the KMS

and the promotion and enablement of knowledge worker productivity using the KMS (Drucker,

1999; Karlinsky-Shichor & Zviran, 2016; Iazzolino & Laise, 2018; Shrafat, 2018; Shujahat et al.,

2019; Zhang, 2017; Zaim et al., 2019). A review of the literature supports employee satisfaction

as a final theme in this study identifying user satisfaction during KMS use, and KMS knowledge

quality constructs including KM process/strategy, richness, and linkage to indicate performance

success of KM activities (Jennex, 2017; Jennex & Olfman, 2006; Zamir, 2019; Zhang &

Venkatesh, 2017).

The historical evolution in the separation of data, information, and knowledge into a

unique asset within organizations led to the natural progression of framework models. The

Information System Theory (Langefors, 1977), DeLone and McLean IS Success Model

(DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004), and the

Jennex and Olfman KM Success Model (Jennex & Olfman, 2006) arose from the need to offer

business performance indicators to assess the organization’s successful utilization of these

assets from an information system perspective. The theoretical framework supports the five

major knowledge domains as subtopics: knowledge workers, Knowledge Management,

Knowledge Kanagement Systems, knowledge worker productivity, and employee satisfaction.

Each major theme incorporates subdomains supported by literature throughout the literature

review’s remaining body through the lens of the Jennex and Olfman KM Success Model

(Jennex, 2017; Jennex & Olfman, 2006).

28

Knowledge Worker

Researchers point to Peter Drucker as the creator of the term knowledge worker in

response to the innovation of technology and changes in workforce needs (Archibald et al., 2018;

Banerjee et al., 2017; Ebert & Freibichler, 2017; Surawski, 2019; Turriago-Hoyos et al., 2016).

Drucker (1999) predicted that knowledge workers would become the most valuable asset within

an organization in the 21st century. Though a review of the literature does not set one definitive

characteristic for all knowledge workers, a common prerequisite theme indicates intellectual and

cognitive job tasks as acceptable requirements (Moussa et al., 2017; Surawski, 2019; Turriago-

Hoyos et al., 2016). Knowledge workers shift from previous expectations to produce a quantity

of work to results-based output and the ability to self-govern and generate knowledge (Turriago-

Hoyos et al., 2016). Today, knowledge workers exist in technical and nontechnology industries

(Surawski, 2019).

Researchers describe additional characteristics of the knowledge worker to contain self-

sufficiency in planning, managing, and auditing the quality of the knowledge work tasks to

complete the intended business process (Iazzolino & Laise, 2018; Shrafat, 2018; Surawski, 2019;

Zhang, 2017). Surawski (2019) describes multiple references to knowledge workers within the

business and education with labels comprising information workers, data workers, professionals,

specialists, experts, white-collar workers, and office workers. Surawski believes white collar is

synonymous with present-day knowledge workers with subcategories of office workers,

information workers, and data workers. Some authors delineate professionals as a type of skilled

knowledge worker often associated with a management role within the business or education

industry (Lee et al., 2019; Surawski, 2019; Vuori et al., 2019). Specialists contained within the

category of a knowledge worker are associated with staff possessing a specific skill set, not

29

within a management position (Nikolopoulos & Dana, 2017; Surawski, 2019). On the other

hand, subject matter experts possess a skill set within a non-management role obtained through

experience often sought after by their peers in a specific knowledge area (Surawski, 2019; Vuori

et al., 2019). Knowledge worker themes found in the literature incorporate knowledge work,

knowledge workers in the technology industry, and the 21st-century role of knowledge workers

in support of this study.

Knowledge Work

To operationalize knowledge workers, one must distinguish knowledge work as the act of

creating and using knowledge to perform organizational tasks required to generate output

necessary for an organization’s products and services (Drucker, 1999; Moussa et al., 2017;

Shujahat et al., 2019). The role of knowledge workers requires the self-management of tasks to

perform these expected deliverables as knowledge work. In direct contrast to manual labor

workers dependent upon the completion of another co-worker’s laborious tasks, the knowledge

worker possesses an intellectual aptitude necessary for performing each task to complete their

work based on their skillset (Costas & Karreman, 2016; Drucker, 1999; Shujahat et al., 2019).

Costas and Karreman (2016) further describe knowledge work on a higher level as a fulfillment

aspect, inspiring innovation, and autonomy for the knowledge worker. Knowledge work, as

performed by qualified knowledge workers, often allows independence creating connectivity

between the knowledge worker and the organization through ownership of knowledge work tasks

according to Costas and Karreman. These tasks require performance by experienced knowledge

workers possessing specific skillsets to create, transfer, and utilize knowledge to perform the

assigned knowledge work task (Costas & Karreman, 2016; Surawski, 2019). Knowledge work

tasks often heed quality over quantity to retrieve the desired outcome fostering the organization’s

30

challenge to measure the output of knowledge worker tasks due to the intangible nature of

knowledge work (Iazzolino & Laise, 2018; Shrafat, 2018).

Knowledge Workers in the Technology Industries

A review of the literature reveals knowledge workers span across multiple industries

while much of the research denotes the majority attributed to technology roles or industries

(Nikolopoulos & Dana, 2017; Surawski, 2019; Zelles, 2015). Within our digital age, technology-

based businesses require the employment of knowledge workers possessing a specific skill set to

perform the intellectual knowledge work (Surawski, 2019). The technical skill sets of knowledge

workers across a variety of industries within the technology-focused industries (Zelles, 2015).

Businesses within the technology industry fall within the “Information Sector” industry,

according to the U.S. Bureau of Labor Statistics spanning a wide range of technology-specific

products and services (“Industries at a Glance,” 2020). Software-specific industry firms

publishing software to market these products for installation or offerings of services for

installation of software fall within the software industry category of NAICS code #51121

(“Banner Reports,” 2019). Knowledge workers in the technology industries become

organizational assets offering intellectual capabilities to ensure firm innovation through the

creation of value without physical labor (Vuori et al., 2019; Zelles, 2015). Just as innovation

burst onto the scene with the emergence of the 21st Century, knowledge workers’ role

exponentially experienced a shift of knowledge work due to a growing digital era (Vuori et al.,

2019; Shujahat et al., 2019).

21st Century Role of Knowledge Workers

Changes in the knowledge workers’ role in the workplace influenced the activities needed

to perform expected business outcomes (Turriago-Hoyos et al., 2016; Vuori et al., 2019; Wessels

31

et al., 2019). Surawski (2019, p. 114) outlines the progression of standard terms to describe

present-day knowledge workers described as “knowledge age workers,” “professionals,”

“specialists,” and “intellectual workers.” Generating knowledge for contributions to

organizational innovation and value-added activities toward the business’s successful

performance has become the norm in the 21st Century knowledge worker. The advancements in

technology continue to impact the knowledge worker’s role through improved tools to produce

knowledge more efficiently and enhanced work conditions for the knowledge worker (Wessels et

al., 2019). Regardless of the industry, multiple employment titles meet the definition of

knowledge worker found within the occupational employment code “15-0000 Computer and

Mathematical Occupations,” according to the Bureau of Labor Statistics (“Occupational

Employment Statistics,” 2018). However, the role of knowledge workers in the 21st Century will

not be limited to only technical and intellectual work tasks, rather the proficiencies in creativity,

visionary, and collaborative insights will embody the very nature of knowledge work in this

digital era (Archibald et al., 2018; Vuori et al., 2019). Challenges faced by businesses throughout

the sequence of knowledge workers coinciding with technological advancements include

managing the knowledge assets and processes (Shujahat et al., 2019).

Knowledge worker themes found in the literature incorporate knowledge work,

knowledge workers in the technology industry, and the 21st-century role of knowledge workers

in support of this study. Current findings indicate knowledge as the foundation for innovation

requiring knowledge workers to generate knowledge assets (Costas & Karreman, 2016;

Turriago-Hoyos et al., 2016). Researchers want future research to incorporate the office space

and conditions of knowledge workers to support knowledge work activities (Krozer 2017;

Surawski, 2019). Additionally, researchers seek contributions in the role of organizational

32

management impacts on knowledge workers (Turriago-Hoyos et al., 2016; Ullah et al., 2016;

Wei & Miraglia, 2017).

Knowledge Management

Knowledge management allows business leaders to leverage the accumulation of

knowledge assets originating from people, processes, and technology acting as sources for

strategic decisions, innovation, and competitive advantage (Koc et al., 2019; Shujahat et al.,

2019; Yuqing Yan & Zhang, 2019). Business leaders must assimilate multiple facets of

knowledge assets to determine strategic business decisions aimed to leverage innovation and

ensure a competitive advantage in the marketplace (Martinez-Conesa et al., 2017). Knowledge

management supports an organization’s innovation capabilities based on processes encouraging

knowledge exchange activities inspiring new products and services unique to the business

(Martinez-Conesa et al., 2017). The way knowledge becomes capitalized determines the

organization’s innovative ability during the Knowledge Management process cycle in the

capture, creation, storage, retrieval, sharing, and utilization of knowledge (Al-Emran et al., 2018;

Shujahat et al., 2019).

Innovative products and services require support from the restructuring of business

processes integrated with Knowledge Management to address emerging technologies and

et al.

facilitate improved business performance, laying the foundation for the competitive advantage

based on effective Knowledge Management practices (Martinez-Conesa et al., 2017; Roldán et

al., 2018). Researchers Al Ahbabi et al. (2019) found a significant positive relationship between

the Knowledge Management processes and the organization’s performance, proposing future

research in the information sector and inclusion of additional contexts within Knowledge

33

Management processes. Additional subdomains surrounding Knowledge Management found in

the literature incorporate the history of Knowledge Management, components of Knowledge

Management, and organizational units of Knowledge Management within the Knowledge

Management theme.

History of Knowledge Management

The first efforts to connect the management of knowledge assets to the firm’s ability to

achieve business goals and impact performance originated three decades ago by knowledge

experts Ikujiro Nonaka and Hirotaka Takeuch (Hoe, 2006; Nonaka, 1991). Early developments

in Knowledge Management efforts pursued asset management of skilled employee knowledge,

business processes, and all organizational learned knowledge for capturing intellectual capital

(Koc et al., 2019). As technology systems evolved, the capability to digitally manage knowledge

covering all facets of the organization set the stage using the Knowledge Management System

et al., 2018; Yuqing Yan & Zhang, 2019). The capability to manage knowledge assets

cultivated by innovative technology in the workplace gave life to Knowledge Management in

supporting an improved competitive advantage (Yuqing Yan & Zhang, 2019). Researchers’

descriptions of these components of Knowledge Management have changed although the end

goal is the same in managing the organization’s knowledge in a manner leading to a net benefit

for the organization (Hashemi et al., 2018; Hoe, 2006; Mousavizadeh et al., 2015; Oyemomi et

al., 2018; Shujahat et al., 2019).

Components of Knowledge Management

Knowledge management represents the organization’s capabilities to create, capture,

share, store, and consume knowledge assets as the gateway to prevent loss of knowledge, inspire

innovation, and gain a competitive advantage (Caruso, 2017; Costa & Monteiro, 2016; Intezari et

34

al., 2017; Martinez-Conesa et al., 2017; Navimipour & Charband, 2016; Shujahat et al., 2019;

Yuqing Yan & Zhang, 2019). Standard components of Knowledge Management listed in the

research represent the creation of knowledge assets, capturing knowledge assets, sharing

knowledge assets, storage of knowledge assets, and the consumption of knowledge assets

necessary to promote the successful management of knowledge (Hashemi et al., 2018; Hoe,

2006; Mousavizadeh et al., 2015; Oyemomi et al., 2018; Shujahat et al., 2019). Each component

contributes to the cyclical phases within Knowledge Management processes organizations’

follow contributing to innovation and competitive advantage (Caruso, 2017; Costa & Monteiro,

2016; Intezari et al., 2017; Martinez-Conesa et al., 2017; Navimipour & Charband, 2016;

Shujahat et al., 2019; Yuqing Yan & Zhang, 2019).

The creation of knowledge assets as a component of Knowledge Management supports

the overarching goal of acquiring knowledge and distributing it across the organization to

improve business performance (Caruso, 2017; Levallet & Chan, 2018). The creation of

knowledge assets begins during the synthesis of tacit and explicit knowledge creating new

perspectives and significance of new knowledge through collaborative efforts (Al Ahbabi et al.,

2019; Cannatelli et al., 2017; Shujahat et al., 2019). This generation of new knowledge requires

the harmonious blend of tacit and explicit knowledge as a natural process within a conducive

workplace environment (Hoe, 2006; Oyemomi et al., 2018). Researchers believe the creation of

knowledge assets sets the stage for innovative opportunities and future capabilities supporting

business value assets and performance (Caruso, 2017; Hashemi et al., 2018; Mousavizadeh et al.,

2015; Shujahat et al., 2019). The creation of new knowledge requires collaboration across

organizational boundaries to stimulate creative mixtures of new and existing knowledge contexts

(Al Ahbab et al., 2019; Cannatelli et al., 2017). Researchers relay the benefits of continuous

35

knowledge creation may improve innovative capacities fostering new innovative services and

products, KM activities, and improved business performance (Cannatelli et al., 2017; Costa &

Monteiro, 2016). The use of technology offering multiple tools for generating new knowledge

may also aid in the flow of knowledge assets creation (Roldán, Real, & Ceballos, 2018).

Once knowledge assets are acquired or created, the capturing of this knowledge requires

cooperation from business units in the deliberate collection of explicit knowledge for the desired

purpose to improve business performance and innovation (Muqadas et al., 2017; Rutten et al.,

2016; Shujahat et al., 2019). The capturing of new knowledge assets progresses through a

lifecycle of collection, organization, and defining of explicit knowledge for reuse within the

organization (Al Ahbabi et al., 2019). Knowledge workers may capture new organizational

explicit knowledge gained through the performance of work tasks and processes (Al Ahbab et

al., 2019; Shujahat et al., 2019). Al Ahbab et al. note that capturing tacit knowledge stemming

from individual knowledge workers’ job experience and skillsets is difficult yet possible to

achieve new knowledge assets. Once new knowledge becomes captured, the process of

codification ensures the reuse of the knowledge as a tangible asset throughout the organization

and accessible within the KMS (Al Ahbab et al., 2019; Cannatelli et al., 2017). The capturing of

new knowledge assets is a continuous process invoking the remaining components of the unit’s

Knowledge Management efforts (Cannatelli et al., 2017; Costa & Monteiro, 2016; Mao et al.,

2016; Roldán et al., 2018).

Researchers describe knowledge sharing as a critical component of the Knowledge

Management process due to the distribution of knowledge assets through technology tools or

people across the business units (Al Ahbabi et al., 2019; Al Shamsi & Ajmal, 2018; Loebbecke,

van Fenema, & Powell, 2016; Muqadas et al., 2017; Oyemomi et al., 2018; Shujahat et al.,

36

2019). This critical component in Knowledge Management hinges upon the unlimited collection

of internal knowledge not yet known to others within the organization dwelling within the minds

of knowledge workers (Shujahat et al., 2019). Knowledge workers participating in the capturing

and creating of new knowledge remain essential to the successful sharing of knowledge in an

organization while fostering improved process standards, innovation capabilities, and business

performance (Muqadas et al., 2017).

Influences in workplace knowledge sharing derive from diverse sources within the

organization, including workplace culture, technology, organizational leadership, and knowledge

workers (Al Shamsi & Ajmal, 2018; Shujahat et al., 2019; Wei & Miraglia, 2017). However,

several researchers pose organizational culture as a leading facilitator in the promotion of

knowledge sharing within the organization (AlShamsi & Ajmal, 2018; Caruso, 2017; Costa &

Monteiro, 2016; Mao et al., 2016; Muqadas et al., 2017; Oyemomi et al., 2018). The role of

technology tools and systems toward knowledge sharing as a component of Knowledge

Management is unclear due to varying types of technology and implementation strategies

impacting measuring outcome success (Al Shamsi & Ajmal, 2018; Costa & Monteiro, 2016;

Mao et al., 2016; Oyemomi, 2018). Nevertheless, some researchers reflect the influence of

leadership as a determinant in the compliance of knowledge sharing of this component within the

Knowledge Management process based on the support or lack thereof from business

management (Al Shamsi & Ajmal, 2018; Cannatelli et al., 2017; Costa & Monteiro, 2016).

Ultimately, knowledge workers within the organization determine when to share knowledge

through social interaction and technology or when to withhold knowledge to use as leverage for

personal gain (Costa & Monteiro, 2016; Koenig, 2018; Muqadas et al., 2017).

37

Al Shamsi and Ajmal (2018) conducted a study to examine direct influences on the

knowledge sharing of service organizations within the technology industry. Data collection

efforts retrieved a total of 222 manager-employee responses in service organizations for the

identification of knowledge-sharing behaviors. The results show that organizational leadership is

an essential factor that impacts knowledge sharing in technology-intensive organizations,

followed by organizational culture, organizational strategy, corporate performance,

organizational process, employee engagement, and organizational structure. According to the

results, the least impactful factor is human resource management (Al Shamsi & Ajmal, 2018).

In contrast, many scholars seek to understand the barriers in knowledge sharing within

the workplace from the aspects of the knowledge worker, organization, and management as

barriers (Intezari et al., 2017; Muqadas et al., 2017; Orenga-Roglá & Chalmeta, 2019; Shrafat,

2018). Knowledge workers may function as a barrier agent preventing knowledge sharing within

the organization from a personal fear of job loss or loss of power or control (Intezari et al., 2017;

Muqadas et al., 2017; Orenga-Roglá & Chalmeta, 2019). Recent studies describe organizational

barriers to knowledge sharing based on the corporate culture and behavioral norms formulating

the knowledge worker’s motivation to share knowledge (Intezari et al., 2017; Shrafat, 2018;

Zimmermann et al., 2018). Within the digital arena, barriers to knowledge sharing evolve from

the technology employed by the organization aimed to share knowledge hinders the free flow of

awareness among the workplace (Al Shamsi & Ajmal, 2018; Costa & Monteiro, 2016; Mao et

al., 2016; Oyemomi, 2018).

The storage of knowledge assets requires a continuous cycle in Knowledge Management

as tacit knowledge becomes converted to explicit knowledge and stored using the organization’s

t

38

may encompass various forms of documents, digital media, databases, or data warehouses

selected by the organizational unit. Oftentimes, organizations offer multiple forms of storage

differing by the type of knowledge and knowledge worker skillsets (Hashemi et al., 2018). In

addition to the storage of knowledge assets, Knowledge Management Systems offer convenience

in managing the creation, capture, sharing, and retrieval of knowledge (Santoro et al., 2018;

Zhang, 2017; Yuqing Yan & Zhang, 2019). Regardless of the knowledge storage mechanism,

storage knowledge assets enable the consumption of this knowledge within the Knowledge

Management lifecycle to support innovation and business performance (Al Ahbabi et al., 2019;

Hashemi et al., 2018).

The retrieval of stored knowledge assets impacts knowledge workers’ capability to

consume the knowledge required to perform assigned tasks (Hashemi et al., 2018). The

successful cycle of Knowledge Management as an ever-evolving process in an endless stream of

creating, capturing, sharing, storing, and consuming knowledge assets embodies the goals of an

organization’s Knowledge Management strategy (Shujahat et al., 2019). The consumption of

knowledge produces a value-added asset that allows knowledge workers to apply new

knowledge to inspire innovation, the intellectual capability, and skillset to utilize this knowledge

toward problem-solving or new knowledge (Al Ahbabi et al., 2019; Shujahat et al., 2019).

Overall, the application of new knowledge promotes improved business processes, innovation

competencies, and business performance enabling the knowledge creation process across the

organization to continue (Al Ahbabi et al., 2019; Costa & Monteiro, 2016). Koc et al. (2019)

describe differing organizational units of Knowledge Management within the organization as

information management, process management, people management, innovation management,

and asset management assisting leadership in successful governance. Business leaders ascribe to

39

a variation of these organizational units of Knowledge Management based on organizational

needs (Koc et al., 2019; Shujahat et al., 2019; Yuqing Yan & Zhang, 2019).

Organizational Units of Knowledge Management

The continual demand for effective management of knowledge throughout the

organization formulated the maturity of the approach known today as Knowledge Management

(Koc et al., 2019; Shujahat et al., 2019; Yuqing Yan & Zhang, 2019). Knowledge management

perspectives continue to evolve as technology disruptions emerge. Knowledge management

encompasses cross-functional sectors within the firm requiring business leader oversight into the

management of the organization’s processes, the knowledge workers, and the workspace acting

as an essential business strategy (Al-Emran et al., 2018; Shujahat et al., 2019). Researchers Koc

et al. (2019) present current organizational units within Knowledge Management into categories

encompassing the management of information, processes, people, innovation, and assets within

the organization. These organizational unites of Knowledge Management subcategories are

described below.

Information management as a unit of Knowledge Management encapsulates the explicit

and recorded knowledge transformed from the organization’s intangible knowledge assets (Koc

et al., 2019). The management of information supports the organization’s knowledge strategy by

awareness and utilization of critical information transformed into knowledge suitable for the

organization (García-Alcaraz et al., 2019; Ramayani et al., 2017). Tacit and explicit knowledge

derives from the transformation of information into a useable form captured and stored for the

express purpose of reutilizing the knowledge for the benefit of the organization and preventing

loss of unique knowledge assets (Koc et al., 2019; Martinez-Conesa et al., 2017; Ramayani et al.,

2017; Shrafat, 2018). Information management is the key to unlock the door to initiate

40

Knowledge Management processes aimed at creating, capturing, storing, sharing, and processing

knowledge within the organization (García-Alcaraz et al., 2019; Ramayani et al., 2017).

Process management ensures the embedded knowledge unique to the organization

required to perform business processes reflects the operational knowledge and workflows of

procedures needed to perform knowledge work tasks (Koc et al., 2019; Steinau et al., 2019).

Knowledge management strategies seek process management efforts by putting the

organization’s data activities front and center regardless of the technology tools used to capture

the knowledge (Steinau et al., 2019). Organizations implement multiple business process

management approaches complimenting current business strategies, process tools, and

technology systems in place to support automation of processes, innovation capabilities, and

business performance (Nikiforova & Bicevska, 2018; Steinau et al., 2019).

Managing employees through a Knowledge Management lens encourages knowledge

workers to capture and share tacit and explicit knowledge, further supporting learning and future

access to the organization’s knowledge assets (Koc et al., 2019). The management of converting

tacit knowledge embedded in the knowledge worker’s understanding and expertise gained

through experience, research, and communication requires consistent people management efforts

(Yuqing Yan & Zhang, 2019). People management in the promotion of Knowledge Management

business processes must emphasize the knowledge worker cooperation in transcending tacit into

explicit knowledge because of workplace activities (Andrews & Smits, 2019; Olaisen & Revang,

2018). Management support of an environment fostering a cohesive workplace mindset

encourages new knowledge generation (Andrews & Smits, 2019; Koc et al., 2019).

The innovation management in Knowledge Management represents knowledge

conversion enabled from singular and collaborative learned knowledge, leading to discoveries

41

and development, and guiding improved business performance (Koc et al., 2019; Yuqing Yan &

Zhang, 2019). Innovation management relies upon the continual flow of knowledge creation and

innovation processes to increase business performance (Briones-Peñalver et al., 2019; Leopold,

2019). Leopold (2019) reviews innovation processes as the key ingredient to innovation

management, including shared Knowledge Management elements through transforming

organizational employees’ tacit knowledge into explicit knowledge, spurring innovation from a

combination of this knowledge, and the promotion of knowledge sharing. Managing innovation

within an organization falls within the Knowledge Management cycle flowing from the

transformation of tacit knowledge into explicit knowledge and, ultimately, into reusable

knowledge to support the innovation efforts within the organization (Sánchez-Segura et al.,

2016). Leopold describes the initial creation of knowledge through the end product requires a

full circle of the Knowledge Management cycle upheld by the collaboration of knowledge

workers within the organization. Collaboration empowers the creation and attainment of internal

knowledge assets supporting the formation of new products and services, facilitating continued

Briones-Peñalver

et al., 2019).

The asset management component reflects managing the intellectual capital of the

organization utilizing the intangible assets as leverage to gain a competitive advantage (

et al., 2019; Bacila & Titu, 2018; Koc et al., 2019; Prusak, 2017). The components of intellectual

capital within an organization consist of human, internal, and external capital assets supporting

the firm’s business performance and competitive capabilities (Prusak, 2017). Human capital

contributions to the intellectual capital of an organization impacted by the workplace culture and

knowledge worker capabilities to produce new knowledge and develop distinctive products or

42

2017). Internal or organizational capital stems from the knowledge captured within the

organization as explicit knowledge in technology applications and systems later formulated into

et al., 2019; Prusak, 2017). External capital includes the intellectual capital based on marketing

strategies through offerings of select products and services aimed to gain new customers and

demand for these products and services (Prusak, 2017). Asset management is a component of an

organization’s Knowledge Management strategy. It exploits the human, internal, and external

capital comprising the intellectual capital aimed to gain a competitive advantage within the

market (Junior et al., 2019).

A review of Knowledge Management themes applicable to this study supported by the

literature included the history of Knowledge Management, components of Knowledge

Management, and organizational units of Knowledge Management. Regardless of the

organization’s Knowledge Management unit strategies, researchers’ findings report the capture,

creation, transfer, storage, and consumption of knowledge assets contribute to the successful

management of knowledge aimed to prevent loss of knowledge and inspire innovation and gain a

competitive advantage (Costa & Monteiro, 2016; Shujahat et al., 2019; Yuqing Yan & Zhang,

2019; Zaim et al., 2019). Nevertheless, differing research within the literature reports

organizational leadership and organizational culture supporting successful Knowledge

Management contributing to innovation (AL Shamsi & Ajmal, 2018; Intezari et al., 2017;

Mousavizadeh et al., 2015). Intezari et al. (2017) call for future exploration into factors

supporting successful Knowledge Management outcomes. In contrast, other researchers seek to

contribute organizational cultural factors toward knowledge workers’ impacts on Knowledge

43

Management success. (Ullah et al., 2016; Wei & Miraglia, 2017). The emergence of

sophisticated technology systems supports the management of knowledge assets within a

Knowledge Management System (Nurulin et al., 2019; Zhang, 2017). Managers seek Knowledge

Management tools, including Knowledge Management Systems, to capitalize on the creation,

storage, sharing, and utilization of knowledge within the organization (Mao et al., 2016; Peng et

al., 2017; Roldán et al., 2018).

Knowledge Management System

An introduction to Knowledge Management System (KMS) as a significant theme within

the literature provides the history of KMS, types of KMS, implementation of KMS, knowledge

worker use of KMS, and KMS performance indicators as the constructs of this domain

contributing to this study. Zhang (2017) describes the Knowledge Management System as a type

of information system specifically aimed to manage knowledge assets. Innovative technologies

spurred Knowledge Management capabilities through new software and hardware offerings,

leading to a variety of KMS technology platforms and offerings (Demirsoy & Petersen, 2018;

Schwartz, 2014). The development of Knowledge Management Systems evolved throughout the

decades in response to Knowledge Management’s role within organizations (Nurulin et al.,

2019). Initial features of the KMS allowed knowledge workers the capability to search the

system for knowledge, additional access knowledge articles internal or external to the

organization, create or edit knowledge artifacts, and label existing artifacts assisting

collaborators for reuse of contextual knowledge (Orenga-Roglá & Chalmeta, 2019; Zhang &

Venkatesh, 2017). KMS tools continue to evolve, enhancing new capabilities assisting in

collaborative effectivity, encourage knowledge exchange, social, and sharing (Lee et al., 2019;

Orenga-Roglá & Chalmeta, 2019; Zhang, 2017).

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Business leaders leverage KMS as a structured tool to manage knowledge assets and

enable Knowledge Management processes, including capturing, creating, sharing, and applying

knowledge (Santoro et al., 2018). The type of KMS system becomes selected based on the

perceived support toward the needs of the Knowledge Management processes with supportive

features and tools (Demirsoy & Petersen, 2018; Lee et al., 2019). Organizations often find value

in adopting a KMS comprised of collaboration tools allowing the users to communicate

efficiently, exchange knowledge, and manage the stored knowledge and communication

mechanisms (Del Giudice & Della Peruta, 2016; Orenga-Roglá & Chalmeta, 2019; Zhang,

2017). Shrafat (2018) conducted a study of small and medium-sized businesses to identify

additional influences contributing to successful Knowledge Management practices and realized

benefits from implementing KMS. Shrafat analyzed survey results from 247 participants from

multiple small to mid-sized businesses incorporating the adoption of a KMS. Results from the

study indicated that various organizational readiness and capabilities encompassing Knowledge

Management, knowledge sharing, organizational learning, and technology contribute to the KMS

success outcome (Shrafat, 2018). In contrast, researchers conclude there remain barriers to

effective use of an organization’s KMS due to the ever-growing storage of multiple media

formats of knowledge within the system, the semantics of finding data within context, and lack

of standard system features (Kimble et al., 2016; Peng, Wang, Zhang, Zhao, & Johnson, 2017).

History of KMS

In 1978, the first rudimentary Knowledge Management System featured hyperlinks

within internal organizational groupware connecting remote workers across geographical

locations as a method to collaborate and share knowledge (Ceptureanu et al., 2012). The

popularity of internal Knowledge Management Systems within the business arena extended to

45

external networks upon the emergence of the internet, launching Knowledge Management

conferences and associations in the 1990s (Ceptureanu et al., 2012; Koenig, 2018). Multiple

types of Knowledge Management System product offerings soon materialized on the market

equipped with new features promising to provide successful management of knowledge assets

(Koenig, 2018). Initially, technological capabilities drove the type of Knowledge Management

Systems available to organizations and the features offered for the utilization of knowledge

assets (Koenig, 2018; Zhang, 2017).

Types of KMS

Several types of information systems exist for collaboration within the workplace among

knowledge workers aimed to support daily work tasks and share knowledge (Centobelli et al.,

2018; Dong et al., 2016; Lee et al., 2019; Zhang & Venkatesh, 2017). However, a review of the

literature revealed studies with multiple types of Knowledge Management Systems to support

Knowledge Management processes that exist within the workplace (Dong et al., 2016; Lee et al.,

2019; Zhang & Venkatesh, 2017). Therefore, discussions denoting types of KMS within an

organization referred to these types based on the purpose of the KMS and the categories

referenced in the literature. Classifications of the KMS are held dependent upon the use of the

system usage, including codified systems encompassing expert coding knowledge and

knowledge exchange systems aimed to share knowledge among employees (Venters, 2010;

Zhang, 2017).

The goal of the codification systems within the KMS domain is to capture tacit

knowledge from subject matter experts into the system, becoming codified explicit knowledge

available to knowledge workers within the workplace (Zhang, 2017). The codification process

within the KMS supports Knowledge Management efforts capturing multiple inputs of

46

information into a well-defined meaningful knowledge asset capable of reuse among knowledge

workers (Kimble et al., 2016). The progression of transforming data into knowledge, known as

semantics, is the basis of the KMS as a codification tool for retrieving varying forms of explicit

knowledge (Kimble et al., 2016). The KMS classification encompassing knowledge exchange

systems covers a wide range of systems supporting the organization’s Knowledge Management

activities and governance (Centobelli et al., 2018; Dong et al., 2016; Lee et al., 2019; Zhang &

Venkatesh, 2017). These systems encourage the exchange of knowledge, the transfer of tacit and

explicit knowledge, and the application of learned knowledge leading to the creation of new

knowledge (Zhang & Venkatesh, 2017). Zhang and Venkatesh explain the encouragement of

employee learning through interactive KMS offering knowledge workers numerous options to

interact and share knowledge within the workplace.

An organization’s KMS may belong to several categories depending on the features and

capabilities of the system purchased by the business to address specific business needs

(Centobelli et al., 2018; Del Giudice & Della Peruta, 2016; Dong et al., 2016.; Koenig, 2018;

Lee et al., 2019; Zhang, 2017; Zhang & Venkatesh, 2017). Koenig (2018) designates these

categories as content management, enterprise location, lessons learned, and communities of

practice A KMS with the sole purpose of warehousing knowledge assets for retrieval by

knowledge workers without collaboration features fall into the category of content management

(Koenig, 2018; Zhang, 2017). The KMS, as an enterprise location tool, provides built-in lookup

capabilities to find organizational subject matter experts possessing both tacit and explicit

knowledge needed by the knowledge worker required to complete a work task (Centobelli et al.,

2018; Zhang & Venkatesh, 2017). Database systems designed to contain and share knowledge

47

assets reflecting business expertise categorized as best practices or lessons learned systems

(Koenig, 2018).

Zhang (2017) expounds on additional features of KMS as lessons learned systems

provide knowledge workers the ability to create ‘how-to’ processes and knowledge-based articles

based on experiences in exercising tacit and explicit knowledge within the workplace meant to

share these experiences with co-workers. The recent development within the KMS domain

includes the emergence of communities of practice enhancing the capture of knowledge through

social means and the advancement of technology, including Web 2.0 tools (Del Giudice & Della

Peruta, 2016; Dong et al., 2016; Orenga-Roglá & Chalmeta, 2019). KMS inclusive of

communities of practice provides advanced technology features to capture tacit knowledge using

Web 2. 0 technologies such as video and audio intended for social collaboration (Del Giudice &

Della Peruta, 2016). Emerging technologies in the marketplace have revolutionized the

capabilities of KMS, incorporating multiple communication tools to support the Communities of

Practice platform (Lee et al., 2019).

Common characteristics of the KMS include a database providing a repository of

captured knowledge, access to the intranet, and or internet, aimed to support the creation,

capture, storage, and consumption of knowledge assets (Centobelli et al., 2018; Dong et al.,

2016; Lee et al., 2019; Zhang & Venkatesh, 2017). Powerful KMS containing several

capabilities to support all aspects of the organization’s Knowledge Management processes also

offer collaboration spaces for editing of knowledge within the system, conferencing capabilities,

and automation of knowledge flow within the system (Dong et al., 2016; Lee et al., 2019; Zhang

& Venkatesh, 2017).

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Implementation of KMS

The implementation of KMS may include both the initial installation of the KMS within

an organization by setting up a technology system and the application of the Knowledge

Management procedures and knowledge worker expected use of the KMS (Intezari & Gressel,

2017; Zhang, 2017; Zhang & Venkatesh, 2017). Kimble et al. (2016) suggest that organizations

identify the business needs before selecting the technology platform for the KMS. On the other

hand, researchers Orenga-Roglá and Chalmeta (2019) emphasize recognizing the organizational

culture and technology impacts the KMS will generate. Organizations may select the

functionality of the KMS as single-threaded seen in a content management system or the

selection of multi-functional capabilities as experienced with a community of practice

incorporating advanced system features for an enhanced experience within the KMS (Orenga-

Roglá & Chalmeta, 2019; Zhang & Venkatesh, 2017). Researchers agree that a systematic

approach for implementing the KMS is essential to knowledge worker job performance and

successful Knowledge Management while strategies to conduct this task lack within the literature

(Orenga-Roglá & Chalmeta, 2019; Zhang & Venkatesh, 2017).

Researchers describe key benefits from KMS implementations that deliver enhanced

Knowledge Management capabilities, financial gains, increased learning opportunities, and

competitive advantage for the organization (Intezari & Gressel, 2017; Zhang, 2017). Intezari and

Gressel (2017) relay the benefits of this implementation also transforms daily employee

knowledge encounters into new explicit experiences leading to innovative products and services.

Wang and Wang (2016) analyzed completed surveys from 291 Taiwanese businesses and found

relationships between innovation, organizational influences, and environmental factors within

49

KMS implementation. The researchers suggested future studies should include additional

performance indicators between the KMS and implementation factors (Wang & Wang, 2016).

Knowledge Worker Use of KMS

Regardless of the type of KMS or implementation strategies organizations employ to

manage knowledge, innovative technologies continue to improve knowledge worker utilization

of the KMS (Demirsoy & Petersen, 2018; Özlen, 2017). Business leaders desire knowledge

workers to contribute learned knowledge to the content of the KMS, utilize the KMS as a

knowledge-based source, and support knowledge work task efficiency (Sutanto et al., 2018).

Frequent interaction with the KMS encourages knowledge sharing and integration of knowledge

assets for the reuse of knowledge among knowledge workers (Martins et al., 2019; Özlen, 2017;

Shujahat et al., 2019). As knowledge workers utilize the system, these KMS activities enable the

continuous knowledge life cycle to create, capture, store, access and share knowledge (Demirsoy

& Petersen, 2018; Jahmani et al., 2018; Surawski, 2019). Participation within this cycle

contributes to transforming tacit knowledge into explicit knowledge in a reusable format (Tserng

et al., 2016). Knowledge workers then become motivated to seek solutions within the KMS by

searching stored knowledge to retrieve explicit instruction within their work tasks (Demirsoy &

Petersen, 2018; Zhang, 2017).

Researchers have pursued motives contributing to knowledge worker use of the KMS,

such as ease of using the system and collaboration capabilities (Del Giudice & Della Peruta,

2016; Dong et al., 2016; Lee et al., 2019; Zhang & Venkatesh, 2017). The knowledge worker

assesses the ease of using the KMS forms based on experiences resulting from interaction with

the system and perceived success (Del Giudice & Della Peruta, 2016; DeLone & McLean, 2003;

Dong et al., 2016). Based on the features available during the use of the KMS, knowledge

50

workers may be willing to interact with the system to seek available knowledge to perform a

work task or contribute learned knowledge to the KMS (Zhang & Venkatesh, 2017). Also,

knowledge workers continue to rely upon the KMS when the interaction with the system

produces expected results (Del Giudice & Della Peruta, 2016; Lee et al., 2019). Researchers

speculate the degree of utilization of the organization’s KMS contributes to Knowledge

Management success due to contributing factors encumbering organizational culture, the KMS

infrastructure, and knowledge worker impression of the KMS technology infrastructure (Lee et

al., 2019; Özlen, 2017).

Xiaojun (2017) performed a study reviewing the implementation features of one

organization’s KMS to identify potential factors influencing knowledge worker tasks, KMS

usage, and user experience. Xiaojun retrieved surveys and interviews from 1,441 knowledge

workers in finance and budgeting, accounting, personnel, customer management, sales,

advertising, and public relations business units. Xiaojun’s findings from the study signified a

positive relationship for knowledge workers based on the moderating influences from the

specific task, KMS, knowledge worker, and leadership.

KMS Performance Indicators

Jennex and Olfman (2006) outlined six key performance indicators within the Jennex

and Olfman KM Success Model as specific components utilized to measure the performance of

the KMS. These knowledge-specific performance indicators remain intact since the original

model known as knowledge quality, system quality, service quality, intent to use/perceived

benefit, use/user employee satisfaction, and net system benefits (Jennex, 2017; Jennex &

Olfman, 2006). The Jennex and Olfman KM Success Model (2006) are very similar to the

DeLone and McLean IS Success Model (1994) to address Knowledge Management dimensions

51

within each category. These categories include information/knowledge quality, system quality,

service quality, intent to use, use/user employee satisfaction, and net benefits (Jennex & Olfman,

2006). Jennex and Olfman presented the KM Success Model to address the growing need to

measure an organization’s KMS key performance indicators and knowledge dimensions updating

the construct of captured information into reusable knowledge assets (Jennex, 2017; Jennex &

Olfman, 2006; Liu et al., 2008).

Karlinsky-Shichor and Zviran (2016) reveal that scholars continue to interchange

information quality and knowledge quality as the same KMS component. This exchange is

evident as researchers continue to use theoretical constructs within the DeLone and McLean IS

Success Model when analyzing the dimensions of an organization’s KMS in conjunction with the

Jennex and Olfman KM Success Model (Liu et al., 2008; Nusantara et al.2018; Wu & Wang,

2006). Karlinsky-Shichor and Zviran (2016) presented a model in their study similar to both the

DeLone and McClean (2003) IS Success Model the Jennex and Olfman (2006) KM Success

Model analyzing only the information quality, system quality, and service quality. Instead, the

researchers added moderators in the user competence during usage of the system based on the

organization’s Knowledge Management capabilities (Karlinsky-Shichor & Zviran, 2016). In this

study, the researcher’s results based on 100 participants belonging to a knowledge-focused role

within the software industry indicated business leaders must consider both technical and cultural

influences of the KMS to foster acceptance from knowledge workers (Karlinsky-Shichor &

Zviran, 2016).

Alarming IDC survey results reveal the need for performance metrics to identify KMS

impact on business performance. The IDC reported that organizations with at least 1,000

employees would comprise at least 45% of global technology spending in 2020 (Vanian, 2016).

52

Medium-sized businesses consisting of 100 to 999 employees, intend to spend the most on

improving business performance (Vanian, 2016). The ISO recently created ISO 30401:2018 to

support organizations in developing a KMS for promotion and enablement of knowledge worker

productivity (“Knowledge Management Systems,” 2018). This evidence supports the application

of the Jennex and Olfman (2006) KM Success Model for organizations seeking key performance

indicators of KMS success based on the original six critical success factors. The performance

indicators include knowledge quality, system quality, service quality, intent to use/perceived

benefit, use/user employee satisfaction, and net system benefits (Jennex, 2017; Jennex &

Olfman, 2006). A reciprocal stream of influence often flows between the KMS and KM

capabilities within an organization leveraging the ability to harness the effective management of

knowledge, thereby leading to increased KMS acceptance and use (Shrafat, 2018).

Several authors supplement Knowledge Management capabilities with knowledge

sharing and KMS usage, further influencing business performance (Martinez-Conesa et al., 2017;

Meng et al., 2014; Nahapiet & Ghoshal, 1998; Zhang et al., 2018). However, other authors

present the effect of Knowledge Management capabilities incorporating knowledge sharing and

KMS usage as a direct stimulus on increased innovation within the organization (Hamdoun et al.,

2018; Hock, Clauss, & Schulz, 2016; Martinez-Conesa et al., 2017; Oparaocha, 2016). The

Jennex and Olfman KM Success Model (2006) key performance indicators offer the organization

a mechanism to measure the performance of the KMS into individual components and

dimensions as markers of knowledge sharing often connected to business performance. In this

model, the knowledge worker’s perspective of the reliability and accuracy of the retrieved results

within the KMS encouraging knowledge sharing measured by the richness dimension of

knowledge quality serving as the focal of this study.

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KMS Knowledge Quality. Knowledge quality as the first of six components in the

Jennex and Olfman KM Success Model (2006) holds the spotlight as the performance indicator

in examining a potential relationship between KM process/strategy, richness, and linkage as a

dimension of knowledge quality within the KMS. Researchers agree the deficits in knowledge

quality stored within an organization’s KMS prevents knowledge workers from retrieving

accurate information (Jennex, 2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al., 2018;

Zhang, 2017). Researchers attempt to operationalize the knowledge quality of a KMS from the

knowledge worker perception during utilization of the system (Jennex, 2017; Jennex & Olfman,

2006; Karlinsky-Shichor & Zviran, 2016; Sutanto, Liu et al., 2018; Zhang, 2017). In this study,

the construct of information quality and knowledge quality within a KMS synonymously contain

dimensions of the Knowledge Management processes/strategies, the richness of the retrieved

results, and the linkage of the KMS from the KMS knowledge quality construct (Karlinsky-

Shichor & Zviran, 2016). Knowledge workers expect to easily recover knowledge within the

organization’s KMS within the context of the search query in a timely and accurate fashion

(Jahmani et al., 2018). This expected knowledge content quality allows the knowledge worker to

combine current knowledge with the explicit knowledge system to generate new knowledge

assets available for reuse within the system (Jahmani et al., 2018).

The KMS incorporates the foundation of the Information Systems Theory (IST), bridging

the interaction with the system the underlying computational logic based on the models

embedded within the system to return expected results (Langefors, 1977; Lerner, 2004). These

results impact the knowledge worker experience using search queries to retrieve knowledge

determined by the relevant content (Demirsoy & Petersen, 2018; Karlinsky-Shichor & Zviran,

2016). Demirsoy & Petersen (2018) describe the Bayes classifier model and vector space model

54

as existing computational logic used as a framework for systems, including the KMS. These

models search only the vocabulary meanings of each word complicated by word vagueness, and

multiple definitions of one word may return irrelevant query results (Demirsoy & Petersen,

2018). Advanced queries logic often incorporates classifying and clustering combined with

statistical word frequencies to rank the sequence of assumed relevant results (Demirsoy &

Petersen, 2018).

In contrast, semantic information searches combine the computational logic of the KMS

based on the underlying semantic logic model to interpret the definition of words contained in

the search query with continued knowledge worker use of the system to improve the relevancy of

the results (Demirsoy & Petersen, 2018). As a component of knowledge quality within a KMS,

the linkages represent the structure of retrieved results returned after performing a search within

the KMS (Karlinsky-Shichor & Zviran, 2016). The richness as a component of knowledge

quality within a KMS signifies the accuracy and timeliness impacting the context of retrieved

results within the KMS (Karlinsky-Shichor & Zviran, 2016). The technology infrastructure of the

KMS, the continued use of the KMS, and the constant update of knowledge with the system

contribute to the knowledge quality of the KMS (Demirsoy & Petersen, 2018). Therefore, the

Knowledge Management processes and strategies as an element of knowledge quality within a

KMS comprise the unique methods, activities, and procedures the business sets in place to

manage knowledge assets (Demirsoy & Petersen, 2018; Karlinsky-Shichor & Zviran, 2016).

The degree of knowledge content quality retrieved from the KMS impacts the knowledge

worker’s perceived usefulness of the system (Jahmani et al., 2018; Zhang, 2017). This perceived

usefulness produces attitudes and behaviors related to knowledge workers’ motivation to utilize

the KMS for knowledge sharing within the organization (Jahmani et al., 2018; Zhang, 2017).

55

However, (Sutanto et al., 2018) maintain the knowledge worker efficiency during the use of the

KMS does not improve based on the perception of the quality of retrieved results. Jahmani et al.

(2018) concluded a study from multiple hospitals surveying healthcare staff regarding KMS

components finding that knowledge content quality retrieved from the knowledge worker is a

functional requirement for the perceived usefulness of KMS. Eltayeb and Kadoda (2017)

performed semi-structured interviews with experts, managers, and employees from multiple

organizations in the region to identify connections between Knowledge Management and

business performance. The researchers concluded a significant relationship between Knowledge

Management practices, future business strategies, and business performance and request future

researchers to explore the quality of knowledge information stored within the KMS (Eltayeb &

Kadoda, 2017).

Knowledge Worker Productivity

Knowledge worker productivity (KWP) as the fourth major theme within this study was

discussed as KWP measurement challenges, KWP enablement, and the KWP financial

implications while using the organization’s Knowledge Management System. Peter Drucker

rationalized the productivity capability of knowledge workers led to increased business

performance and financial gains (Drucker, 1999; Iazzolino & Laise, 2018). Productivity efforts

may benefit by empowering knowledge workers, instilling autonomy, enacting continuous

improvement for innovation, allowing self-governance of quality, and treating knowledge

workers as assets and not merely resources (Drucker, 1999; Iazzolino & Laise, 2018).

KWP Measurement Challenges

Challenges in measuring knowledge worker productivity arise from capturing potential

intangible tasks aimed to assign a metric for comparison of changes in productivity (Iazzolino &

56

Laise, 2018; Karlinsky-Shichor & Zviran, 2016). Productivity is one of the perceived KMS

benefits often cumbersome to measure (Karlinsky-Shichor & Zviran, 2016; Turriago-Hoyos et

al., 2016). The underlying activities impacting knowledge worker productivity and measurable

outcomes become intertwined with current Knowledge Management processes and knowledge

worker perceptions (Turriago-Hoyos et al., 2016). Iazzolino and Laise (2018) reviewed Drucker

and Public’s model to identify the meaning and measurement of productivity surrounding the

knowledge workers, management, and stakeholders. Iazzolino and Laise perceived like Drucker;

Public believed the measure of knowledge workers was comparable to manual workers based on

the value-added of activities and the ways of the employees. Public’s proposal to translate

knowledge worker’s activities into value-added metrics aligns with the purpose of the income

statement. Identifying the investment of human capital supports the notion of workers as

investments and not merely a line-item cost (Iazzolino & Laise, 2018). Methods rating the

measurement of knowledge worker productivity as a ratio calculation between the value-added

metric of each knowledge worker and the total number of employees and the differences in

productivity vary among the research (Duarte, 2017; Iazzolino & Laise, 2018).

KWP Enablement

The enablement of knowledge worker productivity begins with the effective use of the

KMS by knowledge workers within an organization supporting the Knowledge Management

capabilities (Shrafat, 2018). KMS usage as a dimension of Knowledge Management capabilities

is often noted as the most desired capability an organization strives to achieve expressed as an

exchange of organizational information, knowledge, and skills (Caruso, 2017; Intezari et al.,

2017; Navimipour & Charband, 2016). The knowledge worker productivity may easily be

measured when operationalized into variables measuring the number of times a user successfully

57

retrieved knowledge within an existing KMS (Dey & Mukhopadhyay, 2018). Researchers link

Knowledge Management activities as contributors to increased business performance and

productivity stemming from knowledge capturing, sharing, and application of knowledge assets

(Shrafat, 2018).

KWP Financial Implications

The failure of businesses to implement a successful KMS to retrieve knowledge assets

reported productivity losses well over 5.7 million published by Fortune 500 organizations

(Ferolito, 2015). The International Organization for Standardization (ISO) developed ISO

30401:2018 as the standard for the KMS organization followed for the promotion and

enablement of knowledge creation. The creation of the ISO 30401:2018 certification for

organizations with existing KMS supports organizations’ efforts to empower knowledge worker

productivity through efficient retrieval of organization knowledge for knowledge sharing (ISO

30401, 2018). Losses in knowledge worker productivity continue to plague businesses incapable

of leveraging the organization’s KMS to sustain knowledge sharing activities supporting

increased business performance (Ferolito, 2015; “ISO 30401:2018,” 2018; ).

Scholars report business leaders fail to ensure the organization’s KMS incorporates Knowledge

Management processes necessary for the promotion of knowledge worker productivity (Jennex,

2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al., 2018; Vanian, 2016; Xiaojun, 2017).

While utilizing the organization’s KMS, knowledge worker productivity then requires the

capability to accomplish the knowledge work task in an efficient and timely manner (Shujahat et

al., 2019). According to Vanian (2016), the continued loss of millions of dollars flows from the

failure of businesses to implement an efficient KMS to support knowledge worker productivity

that necessitates further research.

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Employee Satisfaction

Employee satisfaction is one of the desired outcomes after implementing the

organization’s KMS in support of KM activities (Zhang & Venkatesh, 2017). The specific KM

capabilities realized within the organization lead to employee satisfaction as an outcome through

employee empowerment to perform assigned job tasks (Zamir, 2019). Employee satisfaction as

the final major theme in this study is based on literature in context from the KMS usage

perspective (Jennex, 2017; Jennex & Olfman, 2006; Zamir, 2019; Zhang & Venkatesh, 2017). A

review of the literature further reveals subthemes related to this study as the user satisfaction

during the usage of the organization’s KMS and the KMS knowledge quality constructs defined

as KM strategy/process, richness, and linkage (Jennex & Olfman, 2006; Jennex, 2017; Kumar,

2018; Zamir, 2019; Zhang & Venkatesh, 2017).

User Satisfaction

The Jennex and Olfman KM Success Model describe the user satisfaction dimension in

this model as an indicator of the employee’s satisfaction with their interaction with the KMS

(Jennex & Olfman, 2006; Jennex, 2017). The user’s experience during the successful retrieval of

knowledge assets performing KM activities has linked these results with employee satisfaction to

gain the desired knowledge (Popa et al., 2018). During the employee’s use of the organization’s

KMS to support KM activities, user satisfaction comes from the capability to retrieve the

knowledge asset from the system to complete assigned job tasks as noted in the Jennex and

Olfman KM Success Model (Jennex & Olfman, 2006; Jennex, 2017). Employee satisfaction

varies based on the KMS user’s experience evidenced in the knowledge outcome determined by

the user’s specific job task (Khanal and Raj Poudel, 2017).

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KM Strategy/Process. The KM Strategy/process construct is the first indicator of KMS

knowledge quality described as the KM strategies determining the KM processes for knowledge

workers while performing KM planned activities (Jennex & Olfman, 2006; Jennex, 2017). The

organization’s KM strategy determines the processes upheld for the flow of knowledge assets

within the KMS and affects the KMS knowledge quality (Popa et al., 2018). Research study

results connect employee satisfaction with KM’s capabilities and KMS strategies, allowing the

knowledge worker to perform tasks because of KM activities(Khanal and Raj Poudel, 2017).

Richness. The richness constructs depicted as the second indicator of the organization’s

KMS knowledge quality was described as the result of the accuracy and timeliness retrieval of

the knowledge asset within the KMS (Jennex, 2017; Jennex & Olfman, 2006). Richness is an

indicator of retrieving the desired results from performing a search within the KMS and the

success of those results (Zhang & Venkatesh, 2017). Employee satisfaction within the context of

KMS use becomes a consequence of the knowledge worker’s expectations from the KMS to

perform knowledge work (Jennex, 2017; Jennex & Olfman, 2006; Zhang & Venkatesh, 2017).

Linkage. The internal codification of the stored knowledge assets creates an internal

mapping within the KMS. Behind the scenes, codification impacts the knowledge worker based

on the search query contributing as the third indicator of the organization’s KMS knowledge

quality (Jennex, 2017; Jennex & Olfman, 2006). During the implementation of the KMS, the

internal structure originates in an internal network of logic from the available KMS features and

capabilities (Karlinsky-Shichor & Zviran, 2016). Employee satisfaction becomes affected by the

internal link competencies supporting the additional constructs of KMS knowledge quality

(Jennex, 2017; Jennex & Olfman, 2006).

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Summary

The literature review followed the lens of the Jennex and Olfman KM success model’s

knowledge quality within a Knowledge Management System as one of the components (Jennex,

2017; Jennex & Olfman, 2006; Liu et al., 2008). Information systems theory served as the

seminal foundation of this model relevant to the underlying technology influencing the

knowledge quality of the KMS (Langefors, 1977; Lerner, 2004). The Jennex and Olfman KM

Success Model (2006) allowed the appropriate framework for this study. This model adds

specific knowledge aspects asserting comparative dimensions of performance indicators detailed

in the DeLone and McLean IS Success Model (DeLone & McLean, 1992; DeLone & McLean,

2003; DeLone & McLean, 2004; Liu, Olfman, & Ryan, 2005; Zuama et al., 2017). Related

knowledge domains listed within the literature review include knowledge workers, Knowledge

Management, Knowledge Management Systems, knowledge worker productivity, and employee

satisfaction as the five major themes encompassing subdomains forming the context of this

research.

Additional subcategories documented within the knowledge worker domain section

included knowledge work, knowledge workers in the technology industries, and the 21st-century

role of knowledge workers. Researchers Eltayeb and Kadoda (2017) concluded a significant

relationship between Knowledge Management practices, future business strategies, and business

performance upon analysis from interviews with experts, managers, and employees in the region.

Eltayeb and Kadoda request future researchers to explore the quality of knowledge information

stored within the KMS. Within the Knowledge Management major theme, the subcategories are

the history of Knowledge Management, components of Knowledge Management, and

organizational units of Knowledge Management. Additional dimensions within the components

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of the Knowledge Management subcategory included the creation of knowledge assets, capturing

knowledge assets, sharing knowledge assets, storage of knowledge assets, and consumption of

knowledge assets. The organizational unit Knowledge Management also contains additional

dimensions within the subcategory supported by the literature, including information

management, process management, people management, innovation management, and asset

management. AlShamsi and Ajmal’s (2018) report result from a study investigating the critical

success factors to promote knowledge sharing identifying leadership, culture, and strategy as

leading indicators. AlShamsi and Ajmal encourage future studies to review additional essential

elements of success in new industries. The third major theme in this literature review is the

Knowledge Management System. This theme incorporates five subcategories comprised of the

history of KMS, types of KMS, implementation of KMS, knowledge worker use of KMS, KMS

performance indicators, and KMS knowledge quality. Iskandar et al. (2017) accumulated top

research articles to request future studies to identify additional features of the KMS supporting

the organization’s Knowledge Management processes.

The fourth domain supported within the literature is knowledge worker productivity,

including the subcategories KWP measurement challenges, KWP enablement, and KWP

financial implications. Shujahat et al. (2019) collected data from 369 knowledge workers within

the IT industry to identify potential relationships between the knowledge worker productivity

and Knowledge Management processes. The results indicated significant linkages between

knowledge creation and knowledge utilization to increased innovation influenced by

productivity. Shujahat et al. (2019) request future research to include additional Knowledge

Management process influences.

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Employee satisfaction as the final theme in this study supported by a review of the

literature encompasses user satisfaction, and the three constructs of the KMS knowledge quality

dimension of the Jennex and Olfman KM Success Model (Jennex, 2017; Jennex & Olfman,

2006). Researchers agree the common thread to employee satisfaction within the context of KM

activities while using the KMS is the enablement for the knowledge worker to retrieve

knowledge assets required to perform job tasks (Jennex, 2017; Jennex & Olfman, 2006; Zamir,

2019; Zhang & Venkatesh, 2017). Employee satisfaction is one of the desired outcomes after the

implementation of the organization’s KMS in support of KM activities (Zhang & Venkatesh,

2017). The specific KM capabilities realized within the organization lead to employee

satisfaction as an outcome through the empowerment of the employees to efficiently perform

assigned job tasks (Zamir, 2019).

Employee satisfaction as the final major theme in this study is based on literature in

context from the KMS usage perspective (Jennex, 2017; Jennex & Olfman, 2006; Zamir, 2019;

Zhang & Venkatesh, 2017). A review of the literature further reveals subthemes related to this

study as the user satisfaction during the usage of the organization’s KMS and the KMS

knowledge quality constructs defined as KM strategy/process, richness, and linkage (Jennex &

Olfman, 2006; Jennex, 2017; Kumar, 2018; Zamir, 2019; Zhang & Venkatesh, 2017).

Researchers identified correlations between the successful implementation of an organization’s

KMS to promote KM activities, employee performance, and satisfaction (Zamir, 2019; Zhang &

Venkatesh, 2017).

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Chapter 3: Research Method

A thorough review of the literature in chapter two revealed that the Jennex and Olfman

KM Success Model supports the appropriate theoretical framework in this study (Jennex, 2017;

Jennex & Olfman, 2006; Liu et al., 2008). Five domain sections within the literature review in

support of this framework included knowledge workers, Knowledge Management, Knowledge

Management Systems, knowledge worker productivity, and employee satisfaction, forming the

background of this research. The literature review supports the research method and design

implemented in this study to examine the relationship between KMS knowledge quality,

knowledge worker productivity, and employee satisfaction.

The failure of business leaders to implement a KMS capable of providing knowledge

quality reduces knowledge worker productivity and results in millions of dollars in annual losses

(Ferolito, 2015; Vanian, 2016). Numerous researchers have studied how the implementation and

maintenance of an organization’s KMS affect the knowledge workers’ ability to retrieve

knowledge assets (Andrawina et al., 2018; De Freitas & Yáber, 2018; Ferolito, 2015; Xiaojun,

2017; Zhang & Venkatesh, 2017). Deficiencies in the quality of information stored within an

organization’s KMS prevents knowledge workers from retrieving these knowledge assets,

thereby reducing knowledge worker productivity and employee satisfaction (Jennex, 2017;

Jennex & Olfman, 2006; Karlinsky-Shichor & Zviran, 2016; Khanal & Raj Poudel, 2017; Popa

et al., 2018; Sutanto et al., 2018; Xiaojun, 2017; Zhang & Venkatesh, 2017). The International

Data Corporation (IDC) reports that one-third of a knowledge worker’s daily responsibilities will

require the searching for and acquiring of information needed across several knowledge systems,

and only 56% of the time, the information becomes found (Ferolito, 2015; Vanian, 2016).

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The purpose of this quantitative survey research method is to explore the relationship

between the knowledge quality of an organization’s Knowledge Management System, knowledge

worker productivity, and employee satisfaction for firms in the software industry in California.

The theoretical concept to measure the KMS knowledge quality stems from the Jennex and

Olfman KM Success Model adapted after the DeLone and McLean IS Success Model

incorporating the knowledge quality expectations from an organization’s KMS (DeLone &

McLean, 1992; DeLone & McLean, 2003; DeLone & McLean, 2004; Jennex & Olfman, 2006).

The research questions support the statement of the problem and purpose of the study, forming

the basis for the research method and design.

RQ1. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

RQ2. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction?

The remainder of this chapter includes details of this quantitative research methodology

and correlational design supporting the study’s defined problem and purpose. Next is a

description of the population and sample participants intended for this study. This is followed by

the questionnaire and survey as the planned instrument and tool deliver the gateway for the

remainder of the chapter. The details of the independent and dependent variables used in this

study served as the operational definitions of variables supported by research. In the study

procedures section of this chapter, a description of actions used to gather the data for the number

of sample participants provided researchers a clear understanding when replicating this study.

The intended strategies to test the hypotheses when answering the research questions were listed

in this section also used to address the problem in this study based on participant data collection.

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A description of the assumptions with supporting rationale, limitations, delimitations, and ethical

assurances finalized the sections enclosed in this chapter, supplemented the reader’s

understanding of this study’s goals. Finally, the significant points of this study listed in summary

conveyed the foundational concepts supporting the study topic in this chapter.

Research Methodology and Design

This quantitative, correlational research study explored if a relationship exists between

the KMS knowledge quality, knowledge worker productivity, and employee satisfaction. This

quantitative research method was the most relevant to this study and accomplished the goal of

exploring the relationship between the identified variables supporting the problem, purpose, and

research questions from the same participant sample (Mellinger & Hanson, 2016). This

correlational research design applied correlational statistical methods and identified the

relationship between variables (Cavenaugh, 2015). The Pearson’s Correlation test was planned to

be used to examine if a positive, negative, or zero association existed between the variables;

however, the assumption of the normal distribution was not met and replaced by the Spearman’s

coefficient of rank correlation test as the distribution was not normal (Field, 2013). The

quantitative research questions in this study assisted the researcher in devising this study’s

structure, allowed the researcher to answer questions tested by using the hypotheses statements

as a guide during the collection and analysis of the data (Punch, 2013). The quantitative research

design in this study assisted the researcher in implementing the research questions and identify

the relationship between the independent variable identified as KMS knowledge quality and the

dependent variables determined as knowledge worker productivity and employee satisfaction

(Jennex & Olfman, 2006; Jennex, 2017).

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Descriptive, causal-comparative/quasi-experimental, and experimental quantitative

research designs were considered inappropriate for this study due to the noncausal relationship-

focused method (Mellinger & Hanson, 2016; Punch, 2013). Since the descriptive design does not

form a hypothesis until after the participant data collection, this design was determined as

irrelevant as this study’s hypothesis was based on current literature. Causal-comparative/quasi-

experimental design aims to identify cause-effect between identified variables and do not

manipulate the dependent variable. An experimental design that utilizes the scientific method to

manipulate the independent variable to identify the outcome of the dependent variable in a

controlled environment did not apply to this study.

Qualitative and mixed methods research methods determined the least desired as the

methodology due to the purpose of this study to analyze potential relationships between the

independent and dependent variables (Mukhopadhyay & Gupta, 2014). Qualitative research

methods did not meet the goal of the proposed research study due to the variables of the study

identified and the planned statistical measurement (Mukhopadhyay & Gupta, 2014). Another

constraint when considering qualitative research includes the time and cost of gathering and

interpreting the data. Qualitative research includes conducting interviews, observing participants,

and facilitating focus groups to categorize and codify based on the prerequisites to data analysis

(Johnson & Onwuegbuzie, 2004). Mixed methods as a research method did not deem appropriate

for this study based on the variables requiring validity and reliability efforts when reporting the

analysis of the relationships (Johnson & Onwuegbuzie, 2004). Researchers utilize qualitative

designs based on unknown variables and problem generalization, guiding the purpose of the

study and not applicable based on known variables in this study (Flick, 2018). An experimental

design was not amiable for this study. The correlational research design was best suited for this

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study in support of the research questions to determine if a relationship exists between the KMS

knowledge quality as the independent variable and the dependent variables identified as

knowledge worker productivity and employee satisfaction.

Researchers agree that the knowledge quality within an organization’s KMS proves a

vital performance indicator of success within the digital management of knowledge assets

(Jennex, 2017; Jennex & Olfman, 2006; Karlinsky-Shichor & Zviran, 2016). The knowledge

quality of the KMS system comprises multiple dimensions of KM strategy/process, richness, and

linkage working in the background within the KMS knowledge quality construct as the focus of

this study (Jennex, 2017; Jennex & Olfman, 2006). Jennex and Olfman (2006) further describe

knowledge quality as a component of the KM Success Model, signifying the success of the

knowledge worker’s productivity and user satisfaction based on the context of the explicit

knowledge retrieved within the KMS. Researchers also describe correlational ties of knowledge

worker productivity based on interaction activities with the organization’s KMS to innovation

and financial outcomes (Drucker, 1999; Karlinsky-Shichor & Zviran, 2016; Iazzolino & Laise,

2018; Shrafat, 2018; Shujahat et al., 2019; Zhang, 2017; Zaim et al., 2019). Within the Jennex

and Olfman KM Success Model (2006), the productivity outcome of knowledge workers points

to dimensions of KMS knowledge quality. At the same time, researchers report that lack of

knowledge quality within an organization’s KMS affects knowledge workers during the retrieval

of knowledge within the KMS (Jennex, 2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al.,

2018; Zhang, 2017).

According to Bloomfield and Fisher (2019), quantitative research methods enlist the

positivism paradigm approach adopting research guided by logistical collection and analysis to

generate the reality of the phenomena. This study follows the same practices of current research

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employing a quantitative research method collecting data from participants by anonymous online

survey techniques. Next, the researcher plans to identify the population and sample participants,

noting the estimated size and characteristics supported by evidence of this population reflecting

the problem, purpose, and research questions in this study.

Population and Sample

The identified population for this study includes businesses classified within the software

industry. The focus of this study further narrowed the population to businesses within California

within the software industry with headquarters in California. Requirements for participants in

this study introduced as those employed in the software industry referred to as the knowledge

worker category (Moussa et al., 2017; Surawski, 2019; Turriago-Hoyos et al., 2016). Using the A

priori power analysis within the G*Power software allows the identification of the sampling

sizes needed for this correlational study indicating a sample size of 153 participants because of

the output for the priori power analysis (medium effect size = .0625, error = .05, power = .95,

predictors = 1) in Appendix A Figure 3. These factors used an alpha level of p = .05, allowed the

researcher to achieve an 80% probability of accurately finding a significant result supported by

the alpha level indication of a significant difference among the groups. The target sample size

included 153 qualified knowledge worker participants. Survey questions included the constructs

of KMS knowledge quality, knowledge worker usage, knowledge worker productivity, and

employee satisfaction questions for data collection efforts. The data collection efforts for this

study began by contracting Qualtrics panel services to solicit online participation for knowledge

workers in the software industry in California. Qualtrics panel services allowed the targeting of

participants from several organizations for full and part-time employees within the existing

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technology-based departments, considered knowledge workers, to complete an anonymous

online survey.

Instrumentation

The Jennex and Olfman KM Success Model served as the basis for selecting the KMS

knowledge quality as the independent variable and knowledge worker productivity and employee

satisfaction during the use of the KMS as the dependent variables (Jennex, 2017; Jennex &

Olfman, 2006). The researcher gained permission to use Halawi’s (2005) KMS Success survey

instrument for data collection that formulated the research questions as shown in Appendix B

Figure 4. The online survey for this study required a conversion of Halawi’s (2005) original

printed KMS Success survey as an instrument in Appendix C Figure 5 to an online survey

questionnaire using Qualtrics panel services. The online KMS Success survey offered the same

survey questions as the original KMS Success survey. The online KMS Success survey questions

ensured the capture of KMS usage in terms of the knowledge quality of the KMS, knowledge

productivity, employee satisfaction, and six demographic questions using the same 7-point Likert

scale (Halawi, 2005). According to Halawi (2005), the validity of the KMS Success survey

instrument confirmed the measurement of the variables in context, and the reliability of the

survey instrument held consistent as a result of thorough preliminary methods implementing a

documented pre-test, pilot test, factor analysis, and internal consistency validations (Halawi,

2005).

Data collected from the online survey instrument served as the mechanism that provided

the researcher answers to the study research questions and guidance for the acceptance or

rejection of the null hypothesis (Wright, 2017). The researcher implemented the research

questions as a guide to the survey questions and response choices. Advantages exist when using

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a survey to collect data articulating answers representing the data from the population (Kelley-

Quon, 2018). If the sample size is large enough to represent the population, collected data should

yield answers like those received if the entire population took the same survey. Another

advantage of administering online surveys to collect data is the removal of researcher

subjectivity in the participant’s answers (Kelley-Quon, 2018). Disadvantages during the sole use

of online surveys as instruments to collect data may introduce the offending of participants due

to the general question format aimed toward the entire population (Wright, 2017). Another

disadvantage to online surveys prevents capturing the participant’s emotional response to

questions which generally provides the researcher with the depth of the emotion attached to the

question (Wright, 2017).

The researcher contracted the use of Qualtrics panel survey services and implemented

Halawi’s (2005) KMS Success survey online in the collection of desired data to answer the

research questions in this study. The reliability and validity of the survey instrument were

confirmed by using Halawi’s proven KMS Success instrument based on the Jennex and Olfman

KM Success Model (2017) in previous studies. Knowledge workers, Knowledge Management,

Knowledge Management Systems, knowledge worker productivity, and employee satisfaction

comprise the five major themes based on the review of the literature to answer the research

questions. Halawi (2005) performed Cronbach’s alpha to measure internal consistency reliability

and confirmed the variables’ accurate measurement (Allen, 2017). In this study, the researcher

entered the collected data into IBM SPSS

Operational Definitions of Variables

This quantitative, correlational research study consisted of one independent variable,

KMS knowledge quality, and two dependent variables, knowledge worker productivity and

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employee satisfaction, displayed in Appendix D Table 1. The Jennex and Olfman KM Success

Model served as a guide for the context and operational definitions of these variables (Jennex,

2017; Jennex & Olfman, 2006; Liu et al., 2008). Halawi’s (2005) KMS Success survey

instrument was the tool used to answer the research questions further supported by the

independent and dependent variables in the Jennex and Olfman KMS Success Model context. A

7-point Likert scale within the online survey instrument included the same questions based on

Halawi’s (2005) KMS Success survey instrument. The independent variable and dependent

variables utilizing the Likert 7-point scale model followed the standard scores of 1 = Strongly

Disagree, 2 = Moderately Disagree, 3 = Somewhat Disagree, 4 = Neutral, 5 = Somewhat Agree,

6 = Moderately Agree, and 7 = Strongly Agree. Peer-reviewed research articles regarding KMS

knowledge quality, knowledge worker productivity, and employee satisfaction facilitated the

appropriate variable constructs and definitions of each variable. The independent and dependent

variables calculated score comprised the average results from the Likert scale representing

interval scale measurements from each question answered on the KMS Success survey

instrument and formed the transformed independent and dependent variables.

KMS Knowledge Quality

This independent, interval variable was transformed from a subset of ordinal 7-point

Likert scale question objects representing the three levels comprising KMS knowledge quality

defined as KM strategy/process, richness, and linkages as supported within the Jennex and

Olfman KM Success Model (Jennex, 2017; Jennex & Olfman, 2006; Liu et al., 2008).

Researchers have found the implementation of the organization’s KMS to affect the knowledge

worker’s ability to retrieve knowledge assets (Andrawina et al., 2018; De Freitas & Yáber, 2018;

Ferolito, 2015; Xiaojun, 2017; Zhang & Venkatesh, 2017).

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KM Process/Strategy

KM process/strategy is one of three dimensions within KMS knowledge quality serving

as the independent variable contributing to the research questions in this study. As an indicator of

KMS knowledge quality, the KM strategies and processes determine how the knowledge worker

will use the KMS during planned KM activities (Jennex & Olfman, 2006; Jennex, 2017). The

direct result of the KM strategies and processes establishes the capability of retrieving

knowledge assets within the KMS, directly affecting the KMS knowledge quality (Popa et al.,

.2018). Researchers link the capability of the knowledge worker to perform KM activities and

employee satisfaction (Khanal and Raj Poudel, 2017).

Richness

Richness is one of three dimensions within KMS knowledge quality, serving as the

independent variable contributing to the research questions in this study. The knowledge worker

performs search queries within the KMS, expecting successful results of the knowledge assets

within the context of each search (Zhang & Venkatesh, 2017). This indicator of knowledge

quality reflects the accuracy and timeliness of the knowledge assets retrieved from the KMS and

within the context of the expected knowledge return (Jennex, 2017; Jennex & Olfman, 2006). In

KMS use, employee satisfaction becomes a consequence of the knowledge worker’s realized

outcomes from the KMS to complete knowledge work tasks (Jennex, 2017; Jennex & Olfman,

2006; Zhang & Venkatesh, 2017).

Linkage.

Linkage is one of three dimensions within KMS knowledge quality, serving as the

independent variable contributing to the research questions in this study. As an indicator of KMS

knowledge quality, the internal codification of the stored knowledge assets enables an internal

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mapping within the KMS (Jennex, 2017; Jennex & Olfman, 2006). During the implementation of

the KMS, the initial structure of these mappings became revealed to the knowledge worker after

retrieving the knowledge assets based on the internal logic to create those mappings (Karlinsky-

Shichor & Zviran, 2016). Employee satisfaction becomes affected by the internal linkage of

knowledge asset mappings supporting the additional constructs of KMS knowledge quality

(Jennex, 2017; Jennex & Olfman, 2006).

Knowledge Worker Productivity

Knowledge worker productivity as the first dependent, interval variable became

represented by the knowledge worker’s value-added activities resulting from interaction with the

organization’s KMS (Kianto et al., 2019; Shujahat et al., 2019). Researchers link knowledge

worker productivity concerning KM activities as influencers on business performance and

financial results (Shrafat, 2018; Vanian, 2016; ). Knowledge worker

productivity becomes operationalized as a mechanism to the ability of the user to successfully

retrieving knowledge assets within an existing KMS (Dey & Mukhopadhyay, 2018). The

analysis of data collected using the survey instrument measured the first research question to

determine if a statistically significant relationship existed between the knowledge quality of an

organization’s KMS and knowledge worker productivity.

Employee Satisfaction

Employee satisfaction as the second dependent, interval variable depicted the successful

experience based on the knowledge worker’s actual use of the KMS while performing KM

activities (Zamir, 2019; Zhang & Venkatesh, 2017). Similarly, Jennex and Olfman (2006)

describe use/user employee satisfaction as a component of the performance indicator, referencing

a successful experience based on the actual use of the KMS and employee satisfaction from each

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use. The analysis of data collected using the survey instrument measured the second research

question and identified if a statistically significant relationship existed between the knowledge

quality of an organization’s KMS and employee satisfaction.

Study Procedures

The following steps describe the completed actions ensuring this study may be replicated.

Northcentral University’s Institutional Review Board (IRB) approved the study before data

collection efforts began as displayed in Appendix F Figure 6. The questions from the original

printed KMS Success survey instrument created and validated by Halawi (2005) were used for

this study displayed in Appendix C Figure 5. The researcher converted Halawi’s (2005) KMS

Success survey printed questions into an online Qualtrics survey. The online KMS Success

survey offered the same survey questions as the original KMS survey, including the KMS

Success questions. The online survey tabulated KMS usage in terms of the knowledge quality of

the KMS, knowledge productivity, employee satisfaction, and six demographic questions using

the 7-point Likert scale (Halawi, 2005). Once collected, the researcher used the online survey

data to align the 7-point Likert scale responses to the independent and dependent variables for

statistical analysis. Using the transform tool in SPSS, the mean of the KMS knowledge quality

survey question objects computed into the independent variable. Similarly, the SPSS transform

tool was used to convert the mean of the survey question objects into each applicable dependent

variable, knowledge worker productivity and employee satisfaction within the context of KMS

usage (Jennex, 2017; Jennex & Olfman, 2006). As shown in Appendix D Table 1, the

independent and dependent variables were a result of the average calculations from the ordinal

Likert scale questions representing interval scale measurements from each question answered on

the KMS Success survey instrument.

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The researcher contracted Qualtrics panel services to solicit responses from knowledge

workers employed in the software industry residing in California. The survey link was active for

two weeks until the required 153 survey responses were collected. The first webpage of the

online survey listed the purpose of the study and the risks and benefits of participating in the

online survey, and efforts to ensure anonymous participation. On the first page of the survey, the

participant was required to click the ‘I agree’ button to begin the survey registering their consent

to the outlined risks. Once the participants clicked the button, the indication of informed consent

to collect data from each answered survey question was logged. The second page of the online

survey further prequalified the participants listing questions to confirm the participant’s

classification as knowledge workers in the software industry. The researcher stored the data on a

password-protected Microsoft Excel spreadsheet using an alphanumeric labeling system for each

participant’s set of survey responses to secure the participants’ identity. After uploading the

survey data into SPSS, the researcher removed the participants identifying information and

assigned a generic alpha ID for each participant’s responses. Statistical tests were performed on

the collected data, and the production of tables and charts were interpreted and described in the

chapter 4 findings.

Data Analysis

The instrument for data collection was an online survey from Qualtrics with pre-validated

survey questions from the original Halawi’s (2005) KMS Survey in Appendix C Figure 5. Data

collection for this study originated from the survey contracted by Qualtrics panel services that

utilized pre-recruited surveys from knowledge workers in California using the probability

sampling framework. The online KMS Success survey questions matched the same survey

questions as the original printed KMS Success survey. The online survey included the KMS

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questions tabulating KMS usage in terms of the knowledge quality of the KMS, knowledge

productivity, employee satisfaction, and six demographic questions on a 7-point Likert scale

(Halawi, 2005). The research questions examining KMS knowledge quality, knowledge, worker

productivity, and employee satisfaction were measured using an acceptable medium effect size

of .0625 (Field, 2013). The assumption in using an alpha level of p = .05 for the Type 1 error

probability rejecting the null hypothesis when it is true applies to this quantitative, correlational

survey research method. The researcher performed the analysis with IBM SPSS after

transforming the survey question ordinal objects into the interval variables. These statistical tests

supported the researcher’s ability in answering the research questions that determined the type of

relationship among the variables.

Research integrity represents the threats to internal validity when incorrect data collection

procedures introduce unknown biases during data collection (Dewitt et al., 2018; Siedlecki,

2020). External validity ensures the study methodology is repeatable on the more significant

applicable population requiring the sample data to be a representation of the applicable

population (2020). In this way, future studies to repeat the study design and methodology.

Ethical considerations for this study require receiving informed consent by each participant after

initial contact to participate in the study survey. Informed consent reduces the potential pressure

applied by management to complete the survey in a manner expected by the employee’s

management (Rawdin, 2018; Vehovar & Manfreda, 2017). Care to ensure the employee’s

responses cannot become an identifiable method from the management personnel. Upon the

return of the required 153 surveys, the survey was considered open for participation for pre-

qualified survey partakers. Each response then became coded using an alphanumerical system

for identification purposes only and the initial responses were permanently deleted. Informed

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consent was acquired by providing the risks and benefits on the first page of the survey with an ‘I

Agree’ button clicked by each participant.

The support for this research design reflected the ability to quantify the variable

measurements determining the relationship between the knowledge quality within a KMS,

knowledge worker productivity, and employee satisfaction through Likert scale data collection.

The vulnerability of this research design included self-reported data and potential outlier

variables (Hughes, 2012). The verification of reliability and validity of the research variables for

confirmation factor analysis (CFA) was achieved through structural equation modeling fully

validated by Halawi (2005). Validity efforts of the research variables utilized survey questions

evident in research studies, including a pre-test, pilot, and complete analysis performed by

Halawi’s KMS Success survey. Pre-qualification efforts used by Qualtrics panel services guided

each participant using questions confirming job classification type and assigned department. This

researcher conducted ethical assurances to ensure the anonymous data collection was performed

without risking the employees’ confidentiality, preventing social status and job safety concerns

based on collected responses.

Assumptions

On of the assumptions of this study was that California businesses utilize one type of

electronic Knowledge Management System to capture, store, and share knowledge. Next, this

study presumed that each participant answered honestly to each survey question. A final

assumption is that knowledge workers employed in California make use of an organization’s

KMS to search for knowledge assets. Most organizations have a robust organizational structure

preventing outliers and allowing research data to capture information toward knowledge worker

productivity.

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Limitations

The researcher identified many limitations in this research study. Data collection methods

when using online surveys limit the ability of the researcher to determine if the participant

answered the online questions honestly (Siedlecki, 2020). Halawi’s (2005) KMS Success survey

comprised multiple questions and asked the same context as was validated by Halawi that

performed Cronbach’s alpha reliability tests for each variable (Allen, 2017). The researcher

contracted Qualtrics panel services for data collection that administered the online survey to

increase participation rates and reduce the study limitations. These limitations included a lack of

control to verify participants lived in California participating in the online survey. Finally, the

respondents in the survey may not have represented the desired knowledge worker employed in

the software industry. The Qualtrics panel services prequalified each survey response and

ensured each participant was identified as a knowledge worker employed in the software

industry living in California before the survey closed with over 153 qualified responses

collected.

Delimitations

The purpose of this quantitative, correlational study was to determine if a relationship

exists between KMS knowledge quality, knowledge worker productivity, and employee

satisfaction. A review of the research revealed that knowledge workers using the organization’s

KMS represent employees performing tasks requiring a specific skill set to be productive when

the assigned job role tasks were executed (Levallet & Chan, 2018; Orenga-Roglá & Chalmeta,

2019; Surawski, 2019; Wang & Yang, 2016; Xiaojun, 2017; Zhang & Venkatesh, 2017). While

knowledge workers exist across the globe, the researcher selected only employees in the software

industry in California that ensured a large enough sample size was acquired for this study. The

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researcher selected only the KMS knowledge quality as one of six components in the Jennex and

Olfman KM Success Model (2006) that identified the relationship between knowledge worker

productivity and employee satisfaction. A review of the literature identified KMS knowledge

quality may enable Knowledge Management capabilities and business performance, yet business

leaders continue to fail to implement an effective KMS (Drucker, 1999; Iazzolino & Laise,

2018); Jennex, 2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al., 2018; Vanian, 2016;

Xiaojun, 2017).

Ethical Assurances

The researcher gained approval from the Northcentral University Institutional Review

Board (IRB) before the data collection began as displayed in Appendix F Figure 6. The

researcher minimized the risk to the participants of the online survey by briefly documenting the

process of the data collection, the storage procedures of the participant data, took steps to ensure

the participant identity and answers remained protected, and the planned destruction of the data

process within the survey (Dewitt et al., 2018). Each participant agreed to informed consent

when the “I Agree” button was clicked on the information page and confirmed the outlined risks

were accepted for participation in the online survey. The researcher stored the data from the

online survey on a password-protected Microsoft Excel spreadsheet followed by an

alphanumeric labeling system for each participant’s set of survey responses which secured the

identity of the participants. The researcher took additional steps that prevented bias during the

data collection and the analysis of data. The researcher contracted with Qualtrics panel services

and retrieved an unbiased collection of data applicable for this research study. Submitted online

surveys with missed answers to the KMS question sections were not used for data analysis to

remove unknown participant bias (Siedlecki, 2020).

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Summary

In summary, this quantitative, correlational study aligned with the problem, purpose, and

research questions that examined if a relationship exists between the knowledge quality

component of the KMS, knowledge worker productivity, and employee satisfaction. The

selection of the quantitative research method and correlational survey design resulted from the

support of research also supported by the framework comprised of knowledge workers,

Knowledge Management, Knowledge Management Systems, knowledge worker productivity,

and employee satisfaction (DeLone & McLean, 1992; DeLone & McLean, 2003; DeLone &

McLean, 2004; Liu, Olfman, & Ryan, 2005; Zuama et al., 2017). Online surveys hinged upon the

direction of the Jennex and Olfman KM Success Model served as the basis for selecting

variables for content validity (Jennex, 2017; Jennex & Olfman, 2006). The operational

definitions of the KMS knowledge quality, knowledge worker productivity, and employee

satisfaction variables were transformed and computed by the means from the collection of the

Likert scale question objects.

The specific study procedures describe the steps taken during data collection and allowed

an informed decision from each participant to complete the survey agreeing to informed consent.

Assumptions, limitations, delineations, and ethical assurances supported the validity and ethical

considerations of this study. Data collection intentions and analysis of the collected data

supported the research method and design of this study. In chapter 4, the findings on the analysis

of collected data were reported and segregated by each research question that identified patterns

in the findings. Descriptive information and explanations for each statistical test allowed the

reader to interpret the results, including inferred assumptions.

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Chapter 4: Findings

The problem addressed in this study was that there is often great difficulty encountered in

trying to retrieve knowledge assets about events in the past required for strategic decision-

making without an effective, in-place Knowledge Management System (KMS) (Oladejo &

Arinola, 2019). Knowledge Management (KM) is challenging to implement requires exploration

and improvement in its’ continued application and development (Putra & Putro, 2017).

Additional difficulties associated with the lack of an effective KMS include knowledge asset

unavailability, improper knowledge asset documentation, excessive time consumption associated

with searching for knowledge assets, decision-making overhead, and duplication of effort

(Oladejo & Arinola, 2019). A substantial number of Knowledge Management System (KMS)

implementations have not achieved their intended outcomes, such as employee performance and

employee satisfaction (Zhang & Venkatesh, 2017).

The purpose of this quantitative, correlational study was to explore the relationship

between the knowledge quality of an organization’s KMS, the knowledge worker productivity,

and employee satisfaction for software industry organizations in California. This study is

relevant and contributes to the Knowledge Management research community as millions of

dollars in losses from unsuccessful KMS implementations fail to satisfy expected benefits in

knowledge assets to support business performance (Fakhrulnizam et al., 2018; Levallet & Chan,

2018; Nusantara et al., 2018; Vanian, 2016). When organizations fail to implement a successful

KMS, KM strategies depending on the use of knowledge assets for knowledge worker

productivity and employee satisfaction also fail (De Freitas & Yáber, 2018; Demirsoy &

Petersen, 2018; Putra & Putro, 2017; Xiaojun, 2017).

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The research questions of this study were used to examine the relationship between the

knowledge quality of an organization’s Knowledge Management System, knowledge worker

productivity, and employee satisfaction. These research questions formed the basis for the

research method and design, reflected the statement of the problem and purpose of the study.

Each research question corresponded with the null and alternative hypothesis as follows:

RQ1. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

H10. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and knowledge worker productivity.

H1a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and knowledge worker productivity.

RQ2. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction?

H20. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and employee satisfaction.

H2a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and employee satisfaction.

In the remaining sections of this chapter, the researcher organized the sections based on

their relevance to the research questions, including the validity and reliability of the data.

Descriptions in this chapter also include the assumptions of statistical tests, results from the data

analysis to answer the research questions, and the hypothesis outcomes. Next, the evaluation of

the findings based on existing research and theory are provided, followed by a summary of the

research findings and Chapter 5.

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Validity and Reliability of the Data

The KM Success survey instrument established by Halawi (2005) to measure the success

of Knowledge Management Systems was converted to an online survey using the same questions

and a 7-point Likert scale. In this study, this researcher performed statistical tests on data

gathered from the online Qualtrics survey and explored if a statistically significant relationship

existed between the knowledge quality of an organization’s KMS, knowledge worker

productivity, and employee satisfaction. Halawi (2005) initiated a pre-test to confirm the survey

question’s clarity among the small set of participants, followed by a pilot study in further

confirmation of modified survey questions to represent identified variables. The evaluation for

the validity of the final KMS Success survey instrument reported by Halawi (2005) consisted of

discriminant, construct, and convergent validity measures.

Throughout the discriminant validity tests, Halawi (2005) dropped constructs based on

factor loading values with correlations lower than 0.5 ensured no overlap of factors existed.

Also, construct validity tests were performed using factor analysis to examine the relationship

between the survey items supporting variables described within the study’s theoretical context of

Halawi’s (2005) study. Halawi also performed convergent validity tests by analyzing the survey

items to the total correlation based on each survey item’s correlation summed by the additional

survey items. Halawi (2005) measured the reliability of the final survey instrument using

Cronbach’s Alpha tests for internal consistency across the study’s constructs. Halawi (2005)

reported that the Pearson’s correlation coefficients test to identify the strength and direction for

the study’s variables were statistically significant at the .01 level.

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KMS Success Survey Instrument

In this study, the same questions from Halawi’s (2005) KMS Success survey instrument

were converted from printed form to an online survey hosted in the Qualtrics web platform

included in Appendix C Figure 5. Halawi’s (2005) survey instrument was used to test the

dimensions of the Delone and McLean model (1992, 2002, 2003) for measuring the success of

an organization’s KMS and the relationship among the dimensions. The online KMS Success

survey for this study served the purpose of data collection in answering the research questions

and explored the relationship between the knowledge quality of an organization’s KMS, the

knowledge worker productivity, and employee satisfaction. According to Halawi (2005), the

validity of the KMS Success survey instrument confirmed the measurement of the variables in

context, and the reliability of the survey instrument was held reliable.

Common Construct Measures

The questions in Halawi’s (2005) original survey measured the six construct dimensions

in the transformed Delone and McLean model (1992, 2002, 2003) contained within the Jennex

and Oflman KMS Success Model (2003). The survey questions and constructs applied to this

study’s variables remained relevant to the same construct dimensions within the Jennex and

Oflman KMS Success Model (2003) identified as knowledge quality, employee satisfaction (user

satisfaction), and knowledge worker productivity (net system benefits). This researcher

combined the common survey question items representing each construct into one specific

variable as specified in this study to answer the research questions. Multiple survey items

representing each dependent variable, employee satisfaction, and knowledge worker productivity

were combined into one variable for each dependent variable construct. Although several

dimensions exist within KMS knowledge quality as the independent variable as discussed in

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Chapter 2, for this study, the independent variable as a single construct was measured against the

dependent variables to answer the research questions and test the null hypothesis.

Assumptions

Spearman’s correlation coefficient was used to answer each of the research questions

assessing the statistical assumptions, including the level of measurement and monotonic

relationship. Pearson’s correlation coefficient was eliminated as the statistical measure due to the

violation of normality and further recommendation to use Spearman’s test for survey ordinal data

(de Winter et al., 2016).

Monotonic Relationship. A Spearman correlation requires that the relationship between

each pair of variables does not change direction (Schober et al., 2018). Schober et al. state that

this assumption is violated if the points on the scatterplot between any pair of variables appear to

shift from a positive to negative or negative to a positive relationship. Figure 1 presents the

scatterplot of the correlation between the KMS knowledge quality independent variable and the

dependent variable knowledge worker productivity. Figure 2 presents the scatterplot of the

correlation between the KMS knowledge quality independent variable and the dependent

variable employee satisfaction.

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Figure 1 Scatterplot of KMS KQ and KWP

Scatterplot of KMS KQ and KWP

Figure 2 Scatterplot of KMS KQ and employee satisfaction

Scatterplot of KMS KQ and employee satisfaction

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Level of Measurement. The level of measurement when using the Spearman

correlational coefficient test is more relaxed than that of Pearson’s correlation coefficient

assumptions (Schober et al., 2018). Schober et al. state the internal, ratio or ordinal levels of

measurement meet the assumption for the Spearman correlational coefficient test.

Test for Normality. The Shapiro-Wilk tests were conducted to identify if the

distributions of KMS knowledge quality, knowledge worker productivity, and employee

satisfaction resulted as significantly different from a normal distribution. The variables had

distributions that significantly differed from normality based on an alpha of 0.05: KMS

knowledge quality (W = 0.90, p < .001), knowledge worker productivity (W = 0.88, p < .001),

and employee satisfaction (W = 0.89, p < .001). The results are presented in Table 2.

Table 2 Shapiro-Wilk Test Results for all Study Variables Test for Normality

Shapiro-Wilk Test Results for all Study Variables Test for Normality

Variable W p

KMS knowledge quality 0.90 < .001

knowledge worker productivity 0.88 < .001

employee satisfaction 0.89 < .001

Results

In this section, results from the data analysis used to answer the research questions and to

test the hypothesis are presented, describing the overall study sample size, descriptive statistics,

and demographic summary. The target sample size of N = 153 was determined using G*Power

software displayed in Appendix A Figure 3. An online survey with eighty-three questions about

the employees’ current KMS was distributed using Qualtrics panel services for two weeks until a

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total of at least 153 valid responses from knowledge workers employed in software industry

firms in California were fulfilled. A total of 154 valid participant responses were recorded and

analyzed for this study. Survey questions were transformed into the independent variable KMS

knowledge quality and the two dependent variables, knowledge worker productivity, and

employee satisfaction.

Descriptive Statistics

Descriptive statistics were calculated for the KMS knowledge quality, knowledge worker

productivity, and employee satisfaction. The results for KMS knowledge quality as the

independent variable were calculated using SPSS as an average of 5.30 (SD = 1.35, SEM = 0.11,

Min = 1.09, Max = 7.00, Skewness = -1.15, Kurtosis = 1.09). The results for knowledge worker

productivity were calculated as an average of 5.35 (SD = 1.39, SEM = 0.11, Min = 1.00, Max =

7.00, Skewness = -1.25, Kurtosis = 1.22). The results for employee satisfaction were calculated

as an average of 5.25 (SD = 1.55, SEM = 0.13, Min = 1.00, Max = 7.00, Skewness = -1.03,

Kurtosis = 0.40). In a comparison of the means, all three variable values were close in value to

one another whereas the employee satisfaction variable with a higher standard deviation was

interpreted as more dispersed from the mean than the KMS knowledge quality or knowledge

worker productivity. The summary of descriptive statistics can be found in Table 3.

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Table 3 Summary of Descriptive Statistics

Summary of Descriptive Statistics

Variable M SD n SEM Min Max Skewness Kurtosis

KMS knowledge quality 5.30 1.35 154 0.11 1.09 7.00 -1.15 1.09

Knowledge worker productivity 5.35 1.39 154 0.11 1.00 7.00 -1.25 1.22

Employee satisfaction 5.25 1.55 154 0.13 1.00 7.00 -1.03 0.40

Demographic Summary

Demographic information was voluntary and collected from participants completing the

online KMS Success survey. Frequencies and percentages were calculated for each participant’s

voluntary demographic data for gender, age, years employed, years of KMS usage, education

level, employment position, and industry in Appendix E. The most frequently noted category of

gender was Male (n = 132, 86%). The noted frequencies for age had an average of 41.07 (SD =

7.90, Min = 20.00, Max = 68.00). The most frequently noted category of years employed was

greater than ten years (5) (n = 57, 37%). The most frequently noted category of years of KMS

usage was greater than five years (5) (n = 51, 33%). The most frequently noted category of

education level was master’s degree or beyond (4) (n = 110, 71%). The most frequently noted

category of employment position was Sr. Manager/Director (3) (n = 63, 41%). Frequencies and

percentages tables and values are presented in Appendix E Tables 4 – 10.

Research Question 1/Hypothesis

RQ1. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

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H10. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and knowledge worker productivity.

H1a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and knowledge worker productivity.

A Spearman correlation analysis was conducted between KMS knowledge quality and

knowledge worker productivity for the first research question to assess if a significant statistical

relationship exists between KMS knowledge quality and knowledge worker productivity. The

minimum required sample size of N = 153 according to the G*Power analysis in Appendix A

Figure 3 was collected over two weeks resulting in 154 valid participant survey responses. The

result of the correlation was examined based on an alpha value of 0.05. A significant positive

correlation was observed between KMS knowledge quality and knowledge worker productivity

(rs = 0.94, p < .001, 95% CI [0.92, 0.96]). The correlation coefficient between KMS knowledge

quality and knowledge worker productivity was .94, indicating a large effect size (Cohen, 1988).

This correlation indicates that as KMS knowledge quality increases, knowledge worker

productivity tends to increase. Table 11 presents the output of the correlation test.

Table 11 Spearman Correlation Result: KMS KQ and KWP

Spearman Correlation Result: KMS Knowledge Quality and Knowledge Worker Productivity

Combination rs 95% CI p

KMS knowledge quality-knowledge worker productivity 0.94 [0.92, 0.96] < .001

Note. n = 154.

Research Question 2/Hypothesis

RQ2. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction?

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H20. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and employee satisfaction.

H2a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and employee satisfaction.

A Spearman correlation analysis was conducted between KMS knowledge quality and

employee satisfaction for the second research question to assess if a significant statistical

relationship exists between KMS knowledge quality and employee satisfaction. The minimum

required sample size of N = 153 according to the G*Power analysis in Appendix A Figure 3 was

collected over two weeks resulting in 154 valid participant survey responses. The result of the

correlation was examined based on an alpha value of 0.05. A significant positive correlation was

observed between KMS knowledge quality and employee satisfaction (rs = 0.93, p < .001, 95%

CI [0.91, 0.95]). The correlation coefficient between KMS knowledge quality and employee

satisfaction was 0.93, indicating a large effect size. This correlation indicates that as KMS

knowledge quality increases, employee satisfaction tends to increase. Table 12 presents the

output of the correlation test.

Table 12 Spearman Correlation Results: KMS Knowledge Quality and Employee Satisfaction

Spearman Correlation Results: KMS Knowledge Quality and Employee Satisfaction

Combination rs 95% CI p

KMS knowledge quality-employee satisfaction 0.93 [0.91, 0.95] < .001

Note. n = 154.

Evaluation of the Findings

The assessment of the findings in this study was interpreted based on the Jennex and

Olfman KM Success (2017) forming the theoretical framework discussed in chapter 1 and

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chapter 2. As presented in existing research, the findings support the theoretical framework

representing the importance in the success of the KMS knowledge-centric practices providing

knowledge assets utilized to accomplish an organizational business purpose (Alavi & Leidner,

2001; Ermine, 2005; Jennex, 2017; Jennex & Olfman, 2006; Wu & Wang, 2006). The theoretical

framework in this study was the basis for the research questions and interpretation of the results

using the Jennex and Olfman KM Success Model as performance indicators while using the

organization’s Knowledge Management Systems (Jennex, 2017; Jennex & Olfman, 2006).

Research Question 1

To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

The null hypothesis for this research question was that there is not a statistically significant

relationship between the knowledge quality of an organization’s KMS and knowledge worker

productivity and was rejected. The results of the inferential test analysis using Spearman’s

correlation resulted in a significant strong positive correlation between the knowledge quality of

an organization’s KMS and knowledge worker productivity. There was an indication between the

variables in that when KMS knowledge quality increases, knowledge worker productivity tends

to increase.

Research Question 2

To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction? The null hypothesis for

this research question was that there is not a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction and was rejected. The

results of the inferential test analysis using Spearman’s correlation resulted in a significant strong

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positive correlation between the knowledge quality of an organization’s KMS and employee

satisfaction. There was an indication between the variables in that when KMS knowledge quality

increases, employee satisfaction tends to increase as seen in the hypothesis testing results for the

first research question.

Summary

The purpose of this quantitative, correlational study was to explore the relationship

between the knowledge quality of an organization’s KMS, the knowledge worker productivity,

and employee satisfaction for software industry organizations in California. The researcher

conducted this study using an online survey as the research instrument and collected data from

knowledge workers employed in software industry firms in California. The data collected were

analyzed based on the research questions from the 154 completed participant responses in this

study. Halawi’s (2005) past validity and reliability efforts for the KMS Success survey

instrument were considered appropriate to support this study.

Two research questions were tested against the null hypothesis, and both were rejected

based on the statistical significance results. The Spearman’s correlational nonparametric test was

used to investigate if a relationship existed between the KMS knowledge quality, knowledge

worker productivity, and employee satisfaction. Descriptive statistics results were noted for the

independent and dependent variables with similar results. A demographic summary of voluntary

participant information was captured for gender, age, years employed, years of KMS usage,

education level, employment position, and type of software industry as displayed in Appendix E.

An evaluation of the findings shows the null hypothesis for both research questions was rejected

as the significant relationship test results were evident between the KMS knowledge quality and

the knowledge worker productivity and the KMS knowledge quality and employee satisfaction.

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The findings in this chapter will be used as the basis for the implications, recommendations, and

conclusions of this study in chapter 5.

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Chapter 5: Implications, Recommendations, and Conclusions

This chapter continues the study topic that explored the relationship between the quality

of a KMS, knowledge worker productivity, and employee satisfaction. The problem addressed

by this study was that there is often great difficulty encountered in trying to retrieve knowledge

assets about events in the past required for strategic decision-making without an effective, in-

place Knowledge Management System (KMS) (Oladejo & Arinola, 2019). The purpose of this

quantitative, correlational study was to explore the relationship between the knowledge quality

of an organization’s KMS, the knowledge worker productivity, and employee satisfaction for

software industry organizations in California. The quantitative research method achieved the

goal of exploring the relationship between the identified variables supporting the problem,

purpose, and research questions from the same participant sample (Mellinger & Hanson, 2016).

This correlational research design using correlational statistical methods identified the

relationship between independent and dependent variables (Cavenaugh, 2015). The results from

the assumption of the normal distribution compelled Spearman’s coefficient of rank correlation

test as the statistical method of choice (Field, 2013). Statistical tests on data gathered from the

online Qualtrics survey were used to explore if a statistically significant relationship existed

between the knowledge quality of an organization’s KMS, knowledge worker productivity, and

employee satisfaction. The results of this study were based on two research questions tested

against the null hypothesis. both research questions’ null hypothesis were rejected based on the

statistical significance results. A significant positive correlation was observed between KMS

knowledge quality and knowledge worker productivity. Likewise, a significant positive

correlation was observed between KMS knowledge quality and employee satisfaction. The

researcher identified limitations in this research study beyond control. The Qualtrics panel

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services acquired 154 participants responses self-identifying as meeting the requirements over 18

years old, read/understand English, interact with the organization’s KMS, live in California, and

currently employed in the software industry. Another limitation impacting this study arises from

the lack of additional research similar to the Jennex and Olfman KM Success Model (2006)

addressing recent KMS applications.

The research questions and corresponding hypothesis served as a guide for the basis of

this study, supported by the research method and design alignment with the statement of the

problem and purpose. The researcher used the first research question to determine if a

statistically significant relationship existed between the knowledge quality of an organization’s

KMS and knowledge worker productivity. The researcher used the second research question to

determine if a statistically significant relationship existed between the knowledge quality of an

organization’s KMS and employee satisfaction. Each research question was used to test the

hypothesis. The remainder of this chapter will include a review of the implications of this study,

recommendations for practice based on the results of this study, and recommendations for future

research to further explore the results of this study. Finally, the conclusion will summarize the

problem, purpose, and importance of the study, finalizing this section discussing applications for

future professional and academic stakeholders based on the findings of this study.

Implications

The findings reflected in chapter 4 guided the implications of this study. These findings

align with the study’s theoretical framework based on the Jennex and Olfman KM Success

Model. The Jennex and Olfman KM Success Model (2006) supported KMS knowledge quality

as the independent variable followed by knowledge worker productivity and employee

satisfaction during the use of the KMS as the dependent variables for this study (Jennex, 2017;

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Jennex & Olfman, 2006). Five significant themes formed the context of this study’s theoretical

framework supported by a review of the literature, including knowledge workers, Knowledge

Management, Knowledge Management Systems, knowledge worker productivity, and employee

satisfaction. In following this model, two research questions were used to guide this study to

explore the relationship between the knowledge quality of an organization’s KMS, the

knowledge worker productivity, and employee satisfaction. Within each research question

section below, the researcher will discuss results relative to the literature review in chapter 2, the

problem and purpose of the study supported by the theoretical framework, and the significance.

Each research question will include the findings and how the study results contribute to the

existing body of research.

Research Question 1/Hypothesis

RQ1. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and knowledge worker productivity?

H10. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and knowledge worker productivity.

H1a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and knowledge worker productivity.

The first research question guided the analysis of the data to determine if a significant

relationship between the knowledge quality of an organization’s KMS and knowledge worker

productivity existed. The null hypothesis was tested and rejected based on the findings indicating

a significant positive correlation was observed between KMS knowledge quality and knowledge

worker productivity. The results indicated that as KMS knowledge quality increases, knowledge

worker productivity tends to increase. Table 11 presents the output of the correlation test.

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Factors that might have influenced the interpretation of the results originate from the

contextual implications regarding the knowledge quality of KMS and the knowledge worker

productivity definition derived within the literature (Jennex 2017; Jennex & Olfman, 2006). The

knowledge quality of the organization’s KMS served as the independent variable within the first

research question. Jennex and Olfman (2006) outlined six key performance indicators within the

Jennex and Olfman KM Success Model as specific components utilized to measure the

performance of the KMS, noting knowledge quality as the first key indicator applicable to this

study. Additional researchers describe the degree of knowledge content quality retrieved

determines the knowledge worker’s perceived usefulness of the system (Jahmani et al., 2018;

Zhang, 2017). Knowledge worker productivity was identified as the dependent variable within

the first research question. Researchers describe knowledge worker productivity as the value-

added activities resulting from the knowledge worker’s interaction with the organization’s KMS

(Kianto et al., 2019; Shujahat et al., 2019).

The study results address the problem in the difficulty encountered in trying to retrieve

knowledge assets about events in the past. The knowledge quality dimensions comprised of KM

process/strategy, richness, and linkage impact the knowledge worker’s capability to retrieve the

desired knowledge assets within the organization’s KMS (Jennex & Olfman, 2006). The study

results support the purpose of exploring the relationship between the knowledge quality of an

organization’s KMS and the knowledge worker productivity for software industry organizations

in California. Data collected from 154 participant surveys representing knowledge workers

employed in the software industry living in California answered questions regarding KMS

knowledge quality, productivity, and perceived satisfaction during usage of the KMS. The results

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indicated a significant positive correlation observed between KMS knowledge quality and

knowledge worker productivity.

The study results contribute to the existing literature and theoretical framework based on

the accepted KMS knowledge quality and knowledge worker definitions within the literature and

framework derived by the Jennex and Olfman KM Success Model (2006). Also, this researcher

converted the written Halawi’s (2005) KM Success survey into an online survey based on the

foundation of the Jennex and Olfman KM Success Model (2006) used as the basis of the data

analyzed in this study. The researcher determined the existing literature supported the need for

this study as the continued financial losses from unsuccessful KMS implementations fail to

satisfy expected benefits in knowledge assets to support business performance (Fakhrulnizam et

al., 2018; Levallet & Chan, 2018; Nusantara et al., 2018; Vanian, 2016). The study results are

consistent with existing research as discussed by Jahmani et al. (2018) from study conclusions

surveying healthcare staff from multiple hospitals regarding KMS components finding that

knowledge content quality retrieved from the knowledge worker as a functional requirement

perceived usefulness of KMS. Also, several researchers report that the failure to ensure the

organization’s KMS incorporates successful KM process outcomes impact knowledge worker

productivity (Jennex, 2017; Karlinsky-Shichor & Zviran, 2016; Sutanto et al., 2018; Vanian,

2016; Xiaojun, 2017).

The study results are also consistent with existing Jennex and Olfman KM Success

(2006) theory for KMS implementation success indicators as to the degree of knowledge content

quality retrieved during usage of the KMS impacted the knowledge worker’s perceived

usefulness of the system (Jahmani et al.,2018; Zhang, 2017). The study results provided an

unexpectedly large effect size in the correlation strength between the independent variable KMS

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knowledge quality and the dependent variable knowledge worker productivity. While not a

causal relationship, the implications associated with the strength of the correlation coefficient

when measuring the relationship between the variables indicate that as the KMS knowledge

quality improves, the knowledge worker productivity may also improve.

Research Question 2/Hypothesis

RQ2. To what extent, if any, is there a statistically significant relationship between the

knowledge quality of an organization’s KMS and employee satisfaction?

H20. There is not a statistically significant relationship between the knowledge quality of

an organization’s KMS and employee satisfaction.

H2a. There is a statistically significant relationship between the knowledge quality of an

organization’s KMS and employee satisfaction.

The second research question guided the data analysis to determine if a significant

relationship between the knowledge quality of an organization’s KMS and employee satisfaction

existed. The null hypothesis was tested and rejected based on the findings indicating a significant

positive correlation between KMS knowledge quality and employee satisfaction. The results

indicated that as KMS knowledge quality increases, employee satisfaction tends to increase.

Table 12 presents the output of the correlation test.

Factors that might have influenced the interpretation of the results originate from the

contextual implications regarding the knowledge quality of KMS and the employee satisfaction

definition derived within the literature. The knowledge quality of the organization’s KMS served

as the independent variable within the second research question. The Jennex and Olfman KM

Success Model (2006) describes knowledge quality within the KMS as the performance indicator

of KMS success comprised of KM process/strategy, richness, and linkage dimensions. The

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Jennex and Olfman KM Success Model (2006) describes the employee ‘users’ satisfaction as one

component of the KM Success performance indicators, noted as a successful experience based on

the actual use of the KMS and employee satisfaction from each use. Employee satisfaction was

identified as the dependent variable within the second research question. Employee satisfaction

is also portrayed as a successful experience based on the knowledge worker’s actual use of the

KMS while performing KM activities (Zamir, 2019; Zhang & Venkatesh, 2017).

The study results speak to the difficulty retrieving the knowledge asset due to the KMS

knowledge quality impacting the employee satisfaction and potentially the intent not to use the

KMS in the future (Jennex and Olfman, 2006; Oladejo & Arinola, 2019). The study results

support the purpose of exploring the relationship between the knowledge quality of an

organization’s KMS and employee satisfaction for software industry organizations in California.

Data collected from 154 participant surveys representing knowledge workers employed in the

software industry living in California answered questions regarding KMS knowledge quality,

productivity, and perceived satisfaction during usage of the KMS. The results indicated a

significant positive correlation observed between KMS knowledge quality and employee

satisfaction. The study results contribute to the existing literature and theoretical framework

based on the accepted KMS knowledge quality and employee satisfaction definitions within the

literature and framework derived by the Jennex and Olfman KM Success Model (2006). Also,

this researcher converted the written Halawi’s (2005) KM Success survey into an online survey

based on the foundation of the Jennex and Olfman KM Success Model (2006) used as the basis

of the data analyzed in this study. The researcher determined the existing literature supported the

need for this study as the continued financial losses from unsuccessful KMS implementations

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fail to satisfy expected benefits in knowledge assets to support business performance

(Fakhrulnizam et al., 2018; Levallet & Chan, 2018; Nusantara et al., 2018; Vanian, 2016).

The study results are consistent with existing research for employee satisfaction reviewed

by Zamir ( 2019) as realized through employee empowerment to perform assigned job tasks.

Several researchers concur the employee satisfaction is a desired outcome during the usage of the

organization’s KMS based on the successful retrieval of knowledge assets (Jennex & Olfman,

2006; Jennex, 2017; Kumar, 2018; Zamir, 2019; Zhang & Venkatesh, 2017). The study results

are also consistent with existing Jennex and Olfman KM Success (2006) theory for KMS

implementation success indicators as a link between the user’s experience during the successful

retrieval of knowledge assets performing KM activities and employee satisfaction in achieving

the desired knowledge within the KMS (Popa et al., 2018). The study results provided an

unexpectedly large effect size in the correlation strength between the independent variable KMS

knowledge quality and employee satisfaction as the dependent variable. While not a causal

relationship, the implications associated with the strength of the correlation coefficient when

measuring the relationship between the variables indicate that as the KMS knowledge quality

improves, employee satisfaction may also improve.

The most significant implications of this study are the unexpected strength in the

correlation coefficient when examining the relationship for each research question. The

implications from both research questions indicate that as the KMS knowledge quality improves,

the knowledge worker productivity may improve. As the KMS knowledge quality improves,

employee satisfaction may also improve. The consequences of the study results support the

continued efforts to identify indicators to effectively manage the knowledge assets within an

organization’s KMS to achieve the desired productivity result (Ferolito, 2015; Vanian, 2016).

103

This study was presented to contribute to the body of knowledge due to the failure of

organizations to implement a successful KMS to support knowledge worker productivity and

employee satisfaction in the workplace. The results of this study found a statistically significant

relationship between the knowledge quality of an organization’s KMS and knowledge worker

productivity and between the knowledge quality of an organization’s KMS and employee

satisfaction. The study results contribute to the existing body of research as the continued

unsuccessful KMS implementations failing to efficiently manage knowledge assets have resulted

in millions of dollars in loss of employee productivity to support business performance

(Fakhrulnizam et al., 2018;

et al., 2019).

Recommendations for Practice

The literature review based on the Jennex and Olfman KM Success Model (2006)

framework sought to address organizations need to implement an effective KMS to retrieve

knowledge assets (Fakhrulnizam et al., 2018; Levallet & Chan, 2018; Nusantara et al., 2018;

Oladejo & Arinola, 2019; Vanian, 2016). The recommendations for practice are based on the

study findings guided by the research questions to explore the relationship between the

knowledge quality of an organization’s KMS, knowledge worker productivity, and employee

satisfaction.

Knowledge Management Business Leaders

Organizational business leaders responsible for the Knowledge Management strategies

should follow ISO 30401:2018 Knowledge Management guidelines due to the global problem of

KMS implementation failures (“ISO 30401:2018,” 2018). Attempts to implement KMS online

systems have not provided the desired productivity result across the globe based on the lack of

104

organizational standards (Ferolito, 2015; Vanian, 2016). The results showed a significant

positive correlation between KMS knowledge quality and knowledge worker productivity

displayed in Table 11. Also, a significant positive correlation was observed between KMS

knowledge quality and employee satisfaction displayed in Table 12. Business leaders should note

the results of this study as the survey participants included 63% of participants who had

interacted with the organization’s KMS for at least three or more years. Moreover, 94.2 % of

participants held the position of manager or higher.

Recommendations for Future Research

Several recommendations for future research are provided based on the limitations noted

in this study and gaps realized within the literature. A change in the participant requirements is to

open the online survey to participants on a global scale and not just in California. Also, the

online survey should allow participants to translate into any supported language and not just

English. Finally, the survey should be expanded to allow participants employed in health and

education in addition to the software industry. A new survey instrument is recommended

creating a new pilot survey further approved by subject matter experts in each industry updating

the existing survey questions to capture newer KMS capabilities. Two options to include

additional performance indicators are recommended. First, this study only analyzed some of the

Jennex and Olfman KM Success Model (2006) performance indicators. Future research should

include all performance indicators to replicate all Jennex and Olfman KM Success Model (2006).

Second, an updated KM Success Model includes changes in the Knowledge Management

strategies and KMS performance indicators. The next logical short-term step for future

researchers could be to expand the participant requirements, as previously noted. Next, a

modification of the current online survey should include the key performance indicators as

105

mentioned in the Jennex and Olfman KM Success Model (2006) or additional key performance

indicators as supported by the literature.

Conclusions

This quantitative, correlational study explored the relationship between the knowledge

quality of an organization’s KMS, knowledge worker productivity, and employee satisfaction

within the software industry in California. These findings align with the study’s theoretical

framework based on the Jennex and Olfman KM Success Model (2006). The Jennex and Olfman

KM Success Model supported KMS knowledge quality as the independent variable followed by

knowledge worker productivity and employee satisfaction during the use of the KMS as the

dependent variables for this study (Jennex, 2017; Jennex & Olfman, 2006). The study results

supported the difficulty encountered in retrieving knowledge assets about events in the past and

the KMS knowledge quality impacting employee satisfaction (Jennex and Olfman, 2006;

Oladejo & Arinola, 2019). The most significant implication from this study was the unexpected

strength in the correlation coefficient when measuring the relationship for each research

question. The study results support the importance of continuing efforts to identify performance

indicators to effectively manage the knowledge assets within an organization’s KMS to achieve

the desired productivity result (Ferolito, 2015; Vanian, 2016). This study contributed to the body

of knowledge and future research efforts for Knowledge Management business leaders due to the

failure of organizations to implement a successful KMS to support knowledge worker

productivity and employee satisfaction in the workplace. Further research to include updated KM

performance indicators for the KM Success for organizations with an existing KMS may be

helpful to organizations in several industries worldwide. Efforts to identify key KM performance

indicators may provide helpful information for business leaders to improve the successful

106

retrieval of knowledge assets, improve the knowledge quality of the KMS, and improve

knowledge worker productivity.

107

References

Al Ahbabi, S.A., Singh, S.K., Balasubramanian, S. and Gaur, S.S. (2019). Employee perception

of impact of Knowledge Management processes on public sector performance. Journal of

Knowledge Management, 23(2), 351-373. https://doi.org/10.1108/JKM-08-2017-0348

Al Shamsi, O., & Ajmal, M. (2018). Critical factors for knowledge sharing in technology-

intensive organizations: Evidence from UAE service sector. Journal of Knowledge

Management, 22(2), 384-412. https://doi.org/10.1108/JKM-05-2017-0181

Alaarj, S., Zainal, A. M., & Bustamam, U.S.B.A. (2016). Mediating role of trust on the effects of

Knowledge Management capabilities on organizational performance. Procedia – Social

and Behavioral Sciences, 235(2016), 729–738.

http://doi.org/10.1016/j.sbspro.2016.11.074

Alattas, M., & Kang, K. (2016). The relationship between organizational culture and knowledge

sharing towards business system success. arXiv.org. https://arxiv.org/abs/1606.02460.

Alavi, M., & Leidner, D. E. (2001). Knowledge Management and Knowledge Management

Systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.

https://doi.org/10.2307/3250961

Allen, M. (2017). In the SAGE Encyclopedia of Communication Research Methods (Vol. 1-4).

SAGE. https://doi.org/10.4135/9781483381411

Al-Emran, M., Mezhuyev, V., Kamaludin, A., & Shaalan, K. (2018). The impact of Knowledge

Management processes on information systems: A systematic review. International

Journal of Information Management, 43, 173–187.

https://doi.org/10.1016/j.ijinfomgt.2018.08.001

108

Ali, N., Tretiakov, A., Whiddett, D., & Hunter, I. (2016). Knowledge management systems

success in healthcare: Leadership matters. International Journal of Medical

Informatics, 97, 331–340. https://doi.org/10.1016/j.ijmedinf.2016.11.004

A literature study. Business Management Dynamics, 8(12), 1–12.

https://doi.org/10.1016/j.chb.2016.03.075

Andrawina, L., Soesanto, R. P., Pradana, S. I., & Ramadhan, G. (2018). Measuring Knowledge

Management System implementation readiness. Pertanika Journal of Social Sciences &

Humanities, 26, 219.

Andrews, M., & Smits, S. (2019). Using tacit knowledge exchanges to improve teamwork. ISM

Journal of International Business, 3(1), 15–23. doi.org/10.1201/

1078/43194.18.1.200101

Archibald, T., Sharrock, G., Buckley, J., & Young, S. (2018). Every practitioner a “knowledge

worker”: Promoting evaluative thinking to enhance learning and adaptive management in

international development. New Directions for Evaluation, 2018(158), 73–91.

https://doi.org/10.1002/ev.20323

of R&D spillovers in innovation development. Journal of Security & Sustainability, 9(2),

409–420. https://doi.org/10.9770/jssi.2019.9.2(1)

Bacila, M. L., & Titu, M. A. (2018). Structural capital and organizational culture – an approach

regarding the development of valuable intellectual capital. Review of General

Management, 28(2), 66–74. https://doi.org/10.1007/s11135-015-0183-3

109

Banerjee, P., Gupta, R., & Bates, R. (2017). Influence of organizational learning culture on

knowledge worker’s motivation to transfer training: testing moderating effects of learning

transfer climate. Current Psychology: A Journal for Diverse Perspectives on Diverse

Psychological Issues, 36(3), 606. https://doi.org/10.1007/s12144-016-9449-8

Barnes Reports: Software Publishing Industry (NAICS 51121). (2019). United States

remediation services industry report, 1–196. https://www.barnesreports.com

Becerra-Fernandez, I., Leidner, D. E., & Leidner, D. (2008). Knowledge management: An

evolutionary view. https://ebookcentral.proquest.com

Bloomfield, J., & Fisher, M. J. (2019). Quantitative research design. Journal of the Australasian

Rehabilitation Nurses’ Association (JARNA), 22(2), 27–30.

https://doi.org/10.33235/jarna.22.2.27-30

Briones-Peñalver, A. J., Bernal-Conesa, J. A., & de Nieves Nieto, C. (2019). Knowledge and

innovation management model. Its influence on technology transfer and performance in

Spanish Defense industry. International Entrepreneurship and Management Journal, 1.

https://doi.org/10.1007/s11365-019-00577-6

Byrne, B. (2019). Return on Expectations: An Academic Assessment of a Large KM

Project. Proceedings of the European Conference on Knowledge Management. Journal of

Knowledge Management Application and Practice, 1, 201. Retrieved from

http://www.naturalspublishing.com/

Cannatelli, B., Smith, B., Giudici, A., Jones, J., & Conger, M. (2017). An expanded model of

distributed leadership in organizational knowledge creation. Long Range Planning, 50(5),

582–602. https://doi-org.proxy1.ncu.edu/10.1016/j.lrp.2016.10.002

110

Carlson, A. (1969). Information Systems: Theory and Practice. Accounting Review, 44(4), 852–

854. Retrieved from http://www.jstor.org/stable/243690

Caruso, S. J. (2017). A Foundation for Understanding Knowledge Sharing: Organizational

Culture, Informal Workplace Learning, Performance Support, and Knowledge

Management. Contemporary Issues in Education Research, 10(1), 45–52.

https://doi.org/10.19030/cier.v10i1.9879

Cavanagh, R. (2015). A unified model of student engagement in classroom learning and

classroom learning environment: one measure and one underlying construct. Learning

Environments Research, 18(3), 349–361. https://doi.org/10.1007/s10984-015-9188-z

Centobelli, P., Cerchione, R., & Esposito, E. (2018). How to deal with Knowledge Management

misalignment: a taxonomy based on a 3D fuzzy methodology. Journal of Knowledge

Management, 22(3), 538. https://doi.org/10.1108/JKM-10-2016-0456

Ceptureanu, S.I., Ceptureanu, E. G., Zgubea, F., & Tudorache, A. (2012). Economic Survey on

Knowledge Based Management in Romanian Companies. Review of International

Comparative Management. Revista de Management Comparat International, 13(2), 325–

336. Retrieved from https://www.ceeol.com/

Corney, P. J. (2018). As KM evolves, so will the ISO standard. Business Information

Review, 35(4), 165. https://doi.org/10.1177/0266382118810825

Costas, J., & Karreman, D. (2016). The bored self in knowledge work. Human Relations, 69(1),

61–83. https://doi.org/10.1177/0018726715579736

-Sikora, A., Sikora, J., Rorat, J., & Niemiec, M.(2018). Information

technology tools in corporate Knowledge Management. Ekonomia i Prawo, 1(1), 5.

https://doi.org/10.12775/EiP.2018.00. (2018).

111