knowledge interaction: social capital and electronic

Knowledge Interaction: Social Capital and

Electronic Knowledge Repository Utilization

By Eliot Adam Jardines

B.A. in Political Science and Latin American Studies, December 1993, University of New Mexico

M.A. in International Studies, August 1996, University of Connecticut M.S. in Strategic Intelligence, September 2000, National Intelligence University

A Dissertation Submitted to

The Faculty of The Graduate School of Education and Human Development

of The George Washington University in partial fulfillment of the requirements

for the degree of Doctor of Education

August 31, 2015

Dissertation directed by

Diana L. Burley Professor of Human and Organizational Learning

ii

The Graduate School of Education and Human Development of the George Washington

University certifies that Eliot Adam Jardines has passed the final examination for the

degree of Doctor of Education as of July 17, 2015. This is the final and approved form of

the dissertation.

Knowledge Interaction: Social Capital and Electronic Knowledge Repository Utilization

Eliot Adam Jardines

Dissertation Research Committee:

Diana L. Burley, Professor of Human and Organizational Learning, Dissertation Director David R. Schwandt, Professor of Human and Organizational Learning, Committee Member Karen R. Detweiler, Enterprise Systems Engineer, The MITRE Corporation, Committee Member

iii

© Copyright 2015 by Eliot A. Jardines All rights reserved

iv

Dedication

To my wife, Teresa

To my sons, Adam, Ethan, and Graham

To my parents, Elidad and Mayra Jardines

v

Acknowledgments

This dissertation was many years in the making and was shaped by many

individuals. First, I would like to express my gratitude to my committee chair, Dr. Diana

Burley, for her counsel and ability to keep me moving forward. To my committee

members, Dr. David Schwandt and Dr. Karen Detweiler, thank you for your strong

support and guidance. I would also like to thank my readers, Dr. Joel Gomez and Dr.

John Bordeaux, for their willingness to take time from their very busy schedules to be

part of the defense, as well as Dr. Michael Marquardt, chair of the Department of Human

and Organizational Learning—thank you for your support. For patiently answering

numerous questions about the position generator instrument, my thanks to Dr. Nan Lin of

Duke University for indulging a doctoral student he has never met.

Reaching this point would have been impossible were it not for the support of a

few great friends. To John Musser, a gifted practitioner of the social capital arts who put

up with countless discussions on the topic and always encouraged my efforts—thank you,

my brother. To Barbara Alexander, who went from the customer I supported at the

Department of Homeland Security to a close friend and tireless editor of innumerable

drafts of my dissertation proposal—thank you for your friendship and gifted red pen.

I thank my classmate, Dr. Faith Power, who not only provided moral support, but

also endured my antics during cohort weekends. My heartfelt thanks to Dr. Amy Mazur,

a great friend who has guided two Jardines through GSEHD and is spoken of with great

reverence by the young men of this household. To my in-laws, Drs. Gerardo and Ruth

Gross, for their steadfast prayers and encouragement.

vi

I thank Sue Doby and Tom Budnar, my supervisors at Oracle Consulting, and my

former boss, Dr. Harold Rosenbaum at CENTRA, for their support during this process.

Thank you to the staff and leadership at X Corp, who allowed me to conduct my research

on their electronic knowledge repository. I am especially appreciative of T. S. for his

executive sponsorship of this research, to J. R. for his guidance and advocacy, and K. F.

for his help as an expert informant and his willingness to pull the metadata from the

server.

To my beloved and talented wife, Teresa—you have sacrificed more than anyone

during the decade it took to finish this doctorate. Although completing this degree has

been a transformative experience, the years since we met at UNM have been the most

deeply transformative of all. To our three sons, Adam, Ethan, and Graham—thank you

for your understanding when I had to study and for playing quietly while I wrote. You

three are our greatest achievements. Para mis padres, quienes sacrificaron tanto por mí e

inculcaron la importancia de la educación.

To Him who orders my steps and shows me unmerited favor, I endeavor to

understand Your grace and strive to reflect it.

vii

Abstract of the Dissertation

Knowledge Interaction: Social Capital and Electronic Knowledge Repository Utilization

This dissertation explored the relationship between the three social capital

measures of extensity, upper reachability, and range, as well as the electronic knowledge

repository (EKR) utilization elements of knowledge seeking and knowledge contribution

via a range of specific EKR functionalities. This quantitative research study consisted of

a regression analysis of the social capital scores generated via the position generator

survey instrument, which were compared to the usage metadata available from the EKR

server logs. The study population consisted of a business unit of a U.S.-based

multinational information technology corporation that utilized the Oracle Social Network

EKR as a means of facilitating business processes and knowledge management.

The guiding theoretical constructs for this study were Lin’s conceptualization of

instrumental and expressive social capital outcomes, which were then coupled with Lin

and Huang’s articulation of EKR utilization elements. This measurement study sought to

propose and validate a means by which the social capital implications of EKR use could

be identified and measured. A classification rubric was developed to aid the scholar and

practitioner alike in categorizing specific EKR functionalities.

The findings of this study suggest that a relationship exists between an EKR

user’s social capital and his or her EKR behaviors as recorded in the server logs. The

resulting methodology may serve as a theoretically based and transparent method of

measuring social capital in the digital environment. Hypothesis testing revealed that the

social capital measure of extensity was a significant predictor of EKR functionality

viii

usage. However, the lack of variability of the remaining two social capital measures

hindered the analysis of the overall model. Based on these findings, recommendations

were offered for improving the accuracy of social capital measurement efforts within the

digital environment of the EKR.

ix

Table of Contents

Page

Dedication .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Abstract of the Dissertation .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiv

CHAPTER 1: OVERVIEW ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Problem Statement .............................................................................................................. 3

Research Questions ............................................................................................................. 5

Theoretical Constructs ......................................................................................................... 7

Electronic Knowledge Repositories ............................................................................... 8

Social Capital ................................................................................................................ 11

Conceptual Framework ................................................................................................ 15

Statement of Potential Significance .................................................................................. 17

Summary of Methodology ................................................................................................ 19

Limitations ........................................................................................................................ 23

Definition of Key Terms ................................................................................................... 24

Summary ........................................................................................................................... 25

CHAPTER 2: LITERATURE REVIEW ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Social Capital .................................................................................................................... 28

Capital ........................................................................................................................... 28

x

Human Capital .............................................................................................................. 30

Cultural Capital ............................................................................................................ 31

Emergence of the Modern Social Capital Concept ...................................................... 32

Embedded Resources .................................................................................................... 39

Instrumental Action ...................................................................................................... 43

Instrumental Action Research Questions and Hypotheses ........................................... 45

Expressive Actions ....................................................................................................... 45

Expressive Actions Research Questions and Hypotheses ............................................ 48

Electronic Knowledge Repositories .................................................................................. 49

Alternative Models ............................................................................................................ 52

Social Cognitive Theory ............................................................................................... 52

Technology Acceptance Model .................................................................................... 54

Task-Technology Fit .................................................................................................... 55

Summary ........................................................................................................................... 57

CHAPTER 3: METHODS .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Research Questions and Hypotheses ................................................................................. 61

Research Design ................................................................................................................ 62

Research Site ................................................................................................................ 65

Participants ................................................................................................................... 67

Apparatus .......................................................................................................................... 69

Position Generator ........................................................................................................ 69

EKR Utilization Metrics ............................................................................................... 71

Data Collection Procedures ............................................................................................... 72

xi

Data Handling ................................................................................................................... 74

Data Analysis .................................................................................................................... 75

Human Participants and Ethics Precautions ...................................................................... 77

Confidentiality .............................................................................................................. 77

Voluntary Participation ................................................................................................ 78

Summary ........................................................................................................................... 78

CHAPTER 4: DATA ANALYSIS .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Preliminary Analysis ......................................................................................................... 81

Primary Analysis ............................................................................................................... 87

Research Question 1a ................................................................................................... 87

Research Question 1b ................................................................................................... 89

Research Question 2a ................................................................................................... 90

Research Question 2b ................................................................................................... 92

Summary ........................................................................................................................... 93

CHAPTER 5: INTERPRETATIONS, CONCLUSIONS, AND

RECOMMENDATIONS .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Interpretations and Conclusions ........................................................................................ 97

Social Capital Measures ............................................................................................... 98

Hypothesis 1 ............................................................................................................... 100

Hypothesis 2 ............................................................................................................... 102

Hypothesis 3 ............................................................................................................... 103

Hypothesis 4 ............................................................................................................... 104

Recommendations ........................................................................................................... 107

xii

View EKRs as Change Management Efforts and Not Simply IT Projects ................ 108

Utilize the Full 22-Occupation Position Generator Instrument .................................. 109

Modify the Instrument ................................................................................................ 110

Adopt a Social Physics Approach .............................................................................. 112

Assess the Potential for Bidirectionality .................................................................... 114

Expand to Additional Functionalities ......................................................................... 114

Conclusion ....................................................................................................................... 116

REFERENCES .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

APPENDIX A: Informed Consent Form and Survey .. . . . . . . . . . . . . . . . . . . . . . . 130

APPENDIX B: Study Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

xiii

List of Figures

Page

1. Oracle Social Network Software Suite ........................................................................ 9

2. Model of Social Capital in Electronic Knowledge Repository Utilization ................ 16

3. Lin’s (2001a) Model of Social Capital ...................................................................... 43

4. Creswell’s (2003) Model of the Deductive Approach to Quantitative Research ...... 63

xiv

List of Tables

Page

1. EKR Utilization Elements, Behaviors, and Social Capital Outcomes ......................... 6

2. Position Generator Instrument—Measuring Social Capital ....................................... 15

3. Research Variables, Underlying Theory, and Data Collection Methods ................... 23

4. Definitions .................................................................................................................. 25

5. EKR Behaviors and Corresponding OSN Functionalities ......................................... 71

6. Initial Descriptive Statistics (N = 103) ....................................................................... 81

7. Categorization of Range Social Capital Measure ...................................................... 84

8. Categorization of OSN Functionalities ...................................................................... 85

9. Final Descriptive Statistics (N = 103) ........................................................................ 86

10. Pearson’s Product-Moment Correlations (N = 103) .................................................. 86

11. Multinomial Logistic Regression Parameter Estimates (N = 103) ............................ 88

12. Please Respond Flag Coefficients (N = 103) ............................................................. 89

13. Post Coefficients (N = 103) ....................................................................................... 90

14. FYI Flag Coefficients (N = 103) ................................................................................ 91

15. Reply Coefficients (N = 103) ..................................................................................... 92

16. Like Coefficients (N = 103) ........................................................................................ 93

17. Hypothesis Outcomes ................................................................................................ 95

1

CHAPTER 1:

OVERVIEW

Billions of dollars are spent each year on knowledge management systems

throughout the business sector. According to surveys conducted at the onset of the new

millennium, at least one half of U.S. firms had a knowledge management initiative

underway (Allee, 2000). In 2008, American companies spent an estimated $85 billion on

knowledge management (Murphy & Hackbush, 2007).

Since the advent of the information age, there has been explosive growth in

knowledge management systems throughout corporate America. More specifically, the

electronic knowledge repository (EKR), such as a wiki, expert database, data warehouse,

or enterprise social network, has become nearly ubiquitous. The ultimate goal of these

EKR deployments is to provide a competitive advantage. However, in spite of the

prevalence of EKRs in modern corporate America, many EKR deployments fail or

remain underutilized.

The advisory firm KPMG (2000) reported that approximately 36% of knowledge

management initiatives in U.S. corporations fail as a result of lack of attention by

management to adoption or implementation issues. Some comfort can be gleaned by the

remaining 64% of KM initiatives that do not fail, but the assumption should not be made

that the remainder are completely successful. Another study (Akhavan, Jafari, & Fathian,

2005) quoted Daniel Morehead, the director of organizational research at British

Telecommunications PLC, who noted that close to 70% of knowledge management

endeavors do not meet their stated goals and objectives.

2

In spite of the general underperformance of EKR deployments, they have

remained popular with American corporations, even during the recent economic

recession. According to Kolbasuk McGee and Soat (2008), as the economy began its

decline in mid-2008, many corporate leaders saw the downturn as an opportunity to

invest in knowledge management initiatives, such as EKRs, as a means of leaping ahead

of their weakened, or soon-to-be weakened, competitors, disregarding the expectation

that information technology (IT) budgets would also decline.

Given the aforementioned underperformance of knowledge management

initiatives over the past decade, a clear understanding of how social capital is measured in

the EKR environment is critical. A thorough review of the literature indicates that

surprisingly little is known regarding the relationship between individuals’ social capital

measures and their utilization of EKRs.

Social capital measures, as articulated by Lin (2001b), include range of

accessibility to different hierarchical positions, heterogeneity of accessibility to different

positions, and upper reachability of social capital access. These measures reflect Lin’s

(1982) theoretical assertion that occupational prestige is an indication of wealth, power,

and status, which are viewed as universally valued resources. Without a clear

understanding of how each of these three measures relates to the elements of EKR

utilization, the applicability of social capital theory to the digital environment, as well as

its explanatory and predictive strengths, is limited.

A better understanding of how the various social capital measures and EKR

utilization interrelate will aid corporate leadership in developing and deploying EKRs,

which enhance knowledge diffusion, as well as developing and maintaining

3

organizational social capital. According to Kilduff and Tsai (2003), businesses succeed or

fail based on how effectively their networks share expertise, learning, and resources.

Problem Statement

U.S. corporations spend billions of dollars annually on knowledge management

applications, such as EKRs, only to see many of those investments ignored, underutilized,

or undermined by the workforce. These corporations are not harvesting sufficient return

on investment (ROI) on EKR deployments. The level of technical sophistication and

experience of firms that develop and deploy EKRs has grown tremendously during the

past decade, and thus, initial issues with implementation or difficulty of use are far less

common now than they were at the beginning of the new millennium. Yet, despite

overcoming these early challenges, EKRs all too often underperform or remain

underutilized.

This poor ROI for EKR deployment has an increasingly negative impact on the

organization and its profitability due to the ever-increasing demands of the information

age. Not only has IT improved, but so has the technological sophistication of modern

white-collar workers, now aptly named “knowledge workers.” As the ranks of knowledge

workers have swollen, their interest in, and need for, knowledge repositories has grown

exponentially. According to The Economist (2010), a recent study found that knowledge

workers spend between 6 and 10 hours per week searching for information—frequently

doing so in EKRs.

EKR use does not occur in a vacuum, but rather within a broader social network,

which provides structure, context, and resources. According to Kankanhalli, Tan, and

Wei (2005), “While technological capabilities are important, having sophisticated KM

4

[knowledge management] systems does not guarantee success in KM initiatives. This is

because social issues appear to be significant in ensuring knowledge sharing success”

(p. 114). Knoke and Yang (2008) defined the concept of a social network as “a structure

composed of a set of actors, some of whose members are connected by a set of one or

more relations” (p. 8). An EKR deployment that ignores the underlying social network or

hinders the relations of its members is likely to face difficulties with utilization.

The adoption of EKRs notwithstanding, many corporations and their executives

have a tendency to ignore the power of social networks. Cross and Prusak (2002)

addressed this point, stating:

Most corporations, however, treat informal networks as an invisible enemy—one that keeps decisions from being made and work from getting done. To many senior executives, these intricate webs of communication are unobservable and ungovernable—and, therefore, not amenable to the tools of scientific management. (p. 105)

EKR deployments that stifle or limit social interactions may decrease the value of

a social network by inhibiting a member’s ability to reach or use the resources embedded

within the network. This interaction with the resources of the social network is, in

essence, social capital. Lin (2001a) defined social capital as “resources embedded in a

social structure that are accessed and/or mobilized in purposive actions” (p. 29). Thus,

social capital can be seen as that which feeds or sustains the social network. Without the

constant interaction as well as knowledge-seeking and knowledge-contribution behaviors

that are prevalent in successful EKR deployments, there are no mechanisms or means for

providing access to social capital through the digital medium.

In order to ensure maximum knowledge diffusion and ROI with EKR systems, a

clear understanding of how social capital measures relate to EKR utilization is a critical

5

first step. Maximum ROI may also depend on a clear understanding of what role social

capital plays in the EKR. Given the billions of dollars’ worth of EKR investments and the

millions of labor hours spent each year on knowledge discovery, a precise delineation of

the interrelationship between social capital measures and EKR utilization may prove

critical for the knowledge management initiatives of American corporations and,

ultimately, add greater ROI for EKR deployments.

Research Questions

To enhance the understanding of the relationship between social capital and EKR

utilization, two primary research questions (with subquestions) guided this quantitative

research study:

1. What is the relationship between EKR users’ social capital and EKR knowledge

seeking?

a. What is the relationship between social capital and EKR users seeking

information?

b. What is the relationship between social capital and EKR users posting

requests for assistance?


contribution?

a. What is the relationship between social capital and EKR users posting new

information?

b. What is the relationship between social capital and EKR users responding

to postings by other users?

6

These research questions explore how social capital measures relate to EKR

utilization. As identified by Lin and Huang (2009), EKR utilization elements fall into two

categories: knowledge seeking and knowledge contribution. For the purposes of

enhanced specificity, these two elements were further divided into four specific EKR

behaviors, which reflect six specific ERK functionalities. Each of the EKR utilization

elements and their subordinate behaviors were theorized to encompass social capital

outcomes as articulated by Lin (2001b). Table 1 delineates the two EKR utilization

elements, observable EKR behaviors, and their linkage to social capital outcomes, which

can serve as a rubric for categorizing specific EKR functionalities.

Table 1 EKR Utilization Elements, Behaviors, and Social Capital Outcomes Utilization element EKR behavior Social capital outcome Knowledge seeking (Lin & Huang, 2009)

• Seeking information via the EKR (“follow” function)

• Requesting assistance via the EKR (“flag” function)

Instrumental action— gaining added resources (Lin, 2001b)

Knowledge contribution (Lin & Huang, 2009)

• Posting new information to the EKR (including “FYI” function)

• Responding to EKR postings by other users (including the “like” function)

Expressive action— maintenance of possessed resources (Lin, 2001b)

The first research question explored the relationship between EKR users’ social

capital (as measured by range, extensity, and upper reachability) and knowledge-seeking

behaviors, which are instrumental actions according to the literature. In order to provide

greater specificity, two subquestions were identified that comprise all knowledge-seeking

EKR utilization elements. Research Question 1a addressed seeking information via the

EKR, while Research Question 1b dealt with requesting assistance via the EKR. Each

7

subquestion had a hypothesis (presented in chapter 2) that asserted a positive relationship

between the specific social capital measure and instrumental action.

The second research question addressed the relationship between EKR users’

social capital (three measures) and EKR knowledge contribution, which is a form of

expressive action. For added specificity, two subquestions were likewise provided and

comprised all knowledge contribution EKR utilization elements. Research Question 2a

dealt with posting new information to the EKR, while Research Question 2b addressed

responding to EKR postings by others. Here again, each subquestion spawned a

hypothesis that asserted a positive relationship between each of the three social capital

measures and each expressive action.

A caveat is that access to social capital is not something that can be measured

completely accurately. To do so, a researcher would have to document each element of

social capital available within the social network—an impossible task. The researcher

would then have to ascertain how much access to that social capital each member of the

network enjoyed—an equally impossible and ultimately subjective task. As a surrogate,

social capital researchers use name, position, or resource generator instruments. For this

study, the position generator instrument was selected; it measures range of accessibility,

extensity, and upper reachability of access to social capital, which are discussed in detail

in chapter 3.

Theoretical Constructs

With the backdrop of these two research questions and four subquestions, this

section provides an in-depth discussion of the key theoretical constructs that guided this

research study. The first section discusses EKRs, including a definition, examples, and

8

utilization behaviors. The second section examines the theoretical elements of social

capital and the measures used in this study. The final section presents the study’s

conceptual framework.

Electronic Knowledge Repositories

The knowledge management field has, in recent years, come to a general

consensus regarding the definition of EKRs. According to Kankanhalli et al. (2005):

EKRs are electronic stores of content acquired about all subjects for which the organization has decided to maintain knowledge. EKRs can comprise multiple knowledge bases as well as the mechanisms for acquisition, control, and publication of the knowledge. (p. 114)

In a footnote to the above definition, the authors indicated that while EKR capabilities are

comparable to the mnemonic functions of organizational memory information systems,

conceptualization focuses on description at the level of the subsystem, but EKRs do not.

Lin and Huang (2009), while citing the aforementioned definition in their

introduction, went on to offer a slightly different definition of EKRs that focused on

codified expertise. They stated that EKRs are “knowledge repositories that emphasize

codification and storage of knowledge so as to facilitate knowledge reuse through access

to the codified expertise” (p. 168). This focus on codification adds an important

dimension to the EKR definition which is not readily apparent in the definition of

Kankanhalli et al. (2005) cited earlier. Thus, the refined and expanded Lin and Huang

(2009) definition was adopted for this study.

According to Fulk, Heino, Flanagin, Monge, and Bar (2004), specific examples of

EKRs include “expert databases, groupware, data warehouses, project websites, intranets,

9

shared whiteboards, and lessons learned databases” (p. 569). Bock, Kankanhalli,

and Sharma (2006) also cited this listing and added that “technologies such as Microsoft

SharePoint Services provide a platform for implementing EKRs” (p. 358). For the

purposes of this study, wikis, such as Wikipedia, were also categorized as EKRs. The

specific EKR this study focused on was the Oracle Social Network (OSN) software

(Figure 1).

Figure 1. Oracle Social Network software suite.

The OSN software integrates both communication and collaboration technologies

into a single enterprise platform (Oracle, 2013). The software comprises four main

elements: conversations, social objects, content, and activity streams. The conversations

element is the primary collaborative tool that facilitates and archives discussions. The

social objects element provides users the capability to upload documents, images, videos,

or other files, which can then be commented upon or modified in a group setting. OSN’s

10

content element is a series of tools that “assist with the flow of content, documents,

images and other rich media between people and groups to facilitate sharing, rapid and

accurate feedback, and reuse” (Oracle, 2013, p. 8). The final element, activity streams, is

a capability that provides a summary of updates of conversations, social objects, and

people for the OSN user. Given that the activity streams are simply a summary of the

underlying OSN content and do not represent EKR behaviors as articulated in Table 1,

this capability was not addressed in the study.

The creation of the wiki in 1994 by Ward Cunningham marked the beginning of

the popularization of EKRs. Largely due to their relative technological simplicity and

ease of use, wikis proliferated, both as encyclopedic reference for the general public and

the mainstay of many a corporate intranet. As an example of the popularity of the wiki

application, its best known incarnation, Wikipedia, currently boasts over 4 million

English-language articles and over 130,000 current contributors (individuals who have

performed a substantive action in the last 30 days) in its English-language version

(Wikipedia, 2013).

A natural question arising from these statistics is why so many individuals expend

the time and energy necessary to populate millions of articles. While there is a significant

amount of scholarship regarding motivation for contributing, there is little understanding

of the role social capital plays in both EKR contributions and knowledge seeking. To that

end, this study explored the relationship between social capital as well as knowledge

seeking in, and contributing to, EKRs: hereafter, EKR utilization. This duality of focus is

important because, as Lin and Huang (2009) pointed out, it is very difficult to focus

11

exclusively on one role, as the same individual can take on the role of seeker and a few

minutes later change to that of contributor.

Bock et al. (2006) made the point that EKRs are the most common IT supporting

knowledge management and related systems. Use of EKRs encompasses both

contributions of codified knowledge (expressive action), as well as the seeking and use of

such codified knowledge (instrumental action): hereafter, EKR utilization elements. The

focus of this study was on the individuals who make use of EKRs, with the aim of

understanding what relationship exists between social capital measures and knowledge

contribution and seeking behaviors (i.e., EKR utilization) among EKR users.

To add greater specificity, this study further distilled the EKR utilization elements

of knowledge seeking and knowledge contribution into specific EKR behaviors on the

part of users. Knowledge seeking in the EKR environment consists of seeking

information available in the repository (by “following” the public postings of other users)

and requesting assistance from other users via the repository’s “please respond” flag

function. Knowledge contribution in the EKR is broken down into the two behaviors of

information posting and responding to EKR postings by others (including use of the

“like” button and the FYI flag function). In short, knowledge contribution metrics are

captured via the EKR functions of posting, replying, FYI flag, and the like button.

Social Capital

The theoretical basis of social capital is derived from multiple academic

disciplines to include economics, sociology, political science, and organizational

behavior. This plurality of influences and perspectives has led to widespread variability

in definitions of social capital. Nonetheless, the central theme of social capital according

12

to Lin (2001a) is that “capital is captured in social relations and that its capture evokes

structural constraints and opportunities as well as actions and choices on the part of the

actors” (p. 3).

Any discussion of social capital must begin with a review of the conceptualization

of capital, which can be traced back to Marx (1933). He saw capital as emerging from the

social relations between laborers and the bourgeoisie. In Marx’s view, the bourgeoisie

controlled the means of production by which commodities were produced and sold.

Laborers, in turn, were paid for their work at a rate that was significantly lower than the

generated value of the commodity once sold. According to Lin (2001a), Marx viewed

capital as part of the surplus value that in turn created further profit.

Theodore W. Schultz sought to expand Marx’s concept of capital by advancing

the first systematic and theoretical argument for the concept of human capital. In a speech

before the American Economic Association, Schultz (1961) criticized prevailing

economic theories, which failed to view human resources as explicitly a form of capital.

It should be noted that while human capital theory expanded the concept of capital, it

engendered no substantial change to the Marxian definition of capital. Human capital

theory did, however, challenge the Marxian notion of the bourgeoisie monopoly over

capital. Rather than bourgeoisie appropriation and exploitation of labor, human capital

theory posits that the free will and self-interest of workers can result in their

accumulation of capital through specialized skills or education.

Picking up on this thread of education and capital, French sociologist Pierre

Bourdieu and his colleagues in the 1970s offered an alternative theoretical perspective to

human capital: the theory of cultural capital. Bourdieu argued that a society’s dominant

13

class imposes and perpetuates its culture through pedagogic action, by which all others

internalize the symbols and meanings of the dominant class and subsequently transmit

this “symbolic violence” to the next generation (Bourdieu & Passeron, 1977). This

acceptance and appropriation of the dominant culture and values is what Bourdieu

referred to as cultural capital. In a subsequent work, Bourdieu (1986) theorized social

capital as the product of a group’s efforts, a collective asset that is then shared by the

group to which the individual belongs. This conceptualization of social capital diverges

from the Marxian definition of capital as purely economic but retains the notion of

imposition and exploitation by a dominant class.

Bourdieu’s conceptualization of social capital fails to account for the free will,

self-interest, and choice evident in human capital theory. This study eschewed the macro-

level view of class exploitation in favor of the micro-level explanation of how individual

actors access (social) capital through their own efforts as facilitated or mitigated by the

constraints of available resources and structural position. That is to say, while societal

class structures were viewed as playing a role, the breadth and depth of that role was not

viewed as all-encompassing in this study.

A well-known critic of Bourdieu’s view of social capital was American

sociologist James Samuel Coleman. He countered that social capital is not simply the

purview of the dominant class, but rather could be viewed as providing benefits to the

broader society. Coleman (1988) defined social capital as “not a single entity but a

variety of different entities, with two elements in common: they all consist of some

aspect of social structures, and they facilitate certain actions of actors—whether persons

or corporate actors—within the structure” (p. S98). Coleman viewed network closure as

14

advantageous and even necessary for social capital, a view consistent with Bourdieu’s

view of a dominant class, which is restrictive as a means of maintaining its level of

dominance.

While Coleman found network closure a prerequisite for social capital, theorists

Granovetter (1973) and Lin (2001a) maintained that closure is neither necessary nor

advantageous for social capital. It can be said that network closure is preferred for the

preservation of social capital, but both Granovetter (1973) and Burt (1992) have

demonstrated that for the acquisition of new information and resources not currently

available with a social network, closure is detrimental to the long-term viability of the

group. As such, this study rejected Coleman’s requirement of network closure as an

essential element of social capital.

For the purposes of this research study, the social capital conceptualization

proffered by Lin (2001a) was adopted, which eschews the Marxian preoccupation with a

bourgeoisie monopoly over capital. Moreover, Lin’s concept allows for network closure,

but does not rule it a requirement for social capital. Lin defined social capital succinctly

as “resources embedded in a social structure that are accessed and/or mobilized in

purposive actions” (p. 29).

With regards to measuring social capital, this study adopted the position generator

instrument that contains the three distinct measures listed in Table 2, as articulated by Lin

and Dumin (1986). Dr. Nan Lin was asked by the researcher if the position generator

instrument would be a valid means of measuring social capital in the digital environment

of the EKR, and he indicated that the position generator “measures the extent (quantity)

and richness (quality) of a person’s network of ties, through a sample of occupations” and

15

that as such the instrument could be used in this study (N. Lin, personal communication,

January 26, 2010). For a more detailed discussion of the means of measuring social

capital and their theoretical underpinnings, see chapter 2.

Table 2 Position Generator Instrument—Measuring Social Capital

Instrument Theoretical basis Measures Position Generator: • Sample of

ordered structural positions salient in a society

• Participants asked to indicate contacts, if any, in each of the positions

Lin (1982): • Occupational prestige an

indication of universally valued resources (e.g., wealth, power, and status)

Lin, Fu, & Hsung (2001): • “Range—of accessibility to

different hierarchical positions in the society (e.g., the distance between the highest and lowest accessed positions)” (p. 63).

• “Extensity—of accessibility to different positions (e.g., number of positions accessed)” (p. 63).

• “Upper Reachability—of accessed social capital (e.g., prestige or status of the highest position accessed)” (p. 63).

Lin and Dumin (1986): • Occupational prestige

scores used to assess resources associated with network member’s occupational positions

• Content free • Role/location-neutral

Conceptual Framework

The theory of social capital provides the conceptual framework for this study, as

depicted in Figure 2. Lin (2001a) described the premise behind social capital as an

investment made by an individual by way of social relations with an expected return.

This premise is uncomplicated, but it represents a major departure from the macro level

of analysis inherent in the Marxian conceptualization of capital. Specifically, the

conceptual framework employed in this study moved beyond the Marxist macro-

dominant culture rubric to a perspective centered on the group or meso level of analysis.

In this approach, as Lin (2001a) stated, “capital is seen as a social asset by virtue of the

16

actors’ connections and access to resources in the network or group of which they are

members” (p. 19).

Figure 2. Model of social capital in electronic knowledge repository utilization.

This study used the social capital conceptualization as articulated by Lin (2001a),

consisting of resources embedded within a social structure that are accessed and

ultimately mobilized for a specific purpose. The figure depicts, via unidirectional arrows,

the three social capital measures as having independent impacts upon both elements of

EKR utilization. These arrows represent Lin’s (2001b) theoretical assertion of

instrumental and expressive actions. Instrumental actions, as articulated in Table 1, are

focused on gaining added resources that are theorized to relate to the knowledge-seeking

element of EKR utilization. Expressive actions focus on maintenance of possessed

resources and are theorized to relate to the EKR utilization element of knowledge

contribution.

This third element of EKR utilization is composed of knowledge-seeking and

knowledge-contribution behaviors. These two elements of EKR utilization draw upon

17

Polanyi’s (1962) conceptualizations of knowledge. Both knowledge seeking and

knowledge contribution are interactions of EKR utilization (Lin & Huang, 2009). A

detailed discussion of the theoretical framework depicted in the Figure 2 model is

presented in chapter 2.

Statement of Potential Significance

A better understanding of the relationship between social capital measures and

EKR utilization adds greater explanatory power to social capital theory for the scholar

and enriches knowledge management outcomes for the practitioner.

This research study sought to delineate the relationship between Lin’s (2001b)

tripartite social capital measures and EKR utilization elements. For the scholar, the study

adds to literature that points to an increase of social capital evident in digital

environments (Hendriks, 1999; Kankanhalli et al., 2005; Lin, 2001a; Lin & Huang, 2009;

Newell et al., 2004; Robert et al., 2008) by providing data about the relationship between

social capital measures and EKR utilization behaviors. This study sought to add to the

social capital literature by providing empirical data regarding the migration of social

interactions and social capital to the digital realm.

For the purposes of this study, digital environments were defined as social

networks that exist on corporate intranets as well as the Internet. The digital environment

is socially constructed by individuals through interaction with each other via EKRs, as

well as supporting technologies such as the Internet, a corporate intranet, e-mail, chat

groups, or instant messaging. It is important to note that digital social interactions are not

exclusive to EKRs but can, and do, take place via other digital interactions. This study,

however, focused solely on digital social interactions within the EKR of one company.

18

Scholars have engaged in assessments of the impact of social capital on

behaviors, such as employment seeking (Fernandez, Castilla, & Moore, 2000; Smith,

2005); IT, such as enterprise resource planning systems (Newell, Tansley, & Huang,

2004); and digitally enabled teams (Robert, Dennis, & Ahuja, 2008). Few articles to date,

however, have dealt with specific social capital measures and EKRs. Current academic

research lacks the requisite specificity or explanatory power with regards to which social

capital measures relate to elements of EKR utilization, or whether a relationship exists at

all.

In the few studies in which social capital and EKRs were explored, additional

theories, such as the technology acceptance model (TAM) and task-technology fit (TTF)

theory, were included (Goodhue, 1995; Goodhue & Thompson, 1995; Kankanhalli et al.,

2005; Lin & Huang, 2005; Wasko & Faraj, 2005). The incorporation of additional

theoretical perspectives without a clear understanding of which social capital measures

relate to EKR utilization can create greater uncertainty. Study results have tended to point

to an impact upon EKR utilization but obfuscate which theory or theoretical component

is responsible. This study sought to fill the gap in the scholarly literature regarding social

capital and EKR utilization by delineating the relationship between three social capital

measures and two EKR utilization elements, thereby adding greater theoretical specificity

and explanatory power to the existing social capital literature.

For the practitioner, a greater understanding of the relationship between these two

elements may help corporate leadership achieve positive knowledge management

outcomes through the deployment of EKRs that enhance the organization’s social capital.

To that end, this study sheds light on the hypothesized relationship between social capital

19

measures and EKR utilization. The aim of the study was to provide practitioners with the

tools to help correct more than a decade of underperformance by knowledge management

initiatives in general, and EKRs specifically.

Additionally, practitioners can benefit from this study’s efforts towards a

transparent and replicable means of measuring an individual’s social capital in the EKR

environment. Commercially, firms like Klout, PeerIndex, and Empire Avenue have

attempted to score or rank the influence of social media users. According to Stevenson

(2012), individuals with higher Klout scores are targeted for special perks and job offers

and gain quicker responses from customer service representatives. The author pointed

out, however, that these commercial efforts at measuring social media influence are

proprietary and stand accused of measuring popularity versus influence. An oft-quoted

example is Klout’s ranking of boy crooner Justin Bieber as far more influential than the

president of the United States or the Dalai Lama (Nathanson, 2014). This study provides

a theoretically sound framework for measuring social capital in the EKR environment,

which avoids the pitfalls of a focus on popularity and eschews the opaque proprietary

algorithm. This study sought to provide practitioners with the foundation for measuring

social capital in digital environs: a far more useful measure than popularity, likeability, or

even influence when it comes to maximizing ROI for EKR deployments.

Summary of Methodology

According to Creswell (2003), there are “several preliminary considerations that

are necessary before designing a proposal or plan for study” (p. 1), the first of which is

selecting a framework for the overarching design, which, in this case, was a quantitative

20

one. The goal of generalizability, as well as the theoretical constructs of Lin’s (2001a)

network theory of social capital, drove the selection of a quantitative approach.

The research site for this study was a major Fortune 500 corporation with a large

number of users of the OSN EKR software. Slightly more than 100 individuals who had

been using the OSN software for 3 months were studied. The 3-month requirement was

necessary to allow adequate time for development of social interactions and social capital

with the EKR environs, per expert informants within the organization.

This study followed a two-step process. In the first step, the social capital

measures of range of accessibility, extensity, and upper reachability were captured via the

position generator instrument. In the second step, an analysis of the OSN EKR software

metrics was conducted to assess EKR utilization.

This two-step process was developed by the researcher due to the lack of existing

methodologies for social capital assessments in the EKR environment. Measuring the

tripartite levels of social capital of participants with the position generator instrument was

relatively straightforward; delineating EKR utilization elements was less so. Survey

instruments regarding EKR usage exist, but they address either technical aspects of EKR

configurations, individual competency, or self-efficacy; incorporate multiple theoretical

elements; or do not specifically address the two social capital outcomes of instrumental

and expressive action. Some research studies did incorporate social capital measures in

their assessment of EKR measures; however, those measures cannot be aligned with the

EKR elements of knowledge seeking and contribution. Additionally, some instruments

relied on participants’ recollections of EKR usage, which were judged as suboptimal for

the purposes of this study. An approach tailored to the specific needs of this research

21

study was required. This study, therefore, used the approach of leveraging utilization

metrics available directly from EKR server logs. Analysis of the EKR server log was

deemed more accurate and empirical than asking participants about their EKR

utilization. Using this approach, this study avoided the inherent biases and inaccuracies

that are inevitably introduced when participants are asked to recollect their IT

utilization.

The first element of the data collection process was use of the Lin and Dumin

(1986) position generator, which provides three measures of social capital: range of

accessibility to different hierarchical positions, extensity or heterogeneity of accessibility

to different positions, and upper reachability of access social capital. These measures

reflect the theoretical assertion by Lin (1982) that occupational prestige is an indication

of wealth, power, and status, which are seen as universally valued resources. This study

utilized the most current position generator that has been validated for a U.S. audience

(Lin, Fu, & Chen, 2013). A web-based version of the position generator instrument was

provided to participants via SurveyMonkey.

The position generator portion of the data collection phase was designed to

establish the degree of social capital accessibility, extensity, and upper reachability for

each of the participants. The goal of measuring social capital via the position generator

instrument was to understand which measures of social capital (range of accessibility,

extensity, and upper reachability) relate to which EKR utilization elements. Comparing

the data about social capital measures with the EKR’s utilization metrics helped

determine the interplay of the social network and resources.

22

The second element of the data collection process consisted of

downloading the EKR system’s utilization metrics. This was accomplished

through the OSN software’s reporting features. This study’s focus was three of

the four OSN elements: conversations, social objects, and content. The fourth

OSN element, activity streams, provided the user an easy-to-read summary of the

OSN content, and therefore, did not represent an EKR behavior as delineated in

Table 1.

The quantitative analysis conducted for this study consisted of

establishing the existence of any relationship between the independent and

dependent variables articulated in the research questions. As a means of assessing

the relationships between the three social capital measures and the various EKR

behaviors, both multinomial and multiple linear regressions were computed. A

two-step process was used during this analysis phase. Initially, the overall

significance of the model was tested to examine if a significant amount of

variance in the dependent variable could be accounted for by the independent

variables. In the second step, the significance of each independent variable was

tested to determine if it was significantly associated when keeping all other

variables constant. Table 3 outlines the independent variable, dependent variable,

theoretical perspective, and data collection method for each research

subquestion.

23

Table 3 Research Variables, Underlying Theory, and Data Collection Methods

Research question

Independent variable

Dependent variable

Theoretical perspective

Collection method

1a. What is the relationship between social capital and EKR users seeking information?

Social capital measures

EKR users searching for information

Social capital—Lin (2001a)

Position generator—Lin et al. (2013)

EKR utilization metrics

1b. What is the relationship between social capital and EKR users posting requests for assistance?


EKR users posting requests for assistance




2a. What is the relationship between social capital and EKR users posting new information?


EKR users responding to postings by other users




2b. What is the relationship between social capital and EKR users responding to postings by other users?


EKR users posting new information




Limitations

The aforementioned research design was selected as a means of ascertaining

which, if any, relationships exist between social capital measures and EKR utilization

elements in a straightforward and minimally impactful manner on the host organization.

The approach was admittedly quantitative in nature, which may hinder an in-depth

24

understanding of the motivations and perceptions of individual EKR users. Likewise, this

approach did not answer the question of why some EKR users gained little or no social

capital, or conversely, why individuals with a high degree of social capital failed to

mobilize it effectively, or why they did not use EKRs. These questions could be

addressed in future qualitative undertakings.

This study was also limited by its research setting, which is the corporate EKR.

The study, therefore, was not necessarily representative of EKR activity in the broader

cyberspace environment but rather applied only to the for-profit setting. The research

design was also limited in the sense that it focused only on employed individuals and

those who had Internet access and were proficient with computers.

The research setting consisted of English-speaking participants who were

employees of a multinational IT corporation and familiar with American corporate

culture. Thus, this study’s applicability was limited to English-language speakers who

worked in the IT field. Lastly, this study focused on the OSN EKR system and may not

be representative of other EKR platforms, such as wikis or Sharepoint.

Definition of Key Terms

Table 4 defines the terms used in this study.

25

Table 4 Definitions Term Definition Access Resources the individual can access in his or her social networks

(Lin, 1999b). Closure The limiting of access to a social network (Coleman, 1988). Electronic knowledge repository (EKR)

“Knowledge repositories that emphasize codification and storage of knowledge so as to facilitate knowledge reuse through access to the codified expertise” (Lin & Huang, 2009, p. 168).

Gamification

“The use of game design elements in non-game contexts” (Deterding, Dixon, Khaled, & Nacke, 2011, p. 9).

Knowledge management

“A formal, directed process of figuring out what information a company has that could benefit others in the company, then devising ways of making it easily available” (Harvard Management Update, 1999, p. 22).

Mobilization The use of social contacts and their inherent resources in an effort to attain higher status, such as a new or better job (Lin, 1999b).

Social capital “Resources embedded in a social structure that are accessed and/or mobilized in purposive actions” (Lin, 2001a, p. 29).

Social network “A structure composed of a set of actors, some of whose members are connected by a set of one or more relations” (Knoke & Yang, 2008, p. 8).

Utilization The process of individuals either contributing knowledge to an EKR or seeking knowledge from an EKR for reuse (Lin & Huang, 2009).

Wiki A website or database developed collaboratively by a community of users, allowing any user to add and edit content (Oxford University Press, 2014).

Summary

To ensure maximum knowledge diffusion and ROI with EKR systems, it is

necessary to clearly understand how social capital measures relate to EKR utilization. As

discussed in this chapter, this quantitative study addressed this issue by examining the

relationship between an EKR user’s social capital and EKR knowledge seeking and

knowledge contribution using a sample of more than 100 participants from a single

corporation. The conceptual framework considered social capital through the aspects of

26

accessibility, extensity, and upper reachability and considered EKR through the aspects

of knowledge seeking (specifically searching for information and posting requests for

assistance) and knowledge contribution (specifically responding to postings by other

users and posting new information). This study contributes to theory by expanding

research on the relatively new phenomenon of social capital in the digital environment of

an EKR. It also contributes to practice by providing insight that could aid corporate

leaders in knowledge management and gamification efforts. Chapter 2 builds on the

conceptual framework by providing a review of the relevant literature, and chapter 3

offers details on the study’s methodology. Results are provided in chapter 4 and are

discussed in chapter 5.

27

CHAPTER 2:

LITERATURE REVIEW

The purpose of this study was to explore the connection between the three

measures of social capital and two action elements of electronic knowledge repository

(EKR) utilization. This chapter lays a foundation for consideration of the study’s two

main research questions and the conceptual framework based on a review of pertinent

literature. The theoretical underpinnings of social capital and EKRs are presented in

detail.

The chapter is divided into four sections. Section 1 provides an in-depth review of

the social capital literature. Section 2 follows with a review of the literature related to

EKRs. In each section, both the history and a critique of the seminal articles and

publications are presented. Additionally, the study’s research questions and related

hypotheses are presented within the context of the relevant literature. Section 3 addresses

alternative models that were assessed and considered but were ultimately rejected for this

study. The final section proffers a summary.

For the literature review methodology, this researcher sought to identify scholarly

articles and publications that addressed social capital theory and the use of EKRs.

Academic research databases such as ABI/Inform, Dissertations and Theses Online,

Factiva, JSTOR, LexisNexis Academic, and ProQuest were searched going back 20

years. The search of these database resources yielded an initial 447 articles and

publications that were subsequently narrowed to 83 that were directly relevant to this

study. In addition, a detailed assessment of the bibliographies of those selected articles

28

yielded additional works that exceeded the 20-year parameter but were included due to

their seminal nature.

Social Capital

The section discusses the development of the theory of social capital, including

the concepts of capital, human capital, cultural capital, and exchange theory. While social

capital can be measured at the micro, meso, and macro levels of analysis, this study was

conducted at the meso or group level of analysis, as discussed in chapter 3. Given that

social capital is focused on the network of social relations, this study was not focused on

the attributes of the individual actor but rather on the relations and patterns of interactions

of individuals taking place in the informal social network.

The primary goal of this section is to highlight the theoretical underpinnings of

social capital as it has evolved throughout the 20th century and its current advancements

in the new millennium. Following this delineation of theory, the chapter critiques the

work of the prominent social capital researchers with the goal of laying the foundation for

consideration of social capital in the digital environs of the EKR. Finally, this literature

review presents a justification for this study’s focus on the social capital outcomes of

instrumental and expressive action.

Capital

The roots of social capital stem from Karl Marx’s conceptualization of capital.

According to Marx (1933), “Capital consists of raw materials, instruments of labour, and

means of subsistence of all kinds, which are employed in producing new raw materials,

29

new instruments, and new means of subsistence” (p. 28). Marx pointed out that all of the

aforementioned components of capital are created by labor.

The Marxian notion of capital was seen as a social relation of production. To

Marx, capital emerges from the social relations between laborers and the bourgeoisie.

The critical element here for social capital is the assertion that capital is a social process.

Marx (1933) stated, “Capital, consequently, is not only a sum of material products, it is a

sum of commodities, of exchange values, of social magnitudes” (p. 29).

While Marx’s assertion of a social context for capital is less well known, his view

of capital as exploitative in nature is nearly synonymous with his name. To Marx, these

social relations are by no means equal, but rather an intricately constructed social

structure that oppresses the many for the benefit of the privileged few. Marx viewed the

bourgeoisie as controlling the means of production by which commodities were produced

and sold. The worker, in turn, was paid for his labor at a rate that was significantly lower

than the generated value of the commodity once sold. Marx saw capital as surplus value

that, in turn, created further profit (Lin, 2001a).

An important element of Marx’s view of capital is the costs associated with

maintaining it. First, there are the costs of maintaining the worker as a worker, such as

training and education. Second, there are the costs associated with maintaining the means

of production, as machines and tools wear out and must be replaced. Lastly, Marx (1933)

indicated that there are social costs of maintaining capital, which take the form of a class

struggle between the workers and the bourgeoisie. The bourgeoisie, as the powerful

element of society, “preserves itself and multiplies by exchange with direct, living

labour-power” (p. 30).

30

Human Capital

Scottish economist Adam Smith (1937), in his treatise on the wealth of nations,

expanded the conceptualization of capital at the dawn of the industrial revolution by

including the skills and abilities of a nation’s population, or human capital. However, at

the 1960 annual meeting of the American Economic Association, Theodore W. Schultz,

then president of the association, advanced the first articulated systematic argument for

the concept of human capital.

During the speech, Schultz (1961) was critical of the prevailing economic theories

which, in his view, failed to consider human resources as an explicit form of capital. In

his view, economists were guilty of treating labor “as if it were a unique bundle of innate

abilities that are wholly free of capital” (p. 2). He insisted that individuals can, by

investing in themselves, broaden their available choices.

Schultz (1961) expanded the Marxian concept of capital to encompass the

capabilities and production capacity of workers while simultaneously challenging

Marxist notions of the bourgeoisie monopoly on capital. Rather than being preoccupied

with inequality and bourgeoisie exploitation of labor, human capital theory asserts that

workers with free will can accumulate capital through specialized skills or education.

Schultz (1961) attributed much of America’s economic power to the nation’s investments

in human capital (versus physical capital). He stated:

Although it is obvious that people acquire useful skills and knowledge, it is not obvious that these skills and knowledge are a form of capital, that this capital is in substantial part a product of deliberate investment, that it has grown in Western societies at a much faster rate than conventional (nonhuman) capital, and that its growth may well be the most distinctive feature of the economic system. (p. 1)

31

Ultimately, Schultz argued that laborers have become capitalists not by owning

traditional capital, but rather by accumulating knowledge and skills of economic worth.

This knowledge and skill, in turn, when united with other human investments, can

account for the dramatic increase in productivity among first-world countries. Without

such a human capital perspective, economists would continue to view labor as individuals

who are more or less equally capable.

Cultural Capital

During the 1970s, French sociologist Pierre Bourdieu and colleagues developed

an alternative to the human capital theoretical perspective: cultural capital. Bourdieu

argued that through a deeply ingrained pedagogic action, a society’s dominant class

imposes and perpetuates its preferred culture. His notion of dominant class was, in other

words, the preeminent class, a term that does not necessarily imply domination.

According to Detweiler (2012), “One’s class (a set or cohort, rather than a purely

economic class, such as Marx specified) expresses and perpetuates its habitus,

particularly through institutions (such as educational institutions)” (p. 32).

In this manner, Bourdieu’s concept of social class differs from Marx’s concept of

class, which focuses on the economics of exploitation. For Bourdieu, cultural capital is

the result of pedagogic action ensuring that all others internalize the symbols and

meanings of the dominant class, subsequently transmitting this pedagogic action to the

subsequent generation (Bourdieu & Passeron, 1977). Bourdieu’s cultural capital is the

acceptance and appropriation of the dominant culture and its values by the rest of society.

Bourdieu’s (1986) work evolved over time, and he came to adopt the term social

capital, defining it as the product of a social group’s efforts, which becomes a collective

32

asset shared by the group. This conceptualization of cultural capital turned social capital

is consistent with Marx’s definition of capital and notion of inequality but not his notion

of economic exploitation. Bourdieu’s work culminated with a further refining of his

concept of social capital (Bourdieu & Wacquant, 1992), which he defined as “the sum of

resources, actual or virtual, that accrue to an individual or group by virtue of possessing a

durable network of more or less institutionalized relationships of mutual acquaintance

and recognition” (p. 119).

Emergence of the Modern Social Capital Concept

Social capital theory can trace its origins back through multiple academic

disciplines, including economics, sociology, political science, and organizational

behavior. The mixed patrimony of social capital theory has led to widespread variability

and, in some cases, outright contradictions in definitions of social capital.

Notwithstanding, a central theme of social capital, according to Lin (2001a), is that

“capital is captured in social relations and that its capture evokes structural constraints

and opportunities as well as actions and choices on the part of the actors” (p. 3).

One of the first instances of the term social capital, with its modern meaning

relating to social cohesion and personal investment, is found in Hanifan’s (1916) article

regarding local support for rural schools. Hanifan, the state supervisor of rural schools in

West Virginia, offered the first explicit definition of social capital:

I do not refer to real estate, or to personal property or to cold cash, but rather to that in life which tends to make these tangible substances count for most in the daily lives of people, namely, goodwill, fellowship, mutual sympathy and social intercourse among a group of individuals and families who make up a social unit. . . . If he may come into contact with his neighbor, and they with other neighbors, there will be an accumulation of social capital, which may immediately satisfy his social needs and which may bear a social potentiality sufficient to the substantial

33

improvement of living conditions in the whole community. The community as a whole will benefit by the coöperation of all its parts, while the individual will find in his associations the advantages of the help, the sympathy, and the fellowship of his neighbors. (pp. 130–131)

As mentioned above, Bourdieu refined his initial concept of cultural capital to one

more in line with the modern concept of social capital, albeit with a focus on class

domination. In Bourdieu’s conceptualization of social capital, members of a stable group

build and maintain trust, thereby providing safety and status for group members. The

group member’s individual relationships are maintained via material and/or symbolic

exchanges that serve to cement or reinforce existing relationships (Häuberer, 2011,

referencing Bourdieu, 1983).

A critic of Bourdieu’s concept of social capital was American sociologist James

Samuel Coleman, for whom social capital was not solely the purview of the dominant

class but rather a form of capital providing benefits for the wider society. Coleman

(1988) defined social capital as “not a single entity but a variety of different entities, with

two elements in common: they all consist of some aspect of social structures, and they

facilitate certain actions of actors—whether persons or corporate actors—within the

structure” (p. S98).

Of particular import to this study, Coleman (1988) identified a crucial form of

social capital as “the potential for information that inheres in social relations. Information

is important in providing a basis for action” (p. S104). In short, the theorist viewed social

relations, which are at their core a function of information sharing, as a form of social

capital that ultimately facilitates action. This form of social capital exists outside the

traditional social capital boundaries of trust and reciprocity but is of value solely based on

the information it provides.

34

Coleman delineated three forms of social capital: the traditional obligations and

expectations (reciprocity), information channels, and social norms (trust). In this, he drew

from Granovetter’s (1985) concept of embeddedness of economic transactions in the

social network as important for establishing trust, expectations, and norms. Coleman

(1988) outlined four distinct types of social capital: relations of mutual trust, information

potential, limiting norms, and appropriable social organizations. Of particular relevance

to this study was Coleman’s information potential form of social capital, which is defined

as “the capability to provide its members with information helpful in the utility

maximization process” (Häuberer, 2011, p. 44, referencing Coleman, 1995). Information

can serve as the starting point for action on the part of an individual in the social network.

By leveraging preexisting relationships formed for other purposes, individuals can

acquire information with little additional effort or cost. Coleman (1988) pointed out that

underinvestments can occur as individuals who serve as sources of information for others

may withhold information as a means of maximizing their own advantage.

Underinvestments notwithstanding, Häuberer (2011) highlighted Coleman’s view of the

criticality of information as a form of social capital when she stated, “But to preserve the

relationships and their information potentials it is indispensable to share information with

other actors in the social structure” (p. 44).

Limiting norms, according to Coleman (1988), are developed as a means of

throttling negative external effects or, conversely, encouraging positive ones. Ultimately,

these limiting norms mature into network closure that has as a consequence the

development of effective sanctions within the social network that establish standards and

35

sanctions to guide group behavior. Coleman saw network closure as necessary and

advantageous for social capital as a means of maintaining its level of dominance.

Theorists Granovetter (1973) and Lin (2001a) countered Coleman’s assertion by

stressing that closure is neither necessary nor advantageous for social capital. Network

closure has value in some instances, such as when preserving social capital is the

overriding concern. While network closure can aid in preservation, it does so at the

expense of the group’s ability to acquire new information and resources, ultimately

impacting the long-term viability of the group, as both Granovetter (1973) and Burt

(1992) have demonstrated. Lin (2001b) indicated that arguing for network closure as a

requirement for social capital denies the significance of weak ties, bridges, or structural

holes. As stated in chapter 1, this study rejected Coleman’s requirement of network

closure as an essential element of social capital.

Robert Putnam’s seminal work on social capital, Bowling Alone (2000), built on

Coleman’s work and focused on the value proposition that social networks represent for

individuals. He asserted that interactions between individuals eventually form

relationships, which, in turn, form social networks in which trustworthiness and norms of

reciprocity develop. These networks allow their members to act together towards

common goals—what he called civic engagement. Putnam (2000) defined social capital

thus:

While physical capital refers to physical objects and human capital refers to properties of individuals, social capital refers to connections among individuals—social networks and the norms of reciprocity and trustworthiness that arise from them. (pp. 18–19)

Accordingly, Putnam (2000) delineated three elements of social capital: trust,

networks of civic engagement, and norms of reciprocity. Trust, according to Putnam, is

36

the lubricant of a social network that is an outgrowth of the other two social capital

elements. Networks of civic engagements can be formal (e.g., an association with

membership requirements) or informal, and they can be horizontally focused (bringing

individuals of similar status together) or vertically focused (hierarchical). As a general

rule, instances of purely horizontal or vertical networks are rare, with most networks

comprising a mixture of both. Putnam (1993) pointed out that horizontal networks

facilitate communication. Information flows in vertical networks, however, are generally

less reliable because individuals who are subordinate tend to hold back information as a

means of protection against exploitation, while those in positions of authority may

withhold information to reinforce their privileged position.

Putnam’s (1993) view of norms of reciprocity borrowed heavily from Coleman’s

(1988) view of limiting norms. These norms are established through socialization and

maintained through sanctions from the group. The value of norms is that they increase

cooperation among group members and decrease transaction costs (Putnam, 1993).

Putnam (2000) articulated two characteristics of social capital: inward- and

outward-looking. Inward-looking or bonding social capital seeks to create connections

between similar individuals, and outward-looking or bridging social capital seeks to bring

together dissimilar individuals. Bonding social capital strengthens group loyalty while

conversely developing antagonism for outsiders. Bridging social capital facilitates

information flows and connections to external resources (pp. 22–23).

Although there are numerous critiques of Putnam’s conceptualization of social

capital, of particular import to this study is the critique regarding his focus on the macro

or societal level of analysis. Social capital clearly has a social component, but it is

37

problematic to advance a conceptualization that does not directly address the individual

element of social capital. EKR behaviors manifest as individual actions (knowledge

seeking and contributing), and a conceptualization that does not address the individual or

micro level prohibits the development of valid measures.

Addressing this point, Häuberer (2011), a recent critic of Putnam, indicated that

the theorist does not address social capital at the individual level, but rather sees social

capital as evident only at the group level: the byproduct of other social activities.

However, all three elements of Putnam’s conceptualization of social capital are

developed by individuals over time with individual interactions. Putnam’s

conceptualization also leaves a great deal of ambiguity regarding cause and effect in

social capital, which makes establishing outcome measures for empirical testing

extremely difficult. This is amply demonstrated as Putnam articulated only one outcome:

the public good measure.

Another critique relevant to this study is Putnam’s reliance on the concept of civic

engagement as the full scope of social capital. Putnam dismissed the notion that social

capital can exist in the digital realm. Häuberer (2011) stated, “Putnam upholds that new

organizations (e.g., Internet communities, fitness centers etc.) do not produce social

capital, because they don’t support direct personal interactions” (p. 60). This assertion

that face-to-face interaction is a necessary precursor of social capital has been refuted by

a number of social capital theorists, to include Bhalchandra et al. (2010), Kavanaugh and

Patterson (2001), Lampe, Ellison, and Steinfield (2007), Sobel (2002), and Valenzuela,

Park, and Kee (2009). Accordingly, this researcher rejected Putnam’s assertion that face-

to-face interaction is a requirement for social capital.

38

Ronald Burt (2004), a theorist who built on Putnam’s bridging concept, focused

on structural holes and brokerage. Burt (2004) advanced the notion that structural holes

represent social capital. He stated, “Social capital exists where people have an advantage

because of their location in the social structure” (p. 351). The author aligned his

conceptualization with that of other social capital structuralists, to include Coleman

(1988) and Lin (2001a).

Burt’s (1982) concept of structural holes consists of a relationship between two

contacts that are not redundant (in other words, not leading to the same people). As these

two contacts do not directly overlap, they represent new network benefits (chiefly in the

form of new information). These relationships are seen as weak ties (Burt, 2004).

Brokers, according to Burt (1992), are individuals who span or connect structural

holes. A common example of a broker is the entrepreneur who spans the gap between the

supplier of a good or service and the customer who desires that good or service.

Additionally, brokers can gain significant benefit from simply moving information from

one side of the structural hole to the other where it is seen as “new” information (Burt,

1992, p. 33).

Burt (2004) referred to this advantage as “information arbitrage,” stating, “They

are able to see early, see more broadly, and translate information across groups. Like

over-the-horizon radar in an airplane, or an MRI in a medical procedure, brokerage

across the structural holes between groups provides a vision of options otherwise unseen”

(p. 354). From an empirical standpoint, Burt has accumulated significant evidence of

increased returns for individuals who take on the broker role. Specifically, brokers are

39

more apt to get a positive performance rating, achieve promotions quicker, and enjoy

greater salaries than their nonbrokering colleagues (Burt, 2000, 2002; Lin, 2001a).

Another theorist who viewed social capital as a structural entity is Dr. Nan Lin.

Lin (2001a) defined social capital as “investment in social relations by individuals

through which they gain access to embedded resources to enhance expected returns of

instrumental or expressive actions” (p. 17). As such, Lin’s social capital consists of three

processes: investment, access and mobilization, and returns of social capital.

Addressing the issue of Putnam’s (2000) civic engagement requirement for social

capital, Lin (2001a) allowed for formal ties but did not see them as the exclusive form of

ties in social capital. Lin (2001a) likewise incorporated the views of both Coleman (1988)

on strong ties and Burt (1992) on weak ties but not in a mutually exclusive manner. He

indicated that social structures benefit from both closure and openness. The openness of a

social network increases access to social capital for its members; while closure increases

a member’s ability to mobilize social capital, the value is tied to the nature of the action.

Embedded Resources

Rather than focusing on the location of an individual in the social network, Lin

concentrated on embedded resources. The assertion is that in most societies, valued

resources are represented by wealth, power, and status (Lin, 1982). Lin (2001a) stated,

“Thus, social capital is analyzed by the amount or variety of such characteristics in others

with whom an individual has direct or indirect ties” (p. 13).

Social capital can be measured by evaluating network resources and contact

resources. Lin (2001a) indicated that network resources are those resources embedded in

an individual’s network of direct contacts, while contact resources are resources found in

40

individuals with whom contact is made for an instrumental action, such as finding a new

job. In short, network resources represent that which can be accessed through social

capital, while contact resources represent that which can be mobilized using social

capital.

Measurement of contact resources, according to Lin (2001a), “is relatively

straightforward—the contact’s wealth, power, and/or status characteristics, typically

reflected in the contact’s occupation, authority position, industrial sector, or income”

(p. 13). Measurement of embedded resources is a bit more complicated, as there is

significant debate in the literature as to whether network locations are measures of social

capital or merely precursors. Lin (2001a) addressed the controversy by indicating his

belief that network locations can facilitate, but not necessarily dictate, access to a social

network’s embedded resources.

Lin went on to proffer the position generator instrument he and a colleague (Lin

& Dumin, 1986) developed in response to the problems associated with the name

generator instrument pioneered by Laumann (1966). The name generator is a commonly

used technique in which an individual is asked to name all the individuals he or she

knows in a specific social context and the degree of closeness (e.g., frequency of

interaction or level of trust) over a specified period of time. This approach allows the

researcher to develop a detailed list of contacts that is then used to delineate ego-centric

networks.

Using this approach, researchers measure social capital in three distinct ways.

One approach consists of using the characteristics of the network as indicators of social

capital. A more structuralist approach uses the location of the ego relative to the

41

alterations in the network to establish the relative positional advantage with regards to

access to social capital, as Burt (2004) did with structural holes. The last method entails

assessing compositions of alter characteristics, either as possessed collective resources

(e.g., occupational prestige, education, or income) or as best possible resources (e.g.,

highest occupational prestige, education, or income).

The name generator instrument is a common means of measuring social capital,

but it is not without drawbacks. Addressing this issue, Lin (2001a) stated:

There are a number of problems associated with the use of the name generators to measure social capital. In short, it tends (1) to be bound with specified content areas (the generating items), (2) to elicit stronger rather than weaker ties, and (3) to locate access to individuals rather than social positions. (p. 63)

The name generator was found to have limited utility for this study for two

reasons. First, the name generator requires saturation sampling to completely map the

social network, which is an extensive and impractical amount of surveying, both in terms

of individuals surveyed and the extensity of the instrument. Second, the name generator

may not adequately address weak ties, as Lin indicated, which are critical in the EKR

context. Granovetter’s (1985) research provided empirical evidence of the value of weak

ties for greater access to information and network resources, and weak ties are, at best,

underreported with the name generator.

As a result of these limitations, Lin and Dumin’s (1986) position generator

instrument was selected for this study. The instrument consists of a list of ordered

occupations that are appropriate to a given society. Participants are asked to indicate if

they have contact with members of these occupations whom they know on a first name

basis. From these responses, the researcher can glean three social capital measures: range,

extensity, and upper reachability.

42

Range is defined as the ability to access different hierarchical positions in a

society. In other words, what is the distance between the lowest and highest positions the

individual can access? Extensity is defined as the number of different positions the

individual can access. Upper reachability is the position of most prestige the individual

can access.

The position generator instrument was selected for this study because of its close

alignment with Lin’s conceptualization of social capital and its content-free nature. The

advantage of utilizing an instrument that is not content bound is the ability to

systematically sample without the need for detailed information about the population. In

this manner, the position generator greatly simplifies data collection for the researcher

and minimizes disruption for the organization and individual participants. Addressing

these two issues, Lin (2001a) stated:

We should note that the position generator derives from certain theoretical decisions. For example, it chooses to sample positions in a hierarchal structure, rather than sampling ego-centered interpersonal ties. To the extent that social capital reflects embedded resources in the structure, then this approach should yield meaningful information regarding ego’s access to such structurally embedded resources. The measurement is also deliberately content-free and role/location-neutral. (p. 63)

Shifting away from social capital measures that are critical to this study is Lin’s

(2001a) discussion of purposive actions. The actions of the individual from the social

capital context center on purposive actions. Although any action, whether purposive or

not, may have social capital ramifications, the focus in this study was on the latter.

Discussing the purposive actions and motivations of individuals in a social

network, Lin (2001a) stated:

43

Actors are motivated to either maintain or gain their resources in social actions—purposive actions. Action to maintain resources can be called expressive action, and action to gain resources can be called instrumental action. Maintaining resources is the primary motivation for action; therefore, expressive action is the primary form of action. (p. 75)

The relevance to this study is found in Lin’s (2001a) conceptualization of

instrumental action (see Figure 3) and its relation to knowledge-seeking and knowledge-

contribution behaviors in the digital EKR environment, which is discussed in the

following section.

Figure 3. Lin’s (2001a) model of social capital.

Instrumental Action

The question naturally arises: Does information contribute to or create social

capital? Adler and Kwon (2002) addressed this issue, stating, “The first of social capital’s

direct benefits is information: for the focal actor, social capital facilitates access to

broader sources of information and improves information’s quality, relevance and

timeliness” (p. 29). Wah, Menkhoff, Loh, and Evers (2007) identified four resources

44

inherent in social capital. First, social capital provides individuals in the network with

valuable information about opportunities and options available to them. Second, social

network connections play an important part in affecting decision-making within the

network. Third, an individual’s standing in the social network is reflected in the social

credentials of that individual. Lastly, social relations reinforce identity and aid in gaining

public acknowledgment.

The structuralist theorist camp addresses the importance of information in social

capital. Take, for example, the following quote by Rodan and Galunic (2004), who were

discussing Burt’s (1992) work about tertius orientation, “In other words, while a tertius

position in the network provides the opportunity for brokering and arbitrage, the

‘currency’ of these tertius strategies is often information” (p. 546).

If information is important to social capital, then it follows that knowledge

contribution is a means by which social capital can be increased. Knowledge

management practitioners have apparently already embraced this logic, as Adler and

Kwon (2002) indicated that some companies that are interested in fostering greater social

capital have adopted “collaborative technologies,” such as EKRs.

This study applied Lin’s (2001b) conceptualization of purposive instrumental

action to the EKR environment. Individuals utilizing an EKR can exhibit instrumental

action behaviors that, as Lin indicated in the abovementioned quote, are designed to gain

additional or new resources. As delineated previously in Table 1, the EKR utilization

element of knowledge seeking (Lin & Huang, 2009) is broken down into two specific

EKR behaviors: seeking information via the EKR and requesting assistance via the EKR.

The EKR functionality of follow enables an individual to connect with another EKR user

45

and gain new information when the system automatically routes to the followers new

content posted by an EKR user who is being followed. Given that information is the

principal social capital resource available in the EKR environment, such efforts to

acquire new information are instrumental actions.

Instrumental Action Research Questions and Hypotheses

This study sought to determine if a relationship exists between an individual’s

social capital and his or her instrumental actions in the EKR environment. To that end,

the first research question asked: What is the relationship between EKR users’ social

capital and EKR knowledge seeking?

In order to provide greater specificity for empirical research, two subquestions

and accompanying hypotheses were devised relating to the Lin and Huang (2009)

knowledge-seeking EKR utilization element. For Research Question 1a, What is the

relationship between social capital and EKR users seeking information?, a positive

relation was hypothesized based on the literature review: H1. Social capital is positively

related to EKR users seeking information. Research Question 1b asked: What is the

relationship between social capital and EKR users posting requests for assistance?

Hypothesis H2 was developed from both the EKR and social capital literature as a means

of testing Question 1b with empirical data: Social capital is positively related to EKR

users posting requests for assistance.

Expressive Actions

Because social capital is a type of capital as previously defined, it follows that

social capital requires investment. This study asserts that the investment required in

46

social capital is bound up in maintenance functions. There has been little discussion,

however, regarding social capital maintenance functions in the literature, despite

assertions by many theorists that there are costs involved in social capital.

Drawing from previous research on the tenets of exchange theory, Homans (1958)

asserted that social behavior, boiled down to a basic definition, is simply the exchange of

goods (both material and nonmaterial). There are, however, costs associated with this

exchange: costs that occur not only at the point of exchange but also in terms of requiring

investment (presumably prior to the exchange).

In discussing the issue of costs associated with social exchange, Homans (1958)

referenced Blau (1963) and his study of a federal enforcement agency. Blau described a

dyad of costs related to exchange. He stated:

A consultation can be considered an exchange of values; both participants gain something, and both have to pay a price. The questioning agent is enabled to perform better than he could otherwise have done, without exposing his difficulties to the supervisor. By asking for advice, he implicitly pays his respect to the superior proficiency of his colleague. This acknowledgement of inferiority is the cost of receiving assistance. The consultant gains prestige, in return for which he is willing to devote some time to the consultation and permit it to disrupt his work. (p. 130)

Additionally, other costs of exchange within social networks must be considered,

according to Blau (1963). He described the costs that begin to surface when multiple

consultations or transactions take place between two individuals. For the consulted, the

cost of his or her time and effort grows with each subsequent request, but the relative

value in terms of added prestige and respect correspondingly decreases with each

transaction. For the requestor, the cost to self-confidence increases with each subsequent

request. Those who make such repeated requests frequently report “feeling stupid” if they

continue asking for help from the same individual and usually begin to seek help

47

elsewhere. Likewise, Blau (1963) found that within the federal enforcement agent’s

social network, repeat requestors suffered a decrease in status. Blau (1963) stated, “The

more often he consults others, however, the more threatening it will be for his self-respect

and his standing in the group to have to admit that he still needs more help from his

colleagues” (p. 138). Strangely, little coverage in the literature is devoted to the topic of

costs in social capital.

It follows that if there are costs associated with social exchanges, then one’s

social capital can wane over time and as a result of multiple transactions. In order to

maintain social capital, individuals must engage in maintenance functions that minimize

the costs of social exchange. Adler and Kwon (2002) explicitly discussed the issue of

maintenance of social capital. They stated, “Like physical capital and human capital, but

unlike financial capital, social capital needs maintenance” (p. 22).

In the everyday flow of information and social exchange, social capital can wax

and wane. Though an individual may incur a cost for multiple requests for assistance

from an individual, over time, the costs may be ameliorated through reciprocal

interactions. In the work environment, today’s expert may be tomorrow’s supplicant as

the tasks at hand evolve or change.

It follows that if social capital theory is applied to the EKR environment, then

behaviors designed to mitigate the costs of social capital, or maintain social capital,

should be evident. This study, therefore, applied Lin’s (2001b) conceptualization of

purposive expressive action to the EKR environment. Individuals utilizing an EKR can

exhibit expressive action behaviors that, as Lin (2001a) indicated, are designed to

maintain existing social capital resources. Table 1 presented the EKR utilization element

48

of knowledge contribution (Lin & Huang, 2009), which was divided for the purposes of

this study into two specific EKR behaviors: posting new information to the EKR and

responding to EKR postings by other users.

Expressive Actions Research Questions and Hypotheses

Addressing the issue of costs of social capital, this study sought to determine if a

relationship exists between an individual’s social capital and his or her expressive actions

in the EKR environment. Expressive actions are a means of preserving social capital by

ameliorating the costs associated with social capital. This study’s second research

question asked: What is the relationship between EKR users’ social capital and EKR

knowledge contribution?

In order to provide greater specificity for empirical research, two subquestions

and related hypotheses were devised relating to the Lin and Huang (2009) knowledge

contribution EKR utilization element. Research Question 2a was: What is the relationship

between social capital and EKR users posting new information? A positive relation was

hypothesized (H3) based on the literature: Social capital is positively related to EKR

users posting requests for assistance. Research Question 2b was: What is the relationship

between social capital and EKR users responding to postings by other users? In order to

test Question 2a with empirical data, Hypothesis H4 was developed from both the social

capital and EKR literature: Social capital is positively related to EKR users responding to

postings by other users.

49

Electronic Knowledge Repositories

EKRs were popularized by Ward Cunningham in 1994 when he created a web-

based knowledge base he called WikiWikiWeb on his company website, which was the

world’s first wiki. Due to its ease of use and technological simplicity, the wiki has

proliferated both in applications for the general public, such as Wikipedia, and in the

business environment, where it has achieved almost ubiquitous status in the corporate

intranet.

A frequently cited EKR definition is that articulated by Kankanhalli et al. (2005):

EKRs are electronic stores of content acquired about all subjects for which the organization has decided to maintain knowledge. EKRs can comprise multiple knowledge bases as well as the mechanisms for acquisition, control, and publication of the knowledge. (p. 114)

In the introduction to their article about EKRs and social capital, Lin and Huang

(2009) cited this definition but went on to offer a different definition focusing on codified

expertise. They defined EKRs as “knowledge repositories that emphasize codification

and storage of knowledge so as to facilitate knowledge reuse through access to the

codified expertise” (p. 168). The focus on codification is an important dimension added

to the EKR definition, which is not apparent in earlier definitions; therefore, the

expanded Lin and Huang (2009) definition was adopted for this research study.

Kankanhalli et al. (2005) went on to characterize the action of knowledge

contribution to EKRs, which they indicated includes explaining or translating the

contributor’s knowledge in a manner that is understood by the reader. The second

element of knowledge contribution is the codification or, according to Hendriks (1999),

the reconstruction of that knowledge to the EKR.

50

Knowledge is defined as inclusive of information, know-how, and skills that the

ego or organization possesses (Suseno & Ratten, 2007). In the literature, knowledge is

identified as either explicit or tacit in nature (Polanyi, 1962). Explicit knowledge,

according to Suseno and Ratten (2007), is “knowledge that can be codified in formal

symbols, schemata, charts, manuals or reports” (p. 9). By its very nature, explicit

knowledge is easily communicated. Tacit knowledge, on the other hand, is viewed as the

intangible nature of knowledge (Polanyi, 1962), involving deeply embedded erudition,

expertise, and skill sets (Suseno & Ratten, 2007). This component of knowledge

contribution frequently poses a significant challenge for the contributor. Indeed, Kogut

and Zander (1992) stated that tacit knowledge, or know-how, can only be internalized by

examination and practice. For instance, a tacit task, such as whistling, may prove very

difficult, if not impossible, to adequately codify in an EKR.

According to Fulk et al. (2004), specific examples of EKRs include “expert

databases, groupware, data warehouses, project websites, intranets, shared whiteboards,

and lessons learned databases” (p. 569). Bock et al. (2006), citing this list, added that

“technologies such as Microsoft SharePoint Services provide a platform for

implementing EKRs” (p. 358).

EKRs are the most common information technology (IT) that supports knowledge

management and related systems, according to Bock et al. (2006). Use of EKRs

encompasses both contributions of codified knowledge as well as the seeking and use of

such codified knowledge. The study focused on individual users of EKRs with the aim of

understanding the role of social capital in knowledge-seeking or knowledge-contribution

behaviors.

51

Roy Pea and John Seely Brown made the point that computer and communication

technologies are radically transforming patterns of communication and knowledge in

societies (Lave & Wagner, 1991). In many ways, EKRs represent a digital manifestation

of communities of practice. EKRs permit newcomers to participate in peripheral and low-

risk tasks that further the goals of the business while enabling the newcomer to acquire

the vocabulary and to understand the tasks and the processes inherent to the organization.

Lave and Wagner (1991) termed this legitimate peripheral participation. EKRs enable

newcomers to directly observe the interactions of more experienced employees as they

navigate through various corporate processes and gain a better understanding of the full

breadth of actors encountered. Moving beyond simple observation, EKR behaviors, such

as reacting to the postings of others (e.g., clicking the like button) or engaging in

knowledge seeking, are low-risk behaviors that may allow the newcomer to accrue some

level of social capital with minimal risk or cost. In this way, the newcomer learns by

participation: through the EKR’s controls, which mediate participation in ways that are

legitimate and do not upend corporate processes.

A possible benefit of this effort is postulated to be greater access to social capital

by the EKR contributor. Lin and Huang (2009) raised this issue, stating, “As validated by

recent studies, social capital is a useful theory when examining social relationships in

EKR usage” (p. 166). Kogut and Zander (1992) also pointed to a benefit in EKR usage as

a means by which knowledge resident in an individual can be transmitted to other

members of the social community via frequent interactions where a coding scheme

develops.

52

It is not uncommon for seeking and contributing behaviors to be reciprocal in

EKR utilization, with the knowledge seeker subsequently proffering a contribution in the

form of feedback or posting lessons learned from applying the information retrieved from

the EKR. Likewise, a contributor may come across new information in the process of

posting his or her own knowledge, so the seeking and contributing EKR interactions are

depicted as reciprocal.

Alternative Models

A number of theories and models were initially considered as a means of

exploring EKR utilization for this study, but were ultimately rejected due to their

limitations. Those theories included social cognitive theory, the technology acceptance

model (TAM), and the task-technology fit model (TTF). Each is addressed below with a

discussion of the limitations that ultimately removed them from consideration for this

study.

Social Cognitive Theory

A common approach in the literature is the focus on the individual’s perception of

his or her capability to complete an IT-related task. A subset of social cognitive theory,

Bandura’s (1986) concept of self-efficacy, is an excellent means of understanding

individual perceptions towards IT competence (Rainer & Harrison, 1993). Marcolin et al.

(2000) pointed out that the IT literature has come to view self-efficacy as serving either

as an antecedent or an outcome of individual IT competency.

Technology self-efficacy (TSE) is the specific application of self-efficacy to the

IT arena. TSE was defined by McDonald and Siegall (1992) as “the belief in one’s ability

53

to successfully perform a technologically sophisticated new task” (p. 465). According to

the authors, TSE is purposefully vague with regards to specific technological tasks; the

conceptualization is designed to address general feelings towards learning new

technology, not a specific application or system.

The applicability of self-efficacy or TSE to this study was compromised due to its

inability to address the broader social elements of EKR utilization. Bandura’s

conceptualization does not account for the impact that social interactions and the social

environment can have on EKR utilization. Rooted firmly in the individual or micro level

of analysis, the self-efficacy conceptualization is critically important for understanding

the individual’s perceptions towards IT usage but does not lend itself to the social capital

focus of this study.

Bandura’s (1986) incorporation of self-efficacy into Miller and Dollard’s (1941)

theory of social learning and imitation (hereafter, social cognitive theory), however, does

result in the incorporation of both individual and social perspectives. Lin and Huang

(2009) demonstrated that the broader social cognitive theory can be used to explain a

user’s knowledge contribution actions.

According to Lin and Huang (2009), social cognitive theory is a widely accepted

and empirically validated model of individual behavior that “argues that a person’s

behavior is shaped and controlled by the influences of the social network as well as the

person’s cognitions” (p. 166). The authors went on to point out that the user’s personal

feelings, namely self-efficacy, play a critical role in EKR usage.

While social cognitive theory does account for the influences of the social

network to some degree, it lacks the specificity necessary to identify which elements

54

within the social network exert influence on the individual’s behaviors. Chiu et al. (2006)

levied this criticism, highlighting the lack of specificity with regards to which social

elements exert influence. Theorists Lin and Huang (2009) were ultimately able to

incorporate social cognitive theory by pairing it with social capital. Unable to find the

requisite specificity in social cognitive theory, the TAM was then considered for this

study.

Technology Acceptance Model

Another theoretical perspective explored for this study was TAM, which first

appeared to provide an adequate explanation for EKR utilization. The model seeks to

explain how users interact with new technologies by focusing on two elements: perceived

usefulness and ease of use.

TAM ultimately proved problematic for the goals of this study, as it assumes that

when the individual decides to act, he or she can do so without limitations. This

assumption belies the influence of the social network, which is critical for understanding

EKR knowledge-seeking and knowledge-contribution behaviors. The TAM assumption

likewise fails to address any other constraints that are external to the user.

Lin and Huang (2009) addressed this shortcoming, stating, “TAM may sound like

a proper theoretical model for the investigation of EKR usage, [but] it fails to account

directly for the factors of social cost and benefit experienced by knowledge contributors,

which may affect their EKR usage” (p. 168). For this reason, TAM was rejected in favor

of social capital’s ability to address social cost and benefit issues. Having rejected TAM

for this study, TTF theory was subsequently considered.

55

Task-Technology Fit

TTF was defined by Goodhue and Thompson (1995) as “the correspondence

between task requirements, individual abilities, and the functionality of the technology”

(p. 218). From a definitional perspective, a task is defined as actions carried out by a user

to convert inputs into outputs (Goodhue, 1995). Technology is defined as the hardware,

software, data, and related services (e.g., training and help desks) that are designed to aid

the user in carrying out a task (Goodhue, 1995).

The TTF perspective portrays the underlying technology as a means by which the

user achieves his or her goal of completing a task, according to Goodhue (1995). He

stated, “TTF focuses on the degree to which systems characteristics match user task

needs” (p. 1827). A central theoretical proposition in TTF is that the greater the fit, the

greater the resulting task performance, and ultimately, the greater the efficacy of the

technology (Goodhue, 1995).

While Goodhue and Thompson (1995) labeled the construct TTF, they

acknowledged the importance of individual ability in a footnote, where they indicated

that perhaps a more accurate label is task-individual-technology fit. Goodhue (1995) was

more explicit about the role of the individual, stating, “The task-technology fit

perspective suggests that a better fit between technology functionalities, task

requirements, and individual abilities will lead to better performance” (p. 1828). While

TTF incorporates a focus on the individual user, it does not, however, address the impact

of the social network and its actors.

In order to address the social dimension, scholars Lin and Huang (2009)

incorporated social capital and social cognitive theory into TTF in their exploration of

56

EKR usage. Addressing this issue, Lin and Huang (2009) stated, “TTF has been

combined with numerous other theories . . . to make up for its deficit in social perspective

concerns. In this study, we bridge the gap of social perspectives, lacking in TTF, by

coupling it with social capital theory” (p. 177).

As a whole, the Lin and Huang (2009) article highlighted the limitations of this

approach in attempting to integrate TTF, social capital, and social cognitive theories.

What ultimately results is multiple segments loosely layered to form a whole. In other

words, Lin and Huang (2009) enticed the reader with the promise of a new ice cream

flavor integrating vanilla, chocolate, and strawberry, but in reality, they simply delivered

a slice of Neapolitan.

The authors purported to incorporate social capital by using trust as a surrogate

measure. Leaving aside the clear limitations of relying solely on the measure of trust as a

means of measuring social capital, Lin and Huang (2009) conceded in their own data that

the mediating effect of trust on EKR usage was found to be only partially supported

(p. 176). Indeed, the social dimension (which includes the social capital element of trust)

had the lowest regression coefficient among all the dimensions in the study (p. 176).

Lastly, the critical issue of how EKR usage was determined was identified by Lin

and Huang (2009) as a significant limitation of their study. They stated, “This study is

based on the participants’ self-determined answers, suggesting that further study may be

needed to include some qualitative data to extend its validity, e.g., calculating the EKR

usage volumes using log files” (p. 178). Relying on individual EKR users’ perceptions

and recollections regarding their EKR usage introduces significant opportunity for error

57

or faulty memory recall. This very issue of measuring EKR utilization drove this study’s

focus on relying on the empirical data found in EKR log files.

For the aforementioned reasons, social cognitive theory, TAM, and TTF theory

were all considered for this study but were subsequently rejected. In addition, Lin and

Huang’s (2009) approach of incorporating all three perspectives into an integrated EKR

usage model proved less than optimal for the purposes of this study and was not used.

Ultimately, Lin’s (2001b) conceptualization of social capital, as described in the first

section of this chapter, was adopted for this study.

Summary

This literature review has reviewed the concept of capital as originally articulated

by Marx (1933). Both Smith (1937) and Schultz (1961) amplified Marx’s concept of

capital, applying it to the skills and abilities of humans in the form of human capital.

These theorists laid the groundwork for Bourdieu’s (1983) cultural capital theoretical

perspective, which evolved into his concept of social capital.

The theoretical discourse among various social capital theorists ranged from

Hanifan’s (1916) initial articulation of the term social capital to Bourdieu’s (1983)

evolution from cultural capital to social capital (1983). Coleman’s (1988) critical

contribution was the idea that information is important in social capital because it

provides the basis for action. However, Coleman’s proposition that network closure is

necessary in social capital was disputed.

Putnam’s (2000) theory of social capital and his bridging concept were presented,

along with the difficulties his articulation faces in addressing the individual element and

actions inherent in social capital. Burt’s (2004) seminal work on structural holes and

58

brokerage was then presented, transitioning to a discussion of Lin’s (2001a) definition of

social capital. Lin’s social capital conceptualization was ultimately selected for this

study. In particular, his focus on network resources and instrumental/expressive actions

was considered most advantageous for this study’s goal of extending social capital theory

into the digital realm.

In terms of measuring social capital, the theoretical underpinnings were presented

for both the name generator and position generator instruments. Each of the survey

instruments was assessed for its applicability to this study and evaluated in terms of the

methodological approach most compatible with the demands and limitations of the

corporate environment. Lin’s position generator proved the most efficacious for the

purposes of this study, as it provided the requisite level of granularity through tripartite

measures; it has the ability to provide meaningful information regarding embedded

resources, and it is content free and role/location neutral (Lin, 2001b).

The EKR was defined, along with the EKR utilization elements of knowledge

seeking and knowledge contribution. These elements were in turn decomposed into four

EKR user behaviors. A discussion of tacit and explicit knowledge was presented, as well

as the reciprocal nature of EKR utilization behaviors. The discussion then turned to the

means by which ERK utilization would be measured via system usage logs, a technique

developed to address the limitations of Lin and Huang’s (2009) study.

The conceptual framework for this study was introduced in chapter 1 as Figure 2.

The model of social capital in EKR utilization begins with social capital and its three

measures of range, extensity, and upper reachability, as articulated by Lin and Dumin

(1986). The model then portrays Lin’s (2001a) two action elements as they relate to EKR

59

utilization: knowledge seeking and knowledge contribution. The specific methodological

approach for this study is presented in the following chapter.

60

CHAPTER 3:

METHODS

U.S. corporations spend billions of dollars on knowledge management

applications, such as electronic knowledge repositories (EKRs), only to see many of

those investments fail or remain underutilized. According to the advisory firm KPMG

(2000), approximately 36% of knowledge management initiatives undertaken by U.S.

corporations failed at the turn of the new millennium. Daniel Morehead, director of

organizational research at British Telecommunications, indicated that close to 70% of

knowledge management endeavors do not meet their stated goals and objectives

(Akhavan et al., 2005).

Knowledge management initiatives, such as deployment of EKRs, have seen an

explosive growth in corporate America yet generally underperform in terms of return on

investment. The purpose of this study was to explore and better characterize the

relationship between EKR users’ social capital measures and their behaviors in the digital

repository. This study sought to add to the social capital literature by providing empirical

data on the relationship between social capital measures and the behaviors of EKR users.

This research study employed a quantitative, nonexperimental research design.

This chapter outlines the study’s research questions and related hypotheses and then

describes their operationalization through the research design; research apparatus;

procedures for data collection, handling, and analysis; and the protection of human

subjects.

61

Research Questions and Hypotheses

According to Lin (2001b), social capital has two outcomes: instrumental action,

or gaining added resources, and expressive action, or maintaining possessed resources.

These social capital outcomes relate to the knowledge-seeking and knowledge-

contribution elements of EKR utilization, as articulated by Lin and Huang (2009) and

demonstrated in Table 1. To enhance the understanding of the relationship between social

capital and EKR utilization, two primary research questions, four subquestions, and four

hypotheses were derived from the EKR and social capital literature to guide this

quantitative research study:


seeking?

a. What is the relationship between social capital and EKR users seeking

information?

H1: Social capital is positively related to EKR users seeking information.

b. What is the relationship between social capital and EKR users posting

requests for assistance?

H2: Social capital is positively related to EKR users posting requests for

assistance.


contribution?

a. What is the relationship between social capital and EKR users posting new

information?

62

H3: Social capital is positively related to EKR users posting new

information.

b. What is the relationship between social capital and EKR users responding

to postings by other users?

H4: Social capital is positively related to EKR users responding to

postings by other users.

Research Design

According to Creswell (2003), there are “several preliminary considerations that

are necessary before designing a proposal or plan for study” (p. 1). The first is selecting a

framework for the overarching design, which in this case is a quantitative one. The goal

of generalizability drove the selection of a quantitative approach. Creswell explained that

quantitative methods are used when the objective of the study is to investigate if

relationships exist between variables:

A quantitative approach is one in which the investigator primarily uses postpositivist claims for developing knowledge (i.e., cause and effect thinking, reduction to specific variables and hypotheses and questions, use of measurement and observation, and the test of theories), employs strategies of inquiry such as experiments and surveys, and collects data on predetermined instruments that yield statistical data. (p. 18)

Specifically, this study sought to determine if a relationship exists between two

variables. The disadvantage of a relational study is the inability to establish a cause and

effect relationship (Zikmund, Babin, Carr, & Griffin, 2010). Quantitative studies are used

to empirically test theory and are considered to be a deductive technique for theoretical

propositions. Creswell (2003) indicated that researchers use a qualitative approach to

explore largely unknown variables and/or an immature theory base.

63

Deductive reasoning has, as its objective, to test or verify a theory through

researcher-generated hypotheses that are subsequently either confirmed or rejected by the

analyzed data. Creswell’s (2003) model of the deductive approach typically used in

quantitative research is presented in Figure 4. This study’s four hypotheses were

generated from the theoretical basis of Lin’s (2001a) theory of social capital and related

measures (Lin & Dumin, 1986), as well as Lin and Huang’s (2009) theoretical

perspective of EKR utilization elements.

Figure 4. Creswell’s (2003) model of the deductive approach to quantitative research.

From a level of analysis perspective, three levels could be considered valid for

this study. The first level is the micro or individual level of analysis, supported by the

literature about knowledge creation, EKR, and social capital. However, to focus solely on

the micro level would prove problematic, as it precludes an in-depth analysis of the

64

impact of the broader social network on both social capital and EKR utilization.

Similarly, a focus on the macro level of analysis is ill-suited, as this study was not

focused at the societal level but rather on business units with an organization.

Additionally, the macro level of analysis would preclude a discussion of the impact that

individuals have on social networks. Therefore, this study focused on the meso level of

analysis.

Addressing the issue of level of analysis for social capital research, Lin and

Erickson (2008) stated:

There is not or should not be any dispute that social capital is rooted precisely at the juncture between individuals and their relations and is contained in the meso-level structure or in social networks. That is, individual actors and their relations form the basis of social capital, and these relations have microconsequences for the individuals as well as macroconsequences for the collectivity. (p. 4)

A quantitative nonexperimental design research design was selected as a means of

investigating the relationship between the variables, social capital measures and EKR

utilization behaviors, in a straightforward and minimally impactful manner on the host

organization. The quantitative nature of the approach could hinder an in-depth

understanding of the motivations and perceptions of EKR contributors. Likewise, this

approach cannot address why some EKR contributors gain little or no social capital, or

conversely, why individuals with high levels of social capital do not contribute to EKRs.

Specifically, this study investigated the relationship between the three measures

of social capital (range, extensity, and upper reachability) that constitute the independent

variables and each of the EKR behaviors (the dependent variables), as delineated in Table

1. As a means of investigating the first research question with its two subquestions, H1

and H2 were developed, which assert a positive relation between the knowledge-seeking

65

EKR utilization element (Lin & Huang, 2009) and the social capital outcome of

instrumental action (Lin, 2001b). In the same manner, Research Question 2 and its

subquestions were investigated with H3 and H4. These two hypotheses assert a positive

relation between the knowledge contribution EKR utilization element (Lin & Huang,

2009) and the social capital outcome of expressive action (Lin, 2001b). Each of the four

hypotheses addressed a specific EKR behavior, which was identified and validated by the

researcher via the EKR utilization and social capital literature.

Utilizing the position generator research instrument addressed below, scores for

the three elements of social capital (i.e., range, extensity, and upper reachability) were

collected for each participant. Once social capital measurements were captured, the

researcher accessed the Oracle Social Network (OSN) server logs in order to quantify the

two specific EKR behaviors listed in Table 1; they equated to the EKR utilization

element of knowledge seeking and the social capital outcome of instrumental action for

each participant. Likewise, the researcher quantified the two EKR behaviors equating to

the EKR utilization element of knowledge contribution and the social capital outcome of

expressive action for each participant. Finally, multinomial logistic regression and

multiple regression were conducted to test the four hypotheses.

Research Site

The criteria for site selection were threefold. The first criterion was that the firm

in question be based in the United States, given the study’s focus on American corporate

knowledge management efforts. It is important to note that the position generator is

country specific for scoring, and as such a multinational focus would require multiple

versions of the instrument. The second criterion for site selection was a research site that

66

employed an EKR with functionalities that covered all four of the EKR behaviors listed

in the classification rubric in Table 1. The final criterion was that the firm capture

detailed usage data in its server logs. Based on the interactions with multiple potential

research sites, it became apparent that few IT departments had foreseen the need to

capture EKR usage metadata at the level required for this study.

The research site that met all criteria and was chosen for this study was X Corp (a

pseudonym), a U.S.-based multinational corporation that develops and sells hardware,

software, and cloud solutions as well as provides information technology (IT) consulting.

The research took place with English-speaking employees as a means of limiting the

impact of multiple languages and cultures. That is not to say the study was open only to

citizens of English-speaking nations, but rather it was open to employees who were fluent

in English and were accustomed to U.S. business and cultural norms. This delimitation of

an English-language-based research site aided in generalizability to U.S. corporate

cultures.

Specifically, this research study was conducted with X Corp’s Advanced

Analytics business unit in the United States. This X Corp business unit provides software

development services for internal X Corp applications. Additionally, the unit is cognizant

that internal applications are eventually packaged and sold as external commercial

products. The total number of potential participants was approximately 450.

The unit made extensive use of the OSN software suite and deeply integrated this

EKR into its employees’ day-to-day activities. This serves as a critical differentiator, as

no other potential research site had an EKR that was so deeply embedded in the business

unit’s business processes. As an example, the Advanced Analytics business unit had all

67

but abandoned e-mail for internal interactions in favor of using the OSN EKR. The entire

business unit, from the vice president to the administrative support staff, was actively and

thoroughly utilizing the EKR.

The leadership at X Corp and the OSN implementation team actively pushed

adoption of the OSN across all of the company’s business units. To that end, business

unit advocates for the EKR were identified and worked with the business unit’s

leadership to encourage its use. The X Corp EKR implementation team also had OSN

ambassadors, such as the corporate librarian who evangelized and assisted business units

in incorporating the OSN platform into their work processes. Business units were rated

on an OSN transformation scale of 1 to 4, where 1 represented a unit that was entirely

dependent on e-mail and 4 represented a unit that used the EKR exclusively rather than

e-mail for internal communications. The Advanced Analytics group would be classified

as a 4 on the OSN transformation scale.

Another critical differentiator was the all-encompassing nature of the OSN EKR

software—to include an instant messaging function. Mimicking the functionality used in

online networks such as Facebook Messenger or mobile phone applications such as

WhatsApp, OSN allowed for instant communication between users. Unlike other instant

messaging applications, which are more ephemeral in nature, OSN maintained a

permanent record (and metrics) of such conversations, which were captured on private

user-to-user pages to which participants had access.

Participants

The target population for this study was X Corp employees who had used the

OSN EKR on the corporate intranet for 3 months. This 3-month period was necessary to

68

ensure sufficient data were available in the OSN server logs relating to the participant’s

EKR utilization behaviors, as per expert informants within the organization. Data from

the OSN EKR server logs that indicated that participants had less than 3 months of tenure

with the software would eliminate participants from the study. Given that gathering data

from the entire X Corp Advanced Analytics unit population was not feasible, this study

used a sampling approach.

To determine an appropriate sample size for this study, an a priori power analysis

was performed using G*Power v. 3.1 (Faul, Erdfelder, Bucher, & Lang, 2009). Power

was defined by Hinkle, Wiersma, and Jurs (2003) as “the probability of rejecting the null

hypothesis when it is false (1 – β)” (p. 299). Assuming adequate power (0.90) for

multiple linear regression with three independent variables with an alpha level of 0.05

and a moderate estimated effect size of 0.15, the required sample size for the position

generator survey instrument used in this research study was at least 99 participants.

To attain the required sample size, the researcher secured approval from X Corp

senior leadership to utilize the EKR to post a notice about the study for personnel in the

business unit as a means of announcing the research study. Additional approval was

granted for two follow-up postings to encourage study participation. Participants were

asked to provide a noncorporate e-mail address as part of the survey to facilitate

subsequent follow-up with specific participants to address missing data, if needed. This

approach also helped protect the confidentiality of survey results by minimizing the use

of X Corp e-mail, which is subject to monitoring and review based on X Corp policy.

Communications with X Corp personnel highlighted the volunteer nature of this study; to

69

incentivize participation, a random drawing was offered for prizes for individuals who

chose to participate.

Apparatus

This research study dealt with the variables of social capital measures and EKR

utilization elements. Social capital cannot be measured directly, and as a result,

researchers use surrogate measures. The specific measures utilized in this study stemmed

from the work of Lin and Dumin (1986), where they presented the position generator

survey instrument, which yields statistically valid surrogate measures for social capital.

The EKR utilization elements articulated by Lin and Huang (2009)—knowledge

seeking and knowledge contribution—were derived from one particular EKR: the OSN

EKR. The researcher was provided access by X Corp to the corporate OSN server logs

and downloaded the usage metadata (but not actual OSN content postings) for each of the

participants who completed the survey and utilized the OSN EKR for at least 3 months.

To protect the confidentiality of individual survey results, the researcher did not share

any survey data with X Corp.

X Corp received a copy of this dissertation prior to publication. The document

was reviewed by the X Corp legal department and the X Corp senior leadership. The

purpose of this review was to ensure that no proprietary information was divulged.

Position Generator

As mentioned previously, Lin and Dumin’s (1986) position generator instrument,

a widely used survey designed to measure individual social capital, was selected for this

study. The instrument measures range of accessibility, extensity, and upper reachability

70

of accessed social capital (Lin, 2001a). In the position generator survey, participants are

presented with a list of 10 occupations and asked to indicate if they know, on a first-name

basis, someone having that occupation. According to Häuberer (2011), “The collection of

jobs represents the possession of collectively valued resources in a given hierarchical

stratification system like occupational status, prestige or authority” (p. 128). Values for

each of the occupations listed were derived from the Standard International Occupational

Prestige Scale (SIOPS) developed by Treiman (1977). By utilizing these prestige scores,

Lin (2001b) has ordered the occupations on the interval level (Häuberer, 2011).

Position generators are used in a wide variety of social capital–related research

studies, including those addressing race and gender issues, mobility, occupational

attainment, and even health-related issues (Li, Savage, & Warde, 2008; McDonald, Lin,

& Ao, 2009; Moerbeek & Flap, 2008; Verhaeghe, Pattyn, Bracke, Verhaeghe, & Van de

Putte, 2012). International researchers frequently substitute specific jobs that are more

appropriate for the particular nation or culture as determined by the SIOPS (Lin &

Erickson, 2008). These occupation list substitutions are enabled by guidelines established

by Lin and Dumin (1986), and despite the differences in occupations used in various

countries and cultures, these different position generator surveys have acceptable

reliability measures, according to Lin and Erickson (2008).

For an instrument to be considered reliable, it should yield the same results across

multiple studies. In their assessment of position generator reliability across differing

occupational lists, Verhaeghe, Van de Putte, and Roose (2012) found high reliability

(0.688) for the instrument when used in conjunction with the SIOPS occupational

prestige list. This study utilized the position generator instrument as articulated by Lin et

71

al. (2013). The specific position generator instrument used in this study appears in

Appendix A.

EKR Utilization Metrics

To provide data on the OSN EKR environment and facilitate analysis of its

relationship with individual social capital access measures, the researcher downloaded

utilization metrics from the OSN EKR server logs encompassing six OSN functionalities.

The goal was to capture the total number of times a particular study participant used each

of the functionalities during the study period of 3 months. Table 5 provides greater detail

regarding these OSN functionalities and how they align with the two EKR utilization

elements and four specific EKR behaviors.

Table 5 EKR Behaviors and Corresponding OSN Functionalities

Utilization element EKR behavior OSN functionality Knowledge seeking (Lin & Huang, 2009)

Seeking information Follow—allows a user to be notified of another user’s public activities on OSN

Requesting assistance

Please respond flag—allows a user to highlight content for another OSN user to respond to

Knowledge contribution (Lin & Huang, 2009)

Posting new information

Post—allows a user to upload a comment or media FYI flag—allows a user to alert another OSN user to new content

Responding to postings by other users

Reply—allows a user to respond to content posted by another user Like—allows a user to indicate a positive attitude towards content posted by another user

72

Data Collection Procedures

Data were collected in two phases: the administration of the position generator

instrument and the systematic exploitation of the X Corp OSN EKR server logs.

The Internet survey website, SurveyMonkey (www.surveymonkey.com), was

used to gather the responses to the position generator instrument. This survey website

was selected because it offers the researcher the most cost-effective means of hosting the

instrument, and it has been used extensively in dissertations and in The George

Washington University research community.

The Internet-based approach of data collection for this study was selected due to

the myriad advantages this medium affords. Internet-based administration of surveys

enables the researcher to reach a broad audience at minimal cost and with higher response

rates (O’Neill, 2004). The Internet allows the researcher to conduct data collection

asynchronously, with survey participants accessing the instrument wherever and

whenever best fits their schedule. Internet-based surveys also minimize the potential for

interviewer bias (O’Neill, 2004). Given that the position generator survey was conducted

asynchronously, the interviewer was not present to potentially affect the participants’

answers.

The survey was conducted in a manner to minimize the two coverage errors

commonly associated with Internet-based surveys. According to de Leeuw, Hox, and

Dillman (2008), one cause of coverage error is the need for access to the Internet, a

computer, and some level of skill with a computer. Given that members of the

organization or unit in question routinely used an EKR, the risk of this type of coverage

error was minimal. The second cause of coverage error is reported as hardware or

73

software misconfiguration or differences that may make the Internet-based survey

inaccessible for some users. Again, given that survey participants routinely had access to

and interacted with an EKR, the technical sophistication of an Internet-based survey was

expected to be unproblematic. To minimize any possibility of technical difficulties, the

position generator instrument was extensively beta-tested prior to its use.

All X Corp personnel in the Advanced Analytics business unit received the

invitation to participate in the study via a posting to the EKR. The posting contained a

hyperlink to the SurveyMonkey position generator instrument. When the participant

visited the Internet survey site, the welcome page included an informed consent

checkbox, which outlined the two elements of the study: the position generator survey

and metadata collection from their X Corp OSN server logs. Participants were required to

acknowledge that they read the informed consent statement before proceeding. Once

participants initiated the survey, they had unlimited time to finish. It was anticipated the

survey would take approximately 7 minutes to complete for the average participant.

The position generator survey site was available for 8 days. The survey

availability period was selected based on previous research on Internet-based surveys that

found that the most effective length of time between reminders is 2 days (Crawford,

Couper, & Lamias, 2001). The 2-day interval, coupled with the Cook, Heath, and

Thompson (2000) meta-analysis on multiple Internet surveys, which indicated that the

optimal number of reminder e-mails was three, dictated a 8-day period.

Once the survey period closed, the researcher queried the OSN metadata server

logs for the previous 3 full months for each participant who completed the survey. The

EKR usage metrics were only queried for the six EKR behaviors described in the EKR

74

utilization metrics section, and no other server log data were collected. As stated

previously, the metadata recorded by the server logs and queried by the researcher did not

contain the specific content of information posted by OSN users but rather a count of the

number of times the user engaged in the specific EKR behaviors.

Data Handling

The confidentiality of the participants to the survey was assured by assigning a

unique identification number (known only to the researcher) to each participant. Results

from the study, and any subsequent discussion or publication of study results, are only

reported in the aggregate. When individual responses to the survey are provided,

participant names are replaced with a unique identification number. A table correlating

the identification numbers with the names of the participants was kept in the researcher’s

personal safe. The completed surveys with personally identifiable information were also

kept in the researcher’s personal safe to protect the integrity of the data and

confidentiality. A back-up copy of both were provided to the faculty sponsor for safe

keeping in separate sealed envelopes to be kept in a locked file cabinet. A third document

containing the list of identification numbers and when the participants’ surveys were

completed was made readily available, as it contained no sensitive data and was used to

facilitate logging in the data and tracking survey completion. At no time will individual

responses be provided to the leadership of X Corp to maintain assurances of

confidentiality of responses.

Upon completion of the survey collection phase, the resulting survey data were

downloaded into a spreadsheet. Data were visually inspected to ensure there were no

errors, either during the administration of the survey or in transference of the data. For

75

missing items, every effort was made to determine if the data were lost during processing

(at which point, the data were restored from the original survey results). If the data were

missing from the original survey response, the participant was contacted and asked to

complete the missing data.

The server log results from all X Corp survey participants that contained the

requisite metadata (information about the EKR behaviors of users but not the content of

those postings) were downloaded onto a DVD by the researcher and kept in a locked safe

in the researcher’s home, with only the researcher knowing the combination. The

researcher will preserve a record copy of the data for 3 years after dissertation approval.

Data Analysis

Results from the quantitative analysis served as the basis for addressing the

research questions in accordance with the objectives of this study. This analytical

methodology was chosen because it is consistent with the means of measurement, as

espoused by the network theory of social capital. Likewise, the tenets of Lin’s (1999a)

network theory of social capital dictate that these measurements take place at the

individual level of analysis.

The statistical software suite SPSS was used to conduct the analysis of the

resulting quantitative data. An initial test for homoscedasticity and normality of data was

conducted prior to any testing of the hypotheses of this study. Homoscedasticity was

defined by Hinkle et al. (2003) as the normal conditional distribution (the distribution of

Y scores for all those with the same value of X) and the standard deviations of each of

these distributions that are assumed to be equal (p. 132). Additionally, the researcher

tested for normality across all data sets. Any outliers remaining after homoscedasticity

76

and normality testing were redacted prior to performing further analysis. Once the data

sets met the initial assumptions and were configured appropriately in SPSS, the statistical

tests began.

To establish the existence of relationships between the four dependent and three

independent variables delineated in this study, both multinomial and multiple linear

regressions were required. Regression is the analytic methodology employed to

statistically determine how the value of a dependent variable changes when the value of

any one of the independent variables increases or decreases. Green and Salkind (2008)

stated:

Multiple regression is used to analyze data from studies with experimental or non-experimental designs. . . . However, if data are collected using non-experimental methods (e.g., a study in which subjects are measured on a variety of variables), the variables in the regression analysis may be called the predictors and the criterion rather than the independent variables and the dependent variable, respectively. (p. 285)

To that end, this study adopted such terminology.

By comparing the social capital scores to the EKR behaviors, the regression

analytic methodology determined the influence, if any, that the predictors had upon the

criterion. Specifically, the regression methodology was used to compare data for each of

the social capital predictors (i.e., range, extensity, and upper reachability) with each of

the EKR behavior criteria (i.e., seeking, requesting, posting, and responding to

information). The researcher conducted this analysis utilizing the SPSS software.

Additionally, detailed descriptive statistics for the predictors and each criterion were

presented.

77

Human Participants and Ethics Precautions

This research study relied on the use of human subjects who voluntarily

participated. The researcher, therefore, had a serious duty to ensure the safety and well-

being of participants and to uphold high ethical standards and any assurance of

confidentiality. Additionally, the researcher must not put in jeopardy the voluntary nature

of the study, nor the security of the collected data. To ensure that proper ethical

consideration was given to participants, the researcher provided and adhered to the

stipulations of an informed consent statement prior to administering the position

generator survey or collecting server log data (see Appendix A). All requirements of The

George Washington University Institutional Review Board were followed, and no data

were collected until this approval was received.

Confidentiality

Abiding by confidentiality assertions to participants is a critical responsibility that

must be maintained throughout the entire research study and with any subsequent

presentation or publication of the research data. This was achieved by maintaining any

personally identifiable information under lock and key by the researcher. Each participant

was assigned a unique identification number that was used on all data and was known

only to the researcher.

One potential complication for this study was that the researcher is an employee

of X Corp. In order to mitigate any concerns regarding the confidentiality of survey

results, the researcher directed potential participants to partake in the survey from their

personal computers and explicitly stated that X Corp computers should not be used. As

mentioned previously, the researcher substituted unique numeric codes for any and all

78

listings of participant names and maintained the master list in a locked safe off of X

Corp’s premises. At no point did the researcher provide X Corp with the master list or

any participant-specific information regarding the completed surveys.

Voluntary Participation

Participation in this study was completely voluntary. The messages distributed via

the X Corp EKR highlighted the voluntary nature of the study and indicated that the

study was to be completed during nonwork hours to avoid any conflict of interest issues.

There were no consequences for any potential participant’s decision not to participate.

Participants were free to opt out of any question or the entire survey either prior to or

after consenting to participate—a right that was clearly indicated in this study’s informed

consent statement. To minimize any potential of a perceived pressure to participate,

members of the researcher’s own business unit were excluded from the invitation to

participate in the study.

Summary

To address this study’s research questions and derivative hypotheses, a

nonexperimental research design was utilized. Participants who had at least 3 months of

experience with the OSN EKR software were recruited as volunteers from the Advanced

Analytics X Corp business unit. To collect data regarding the participants’ social capital,

the position generator survey instrument was used. Data to delineate EKR utilization

behaviors of participants was collected by analyzing the metadata from the X Corp OSN

server logs. The regression analytic methodology was used to statistically establish the

relationship between the study variables and to support or reject the study’s four

79

hypotheses. Throughout the study, attention was given to ethical precautions and strict

adherence to the Institutional Review Board guidelines for conducting research with

human subjects.

80

CHAPTER 4:

DATA ANALYSIS

This quantitative study examined the measurement and relationship between

social capital and electronic knowledge repository (EKR) utilization. Specifically, the

three social capital measures of extensity, upper reachability, and range were derived

from the position generator instrument that was administered to 103 employees of X

Corp who were users of the Oracle Social Network (OSN) EKR. The three social capital

measures were then compared via regression against metadata from the X Corp EKR,

which captured knowledge-seeking and knowledge-contribution behaviors over a

3-month period (i.e., January to March of 2015). Table 5 in chapter 3 provides greater

detail regarding the two utilization elements, four EKR behaviors, and their

corresponding OSN functionalities.

Given that social capital theorists have a keen interest in inequality in social

capital among various racial/ethnic, economic, and gender groups, a discussion of

participant demographics and variances therein would be expected. However, in

consultation with the dissertation committee, the researcher did not collect such

demographic information, as the primary focus of this study was delineating the

relationship between social capital measures and EKR behaviors. By not collecting

demographic data on participants, the researcher minimized institutional review board

and privacy concerns of the research site as well as potentially increased the number of

responses by shortening the time necessary to complete the instrument. That said, a post

facto review of the respondents indicated that of the 103 participants, 75 were men and

28 were women.

81

This chapter presents preliminary analysis elements followed by a primary

analysis section that addresses each of the research questions and accompanying

hypotheses. The data collected from both the position generator instrument and the OSN

EKR server logs are presented and analyzed. The resulting data and regression analysis

for this study are presented via tables and narrative write-up to explain the results.

Preliminary Analysis

Preliminary analyses were conducted to determine the state of the data collected,

as well as to test the assumptions associated with the primary analysis. A review of the

collected data indicated there were no missing data from any of the 103 study

participants’ EKR behaviors or position generator scores. Prior to conducting the primary

analyses, the statistical assumptions for regression were examined, which resulted in

significant violations of normality. The resulting descriptive statistics, inclusive of

skewness and kurtosis, revealed substantial violations across all variables with the

exception of the extensity and upper reachability social capital measures (see Table 6).

Table 6 Initial Descriptive Statistics (N = 103) Min Max Mean SD Skewness (SE) Kurtosis (SE) Extensity 0 9 5.17 2.22 –.17 (.24) –.74 (.47) Upper reach 0 78 73.94 10.34 –4.40 (.24) 25.80 (.47) Range 0 46 35.30 12.74 –1.54 (.24) 1.70 (.47) Follow 0 80 3.70 9.38 5.90 (.24) 43.88 (.47) Please respond flag 0 428 36.96 69.02 3.46 (.24) 14.17 (.47) Post 0 1824 245.07 284.08 2.35 (.24) 8.83 (.47) Reply 0 1926 295.65 359.52 2.22 (.24) 6.09 (.47) FYI Flag 0 833 113.34 170.50 2.29 (.24) 5.18 (.47) Like 0 742 62.59 111.15 3.35 (.24) 14.78 (.47) Note. SD indicates standard deviation; SE, standard error.

82

An additional issue was discovered regarding the social capital measure of upper

reachability. There was a significant lack of variability for upper reachability scores

among the 103 study participants. Due to the highly educated nature of the study

population, 71.8% of the population indicated they knew a college professor, which was

the highest possible score in the position generator instrument with 10 occupations. An

additional 16.5% of respondents indicated they knew a lawyer, which was the occupation

with the second highest Standard International Occupational Prestige Scale (SIOPS)

score. Of the remaining occupations, 3.9% indicated they knew someone who was a

middle school teacher or personnel manager, the third highest occupations. This

demonstrates a clear example where the survey instrument limited variability by

including two occupations with the same SIOPS score.

Rounding out the rest of the distribution, 1.9% of respondents indicated the

highest occupation they knew was a nurse, and 4.9% of respondents indicated their

highest known occupation was a computer programmer. One respondent indicated that he

did not know anyone in any of the occupations. Given regression’s assumption of

normality, the upper reachability measure reflected values of –4.40 for skewness and

25.80 for kurtosis, both of which would be considered extreme according to Tabachnick

and Fidell (2013).

This short-form version of the position generator was recommended to the

researcher via correspondence with the instrument’s creator (N. Lin, personal

communication, November 19, 2014). In subsequent correspondence, he indicated that

given the size of the study sample, the social capital measure of extensity may be a more

83

appropriate measure (N. Lin, personal communication, April 14, 2015). Therefore, the

upper reachability measure was not used in the final analysis.

The social capital measure of range is determined by subtracting the SIOPS value

of the lowest occupation listed in the survey from the SIOPS value of the highest

occupation (Lin & Ao, 2008). Given the significant lack of variability in the upper

reachability score, the observed distributions for range were somewhat problematic. For

the range social capital measure, the minimum statistic was zero and the maximum

statistic was 46, with 30.1% of respondents receiving the maximum score and an

additional 28.2% receiving a range score of 40. As a result, a log transformation was

attempted to correct the problematic observed distributions, but the resulting

transformation did not improve the distribution.

Ultimately, this issue was addressed by a transformation to discrete categories

based on the observed distribution. According to Tabachnick and Fidell (2013), “With

grouped data, the assumption of normality is elevated with respect to the sampling

distribution of means and the Central Limit Theorem predicts normality with decently

sized samples” (p. 85). In accordance with the process outlined by the authors, multiple

variations were attempted with regards to a categorization transformation, and each was

subsequently tested for normality. The five-scale categorization of the range measure

delineated in Table 7 demonstrated the best skewness and kurtosis scores and was

adopted for the study.

84

Table 7 Categorization of Range Social Capital Measure

Score Frequency Percentage Category 0–18 9 8.7% 1 19–29 19 18.4% 2 30–39 10 9.7% 3 40–45 34 33.0% 4

46 31 30.1% 5

With regards to the six OSN functionalities (i.e., follow, please respond flag, post,

reply, FYI flag, and like), the violations of normality were addressed by the creation of

discrete categories based on observed distribution following the transformation process

outlined by Tabachnick and Fidell (2013). The OSN functionality of follow was divided

into three categories. Further division of the distribution into additional categories was

not possible due to the frequency of the minimum statistic of zero, which accounted for

45.6% of participants.

The OSN functionality of the please respond flag was divided into four categories

based on the observed distribution. The remaining distributions for the four OSN

functionalities were divided into five distinct categories as delineated in Table 8. All of

these categorical transformations followed the process outlined by Tabachnick and Fidell

(2013) to ensure the best possible skewness and kurtosis scores. Each study participant’s

use of the six OSN functionalities was captured via the OSN server logs over a 3-month

period from January to March 2015.

85

Table 8 Categorization of OSN Functionalities

Category Instances of

behavior Frequency Percentage Category Follow 0 47 45.6% 1

1–2 30 29.1% 2 3–80 26 25.2% 3

Please respond flag

0 25 24.3% 1 1–10 27 26.2% 2 11–42 25 24.3% 3 43–428 26 25.2% 4

Post 0–30 23 22.3% 1 31–100 18 17.5% 2 101–200 18 17.5% 3 201–500 26 25.2% 4 501–1824 18 17.5% 5

Reply 0–20 21 20.4% 1 21–120 21 20.4% 2 121–250 20 19.4% 3 251–500 20 19.4% 4 501–1926 21 20.4% 5

FYI flag 0–3 22 21.4% 1 4–25 21 20.4% 2 26–70 20 19.4% 3 71–170 19 18.4% 4 171–833 21 20.4% 5

Like 0 22 21.4% 1 1–6 19 18.4% 2 7–30 21 20.4% 3 31–90 20 19.4% 4 91–742 21 20.4% 5

Once the social capital measure of range and the six OSN functionalities were

categorized, the researcher conducted a subsequent test for normality. The resulting

descriptive statistics, inclusive of skewness and kurtosis, were all well within acceptable

parameters. Table 9 delineates the final descriptive statistics for the data set. As

86

previously mentioned, the social capital measure of upper reachability was not used due

to issues with lack of variability.

Table 9 Final Descriptive Statistics (N = 103) Min Max Mean SD Skewness (SE) Kurtosis (SE) Extensity 0 9 5.17 2.22 –.17 (.24) –.73 (.47) Upper reach 0 78 73.94 10.34 –4.40 (.24) 25.80 (.47) Range 1 5 3.57 1.33 –.59 (.24) –.93 (.47) Follow 1 3 1.80 .82 .40 (.24) –1.41 (.47) Please respond flag 1 4 2.51 1.14 –.02 (.24) –1.40 (.47) Post 1 5 2.98 1.41 –.07 (.24) –1.40 (.47) Reply 1 5 3.07 1.38 –.03 (.24) –1.24 (.47) FYI flag 1 5 3.00 1.41 .02 (.24) –1.28 (.47) Like 1 5 3.05 1.44 –.09 (.24) –1.32 (.47) Note. SD indicates standard deviation; SE, standard error.

Pearson’s product-moment correlations were computed among all the variables in

the data set, which yielded consistent significant relationships for linearity (Table 10).

Likewise, the relationships among the variables demonstrated r values below 0.85 and

did not pose an issue with multicollinearity. Again, upper reachability was not used.

Table 10 Pearson’s Product-Moment Correlations (N = 103)

Extensity Upper reach Range Follow Flag Post Reply FYI Like

Extensity 1 .528** .773** .137 .245* .214* .188 .153 .205* Upper reach .528** 1 .598** -.029 .194* .160 .125 .058 .152 Range .773** .598** 1 -.027 .154 .074 .080 .016 .093 Follow .137 -.029 -.027 1 .429** .523** .402** .338** .521** Please respond

flag .245* .194* .154 .429** 1 .650** .671** .634** .612**

Post .214* .160 .074 .523** .650** 1 .788** .606** .748** Reply .188 .125 .080 .402** .671** .788** 1 .567** .731** FYI flag .153 .058 .016 .338** .634** .606** .567** 1 .509** Like .205* .152 .093 .521** .612** .748** .731** .509** 1 * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).

87

Primary Analysis

The purpose of this study was to enhance the understanding of the relationship

between social capital and EKR utilization. Drawing from Lin’s (2001b) work on social

capital, two outcomes were established: that of instrumental action (gaining added

resources) and that of expressive action (maintaining resources). As the literature review

in chapter 2 demonstrated, these social capital outcomes relate to the knowledge-seeking

and knowledge-contribution elements of EKR utilization as articulated by Lin and Huang

(2009) and delineated in Table 1.

To enhance the understanding of the relationship between social capital and EKR

utilization, two primary research questions, four subquestions, and four hypotheses were

developed for this study. In order to test this study’s four hypotheses, both multinomial

logistic regressions and multiple regressions were utilized. The social capital measures of

extensity and range were the only predictors (independent variables) utilized, as upper

reachability was not employed due to lack of variability in the data set. This results

section individually addresses each of the hypotheses and accompanying results. The

anonymized study data for the individual participants appear in Appendix B.

Research Question 1a

Based upon the EKR and social capital literature, Research Question 1 was

developed with two subquestions. Research Question 1 was: What is the relationship

between EKR users’ social capital and EKR knowledge seeking? To add greater

specificity, Subquestion 1a was developed: What is the relationship between social

capital and EKR users seeking information? To that end, H1 asserted that social capital is

positively related to EKR users seeking information.

88

To determine whether the social capital measures of extensity and range were

predictive of the criterion of follow, a multinomial logistic regression was conducted

(Table 11). The overall model predicting the follow criterion was not significant, χ2 (10)

= 11.49, p = .321, Nagelkerke R2 = .120. These results indicate that, as a set, the

predictors of extensity and range could not significantly predict a study participant’s use

of the follow OSN functionality.

In terms of individual predictors, extensity scores were a significant predictor of

the highest level of follow frequencies, Wald = 4.69 (1), p = .030. With every 1 point

increase in extensity score, study participants were over 1.5 times more likely to use the

follow functionality at the highest level, odds ratio (OR) = 1.528. The social capital

measure of range was also a significant predictor of the second level of follow

frequencies, Wald = 6.03 (1), p = .014. With every 1 point increase in range score, study

participants were 14 times more likely to use the follow OSN functionality at the second

Table 11 Multinomial Logistic Regression Parameter Estimates (N = 103)

Followa B Std. error Wald Df Sig. Exp(B)

95% CI for Exp(B) Lower bound

Upper bound

2.00 Intercept –2.02 1.27 2.51 1 .11 Extensity .23 .18 1.67 1 .20 1.26 .89 1.77 [Range=1] .62 1.33 .21 1 .64 1.85 .14 25.08 [Range=2] .62 .99 .39 1 .53 1.85 .27 12.81 [Range=3] 1.19 .93 1.63 1 .20 3.29 .53 20.51 [Range=4] .42 .63 .45 1 .50 1.52 .45 5.19 [Range=5] 0b . . 0 . . .

3.00 Intercept –4.14 1.51 7.55 1 .01 Extensity .42 .20 4.69 1 .03 1.53 1.04 2.24 [Range=1] 1.75 1.65 1.13 1 .29 5.76 .23 144.88 [Range=2] 2.64 1.08 6.03 1 .01 14.05 1.71 115.73 [Range=3] 2.02 1.07 3.59 1 .06 7.56 .93 61.41 [Range=4] 1.33 .72 3.41 1 .07 3.80 .92 15.64 [Range=5] 0b . . 0 . . . .

a. The reference category is 1.00. b. This parameter is set to zero because it is redundant.

89

level, OR = 14.05. Given that H1 asserted that social capital is positively related to EKR

users seeking information, based on these results the hypothesis is supported—albeit only

for the social capital measures of extensity and the second category of range.

Research Question 1b

Research Question 1b asked: What is the relationship between social capital and

EKR users posting requests for assistance? H2 asserted that social capital is positively

related to EKR users posting requests for assistance. To determine whether the social

capital measures of extensity and range were predictive of the criterion of the please

respond flag functionality, a multiple regression was conducted. The overall model

predicting please respond flag scores was significant, F (2, 102) = 3.39, p = .038, R2 =

.063. These results indicate that taken as a set, extensity and range could significantly

predict the use of the please respond flag.

At the individual predictor level, an EKR user’s extensity score was a significant

predictor of the use of the OSN please respond flag functionality, t = 2.06, p = .042.

Higher extensity scores were associated with higher frequencies of the please respond

flag, Beta = .314. The social capital measure of range was found not to be significant.

Therefore, H2 was partially supported at the individual predictor level (Table 12).

Table 12 Please Respond Flag Coefficients (N = 103)

Unstandardized

t p B SE Beta 1 (Constant) 1.96 .32 6.14 .000

Extensity .16 .08 .31 2.06 .042 Range –.08 .13 –.09 -.58 .562

90

Research Question 2a

Based upon the EKR and social capital literature, the second research question

was likewise developed with two subquestions. Research Question 2 asked: What is the

relationship between EKR users’ social capital and EKR knowledge contribution? To add

greater specificity, Subquestion 2a was developed: What is the relationship between

social capital and EKR users posting new information? To address this subquestion, H3

asserted that social capital is positively related to EKR users posting new information.

For this hypothesis, two OSN EKR functionalities that correspond to the EKR behavior

of posting information were measured: post and FYI flag. In this section, each EKR

behavior is analyzed separately.


predictive of the criterion of the OSN post functionality, a multiple regression was

conducted (Table 13). The overall model predicting post scores was significant, F (2,

102) = 3.56, p =.032, R2 = .066. At the individual level, an EKR user’s extensity score

was a significant predictor of the use of the OSN post functionality, t = 2.56, p = .012.

Higher extensity scores were associated with higher frequencies of the post functionality,

Beta = .389. The social capital measure of range was found not to be significant.

Table 13 Post Coefficients (N = 103) Unstandardized


Extensity .25 .10 .39 2.56 .012 Range –.24 .16 –.23 –1.49 .141

91

The second EKR functionality measured for H3 was the FYI flag. A multiple

regression was conducted to determine whether the social capital measures of extensity

and range were predictive of the criterion of the OSN FYI flag functionality (Table 14).

The overall model predicting FYI flag scores was not significant, F (2, 102) = 2.60, p =

.080, R2 = .049. These results indicate that, as a set, the two predictors could not

significantly predict a study participant’s use of the FYI flag functionality. At the

individual level, an EKR user’s extensity score was a significant predictor of the use of

the OSN FYI flag functionality, t = 2.27, p = .025. Higher extensity scores were

associated with higher frequencies of the FYI flag functionality, Beta = .349. However,

the social capital measure of range was found not to be significant.

Table 14 FYI Flag Coefficients (N = 103) Unstandardized


Extensity .22 .10 .35 2.27 .025 Range –.27 .16 –.25 –1.65 .101

With regards to Research Question 2a, mixed results were apparent. The overall

model for the post OSN functionality was significant, but the overall model for FYI flag

was not. However, at the level of individual social capital measures, extensity was found

to be a significant predictor for both OSN functionalities. As a result, H3 was partially

supported.

92

Research Question 2b

Research Question 2b asked: What is the relationship between social capital and

EKR users responding to postings by other users? Accordingly, H4 asserted that social

capital is positively related to EKR users responding to postings by other users. For this

hypothesis, two OSN EKR functionalities corresponding to the EKR behavior of

responding to postings by others were measured: reply and like. In this section, each EKR

behavior is analyzed separately.


predictive of the criterion of the OSN reply functionality, a multiple regression was

conducted (Table 15). The overall model predicting reply scores was not significant, F (2,

102) = 2.40, p =.096, R2 = .046. At the individual level, an EKR user’s extensity score

was a significant predictor of the use of the OSN reply functionality, t = 2.03, p = .045.

Higher extensity scores were associated with higher frequencies of the reply

functionality, Beta = .312. The social capital measure of range was found not to be

significant.

Table 15 Reply Coefficients (N = 103) Unstandardized


Extensity .19 .10 .31 2.03 .045 Range –.17 .16 –.16 –1.05 .298 The second EKR functionality measured for H4 was like. To determine whether

the social capital measures of extensity and range were predictive of the criterion of the

like functionality, a multiple regression was conducted (Table 16). The overall model

93

predicting like scores was not significant, F (2, 102) = 2.78, p =.067, R2 = .033. At the

individual level, an EKR user’s extensity score was a significant predictor of the use of

the like functionality, t = 2.16, p = .033. Higher extensity scores were associated with

higher frequencies of the reply functionality, Beta = .331. The social capital measure of

range was found not to be significant (Table 16).

Table 16 Like Coefficients (N = 103) Unstandardized


Extensity .22 .10 .33 2.16 .033 Range –.18 .17 –.16 –1.06 .291

Regarding Research Question 2b, the two criteria yielded mixed results. From an

overall model perspective, both the reply and like OSN functionalities were not

significant. However, at the level of individual social capital measures, extensity was

found to be a significant predictor of the aforementioned criteria. Therefore, H4 was

supported at the individual predictor level for extensity but not for the range predictor. At

the overall model level for the reply or like criterion, H4 was not supported.

Summary

The position generator survey instrument was administered to 103 employees of

X Corp who were users of the OSN EKR. The survey instrument yielded scores for the

social capital measures of extensity, upper reachability, and range. Due to a lack of

variability for upper reachability, this social capital measure was not used. The two

remaining social capital measures were compared via regression against metadata from

94

the OSN EKR, which captured knowledge-seeking and knowledge-contribution

behaviors through six different OSN functionalities. Regression analysis was conducted

to determine if each of the study’s four hypotheses was supported.

Based upon the results of both H1 and H2, which were partially supported, it was

determined that there was a positive relationship between social capital and EKR users

seeking information, as well as requesting assistance. These behaviors are indicative of

the EKR utilization element of knowledge seeking (Lin & Huang, 2009). Therefore,

Research Question 1 can be addressed based on these results by asserting a positive

relationship between an EKR user’s social capital measure of extensity and EKR

knowledge seeking. Likewise, the social capital measure of range did not consistently

reflect significance across all information seeking (follow functionality) categories and

had no significant relationship with the requesting assistance behavior (please respond

flag functionality).

The overall model for H3 was supported by one of the two criteria—post, but not

FYI flag. H4 was not supported at the overall model level for either criterion—reply or

like. However, both H3 and H4 were partially supported at the individual predictor level

for the social capital measure of extensity. Based on this analysis, it was determined that

there was a positive relationship between the social capital measure of extensity and EKR

users posting new information, as well as responding to postings by others. These

behaviors are indicative of the EKR utilization element of knowledge contribution (Lin &

Huang, 2009).

Research Question 2 can be addressed based on these results by asserting a

positive relationship between an EKR user’s social capital measure of extensity and EKR

95

knowledge contribution. The social capital measure of range did not reflect significance

across any posting new information categories (post and FYI flag functionalities).

Likewise, there was no significant relationship between range and responding to postings

by other users (reply and like functionalities).

The two overarching research questions and the four subquestions were

subsequently addressed by articulating a positive relationship, whereby the social capital

measure of extensity was a significant predictor of the six functionalities of the OSN

EKR. In short, this study established a positive relationship between extensity and the

two EKR utilization elements of knowledge seeking and knowledge contribution as well

as the four EKR behaviors as evidenced through the six OSN functionalities. Table 17

provides a summary of the high-level results. The following chapter addresses the

interpretations of these results as well as the recommendations for future research.

Table 17 Hypothesis Outcomes

Hypothesis Functionality Overall model Extensity Range

H1: Social capital is positively related to EKR users seeking information.

Follow Not

supported Partially

supported Partially

supported

H2: Social capital is positively related to EKR users posting requests for assistance.

Please respond flag

Supported Supported Not supported

H3: Social capital is positively related to EKR users posting new information.

Post Supported Supported Not supported

FYI flag Not supported

Supported Not supported

H4: Social capital is positively related to EKR users responding to postings by other users.

Reply Not supported


Like Not supported


96

CHAPTER 5:

INTERPRETATIONS, CONCLUSIONS, AND RECOMMENDATIONS

This study sought to address the paucity of literature relating to how social capital

is measured in an electronic knowledge repository (EKR) environment. Surprisingly little

was documented in the literature regarding the relationship between an individual’s social

capital and his or her EKR utilization. That is not to say there are not EKR utilization

studies that address social capital; however, those studies include social capital as one of

multiple theoretical perspectives, which results in a lack of specificity.

This measurement study provides a more detailed explanation of the relationship

between the three social capital measures as defined by Lin (2001b) and the two elements

of EKR utilization as articulated by Lin and Huang (2009). The two EKR utilization

elements of knowledge seeking and knowledge contribution were further divided into

four EKR behaviors and six corresponding functionalities of the OSN EKR system.

For the scholar, this enhanced understanding of social capital in the EKR

environment adds to both the social capital and EKR literature by providing greater

specificity regarding the interaction between the two. The resulting analysis of the data

points to a statistically significant relationship between the social capital measure of

extensity and all six of the OSN EKR functionalities. The study also brings to light issues

with administering the Lin et al. (2013) short form position generator instrument, which

resulted in a lack of observed variability for the social capital measure of upper

reachability. It is surmised that this lack of variability also impacted the range measure.

For the practitioner, this study laid the groundwork for a transparent and

replicable means of measuring the relationship between an individual’s social capital and

97

his or her EKR interactions. The commercial firms Klout, PeerIndex, and Empire Avenue

have attempted to score the influence of social media users, but are frequently accused of

measuring popularity rather than social capital. This study points the way to a fully

transparent and theoretically sound means of measuring influence in EKRs that is based

not on popularity, but rather on social capital measures. These results have direct

applicability to improving EKR gamification efforts.

This chapter addresses interpretations, conclusions, and recommendations to add

to both the social capital and EKR literature. The interpretations and conclusions section

expands upon the data analysis conducted in chapter 4 with a particular emphasis on

drawing connections between the social capital and EKR literature. Recommendations

for future research are discussed in the last section of this chapter with a focus on

addressing the problem statement as delineated in chapter 1.

Interpretations and Conclusions

This study, through a detailed literature review, was able to draw an unambiguous

linkage from the EKR utilization concepts of knowledge seeking and knowledge

contribution to Lin’s (2001b) social capital outcomes of instrumental and expressive

action. A model of social capital in EKR utilization was developed and presented in

Figure 2. Leveraging the model and the literature review, four distinct EKR behaviors

were identified and characterized in Table 1 as a means of creating a classification rubric.

Once the research site was selected and the specific EKR assessed, a listing of six OSN-

specific functionalities (i.e., follow, please respond flag, post, FYI flag, reply, and like)

corresponding to the EKR behaviors was identified in Table 4.

98

Social Capital Measures

This study focused on assessing the predictive value of the three social capital

measures of extensity, upper reachability, and range as articulated by Lin (2001b). Upon

completion of the initial descriptive statistics phase, it became immediately apparent that

the social capital measure of upper reachability suffered from very limited variability. As

indicated in chapter 4, 72.1% of the study participants indicated they knew a college

professor, which garnered the highest possible Standard International Occupational

Prestige Scale (SIOPS) score of 78 in the short form of the position generator instrument.

A significant influence on this result was the highly educated nature of the X Corp

employees participating in this study. Based on the employment requirements for

applicants to the X Corp Advanced Analytics business unit, it is very likely that all 103

study participants possessed at least a bachelor’s degree, with many holding advanced

degrees.

Although the highly educated nature of study participants no doubt had a

significant influence, additional factors may have been at play. In consulting the author of

the position generator instrument, Lin indicated that the relatively small sample size of

the study might also be a factor (N. Lin, personal communication, April 14, 2015).

Additionally, the means by which the position generator instrument was administered

may have contributed to the lack of variability among upper reachability scores. The

position generator survey begins with the following statement:

Next, I am going to ask some general questions about jobs some people you know may now have. These people include your relatives, friends, and acquaintances. (Acquaintances are people who know each other by face and name.) If there are several people you know who have that kind of job, please tell me the one that occurs to you first.

99

The critical part of that statement is the qualifier now. Even though the word was

bolded for emphasis on the SurveyMonkey site, it could have easily been overlooked by

study participants. The instrument is frequently administered in person by a researcher

who may add visual and vocal emphasis to the point. Absent such visual or vocal

emphasis, the questions could have been interpreted as “have you ever known . . . ” rather

than “do you currently know. . . .” Thus, this misinterpretation would likely yield many

more X Corp study participants who have known a college professor compared to those

who currently know one.

The social capital measure of range was also impacted by the lack of variability

evident in the upper reachability measure. The range score is computed by subtracting the

SIOPS score from the lowest-ranked occupation selected by the study participant from

that of the upper reachability score. Though not as pronounced as in the case of upper

reachability, the variability for range was nonetheless impacted by its reliance on the

upper reachability score.

Additional elements that likely impacted the variability of the range score include

the specific occupations listed in the 10-occupation position generator instrument, as well

as two sets of occupations that had the same SIOPS scores. Given that the survey

participants were all from X Corp’s Advanced Analytics group, it was not surprising to

find that 96.3% of study participants reported knowing a computer programmer. Further

limiting the variability in range scores was the position generator’s use of two

occupations with a SIOPS score of 38 (farmer and receptionist) and two occupations with

a SIOPS score of 60 (middle school teacher and personnel manager).

100

On the positive side, the social capital measure of extensity was consistently a

significant predictor across all six OSN functionalities, albeit partially in the case of

follow. The consistent performance of the extensity score not only demonstrates its value

as a predictor across both knowledge-seeking and knowledge-contribution EKR

behaviors (Lin & Huang, 2009), but also validates the linkage with these behaviors and

the two social capital outcomes of instrumental action and expressive action (Lin, 2001b)

as delineated in Table 1. For the practitioner, extensity may be a critical element for

gamification efforts within EKRs given its positive relationship with a multitude of

functionalities. Extensity of contacts has the added value of being the easiest of social

capital measures to capture within the EKR environment.

Hypothesis 1

Addressing specifics for H1, which asserted that social capital is positively related

to EKR users seeking information, a multinomial logistic regression was conducted to

determine which of the social capital measures of extensity and range were predictive of

the follow EKR functionality. The analysis did not find a significant relationship between

the social capital measures and the follow functionality. The failure of the two social

capital measures to be of significant predictive value of the follow criterion may be due to

the issues related to lack of variability in the range measure.

However, additional factors should be considered. The lack of significant

predictive value for the overall model may indicate that the model was incomplete, as it

lacked the upper reachability measure. Alternatively, the follow OSN functionality may

not have been the strongest criterion for the seeking information EKR behavior. One of

the limitations of this study was the availability of metadata for EKR functionalities in

101

the OSN platform, as not all functionalities under initial consideration had usage

metadata captured in the server logs.

At the individual predictor level, the social capital measure of extensity was a

significant predictor at the highest level of the follow functionality. In other words,

extensity was a significant predictor only for those individuals who used follow

frequently. This result is due in large part to the nonlinearity of the original distribution

for follow.

A total of 45.6% of participants in this study had no reported following behaviors

during the time period of the study. This may be a result of having used the functionality

significantly when they first joined OSN and thus having no reason to use it again unless

new personnel of interest were hired. Alternatively, follow simply is not a frequently used

functionality.

The second category for follow consisted of individuals who had used the

functionality once or twice, comprising 29.1% of study participants. The final category

consisted of study participants who used the follow functionality 3 to 80 times during the

study period, which amounted to 25.2% of the study population. The lack of significance

at the first and second levels of the functionality may simply represent insufficient

variability at the lowest levels.

Interestingly, the second category of the follow functionality was the only instance

in the study where the social capital measure of range was supported. Within the second

category of range, study participants were 14 times more likely to use this functionality

for every one point increase in range score. This may again be attributable to the unique

distribution of the follow functionality where those who used the functionality were in the

102

minority but used it extensively, while those in the majority rarely, if ever, used it. As an

additional data point, the third and fourth range scores were in the marginal range of

significance (.058 and .065, respectively).

The overall model did not support H1, nor was it supported at all levels of the

individual predictors. However, H1 was supported by both predictors within certain

categories, and thus the positive relationship between social capital and EKR users

seeking information was partially supported. This partial support may indicate that a

predictor (upper reachability) is missing, or that another OSN functionality associated

with the EKR utilization element of knowledge seeking might be more relevant.

Hypothesis 2

H2 asserted that social capital is positively related to EKR users posting requests

for assistance. A multiple regression was conducted to determine if the two social capital

measures were predictive of the please respond flag functionality. The empirical results

demonstrated that the overall model was significant. This outcome is likely due to a

direct connection between this functionality and the individual’s goal of gaining

additional resources. As Lin (2001a) pointed out, actors within a social network are

motivated to either gain new resources or maintain the ones they already possess, what he

called “purposive social actions.”

Adler and Kwon (2002) made the point, raised earlier in chapter 2, that the first

direct benefit of social capital is information. The authors went on to state that social

capital not only broadens the sources of information, but also improves its quality,

relevance, and timeliness. The EKR software’s please respond flag functionality

perfectly encapsulates a user’s purposive social action to gain additional information and

103

thus may explain why the null hypothesis was rejected. Again, given the relatively weak

R2 value, the reader may infer that additional factors, yet to be identified, were at play.

At the individual level, the social capital measure of extensity was a significant

predictor of the use of the please respond flag functionality. This is likely due to EKR

users with higher extensity scores having more individuals to whom they could address a

request for response. With a broader range of contacts across the organization, the high

extensity EKR users simply had more resources at their disposal to address a wider range

of issues. As indicated previously, the social capital measure of range was not a

significant predictor, which may be rooted in a lack of variability, or impacted from the

problematic upper reachability measure.

Hypothesis 3

H3 asserted that social capital is positively related to EKR users posting new

information. For this hypothesis, two OSN functionalities were available for analysis—

post and FYI flag. A multiple regression was conducted for each to determine if the social

capital measures were predictive. In this instance a mixed result was observed, with the

overall model for the post functionality supporting H3 and the overall model for FYI flag

supporting the null hypothesis.

The overall model rationale for post supporting H3 may be due to the link

between the purposive social action of knowledge contribution and the EKR’s post

functionality. Lin (2001a) asserted that actors within a social network are motivated to

either gain or maintain resources through these purposive actions. In this instance, the

support for the overall model may demonstrate the posting of new information serving as

a means of maintaining the EKR user’s social capital resources.

104

At the individual level, the social capital measure of extensity as a predictor for

post was supported. If Lin’s (2001a) assertion that actors are motivated to maintain

resources available to them within the social network is true, then it is likely that

individuals with higher extensity scores would be apt to use the post functionality more

often than those with lower extensity scores. The biblical adage “to whom much is given,

much is required” appears to be at play here, as users with a broader range of social

contact have the need to maintain more social capital by posting more content. The

measure of range was not supported, as was the case throughout five of the six EKR

functionalities.

Turning to the FYI flag functionality, the multiple regression conducted indicated

the overall model was not significant. This may be due to a weaker connection between

the FYI flag functionality and the social capital outcome of expressive action. Study

participants may perceive the FYI flag as less apt to be noticed compared to the please

respond flag and may opt for the latter. Alternatively, given that the range predictor was

not found to be significant at the individual level, the impact from that one predictor may

have affected the overall model. However, at the individual predictor level, the extensity

measure once again was found to be a significant predictor for the FYI flag.

Hypothesis 4

H4 asserted that social capital is positively related to EKR users responding to

postings by other users. As in H3, two OSN functionalities were available for analysis in

this hypothesis—reply and like. For each functionality, a multiple regression was

conducted to determine if the social capital measures were predictive. In both instances

the overall models were not supported.

105

That the overall model for the EKR functionality of reply was not supported was

surprising given the clear linkage between the functionality and the utilization element of

knowledge contribution (Lin & Huang, 2009). This functionality allows the user to reply

to another user’s comment or query, which adds to the discussion either by providing

new content or at the very least providing validation—an expressive action that serves to

maintain possessed resources (Lin, 2001b).

This failure to support the overall model may be due to the impact of the range

social capital measure on the model. The absence of the upper reachability social capital

measure may also have impacted the overall model. Alternatively, the reply post may not

have as strong a predictive value as the post functionality does within the knowledge

contribution utilization element.

With regards to the like functionality, the failure of the overall model to support

H4 was not entirely surprising. Of the six EKR functionalities selected for this study, the

like functionality has perhaps the most tentative relationship to both the knowledge

contribution utilization element and the expressive action social capital outcome, as

described in Table 1. According to expert informants within the organization, the like

functionality is primarily used as a way of simply providing positive feedback to another

user, not necessarily a vote of validation for the posted content. If study participants used

the functionality as such, there is little to no connection with knowledge contribution,

although it could be argued that it would demonstrate an expressive action designed to

maintain possessed resources. Given the near ubiquitous proliferation of the like

functionality in the most popular EKRs, further study is needed to better understand how

106

the functionality fits within the EKR and social capital literatures, as well as how EKR

users specifically view this functionality and why they choose to utilize it.

An additional factor for consideration is the distribution of the like functionality in

the dataset. The distribution was similar to that of the follow functionality, where a

significant number of users simply did not use this function. Within the like dataset, 21%

of users did not avail themselves of the functionality. The distribution of the dataset was

also similar to the follow functionality in that those participants who used like tended to

use it a great deal. For example, the highest category for like ranged from 91 to 742

instances in the study’s 3-month time period. Given regression’s assumption of linearity,

the failure of the overall model for like may simply be a result of the nonlinearity of the

original distribution.

Analysis of the individual social capital predictors demonstrated support for both

reply and like functionalities by the extensity measure. Again, this may indicate that

individuals with more connections may feel obliged to engage in more knowledge

contribution efforts as a means of maintaining their social capital. The measure of range

continued a consistent pattern of nonsignificance as a predictor for these two

functionalities, as was the case for three of the previous four functionalities.

Given the discussion of consistency, an additional data point for consideration is

the consistency of Beta scores for all five EKR functionalities for which a multiple

regression was conducted. A high degree of consistency was observed across these five

functionalities, with a range of only 0.312 to 0.389. In summary, that extensity was

supported as a predictor for all five functionalities analyzed with multiple regressions,

and that their Beta effects were so consistent, adds validity to this study’s assertion that

107

social capital can be measured within the EKR environment. The following section

addresses recommendations for future inquiries.

Recommendations

The primary purpose of this study was to demonstrate a linkage between social

capital and the digital environs of the EKR through research. Although a significant

corpus of literature exists in both the social capital and the EKR arenas, there is little that

focuses on the relationship between social capital measures and EKR user behaviors.

What little literature exists with regards to social capital and EKRs is bound up in

multitheoretical models that obfuscate the relationship between these two critical

elements, as discussed in chapter 2.

As addressed in the previous section, this study was successful in establishing the

social capital measure of extensity as a significant predictor of the six OSN

functionalities, which were associated with the EKR elements of knowledge seeking and

knowledge contribution as delineated by Lin and Huang (2009). However, a series of

issues were encountered during the data collection and analysis phases of the study that

impacted the remaining social capital measures of upper reachability and range. This

section proffers five specific recommendations for future research with the hope of

facilitating studies that can more effectively explore the linkages in an overall model that

encompasses all three social capital measures and the two EKR elements.

108

View EKRs as Change Management Efforts and Not Simply IT

Projects

An important lesson learned in this study is the criticality of selecting the

appropriate research site and a viable EKR. In terms of a research site, while EKRs are

the single most popular knowledge management solution, the majority underperform, as

indicated in chapter 1. Thus, finding a research site with an EKR deployment that is

actively in use by a significant portion of business unit members was a challenge.

In addition to active use of the EKR, a viable site requires active engagement by

company or business unit managers. This point is critical, as most potential research sites

encountered by the researcher had management that practiced a “field of dreams”

approach, assuming that if they deployed an EKR the users would come. This approach is

emblematic of misunderstanding the task at hand; EKR deployments are at their core not

an IT project, but rather a change management initiative. It is all but an axiom among

change management theorists that the active sponsorship and participation of

management is necessary for success. Managers who practice benign neglect of their

EKRs and are absent from the interactions therein cannot expect others to embrace what

they themselves have not. In the case of X Corp, management was deeply involved with

and active in the EKR, with the unit’s vice president and several managers ranking near

the top in terms of EKR functionality usage.

A further amplification of EKR deployments as change management efforts was

this study’s requirement that EKRs be fully embedded into business processes. The EKR

deployed in the X Corp Advanced Analytics business unit was selected for this study

because it was central to the business processes within the unit. As mentioned in chapter

109

3, X Corp utilized an EKR transformation scale where level of adoption was measured.

The Advanced Analytics unit demonstrated the highest level of adoption of the EKR,

where e-mail was no longer used for internal communication. The OSN EKR therefore

became not only integrated in business processes but central to those processes. This

constant interaction within the EKR became a normative social influence that fomented

the development of social capital and facilitated Lin’s (2001b) instrumental and

expressive outcomes.

Lastly, the X Corp research site was selected because the OSN instantiation in use

by the Advanced Analytics business unit captured detailed metadata on the various OSN

functionalities examined in this study. None of the other EKRs considered for this study

captured metadata on the various EKR functionalities at a level of granularity necessary

for this measurement study. Therefore, it is strongly recommended that managers and IT

personnel seeking to deploy EKRs consider how they can capture detailed metadata

regarding EKR functionality usage and how that data can be useful in terms of

gamification and return on investment (ROI) measures.

Utilize the Full 22-Occupation Position Generator Instrument

Based on feedback from the author of the position generator instrument (N. Lin,

personal communication, November 19, 2014), the 10-occupation short form of the

survey was adopted for this study. Lin’s rationale for advising use of the short form of the

instrument was based on his correlational analysis. This analysis revealed there were no

significant differences in terms of validity between the two versions of the position

generator instrument.

110

The decision to adopt the short form of the survey was based on the researcher’s

belief that a shorter survey would increase the response rate and minimize survey

abandonment. An additional benefit to using the short form was Lin’s analysis indicating

that the occupations in the short form were more highly correlated to each other than

those in the 22-occupation long form. The only potential drawback identified by Lin to

the use of the short form was a small decrease in variance for the extensity and range

scores as well as a small increase in the upper reachability score, which at the time did

not appear to be a major issue.

Given the issues encountered during data analysis with the lack of variability in

the upper reachability social capital measure, the use of the full 22-position generator

version of the survey is recommended. Transitioning to the long form position generator

instrument also has the potential value of adding greater variability to the range social

capital measure. The long form of the instrument includes positions with higher SIOPS

scores, as well as positions with lower SIOPS scores than those in the short form. This

added potential variability for the range social capital measure may result in a more linear

distribution, thus aiding regressional analysis.

Modify the Instrument

In keeping with the theme of increasing variability in both the upper reachability

and range social capital measures, three modifications to the instrument are

recommended. As addressed in the social capital measures section of this chapter, two

sets of occupations used in the short form of the position generator instrument had the

same values. Each of the occupations listed in the short form of the survey had a value

assigned that was derived from Treiman’s (1977) SIOPS.

111

The first suggested modification to the instrument is ensuring that no two

occupations have the same SIOPS value. Analysis of the raw data from the survey

responses revealed that many study participants either indicated knowing both positions

with SIOPS values at the lower prestige range (farmer and receptionist) or both positions

at the mid prestige range (middle school teacher and personnel manager). This

duplication of scores resulted in a lost opportunity for increased variability within the

upper reachability and range measures.

The second recommended modification is geared towards researchers who wish to

use the position generator instrument with IT units or organizations. The short form of

the survey used for this study included the computer programmer occupation—which is

nonproblematic in a general population, but can negatively impact variability across all

three social capital measures within an IT element. As highlighted previously, 97% of

study participants at X Corp indicated they knew a computer programmer—a figure so

high as to make the occupation useless as a discriminator. Applied more broadly,

researchers should consider which occupations used in the instrument might be

overrepresented within the study population and then substitute another occupation from

the SIOPS list.

The last suggested modification involves adjusting the wording for each

occupation question in the survey to highlight the requirement that the participant must

currently know a person in this occupation. This modification is unnecessary when the

position generator instrument is traditionally administered in person, as the individual

administering the instrument can add emphasis with both speech and demeanor in a way

that is less apt to be forgotten. By contrast, the qualifier may now have is easily forgotten

112

when it is part of the instrument’s long introductory text. It is not difficult to imagine that

the qualifier in this sentence would be overlooked when read or skimmed by a participant

using an Internet survey.

Specifically, each top-level survey question should include the qualifier currently

in the question, such as “Is there anyone you currently know who is a NURSE?” This

modification may minimize overreporting of contacts, which could improve the accuracy

of all three social capital measures. Without this emphasis on currency within the Internet

version of the instrument, it is overly reliant on the study participant’s ability to recall a

single word in the survey’s introductory text. As mentioned previously, it is

recommended that the 22-position long form version of the instrument be used, along

with these modifications, to maximize data variability.

Adopt a Social Physics Approach

For the practitioner, this study presents a roadmap for understanding the

relationship between social capital and EKR usage. While this study exceeded the

minimum sample size of 99 participants, the issues with lack of variability among two of

the three social capital measures and one of the six OSN functionalities could be

improved with an expansion of the sample size as well as duration of observation.

Pentland’s (2014) landmark work on social physics has demonstrated the value of a

greatly expanded sample size and duration/intensity of observation. Dubbed reality

mining, this approach to logging massive amounts of social interaction data is now

feasible, especially in an EKR environment.

With such vast amounts of interaction data now available through a multitude of

data collection platforms such as sociometric badges, smart phone applications, and even

113

credit card readers, significant insights and predictive methodologies have been

developed through the social physics approach. According to Pentland (2014),

Social physics is a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other. Social physics helps us understand how ideas flow from person to person through the mechanism of social learning and how this flow of ideas ends up shaping the norms, productivity, and creative output of our companies, cities and societies. (p. 4)

Although much of what is addressed in social physics is outside the bounds of this study,

the methods employed can easily be adopted in the EKR environment by researcher and

practitioner alike.

Indeed, there is significant value in exploring social physics’ methodology for

upward scalability, which, based on the author’s research, enhances predictive capability.

Pentland (2014) stated, “Almost uniquely among the social sciences, this new social

physics framework provides quantitative results at scales ranging from small groups, to

companies, to cities, and even to entire societies” (p. 7). Additional research into

methodologies for scaling upwards and integrating data from multiple collectors (or EKR

functionalities) would add materially to the EKR literature.

A final element of social physics worthy of future study is the theory’s

conceptualization of idea flow. The assertion that idea flow within social networks can be

separated into the exploration and engagement processes is described as one of the two

most important concepts within social physics (Pentland, 2014). It appears the social

physics concept of idea flow is closely related to the social capital outcomes of

instrumental and expressive actions as articulated by Lin (2001b). The idea flow concept

likewise appears to be related to Lin and Huang’s (2009) EKR utilization elements of

knowledge seeking and knowledge contribution. A detailed comparative literature review

114

could create a bridge between the three theoretical perspectives, as has been done in this

study for EKR utilization elements and social capital outcomes.

Assess the Potential for Bidirectionality

Another area worthy of future research is that of determining whether the

relationship between the social capital measures and the EKR functionalities is

bidirectional. This study has demonstrated that the social measure of extensity is

consistently a moderately significant predictor for each of the six functionalities studied.

It can be inferred that there is a likelihood that EKR functionalities can serve as a

predictor of social capital measures, but further research is required to explore this issue.

Expand to Additional Functionalities

The last recommendation involves expanding the number and types of EKRs

studied. The results of this study are limited in applicability, in that they are based on

functionalities of a single EKR—the OSN (recently renamed the Social Network Cloud).

According to Fulk et al. (2004), EKRs include expert databases, intranets, shared

whiteboards, groupware, and lessons learned databases—a wide variety of platforms with

many different functionalities.

This study offers a means by which EKR functionalities can be assessed and

categorized within the four articulated EKR behaviors in Table 1. The alignment between

EKR utilization elements as delineated by Lin and Huang (2009) and Lin’s (2001b)

social capital outcomes is an important discriminator and a means of increasing the

likelihood that EKR functionalities under study have a potential relationship with social

capital. It is recommended that, prior to data collection, the proposed EKR functionalities

115

be assessed for placement within one of the four EKR behavior categories. In the event

that a functionality does not fit within the Table 1 rubric, the researcher is alerted to the

likelihood that the functionality falls outside of the EKR and social capital literature and

is not likely to be a significant criterion for social capital predictors.

Given the wide array of digital knowledge sharing capabilities, the six OSN

functionalities assessed in this study are a fraction of those currently in use. In order to

expand the applicability to a wider range of EKRs, further cataloging of EKR

functionalities is required, with an eye toward expanding the EKR literature.

Additionally, these functionalities should be assessed for their potential relation to social

capital outcomes.

For the practitioner, the insights gained from this study are of value for

gamification and analytics efforts. Based on the experience with the X Corp research site,

not all functionalities available for study had usage metadata captured by the server logs.

For example, the OSN search functionality, which is assessed as highly relevant to both

the knowledge-seeking EKR utilization element and the instrumental action social capital

outcome, could not be studied due to a lack of usage metadata in the X Corp server logs.

If usage data cannot be captured for all of an EKR’s functionalities, then the

rubric established in Table 1 can serve as a means of prioritizing which functionalities

should be captured. Additionally, practitioners should ensure that gamification efforts

include measures for both social capital outcomes, not simply instrumental action, which

tends to be the focus of commercial social influence ranking firms such as Klout,

PeerIndex, and Empire Avenue. A bifurcated measure such as the one used by Kred is

116

more closely aligned to the social capital literature and thus a recommended approach for

gamification and EKR utilization.

Conclusion

U.S. corporations continue to suffer from poor ROI for EKR deployments while

paradoxically hiring ever greater numbers of knowledge workers, for whom searching for

information comprises upwards of 20% of their workweek (The Economist, 2010). A lack

of understanding of the role that social networks and social capital plays within the EKR

environment has contributed to this underperformance. Although some studies have

attempted to address this issue, they have done so from a multitheoretical perspective that

tends to obfuscate the role of social capital in the EKR environment, providing no

roadmap for measuring or predicting effects on EKR functionalities.

The goal of this study was to enhance the understanding of the relationship

between social capital measures and EKR utilization. To that end, a rubric for

characterizing an EKR’s specific functionalities within the elements of both the EKR

utilization elements and social capital outcomes was developed (Table 1). From this

rubric grew a model of social capital in EKR utilization (Figure 2).

Two overarching research questions as well as four hypotheses were developed

and tested. The results of this quantitative study demonstrated a statistically significant

relationship between the social capital measure of extensity and all EKR functionalities.

Partial support was demonstrated for the social capital measure of range with some of the

functionalities, an indicator of a relationship that was obscured by issues with lack of

variability of the short form position generator instrument.

117

Notwithstanding the difficulties encountered with lack of variability in the

instrument, the study unequivocally demonstrated a relationship between social capital

and the EKR behaviors of knowledge seeking and knowledge contribution. This level of

specificity adds to the social capital literature and serves as a point of departure for the

measurement of social capital in digital environments and ultimately predictive

methodologies. For the practitioner, these findings demonstrate a need for corporate

leaders to consider the role of social capital when deploying EKRs or any knowledge

management solution.

The problem statement for this study identified that most U.S. corporate EKRs are

underutilized, which results in poor ROI in spite of the billions of dollars spent annually.

Given that the results of this study indicate a significant relationship between social

capital and EKR utilization, corporate leaders and IT professionals should consider and

plan for the role social capital plays in EKRs. The field of dreams approach to EKR

implementation has for decades led to underperformance—betraying the natural result of

mischaracterizing these implementations as purely IT processes.

EKRs are by definition tools of organizational change and transformation, which

in turn are tightly bound to social networks and social capital. Corporate leadership and

IT managers must tap into the social element of EKRs and effectively manage these

transformational technologies by inculcating EKRs into the organization’s business

processes. This includes providing incentives for their use and creating disincentives for

failing to adopt the technology. Equally important is the need to lead by example, with

leaders and managers adopting and driving use of EKRs by investing their own social

capital in the process. Based on the findings of this study, ROI for EKRs must include not

118

only an assessment of funding and human capital investments, but also a consideration of

the social capital expended. Only by understanding and planning for the role of social

capital in EKRs can these knowledge management efforts truly perform as intended.

119

REFERENCES

Adler, P. S., & Kwon, S. (2002). Social capital: Prospects for a new concept. Academy of

Management Review, 27(1), 17–40.

Akhavan, P., Jafari, M., & Fathian, M. (2005). Exploring the failure factors of

implementing knowledge management system in the organizations. Journal of

Knowledge Management Practice, 6. Retrieved from

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2188273

Allee, V. (2000). Knowledge networks and communities of practice. OD Practitioner,

32(4), 4–13.

Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ:

Prentice Hall.

Bhalchandra, P. U., Deshmukh, N. K., Khamitkar, S. D., Lokhandle, S. N., Phulari, S. S.,

& Shinde, A. R. (2010). Understanding formulation of social capital in online

social network sites (SNS). International Journal of Computer Sciences, 7(1), 92–

96.

Blau, P. M. (1963). The dynamics of bureaucracy: A study of interpersonal relations in

two government agencies (2nd ed.). Chicago, IL: University of Chicago Press.

Bock, G., Kankanhalli, A., & Sharma, S. (2006). Are norms enough?: The role of

collaborative norms in promoting organizational knowledge seeking. European

Journal of Information Systems, 15, 357–367.

Bourdieu, P. (1983). Ökonomischeskapital, Kulturelleskapital, Sozialskapital [Economic

capital, cultural capital, social capital]. In R. Kreckel (Ed.), Sozialeungleichheiten

(pp. 183–198). Göttingen, Germany: Schwartz.

120

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory

and research for the sociology of education (pp. 241–258). Westport, CT:

Greenwood Press.

Bourdieu, P., & Passeron, J. (1977). Reproduction in education, society, culture. Beverly

Hills, CA: Sage.

Bourdieu, P., & Wacquant, L. (1992). An invitation to reflexive sociology. Chicago, IL:

University of Chicago Press.

Burt, R. S. (1982). Toward a structural theory of action: Network models of social

structure, perception, and action. New York, NY: Academic Press.

Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA:

Harvard University Press.

Burt, R. S. (2000). The network structure of social capital. In R. I. Sutton & B. M. Staw

(Eds.), Research in organizational behavior (pp. 345–423). Greenwich, CT: JAI

Press.

Burt, R. S. (2002). The social capital of structural holes. In M. F. Guillen, R. Collins, P.

England, & M. Meyer (Eds.), The new economic sociology (pp. 148–192). New

York, NY: Russell Sage Foundation.

Burt, R. S. (2004). Structural holes and good ideas. American Journal of Sociology,

110(2), 349–399.

Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. New York,

NY: Oxford University Press.

121

Chiu, C. M., Hsu, M. H., & Wang, E. T. G. (2006). Understanding knowledge sharing in

virtual communities: An integration of social capital and social cognitive theories.

Decision Support Systems, 42(3), 1872–1888.

Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal

of Sociology, 94, S95–S121.

Coleman, J. S. (1995). Grundlagen der Sozialtheorie [Foundations of social theory].

Munich, Germany: Oldenbourg.

Cook, C., Heath, F., & Thompson, R. L. (2000). A meta-analysis of response rates in

web- or Internet-based surveys. Educational and Psychological Measurement, 60,

821–836.

Crawford, S., Couper, M. P., & Lamias, M. J. (2001). Web surveys: Perceptions of

burden. Social Science Computer Review, 19, 146–162.

Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods

approaches. Thousand Oaks, CA: Sage.

Cross, R., & Prusak, L. (2002). The people who make organizations go—or stop.

Harvard Business Review, 80(6), 104–112.

de Leeuw, E. D., Hox, J. J., & Dillman, D. A. (2008). International handbook of survey

methodology. New York, NY: Laurence Erlbaum Associates.

Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, September). From game design

elements to gamefulness: Defining gamification. In Proceedings of the 15th

International Academic MindTrek Conference: Envisioning future media

environments (pp. 9–15). New York, NY: ACM.

122

Detweiler, K. R. (2012). Exploring discursive interaction, habitus, and dynamic co-

emergence among an agency leader and congressional oversight committee: A

case study of languaging in practice (Doctoral dissertation, The George

Washington University). Available from ProQuest Dissertations and Theses

database. (UMI No. 30503060)

Economist. (2010, January 30). Yammering away at the office. The Economist. Retrieved

from http://www.economist.com/node/15350928

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses

using G*Power 3.1: Tests for correlation and regression analyses. Behavior

Research Methods, 41, 1149–1160.

Fernandez, R. M., Castilla, E. J., & Moore, P. (2000). Social capital at work: Networks

and employment at a phone center. American Journal of Sociology, 105(5), 1288–

1356.

Fortune Magazine. (2013). Fortune 500: The complete list. Retrieved from

http://money.cnn.com/magazines/fortune/fortune500/

Fulk, J., Heino, R., Flanagin, A. J., Monge, P. R., & Bar, F. (2004). A test of the

individual action model for organizational information commons. Organizational

Science, 15(5), 569–585.

Goodhue, D. L. (1995). Understanding user evaluations of information systems.

Management Science, 41(12), 1827–1844.

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual

performance. MIS Quarterly, 19(2), 213–236.

123

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology,

78(6), 1360–1380.

Granovetter, M. S. (1985). Economic action and social structure: The problem of

embeddedness. American Journal of Sociology, 91(3), 481–510.

Green, S. B., & Salkind, N. J. (2008). Using SPSS for Windows and Macintosh:

Analyzing and understanding data. Upper Saddle River, NJ: Pearson Education.

Hanifan, L. J. (1916). The rural school community center. Annals of the American

Academy of Political and Social Science, 67, 130–138.

Harvard Management Update. (1999, February). Do we know how to do that?

Understanding knowledge management. Harvard Management Update, 4(2), 1–5.

Häuberer, J. (2011). Social capital theory: Towards a methodological foundation.

Weisbaden, Germany: VS Research.

Hendriks, P. (1999). Why share knowledge? The influence of ICT on the motivation for

knowledge sharing. Knowledge and Process Management, 6(2), 91–100.

Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioral

sciences. New York, NY: Houghton Mifflin.

Homans, G. (1958). Social behavior as exchange. American Journal of Sociology, 63(6),

597–606.

Kankanhalli, A., Tan, B., & Wei, K. (2005). Contributing knowledge to electronic

knowledge repositories: An empirical investigation. MIS Quarterly, 29(1), 113–

143.

124

Kavanaugh, A., & Patterson, S. J. (2001). The impact of community computer networks

on social capital and community involvement. American Behavioral Scientist, 45,

496–509.

Kilduff, M., & Tsai, W. (2003). Social networks and organizations. Los Angeles, CA:

Sage.

Knoke, D., & Yang, S. (2008). Social network analysis (2nd ed.). Los Angeles, CA:

Sage.

Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the

replication of technology. Organizational Science, 3(3), 383–397.

Kolbasuk McGee, M., & Soat, J. (2008, July 28). Stare down the bear: IT spending’s

getting squeezed, our latest research finds. CIOs are using these strategies to keep

their business charging ahead. Information Week, 33–34.

KPMG. (2000). Knowledge management research report. Amsterdam, the Netherlands:

Author. Retrieved from http://www.providersedge.com/docs/km_articles/

kpmg_km_research_report_2000.pdf

Lampe, C., Ellison, N. B., & Steinfield, C. (2007). The benefits of Facebook “friends”:

Social capital and college students’ use on online social network sites. Journal of

Computer-Mediated Communication, 12, 1143–1168.

Laumann, E. O. (1966). Prestige and association in urban community. Indianapolis, IN:

Bobbs-Merrill.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation.

New York, NY: Cambridge University Press.

125

Li, Y., Savage, M., & Warde, A. (2008). Social mobility and social capital in

contemporary Britain. British Journal of Sociology, 59(3), 391–411.

Lin, N. (1982). Social resources and instrumental action. In P. Marsden & N. Lin (Eds.),

Social structure and network analysis (pp. 131–145). Beverly Hills, CA: Sage.

Lin, N. (1999a). Building a network theory of social capital. Connections, 22(1), 28–51.

Lin, N. (1999b). Social networks and status attainment. Annual Review of Sociology, 25,

467–487.

Lin, N. (2001a). Social capital: A theory of social structure and action. New York, NY:

Cambridge University Press.

Lin, N. (2001b). Building a network theory of social capital. In N. Lin, K. Cook, & R. S.

Burt (Eds.), Social capital: Theory and research (pp. 3–29). New Brunswick, NJ:

Transaction.

Lin, N., & Ao, D. (2008). The invisible hand of social capital: An exploratory study. In

N. Lin & B. H. Erickson (Eds.), Social capital: An international research

program (pp. 107-132). New York, NY: Oxford University Press.

Lin, N., & Dumin, M. (1986). Access to occupations through social ties. Social Networks,

8, 365–385.

Lin, N., & Erickson, B. H. (2008). Theory, measurement, and the research enterprise on

social capital. In N. Lin & B. H. Erickson (Eds.), Social capital: An international

research program (pp. 1-26). New York, NY: Oxford University Press.

Lin, N., Fu, Y., & Chen, C. J. (Eds.). (2013). Social capital and its institutional

contingency: A study of the United States, China, and Taiwan. London, UK:

Routledge.

126

Lin, N., Fu, Y., & Hsung, R. (2001). The position generator: Measurement techniques for

investigations of social capital. In N. Lin, K. Cook, & R. S. Burt (Eds.), Social

capital: Theory and research (pp. 3–29). New Brunswick, NJ: Transaction.

Lin, T., & Huang, C. (2009). Understanding the determinants of EKR usage from social,

technological, and personal perspectives. Journal of Information Sciences, 35(2),

165–179.

Marcolin, B. L., Compeau, D. R., Munro, M. C., & Huff, S. L. (2000). Assessing user

competence: Conceptualization and measurement. Information Systems Research,

11(1), 37–60.

Marx, K. (1933). Wage-labour and capital. New York, NY: International Publishers.

McDonald, S., Lin, N., & Ao, D. (2009). Networks of opportunity: Gender, race, and job

leads. Social Problems, 56(3), 385–402.

McDonald, T., & Siegall, M. (1992). The effects of technological self-efficacy and job

focus on job performance, attitudes, and withdrawal behaviors. Journal of

Psychology, 126, 465–475.

Miller, N. E., & Dollard, J. (1941). Social learning and imitation. New Haven, CT: Yale

University Press.

Moerbeek, H., & Flap, H. (2008). Social resources and their effect on occupational

attainment through the life course. In N. Lin & B. H. Erickson (Eds.), Social

capital: An international research program. New York, NY: Oxford University

Press.

Murphy, J., & Hackbush, J. (2007). The knowledge management spending report 2007–

2008. Boston, MA: AMR Research.

127

Nathanson, J. (2014). How Klout finally matters. Slate. Retrieved from

http://www.slate.com/articles/business/the_bet/2014/05/klout_is_basically_dead_

but_it_finally_matters.html

Newell, S., Tansley, C., & Huang, J. (2004). Social capital and knowledge integration in

an ERP project team: The importance of bridging and bonding. British Journal of

Management, 15, S43–S57.

O’Neill, B. (2004). Collecting research data online: Implications for extension

professionals. Journal of Extension, 42(3). Retrieved from

http://www.joe.org/joe/2004june/tt1.php

Oracle Corporation. (2013). Oracle white paper on the Oracle social network [White

paper]. Retrieved from http://www.oracle.com/technetwork/middleware/

webcenter/socialnetwork/overview/wp-oracle-social-network-2011-513258.pdf

Oxford University Press. (2014). Oxford dictionary: Wiki. Retrieved from

http://www.oxforddictionaries.com/definition/english/wiki

Pentland, A. (2014). Social physics: How good ideas spread—the lessons from a new

science. New York, NY: Penguin.

Polanyi, M. (1962). Personal knowledge: Towards a post-critical philosophy. Chicago,

IL: University of Chicago Press.

Putnam, R. D. (1993). Making democracy work—civic traditions in modern Italy.

Princeton, NJ: Princeton University Press.

Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community.

New York, NY: Simon & Schuster.

128

Rainer, R. K., & Harrison, A. W. (1993). Toward development of the end user computing

construct in a university setting. Decision Science, 24(6), 1187–1199.

Robert, L. P., Dennis, A. R., & Ahuja, M. K. (2008). Social capital and knowledge

integration in digitally enabled teams. Information Systems Research, 19(3), 314–

334.

Rodan, S., & Galunic, C. (2004). More than network structure: How knowledge

heterogeneity influences managerial performance and innovativeness. Strategic

Management Journal, 25, 541–562.

Schultz, T. W. (1961). Investment in human capital. American Economic Review, 1, 1–

17.

Smith, A. (1937). An inquiry into the nature and causes of the wealth of nations. New

York, NY: Modern Library.

Smith, S. S. (2005). “Don’t put my name on it”: Social capital activation and job-finding

assistance among the black urban poor. American Journal of Sociology, 111(1),

1–57.

Sobel, J. (2002). Can we trust social capital? Journal of Economic Literature, 40, 139–

154.

Stevenson, S. (2012). What your Klout score really means. Wired. Retrieved from

http://www.wired.com/2012/04/ff_klout

Suseno, Y., & Ratten, V. (2007). A theoretical framework of alliance performance: The

role of trust, social capital and knowledge development. Journal of Management

and Organization, 13(1), 4–23.

129

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston,

MA: Pearson.

Treiman, D. J. (1977). Occupational prestige in comparative perspective. New York,

NY: Academic Press.

Valenzuela, S., Park, N., & Kee, K. F. (2009). Is there social capital in a social

networking site? Facebook use and college students’ life satisfaction, trust, and

participation. Journal of Computer-Mediated Communication, 14(4), 875–901.

Verhaeghe, P. P., Pattyn, E., Bracke, M., Verhaeghe, M., & Van de Putte, B. (2012). The

association between network social capital and self-rated health: Pouring old wine

in new bottles? Health and Place, 18(3), 358–365.

Verhaeghe, P. P., Van de Putte, B., & Roose, H. (2012). Reliability of position generator

measures across different occupational lists: A parallel test experiment. Field

Methods, 25(3), 238–261.

Wah, C. Y., Menkhoff, T., Loh, B., & Evers, H. (2007). Social capital and knowledge

sharing in knowledge-based organizations: An empirical study. International

Journal of Knowledge Management, 3(1), 29–48.

Wasko, M. M., & Faraj, S. (2005). Why should I care? Examining social capital and

knowledge contribution in electronic networks of practice. MIS Quarterly, 29(1),

35–57.

Wikipedia. (2013). Wikipedia statistics. Retrieved from http://en.wikipedia.org/wiki/

Special:Statistics

Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2010). Business research

methods. Stamford, CT: Cengage Learning.

130

APPENDIX A:

INFORMED CONSENT FORM AND SURVEY

SCREEN ONE Research Consent Form Knowledge Interaction: Social Capital and Electronic Knowledge Repository Utilization GW IRB number: 121412 Principal Investigator: Dr. Diana Burley - Telephone number: (571) 553-3761 Sub-Investigator: Eliot Jardines - Telephone number: (571) 439-6367 Introduction Welcome! You are invited to participate in a research study conducted by Eliot Jardines, the Sub-Investigator, under the direction of Dr. Diana Burley of the Department of Human and Organizational Learning, Graduate School of Education, The George Washington University (GWU). Taking part in this research is entirely voluntary. You will not be affected should you choose not to participate or if you decide to withdraw from the study at any time. Why is this study being done? This study examines how the use of an electronic knowledge repository such as OSN [the X Corp intranet EKR] relates to a user’s social capital. The term “social capital” refers to the capital (money, resources, or information) owned not by you, but rather your social network (your group of friends, acquaintances, or colleagues). You are being asked to participate in this study because you are a user of X Corp’s OSN electronic knowledge repository. What is involved in this study? If you choose to take part in this study, you will be asked to complete this online survey that will take approximately 7 minutes to complete. The survey consists of a list of 10 occupations where you will be asked to indicate whether you know someone who has this position and how well you know them (but we won’t ask you to name them). This survey is called a position generator [term is hyperlinked to Wikipedia page] and it measures your level of social capital. The second part of this research study involves the analysis of OSN to determine how often participants (like you) “like,” “follow,” “flag,” contribute or respond to postings on OSN. This DOES NOT include collecting information on WHAT is posted but rather the number of times you use the “like,” “follow,” “flag” functionality or contribute or respond to postings on OSN. The results of your social capital survey will be compared to the number of times you use the like, follow, or flag functionality as well as contribute or

131

respond to OSN postings to determine if there is a relationship between social capital and using OSN. What are the risks of participating in this study? Participating in this study poses minimal risks. After reading this informed consent form, you will be asked to acknowledge that you understand and agree to participate before beginning the survey. In addition, you may withdraw at any time from the study and refuse to answer any question(s). Any information that can be used to identify the participants of this study will remain confidential and will only be disclosed with your permission or as required by law. Are there benefits to taking part in this study? Although you will not benefit directly from your participation in the study, it is hoped that the insights from this research study will be used to improve OSN or other electronic knowledge repositories. Additionally, the researcher will provide you with the results of your social capital survey along with an explanation of the results. What are my options? You do not have to participate in this study if you do not want to. You may choose to discontinue participation in this study at any time without penalty or loss of benefits for which you are entitled. Should you decide to participate and later change your mind, you can do so at any time—up until the study is published. In addition, you may refuse to answer any question(s). Will I receive payment for being in this study? You will not be paid for taking part in this study. However, if you participate, you will be entered into a drawing where a randomly selected participant will win a new Apple iPad Mini with two additional drawings for $100 gift cards to Amazon.com. Can I be taken off the study? Yes, you can be taken off this study at any time up until the study is published. How will my privacy be protected? The results of this research study will be included in the researcher’s approved dissertation and may be reported in journals or at scientific meetings. The people who participated in this study will not be named or identified. The George Washington University will not release any information about your research involvement without your written permission, unless required by law. In terms of confidentiality, any information in this study that can be identified with participants will remain confidential and will be disclosed only with the participants’ permission or as required by law. Only Eliot Jardines and Dr. Diana Burley will have access to the data, which will be stored in an electronic file secured in the personal office of Eliot Jardines. Individual survey results will be kept for a maximum of 3 years and destroyed immediately thereafter.

132

Problems or Questions The Office of Human Research of George Washington University, at telephone number (202) 994-2715, can provide further information about your rights as a research participant. Further information regarding this study may be obtained by contacting Eliot Jardines, the sub-investigator in this study, at telephone number (571) 439-6367 or via e-mail at [email protected]. You may print out this summary page in case you want to read it again. If you agree to participate in this study, please read the following statement and click the agree button below:

• I understand the information written above. I will not receive any compensation for this now or at any time in the future. I further certify that I am over the age of 18 years. Clicking the agree button below indicates my willingness to participate in this study as described above and my understanding that I can withdraw at any time.

[Agree] [Leave This Site]

SCREEN TWO First, your name and e-mail address are needed in case we have a question or you win the drawing! Please provide your first and last name. This information is needed in order to be able to link this survey with information about your activity on OSN. The researcher will not be looking at the content of your postings, only reviewing the number of times you search, contribute, or respond to postings by others. If you do not feel comfortable providing your name or e-mail address, you can opt out of the survey now. Your First Name Your Last Name

Please provide a personal e-mail address we can use to contact you. (Don’t worry. We won’t contact you unless we have a question or you win our random prize drawing!) To protect the confidentiality of your survey responses, please do not use your work e-mail address. Your Personal E-mail Address (please do not use your work e-mail)

[Submit Button] [Clear Button]

133

SCREEN THREE Next, I am going to ask some general questions about jobs some people you know may now have. These people include your relatives, friends, and acquaintances. (Acquaintances are people who know each other by face and name.) If there are several people you know who have that kind of job, please tell me the one that occurs to you first. 1. Is there anyone you know who is a NURSE? (1) Yes (0) No [If no, skip to the next position.] 1.1. If yes, what is his/her relationship to you? [Choose only the relationship you consider the most important.] (1) Spouse (current or previous) (18) Co-worker, boss/superior, or (2) Parents subordinate from previous firm (3) Father-in-law/mother-in-law (19) Client (4) Children (20) Person working for another firm, but (5) Sibling known through work relations (6) Daughter-in-law (21) Someone from the same religious group (7) Son-in-law (22) Someone from the same association, (8) Other relatives club or group (9) Old neighbor (23) Close friend (10) Current neighbor (24) Ordinary friend (11) School/classmate (25) Someone known because he/she (12) Compatriot provides a service to me or my family (13) Teacher (26) Someone known from the Internet (14) Student (27) An acquaintance (15) Current co-worker (28) Indirect relationship (known via

(16) Current boss/supervisor someone else) (17) Current subordinate

1.2. Did you get to know him/her through your spouse or partner? [For those who are single or do not have partners, skip to next question.] (1) Yes (0) No 1.3. Is the NURSE male or female? (1) Male (0) Female 1.4. How long have you known each other? ______ Years 1.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

134

1.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 2. Is there anyone you know who is a FARMER? (1) Yes (0) No [If no, skip to the next position.] 2.1. If yes, what is his/her relationship to you? [Choose only the relationship you

consider the most important.] (1) Spouse (current or previous) (18) Co-worker, boss/superior, or (2) Parents subordinate from previous firm (3) Father-in-law/mother-in-law (19) Client (4) Children (20) Person working for another firm, but (5) Sibling known through work relations (6) Daughter-in-law (21) Someone from the same religious group (7) Son-in-law (22) Someone from the same association, (8) Other relatives club or group (9) Old neighbor (23) Close friend (10) Current neighbor (24) Ordinary friend (11) School/classmate (25) Someone known because he/she (12) Compatriot provides a service to me or my family (13) Teacher (26) Someone known from the Internet (14) Student (27) An acquaintance (15) Current co-worker (28) Indirect relationship (known via


2.2. Did you get to know him/her through your spouse or partner? [For those who are

single or do not have partners, skip to next question.] (1) Yes (0) No 2.3. Is the FARMER male or female? (1) Male (0) Female 2.4. How long have you known each other? ______ Years 2.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

135

2.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 3. Is there anyone you know who is a LAWYER? (1) Yes (0) No [If no, skip to the next position.] 3.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 3.3. Is the LAWYER male or female? (1) Male (0) Female 3.4. How long have you known each other? ______ Years 3.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

136

3.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 4. Is there anyone you know who is a MIDDLE SCHOOL TEACHER? (1) Yes (0) No [If no, skip to the next position.] 4.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 4.3. Is the MIDDLE SCHOOL TEACHER male or female? (1) Male (0) Female 4.4. How long have you known each other? ______ Years 4.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

137

4.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 5. Is there anyone you know who is a PERSONNEL MANAGER? (1) Yes (0) No [If no, skip to the next position.] 5.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 5.3. Is the PERSONNEL MANAGER male or female? (1) Male (0) Female 5.4. How long have you known each other? ______ Years 5.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

138

5.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 6. Is there anyone you know who is a HAIR DRESSER? (1) Yes (0) No [If no, skip to the next position.] 6.1. If yes, what is his/her relationship to you? [Choose only the relationship you

consider the most important] (1) Spouse (current or previous) (18) Co-worker, boss/superior, or (2) Parents subordinate from previous firm (3) Father-in-law/mother-in-law (19) Client (4) Children (20) Person working for another firm, but (5) Sibling known through work relations (6) Daughter-in-law (21) Someone from the same religious group (7) Son-in-law (22) Someone from the same association, (8) Other relatives club or group (9) Old neighbor (23) Close friend (10) Current neighbor (24) Ordinary friend (11) School/classmate (25) Someone known because he/she (12) Compatriot provides a service to me or my family (13) Teacher (26) Someone known from the Internet (14) Student (27) An acquaintance (15) Current co-worker (28) Indirect relationship (known via



single or do not have partners, skip to next question.] (1) Yes (0) No 6.3. Is the HAIR DRESSER male or female? (1) Male (0) Female 6.4. How long have you known each other? ______ Years 6.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

139

6.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 7. Is there anyone you know who is a COMPUTER PROGRAMMER? (1) Yes (0) No [If no, skip to the next position.] 7.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 7.3. Is the COMPUTER PROGRAMMER male or female? (1) Male (0) Female 7.4. How long have you known each other? ______ Years 7.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

140

7.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 8. Is there anyone you know who is a RECEPTIONIST? (1) Yes (0) No [If no, skip to the next position.] 8.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 8.3. Is the RECEPTIONIST male or female? (1) Male (0) Female 8.4. How long have you known each other? ______ Years 8.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

141

8.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 9. Is there anyone you know who is a PROFESSOR? (1) Yes (0) No [If no, skip to the next position.] 9.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question.] (1) Yes (0) No 9.3. Is the PROFESSOR male or female? (1) Male (0) Female 9.4. How long have you known each other? ______ Years 9.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

142

9.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other 10. Is there anyone you know who is a POLICEMAN? (1) Yes (0) No [If no, skip to the next position.] 10.1. If yes, what is his/her relationship to you? [Choose only the relationship you




single or do not have partners, skip to next question] (1) Yes (0) No 10.3. Is the POLICEMAN male or female? (1) Male (0) Female 10.4. How long have you known each other? ______ Years 10.5. How close are you to him/her? (1) Very close (2) Close (3) So, so (4) Not close (5) Not close at all

143

10.6. What is the racial/ethnic background of this person? (1) White (non-Latino) (4) Asian (2) African American (5) Native American (3) Latino (6) Other

[Complete Survey Button] [Clear Button]

144

APPENDIX B:

STUDY DATA

Social Capital and Oracle Social Network Electronic Knowledge Repository 3-Month Results

Number Extensity Upper reach Range* Follow*

PR flag* Post* Reply*

FYI flag* Like*

1 7 78 5 2 4 5 5 4 5 2 5 78 5 1 2 2 2 2 1 3 9 78 4 3 3 3 3 5 3 4 4 78 2 1 1 2 1 1 1 5 4 78 2 3 3 5 4 5 5 6 5 78 5 1 2 1 2 2 2 7 8 78 5 2 4 4 5 4 4 8 1 51 1 2 2 1 1 1 2 9 3 78 4 3 4 5 5 4 5 10 5 78 4 1 3 4 5 5 5 11 5 78 4 3 3 5 5 5 5 12 4 73 2 2 2 4 5 3 3 13 8 78 4 3 4 5 5 5 4 14 4 54 2 1 2 2 3 1 2 15 2 78 2 1 3 4 4 3 3 16 9 78 5 1 2 2 2 2 1 17 4 78 4 1 2 1 2 2 3 18 7 78 5 2 4 5 4 4 4 19 3 78 5 1 1 4 4 1 1 20 5 78 2 3 4 5 5 4 5 21 3 78 4 1 3 2 2 5 2 22 6 78 4 3 4 5 4 4 4 23 7 78 4 1 1 1 4 1 1 24 6 78 5 2 4 5 5 4 5 25 6 78 5 2 3 4 5 2 5 26 5 78 5 2 3 4 4 3 5 27 2 78 2 1 1 3 2 2 4 28 8 78 4 1 2 5 4 5 5 29 7 78 4 1 1 4 3 3 3 30 5 78 4 1 2 1 1 2 1 31 4 78 2 2 2 3 2 2 3 32 8 78 5 1 2 2 2 2 4 33 9 78 5 2 2 2 1 5 2 34 7 78 5 3 4 5 5 5 5 35 4 60 2 3 2 4 4 3 4 36 8 78 5 1 1 1 3 1 1

145



FYI flag* Like*

37 5 78 5 1 1 1 1 3 2 38 6 78 5 3 1 3 2 1 3 39 9 78 5 1 4 2 3 2 4 40 7 73 4 2 4 5 4 4 4 41 6 78 5 1 1 1 1 1 1 42 2 78 2 1 1 1 1 1 1 43 5 73 3 3 4 5 5 4 5 44 1 51 1 2 1 2 2 4 3 45 8 78 5 3 3 4 2 2 5 46 5 73 3 1 1 1 1 1 1 47 3 78 2 3 4 3 4 4 2 48 6 78 4 1 3 3 4 2 4 49 1 60 1 1 1 1 1 1 1 50 2 51 2 2 2 4 3 2 3 51 3 78 4 1 1 1 1 1 1 52 4 78 3 2 4 3 3 3 3 53 8 78 4 3 3 4 5 5 5 54 7 73 4 2 3 3 2 2 3 55 6 78 5 1 1 2 2 3 2 56 5 73 4 2 4 5 5 4 5 57 1 51 1 1 1 2 3 2 3 58 5 73 2 3 3 4 5 4 3 59 5 78 4 2 4 4 4 3 4 60 9 78 5 1 3 4 5 3 5 61 6 78 3 3 4 2 3 2 3 62 7 78 4 3 3 4 3 5 4 63 7 78 4 1 3 3 5 4 3 64 2 54 1 1 4 4 4 5 3 65 5 73 3 3 2 2 1 3 3 66 4 73 3 1 2 4 4 3 3 67 4 78 4 2 3 3 3 2 4 68 6 78 5 1 4 2 3 5 2 69 5 78 3 2 2 4 5 5 5 70 8 78 5 3 2 3 2 2 3 71 2 60 1 1 1 1 1 1 1 72 3 78 4 1 3 2 2 5 4 73 8 73 4 2 4 3 3 3 3 74 6 78 5 2 2 2 2 2 2 75 8 73 4 2 3 4 4 4 5 76 2 73 2 1 2 1 2 3 2 77 1 51 1 1 3 3 3 3 4 78 6 78 4 2 3 4 4 3 4 79 6 78 4 1 3 2 3 5 2 80 5 73 3 2 4 4 3 5 4

146



FYI flag* Like*

81 4 78 4 1 4 4 3 4 1 82 7 78 5 3 2 4 3 1 4 83 3 60 1 1 2 3 3 5 2 84 6 78 5 2 2 3 3 2 4 85 4 78 4 3 4 1 5 3 5 86 6 78 4 3 4 5 4 5 2 87 8 78 5 2 4 4 4 3 3 88 3 73 2 2 4 5 4 4 4 89 2 78 2 1 1 1 1 1 1 90 8 78 5 1 1 2 2 3 1 91 7 78 5 1 3 2 2 5 1 92 0 0 1 3 1 1 2 3 1 93 7 73 3 2 3 4 4 4 5 94 6 78 4 2 4 4 5 4 5 95 5 78 4 2 1 3 2 3 2 96 7 78 4 3 2 3 2 2 3 97 3 73 2 3 3 5 5 5 5 98 3 78 2 3 2 3 3 3 4 99 9 78 5 1 1 1 1 1 1 100 4 78 4 1 1 1 3 1 2 101 3 78 2 1 1 1 1 1 1 102 4 73 3 1 1 1 1 1 1 103 6 78 5 1 1 1 1 1 1

Total 532 7616 368 185 259 307 316 309 314

Average 5.17 73.94 3.57 1.80 2.51 2.98 3.07 3.00 3.05 * Represents categories—see Tables 7 and 8.

knowledge interaction: social capital and electronic

Documents