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Dr. Rachid Ameur

Dissertation Work 2015

 

 

FACTORS AFFECTING VIDEOCONFERENCING UTILIZATION

 

AND USAGE IN A SOCIAL-SERVICES ORGANIZATION

 

 

 

 

 

Doctoral Dissertation Research

 

 

 

Submitted to the Graduate Faculty of

 

Argosy University, Orange County Campus

 

College of Business

 

 

 

In Partial Fulfillment of

 

the Requirements for the Degree of

 

Doctor of Business Administration

 

 

 

 

By

 

Rachid Ameur

 

April, 2015

 

 

 

 

 

 

 

 

 

 


FACTORS AFFECTING VIDEOCONFERENCING UTILIZATION

 

AND USAGE IN A SOCIAL-SERVICES ORGANIZATION

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Copyright ©2015

 

Rachid Ameur

 

All rights reserved


 

FACTORS AFFECTING VIDEOCONFERENCING UTILIZATION

 

AND USAGE IN A SOCIAL-SERVICES ORGANIZATION

 

 

 

 

Doctoral Dissertation Research

 

 

 

Submitted to the Graduate Faculty of

 

Argosy University, Orange County Campus

 

Doctor of Business Administration

 

 

 

In Partial Fulfillment

 

of the Requirements for the Degree of

 

Doctor of Business Administration

 

 

By

 

Rachid Ameur

 

 

 

 

 

 

 

Doctoral Research Mentor Approval:

 

                                                                                                                                                

Kambiz Moghaddam, Ed.D., Chair                        Date

 

                                                                               

Mohamad Saouli, D.P.A., Member               

 

                                                                               

Paul Wendee, D.B.A., Program Chair



ABSTRACT

Videoconferencing technology utilization has increased because of globalization and travel security concerns. Yet in spite of government mandates, there are inconsistent levels of videoconferencing utilization and usability experienced within southern California social services organizations.  This correlational study applied the unified theory of acceptance and use of technology (UTAUT) to a new setting in a social services organization to examine the relationship of any and more frequent videoconferencing utilization to four predominant factors from the UTAUT model: (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions.  A quantitative cross-sectional survey design was used.  Data were collected from 122 employees of the Los Angeles County Department of Social Services.  The results indicated a significant relationship between all four factors and videoconferencing utilization, with performance expectancy and valuing videoconferencing for communication and collaboration being the best predictors of reported utilization in multiple regressions.  Performance expectancy for using videoconferencing technology was reported as a positive factor that commonly affects videoconferencing use, whereas effort expectancy was reported as a common factor hindering use.  Facilitating conditions were reported both as common positive and hindering factors that affect use.  More specific measures of videoconferencing utilization may help to more clearly define factors that lead to videoconferencing utilization.  Further research should extend the study to all Los Angeles County departments and use mixed methods and a concerns-based adoption model to evaluate factors related to videoconferencing utilization.


ACKNOWLEDGEMENTS

 

I wish to express my deepest appreciation to my dissertation committee: first, to my chair, Dr. Moghaddam, for his continuous supervision and encouragement during this investigation and throughout the preparation of this dissertation.  And I also wish to express my gratitude to Dr. Saouli for his valuable advice and guidance throughout the methodology section of this research. 

I also want to thank the leadership and the executive management team at the Los Angeles County Department of Public Social Services: in particular, Ms. Spiller, Mr. Phil Ansel, Mrs. Lynn Voden, and Mr. Michael Sylvester for putting their trust in me and allowing me to conduct this research at their organization.  I also want to thank Dr. Michael Bono and the DPSS’ Internal Research Review Board team for their patience and collaboration throughout the IRB approval process and my carrying out the research at DPSS. 

Dr. Richard Sheng; my mentor and friend, thank you for being so supportive throughout this dissertation process and for your effective feedback.  I also want to thank Dr. Dianna Siganoff for her leadership and encouragement in the early stage of this dissertation. 


 

DEDICATION

This dissertation is dedicated to my parents, Zahra Mobarki and Belaid Ameur, for their love, encouragement, and wise advice; to my children, Lina Ameur and Sofia Ameur for their patience; and to my wife, Loubna Bouad, for her continuous understanding and support throughout this journey.  And finally I dedicate this dissertation to my sister, Siham Elboukfaoui; Mr. Rachad; my nephews, Ethan Ameur and Noah Ameur; Othman and Mehdi Rachad;  and my brother, Ahmed Ameur, and his wife, Jean Ameur.


 


TABLE OF CONTENTS

Page

LIST OF TABLES.............................................................................................................. x

 

LIST OF FIGURES............................................................................................................ xi

 

LIST OF APPENDICES................................................................................................... xii

 

CHAPTER ONE: INTRODUCTION................................................................................. 1

Problem Background........................................................................................................... 1

       Value of and Barriers to Using Videoconferencing..................................................... 1

       Videoconferencing at the Los Angeles County Department of Social Services.......... 4

Statement of the Problem.................................................................................................... 5

Purpose of the Study............................................................................................................ 6

Research Questions............................................................................................................. 6

Definitions........................................................................................................................... 8

Significance of the Study.................................................................................................... 9

Theoretical Framework..................................................................................................... 11

Summary............................................................................................................................ 11

 

CHAPTER TWO: REVIEW OF THE LITERATURE..................................................... 13

Theories............................................................................................................................. 14

       Building Theory......................................................................................................... 15

       Technology-Acceptance Model................................................................................. 17

       Theory of Planned Behavior...................................................................................... 18

       Technology Acceptance Model 2............................................................................... 21

       Unified Theory of Acceptance and Use Technology................................................. 21

               Performance expectancy..................................................................................... 23

               Effort expectancy................................................................................................ 24

       Social influence.......................................................................................................... 26

       Facilitating conditions................................................................................................ 27

The Four Determinant Factors of Technology Use........................................................... 31

       Performance Expectancy............................................................................................ 32

       Social Influence.......................................................................................................... 36

       Effort Expectancy....................................................................................................... 38

       Facilitating Conditions............................................................................................... 40

Demographic Factors in Technology Use......................................................................... 41

Areas Needing Additional Investigation........................................................................... 43

Summary............................................................................................................................ 46

 


 

Page

CHAPTER THREE: METHODOLOGY.......................................................................... 48

Restatement of the Purpose............................................................................................... 49

Research Design................................................................................................................ 51

Subjects.............................................................................................................................. 53

       Population and Sampling........................................................................................... 53

       Characteristics of the Sample..................................................................................... 54

Instrumentation.................................................................................................................. 55

       Survey Questionnaire Sections.................................................................................. 56

       Reliability and Validity.............................................................................................. 57

Methodological Assumptions, Limitations, and Delimitations......................................... 58

       Methodological Assumptions..................................................................................... 58

       Limitations................................................................................................................. 58

       Delimitations.............................................................................................................. 59

Procedures......................................................................................................................... 59

Data Processing and Analysis........................................................................................... 60

Summary............................................................................................................................ 61

 

CHAPTER FOUR: RESULTS.......................................................................................... 63

Restatement of the Purpose of the Study........................................................................... 63

Research Questions and Hypotheses................................................................................. 63

Reported Use of and Perceptions of Videoconferencing.................................................. 65

Tests of Hypotheses........................................................................................................... 68

Demographic Correlates of Independent Variables.......................................................... 70

Demographic Correlates of Use of Videoconferencing.................................................... 72

Multiple Regression Analyses........................................................................................... 73

Summary............................................................................................................................ 74

 

CHAPTER 5: DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS.......... 76

Restatement of the Purpose of the Study........................................................................... 76

Discussion.......................................................................................................................... 78

       Research Question 1................................................................................................... 79

       Research Question 2................................................................................................... 80

       Research Question 3................................................................................................... 80

       Research Question 4................................................................................................... 81

Conclusions....................................................................................................................... 82

Implications for Practice................................................................................................... 85

       Improve Technology Protocols.................................................................................. 86

       Improve Training and Assessment in Technology..................................................... 87

       Improve Managerial Communication about Technology.......................................... 88


 

Page

Recommendations for Future Research............................................................................ 90

       Use Different Methodologies and Samples................................................................ 90

       Cost-Effectiveness Studies......................................................................................... 90

       Study Videoconferencing and High Performance...................................................... 91

               Strategic planning............................................................................................... 91

               Change process................................................................................................... 92

               Application of Videoconferencing Acceptance Model....................................... 94

 

REFERENCES.................................................................................................................. 97

 


 

LIST OF TABLES

                                                                                                                                        Page

 

Table 1.    Theories Addressing Technology Acceptance Models Frameworks............... 15

Table 2.    Proposed Instrument Factors and Test Construct Sample................................ 44

Table 3.    Demographic and Employee Characteristics of Sample.................................. 54

Table 4.    Reported Use of and Perceptions of Videoconferencing.................................. 66

Table 5.    Correlations of Scales with Each Other............................................................ 67

Table 6.    Attitudes About Videoconferencing and Correlations With

                 Videoconferencing Use.................................................................................... 68

 

Table 7.    Demographic Factors as Predictors of Independent Variables......................... 71

Table 8.    Multiple Logistic Regression Predicting Any Videoconferencing Use........... 73

Table 9.    Multiple Linear Regression Predicting Frequency of

                 Videoconferencing Use.................................................................................... 74

 

Table 10.  Stages of Concern about Technology Use From Concerns-Based

                 Adoption Model................................................................................................ 95


LIST OF FIGURES

                                                                                                                                        Page

 

Figure 1.    The four essential factors of videoconferencing utilization............................ 14

Figure 2.  Technology acceptance model (TAM)............................................................. 18

Figure 3.  The unified theory of acceptance and use technology (UTAUT).................... 22

Figure 4.  Performance expectancy................................................................................... 24

Figure 5.  Effort expectancy.............................................................................................. 25

Figure 6.    Social expectancy............................................................................................ 27

Figure 7.    Facilitating conditions..................................................................................... 28

Figure 8.    Independent variables, possible covariates, and dependent variables............. 57

Figure 9.    Videoconferencing acceptance model............................................................. 93


 

LIST OF APPENDICES

                                                                                                                                        Page

 

APPENDIX A.   Permission to Use MIS Quarterly....................................................... 109

APPENDIX B.    Department of Public Social Services Employees Survey................. 111

APPENDIX C.    Permission to Conduct Research by Department of

                             Public Social Services......................................................................... 119

 

APPENDIX D.    Email Invitation to Department of Public

                             Social Services Employees................................................................. 121

 

APPENDIX E.    Consent Form for Department of Public

                             Social Services Employees …………………………………..…...... 123

 


CHAPTER ONE: INTRODUCTION

To stay ahead of competition means getting rid of old ways of doing business and implementing new and efficient systems that are cost-saving, such as videoconferencing (Johnson, 2013; Olaniran, 2009).  In recent years, organizational management, communications, and decision-making have changed rapidly, making it difficult for organizations to manage business relations without changing the means of communications.  Videoconferencing solutions are new emerging business communication and collaboration technology tools that can be used to improve productivity and increase efficiency (Johnson, 2013).

Problem Background

Value of and Barriers to Using Videoconferencing

Videoconferencing technology allows two or more regional office locations to interact using a monitor, a camera, and a microphone with a codec or software, and this is accomplished through simultaneous two-way video and audio transmissions (Heller, 2010).  For example, using videoconferencing, a virtual team working on a project may collaborate or may hold electronic meetings to coordinate activities.  Teleconferencing video-communications technology also permits business conferences among employees who are not located in the same physical office space but could be located across the county, states, or countries (O'Brien, 2009).  Telepresence combines video and audio conferencing components in a single room, often designed as a videoconferencing conference room; it not only enables employees to connect to a far site but gives those at both sites the feeling of sitting across the same table in the same room (Heller, 2010), permitting richness of information exchange (Daft, 2007). 

The richness of an information channel is influenced by three characteristics: (1) the ability to handle multiple cues simultaneously; (2) the ability to facilitate rapid, two-way feedback; and (3) the ability to establish a personal focus for the communication.  Face-to-face discussion is the richest medium, because it permits direct experience, multiple information cues, immediate feedback, and personal focus. (p. 275)

Videoconferencing technology usage has increased because of globalization and concerns about travel security (Olaniran, 2009).  Increase in energy costs has also augmented travel expenses; therefore, large and small organizations have turned to videoconferencing and web-conferencing to cut costs (Laudon & Laudon, 2012).  Leaders at some companies have hoped to reduce travel expenses by making videoconferencing accessible by demand to all employees (Olaniran, 2009).  A June 2008 report issued by the Global e-Sustainability Initiative and the Climate Group estimated that up to “20 percent of business travel could be replaced by virtual meeting technology” (Laudon & Laudon, 2008, p. 10).  Economy of scale has contributed to making videoconferencing affordable for companies, leading to large deployments throughout the enterprise. 

Communications is the contextual framework in which the researcher plans to develop the lens for videoconferencing utilization.  According to Johnson (2013), 70% of unified communication and collaboration (UCC) technologies will mature within five years and 60% will have transformational or high impact if their implementation is led by genuine business requirements.  Innovations in videoconferencing and other technologies are rapidly developing communications architectures, platforms, and applications based on fast moving network presence, which improve employee and corporate productivity (Johnson, 2013).  Several companies have digitally integrated; they have incorporated the internet platform with their business-communication models to drive profit through new markets (Daft, 2007).  As an example, in 2005, Ebay Inc. acquired Skype technologies to strengthen the companies’ international market place (Ebay, 2005.).

Technology mandates are also backed by signed executive orders at the national level of government to promote efficient government spending (Tuutti, 2012a).  President Barack Obama (2011) directed agencies to find alternatives to government travel, including the exploration and use of videoconferencing.  The executive team has set benchmarks to be realized by 2016; however, many barriers exist to meeting the 2016 goal.  These include lack of videoconferencing solutions available to employees, cost concerns, infrastructure limitations, low level of general use, organizational cultural barriers, lack of awareness of benefits, and lack of managerial buy-in (Telework Exchange, 2012).

Some government agencies have been sluggish in embracing technology mandates or obligations aimed at cutting the cost of business meetings by reducing productive hours spent travelling and other incidental costs relating to meetings funded by the government (Tuutti, 2012b).  Some employees who have tried to use video teleconferencing (VTC) in the past were discouraged by poor audio/visual and video streaming connection disruptions (Tuutti, 2012b).  Negative experiences with using videoconferencing equipment, lack of skills needed to operate equipment, and low motivation have been cited as reasons why technology in not fully utilized (Tuutti, 2012b).  There is a need to assess videoconferencing or telepresence utilization and usage of systems already in place to determine effectiveness and effect change.  System and organizational factors influence users’ attitudes and beliefs about accepting or rejecting technology (Gallion, 2000).  Careful measurement of these factors can allow tracking over time, providing management with a tool for monitoring user acceptance and identifying potential acceptance problems (Gallion, 2000).

Videoconferencing at the Los Angeles County Department of Social Services

This research focused on videoconferencing usage within the Los Angeles County Department of Social Services.  Los Angeles County’s geographical expanse makes it costly and environmentally unfriendly to host face-to-face meetings and impromptu training sessions for the Department of Public Social Services (DPSS) staff at 36 district offices spread throughout the county.  DPSS has faced significant mileage reimbursement costs for staff to attend meetings at various locations.

The central videoconference infrastructure at DPSS was currently used by 22 county departments, including the Department of Mental Health, the Department of Public Health, the Department of Health Services, the Auditor-Controller, the Department of Public Social Services, the Department of Internal Services, and the Chief Information Office, and was also heavily used by the justice system departments.  Within the last 12 months, many videoconferencing business applications were deployed, including remote interviews conducted by the Department of Human Resources, teleconferencing at the Department of Mental Health, emergency responses at the Department of Public Health, training of nurses at the Department of Health Services, attorney-client meetings at the Public Defender and Alternate Public Defender offices, and video-arraignment and inmate visitation at the Sheriff’s offices.  More uses are now under development (Tindal & Sanchez, 2012, para. 9). 

Like many firms, DPSS is actively working to replace traditional face-to-face meetings with “virtual” meetings, leveraging videoconferencing technology and such web-conferencing technology as WebEx from Cisco, Inc.  However, some workers are challenged by these technologies, because they are not familiar with them or they have no experience using them.  Employees have to trust and accept technology capabilities to be willing to learn and use technology to its full capacity (Holzinger, Searle, & Werbacher, 2011).  Technological issues must be identified to determine acceptance.  Telepresence, like many technologies, initially presents boundaries inhibiting acceptance and use (Agnor, 2012).  A necessary step in planning is to remove boundaries to improve the acceptance of videoconferencing and its use.

Statement of the Problem

While some agencies seem handicapped to embrace videoconferencing technology (Tuutti, 2012b), others, such as the Los Angeles County DPSS, continue to invest in building and deploying videoconferencing infrastructure.  Yet, the level of utilization of videoconferencing technology remains unknown, particularly at the Los Angeles County DPSS, where funding was committed to videoconferencing infrastructure (Tindal & Sanchez, 2012).  It is unknown whether this technology is being adequately utilized, the barriers to utilization, as well as solutions or motivational incentives that may be necessary to boost utilization.  Huge technology projects like videoconferencing, researchers contend, require frequent evaluation and justification, as they involve the expenditure of public funds (Tindal & Sanchez, 2012).  Thus, there is a need to identify not only the barriers to implementing videoconferencing in organizations, but also to evaluate usage and factors related to it to justify past and future expenditures on videoconferencing as cost-saving means in government agencies. 

Purpose of the Study

The purpose of this study conducted in the Los Angeles County DPSS was to establish a baseline of videoconferencing utilization and to identify factors related to implementation and usage of videoconferencing as an alternative to face-to-face meetings.  The purpose of the study aimed to identify the barriers to the implementation and effective usage of video conferencing as an alternative to face-to-face meetings, in spite of government mandates, and given the inconsistent levels of videoconferencing utilization and usability experienced within a Southern California social services organization.

In other words, this study entailed an analysis of incongruent patterns of videoconferencing utilization and usability arising from a real world need for a Social Services organization to understand videoconferencing utilization trends and to discover factors and settings that contributes to the acceptance and use of videoconferencing or which limit acceptance and use.

Research Questions

Four research questions and corresponding hypotheses were posed in this study:

Research Question 1 asked, Does performance expectancy of employees influence videoconferencing utilization?

H10:      There is no significant relationship between performance expectancy of employees and videoconferencing utilization.

H1A:    There is a significant relationship between performance expectancy of employees and videoconferencing utilization.

Research Question 2 asked, Does effort expectancy of employees influence videoconferencing utilization?

H20:     There is no significant relationship between effort expectancy of employees and videoconferencing utilization.

H2A:    There is a significant relationship between effort expectancy of employees and videoconferencing utilization.

Research Question 3 asked, Does social influence promote videoconferencing utilization?

H30:     There is no significant relationship between social influence and employees’ intention to use videoconferencing.

H3A:    There is a significant relationship between social influence and employees’ intention to use videoconferencing.

Research Question 4 asked, Do facilitating conditions influence videoconferencing utilization?

H40:     There is no significant relationship between facilitating conditions and videoconferencing utilization.

H4A:    There is a significant relationship between facilitating conditions and videoconferencing utilization.

Research Question 5 asked, Do demographic variables (position, age, gender, education) influence performance expectancy, effort expectancy, social influence, or facilitating conditions?

H50:     There is no significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

H5A:    There is a significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

Definitions

This section provides terms utilized throughout the study.  These terms offer key essentials to understanding the relevance of videoconferencing technology, and the factors that might influence its utilization.  The following terms are directly related to the content of this study:

Attitude: According to Ajzen (1989), an attitude is an individual’s beliefs and reflections combined to illustrate his or her actions toward technology utilization.

Behavioral intention: An employee’s conscious willingness to use technology to perform a task or not to perform that same task (Venkatesh, Morris, Davis, & Davis, 2003).

Computer self-efficacy: The employee’s belief that he or she has the ability to complete an assignment using a computer or technology at large (Venkatesh et al., 2003).

Effort expectancy: The employee’s belief that it is easy to use a system to perform his or her job.  Ease of use is a key element in effort expectancy (Venkatesh et al., 2003).

Performance expectancy: The employee believes that using a system will help him or her increase job performance (Venkatesh et al., 2003).

Social influence: An employee feels and believes that he or she must use the new system to comply with management’s expectation (Venkatesh et al., 2003).

Telepresence: A system that combines video and audio conferencing components in a single room.  It enables employees at distant sites to connect and feel that they are both located in the same room and sitting across the same table (Heller, 2010).  Agnor (2012) suggested use of telepresence provides participants with a sense, and a feeling, as they are physically present in a distant location. 

Usability: According to Theofanos, Stanton, and Wolfson (2008), usability defines how a product can be used by employees to achieve specific organizational goals while improving effectiveness, efficiency, and satisfaction in a specified context of use.

Utilization: A process that is concerned with employee behavior regarding acceptance and usage of technology; it is the employee’s perception of whether technology is perceived to be useful (Laudon & Laudon, 2012).

Videoconferencing: A converged technology application that integrates audio, video, and content.  It has wide modes of application. 

Significance of the Study

The knowledge derived from this study was to allow DPSS to take measures to effectively leverage and influence positive factors in order to improve utilization and usability while at the same time correcting any deficiencies of utilization and usability of videoconferencing.

The findings from this study  will establish a conceptual understanding of usage and utilization of videoconferencing and help to identify a basis for developing a Videoconferencing Acceptance Model (see Chapter 5, Figure 5), which can help organizations achieve high levels of utilization and usability of videoconferencing.  This research will provide preliminary information that could assist the DPSS in developing policies, incentives, and measures focused on areas of need that will boost implementation and usage of videoconferencing and the realization of its benefits.

Los Angeles County produced a Countywide Videoconferencing Report (Tindal & Sanchez. 2012), which provided the Board of Supervisors with videoconferencing directives.  The main points were as follows:

1.            Review the potential benefits from integration of existing independent departmental videoconferencing systems into the Internal Services Department (ISD) managed central system;

2.            Report back within the next 30 days with the assessment and recommendations for the implementation of an expanded and integrated videoconferencing network throughout the County system;

3.            Carry out a thorough network and bandwidth utilization assessment for each County location, prior to any videoconference equipment implementation to ensure usability of the service;

4.            Follow the International Telecommunication Union standards for videoconferencing protocols and audio/video compression to maintain compatibility across County and worldwide systems;

5.            Provide adequate training for County employees and create user-friendly training materials available online and offline;

6.      Mandate the use of videoconference equipment when possible as an alternative for participants to physically attend meetings. (Tindal & Sanchez, 2012, para. 6)

This study addressed several of these main points, and may ultimately lead to increased videoconferencing utilization within the DPSS.  The findings from this study could contribute to creating and adopting an organizational communication policy to encourage employees to accept and adopt videoconferencing as an alternate way to attend meetings rather than to travel (Tuutti, 2012a).  The researcher also sought to add theoretically to models of technology acceptance, providing knowledge about videoconferencing for organizational use and acceptance of videoconferencing. 

Theoretical Framework

The theoretical framework for this study was the unified theory of acceptance and use of technology model (UTAUT), a consolidation of eight technology-acceptance models (see Chapter 2).  The model’s four determinant factors have a direct link to intention to use technology.  As noted by Venkatesh et al. (2003), the model presents a new and useful set of measurement tools for managers to assess the likelihood that new technology utilization will succeed.  This study also applied a theoretical framework from Teng and Calhoun (1996), which suggested a correlation between decision-making and the use of communication technologies (Saouli, 2004).  Huber (1990) stated,

The availability of advanced information technology increases the communicating or decision-aiding options for the potential user, and thus in the long run, unless the selected technology is inappropriately employed, the effect is to increase the quality of the user’s communication or decision-making processes. (p. 51)

Newman (2010) suggested that advanced information technologies have enabled organizations to achieve higher-level decision-making competencies when employees are utilizing technology to its full capacity.  Their research supported the value of technological factors and the part they play in incorporating technology components in the decision-making process.

Summary

Factors such as (a) performance expectancy, (b) effort expectancy, (c) social influence; and (d) facilitating conditions have been proven to have an impact on how technology is utilized in organizations and have been described in the Unified Theory of Acceptance and Use of Technology model (UTAUT; Venkatesh et al., 2003)).  The purpose of this quantitative correlational study was to investigate the influence of these four determinant factors in videoconferencing utilization within a social services organization.  This research not only extended the Venkatesh et al. (2003) model, but also added to the existing research in the field and offered additional critical-success factors and recommendations to improve videoconferencing utilization within an organization.

Videoconferencing is a collaboration tool that allows coworkers to come together visually but without traveling long distances or consuming large amounts of time away from one’s workstation (Tuutti, 2012a).  However, barriers to implementing videoconferencing remain.  This research focused on videoconferencing usage within the Los Angeles County Department of Social Services. 

This chapter included the study’s problem background, statement of the problem, significance of the problem, purpose of the study, theoretical framework, research questions, limitations, definitions of terms, and significance of this study.  The second chapter is a review of the relevant theory, empirical findings, and related literature on videoconferencing utilization and the impact of the TAM/UTAUT factors on these technologies’ acceptance and utilizations.  Chapter 3 details the study’s methodology strategy and protocols used to conduct the study.  Chapter 4 reports the data collection and findings.  The fifth chapter presents recommendations based on the findings and addresses implication of the findings for future research directions.


 

CHAPTER TWO: REVIEW OF THE LITERATURE

This scholarly review considers theoretical and research evidence supporting many theories and factors that could influence video conferencing utilization, and especially predominant theories regarding technology acceptance in the field of technology and videoconferencing.  This section also reviews studies on acceptance and use of videoconferencing in many organizational settings and applications.  Research revealed three research areas that addressed user acceptance of information technology: user satisfaction, innovation adoption, and social psychology (Gallion, 2000).

Studies have adapted different technology acceptance models, such as unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (Davis, 1989).  These models have included behavioral factors that influence acceptance of technology in a social context; the aim of these models was to predicate users’ behavioral actions towards acceptance and usage of technology.  These studies have further relied on such theories as the theory of reasoned action (Ajzen & Fishbein, 1980); the theory of planned behavior (Ajzen, 1991; Taylor & Todd, 1995); social-cognitive theory, which is part of Bandura’s (1986) behavioral model; and diffusion of innovations theory (Rogers, 1962) to understand the influence of such factors as (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions on video-conferencing utilization.  The important determinant factors identified for this study are summarized in Figure 1.

Figure 1. The four essential factors of videoconferencing utilization.  Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, 2003, MIS Quarterly, 27(3), p. 447. (see Appendix A for permission to use)

 

 

Theories

This literature review focuses on several theories of technology adoption that explain technology acceptance.  Wilson and Lankton (2004) emphasized selection among different theories when conducting literature reviews.  Reviews of studies have also shown that many researchers have used theories to validate their literature reviews.

Venkatesh et al. (2003) relied on the use of many theories and constructs, as in the case of their UTAUT study where they concluded that eight prominent theories were able to explain 17% to 53% of the variance in user acceptance and usage; thus major theories have statistically proven their analytical capabilities.  However, some of these theories related to technology acceptance are abstract and can only explain generalized behaviors that may not reflect the same settings and simulation context.  This review found several studies that directly linked predominant theories of technology acceptance and use to video-conferencing technology use (Table 1).  Issues in building theory are described next.

Table 1

Theories Addressing Technology-Acceptance Models’ Frameworks

 

 

Social Cognitive

Planned Behavior

Technology Acceptance Model

Diffusion of Innovation

Planned Behavior

 

Ajzen and Fishbein (1980)

 

X

 

 

 

 

 

Davis (1986)

 

X

X

 

 

 

Bandura (1986)

X

 

X

 

 

 

Venkatesh et.al. (2003)

X

X

X

X

X

 

Ajzen (1989)

 

X

 

 

 

 

Rogers (1962)

 

 

 

X

 

 

Taylor and Todd (1995b)

 

 

 

 

X

 

 

 

Building Theory

A theory is a “systematic explanation for the observations that relate to a particular aspect of life” (Babbie, 1995, p. 50).  Analytical research helps verify a theory or hypothesis by replicating causal effects (Arbnor & Bjerke, 1997).  Particular attention is made to ensure that the researcher does not affect the situation or results.  The basic building blocks of a theory are concepts (Babbie, 1995): “abstract ideas generalized from particular facts” (Davis, 1986, p. 31) in which facts are known or proved to be true.  Arbnor and Bjerke (1997) defined a concept as “an abstraction of observed events or characteristics” (p. 93).  Numerous concepts are known and acknowledged in the analytic method (Arbnor & Bjerke, 1997).  The expansion of new concepts should help develop the IT field by adding new vocabularies or propositions to explain emerging inclinations. Propositions are “conclusions drawn about the relationships among concepts” (Babbie, 1995, p. 50).  Over time propositions have the potential to become paradigms.

Using concepts, researchers create operational definitions, defined as methods of measuring or changing a concept (Davis, 1996).  According to Arbnor and Bjerke (1997), an operational definition should include a statement of which object(s) are to be observed, “a description of the situation in which the observation is to take place, a determination of the type of measuring scale to be applied to the observation data, and rules for how to handle the data obtained through the observation” (p. 94).  Combined, these form an operational paradigm, the purpose of which is "to create a fit between ultimate presumptions about a mythological approach and the nature of the area under study” (p. 217). 

According to Arbnor and Bjerke (1997), in order to use the analytic approach the researcher must have identified not only the situation but also the variables to be researched.  Then a hypothesis is developed based on the relationships determined.  Structured experiments are performed to help measure and determine cause-and-effect relationships to validate the hypothesis.  Consequently, repeated studies validating these hypotheses lead to theory development.

In this research paper's operative paradigm, concepts were major themes related to the way a social networking collaborative utilizes videoconferencing.  These themes included perceived usefulness, perceived ease of use, attitude, perceived enjoyment, subjective norms, self-efficacy, facilitating conditions, perceived behavioral control, behavioral intention to use, and use-behavior determinants.  The way that theories have developed to address these concepts are reviewed next.

Technology-Acceptance Model

Fred Davis (1986) developed a model of technology acceptance with two major objectives in mind: (a) to improve understanding of user-acceptance processes, which could be accomplished through a new theoretical approach to ensure successful design and implementation of information systems, and (b) to design TAM as a framework that could be used as a matrix tool to test and measure employees’ acceptance of technology applications, such as videoconferencing.  For system designers and implementers, it is also important to use the TAM model prior to implementation to assess employees’ level of technology acceptance (Davis, 1986).  User-acceptance testing is a proven method to provide useful information about the relative likelihood of success in technology adoption.  TAM was first introduced in 1986, and Davis’ model to assess technology utilization and usage is still the most widely applied theoretical model in the information-systems field (Lee, Kozar, & Larsen, 2003). 

The TAM (Davis, 1989) built on the theory of reasoned action (TRA, Fishbein & Ajzen, 1975), which indicated that the main detriments to attitudes toward new technology are the perceived ease of use and perceived usefulness.  The TRA model indicated that a person's attitude toward a behavior represents the person's beliefs about the behavior and subsequently affects usage.  In the TAM, usage is modeled as a direct function of intention to use (Davis, 1989).  The TAM focuses on perceived usefulness of technology and perceived ease of use of technology as factors predicting attitudes toward technology, behavioral intention to use it, and actual use (Figure 2).

Figure 2. Technology acceptance model (TAM). Adapted from “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” by F. D. Davis, R. P. Bagozzi, and P. R. Warshaw, 2003, Management Science, 35, p. 985.

 

 

Further investigation (Davis, Bagozzi, & Warshaw, 1989a) determined that extrinsic and intrinsic motivation affect user intention.  According to Davis et al. (1989a), extrinsic motivation occurs when performance of an activity is critical to achieving the outcome; whereas, intrinsic motivation pertains to the performance of an activity without respect to the activity or anticipated reinforcement.  Davis (1989a) thought extrinsic motivation of perceived usefulness would have a stronger impact on IT adoption than would intrinsic motivation alone.  As such, the behavioral intention determined by TAM should better demonstrate the attitudinal component beyond the subjectivity and uncertainty associated with the original TRA study (Davis et al., 1989a).

Theory of Planned Behavior

In his theory of planned behavior (TPB), Icek Ajzen (1991) also illustrated the importance of beliefs and behavior in improving user acceptance of new technology.  His findings added to the theory of reasoned action by including perceived behavioral control.  A deeper look into these theoretical concepts adds richness to researchers’ perceptions of videoconferencing utilization. 

Ajzen (1991) defined perceived behavioral control as one’s perception of the ease or difficulty of carrying out a specific behavior.  Perceived behavioral control depends on control beliefs and perceived facilitation (Ajzen & Madden, 1986; Mathieson, 1991). Ajzen and Madden (1986) defined control belief as “a perception of availability of the skills, resources and opportunity” (p. 457) and perceived facilitation as “the individual's assessment of the importance of these resources to achieve the outcome” (p. 176).

Researchers Taylor and Todd (1995) added perceived behavioral control to TAM to evaluate differences in the way that experienced and inexperienced users make usage decisions.  For experienced users, behavioral intention was expected to mediate fully the relationship between perceived behavioral control and usage.  In contrast, for inexperienced users with no prior knowledge on which to assess control factors, perceived behavioral control “may directly influence behavior since it is this direct experience that makes the influence of control factors apparent” (p. 563).  For experienced users, business intelligence fully mediated the relationship between perceived behavioral control and usage.  “Less experienced users perceived behavioral control had less of an impact on intention, but had a significant influence on usage" (p. 566).

Miller (1994) also recognized the value of TAM constructs in technology acceptance-based research.  In a study involving 66 students taking an online course, he tested the impact of the following factors: Perceived Ease of Use and Perceived Usefulness (Davis, 1989), Subjective Norm (Mathieson, 1991), Task Specific Computer Self Efficacy (Murphy, Coover, & Owen, 1989), and General Computer Self Efficacy (Compeau & Higgins, 1995).  The results of the study supported propositions centered on the technology acceptance model.

Mathieson (1991) compared TAM and TPB to predict user intentions, and found that both theories predicted intention to use a technology quite well.  The TAM had a slight empirical advantage and was easier to apply, but users’ opinions were generalized about a system, whereas TPB was able to provide more specific information.  It also appeared that intention was predicted by both perceived behavioral control and attitude.

Gallion (2000) built upon TAM and TPB frameworks in introducing the construct of relative advantage to the organization to the TAM perceived-usefulness framework.  In addition, relative frequency of use, user performance, and the ratio of completed work assigned were examined as measures of acceptance (Gallion, 2000).

In their book, Garrity and Sanders (1999) also presented topics helpful to the researcher, including theory development and testing, instruments for evaluating system success, validation of system-success measures, and trends in information-systems success evaluation.  As the role of information technology continues to expand in scope and complexity, it is imperative that researchers have a matrix to determine when a system is successful as these measurements tend to achieve four goals: present conceptual and philosophical foundations to gauge systems success; contribute new approaches to identify and understand systems success; and compare system-success instruments while validating systems-success measures (Garrity & Sanders, 1999).

Technology Acceptance Model 2

According to Westland and Clark (2000), since the 1980s, organizations have allocated 50% of all new capital investment towards increasing information on technology availability; however, in order for technologies to improve employees’ performance and the organization’s productivity, they must be accepted and then used by employees.  To address this problem researchers have selected constructs and factors from multiple technology-acceptance models.  According to Venkatesh and Davis (2000a, 2000b), Technology Acceptance Model 2 (TAM2) extended the original TAM model by including subjective norms as an additional predictor of intention to use technology, such as how managers should support use of technology. 

Unified Theory of Acceptance and Use Technology

Reviewing user-acceptance literature, Venkatesh et al. (2003) went on to combine eight technology acceptance models: (a) the theory of reasoned action, (b) the technology acceptance model, (c) the motivational model, (d) the theory of planned behavior, (e) a model combining the technology acceptance model and the theory of planned behavior; (f) the model of PC utilization; (g) innovations diffusion theory; and (h) social cognitive theory.  They formulated a new model called the unified theory of acceptance and use technology (UTAUT; Figure 3). 

Figure 3. The unified theory of acceptance and use technology (UTAUT).  Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh et al., 2003, MIS Quarterly, 27(3), p. 447.  (see Appendix A for permission to use)

 

 

Venkatesh et al. (2003) also consolidated the names of factors addressed in previous acceptance and use models into a defined set of acceptance and use determinants. During their research they tested many acceptance and use factors, such as performance expectancy, effort expectancy, attitude towards using the system, social influence, facilitating conditions, self-efficacy, comfort level, and intention of use.  According to Venkatesh et al., four constructs played an important role in the UTAUT model as direct determinants of user acceptance and usage behavior; these factors were (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions.  If organizations can implement videoconferencing systems that take these factors into account, this can benefit users and the organization at large, resulting in increased utilization.

Performance expectancy. Performance expectancy (see Figure 4) is a psychosocial factor related to the perceived usefulness of videoconferencing (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989b).  In addition, it reflects employee beliefs and perceptions of how technology could help improve job performance (Davis, 1989).  For example, if an employee used videoconferencing it would enable him or her more quickly to accomplish tasks, such as making decisions.  This factor is also used to assess how the capabilities of videoconferencing could enhance an individual’s job performance (Thompson, Higgins, & Howell, 1991).


 

Figure 4. Performance expectancy. From “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh et al., 2003, MIS Quarterly, 27(3), p. 448.  (see Appendix A for permission to use)

 

 

Effort expectancy. Effort expectancy (see Figure 5) is associated with perceived ease of use.  If users find a videoconferencing system easy to use, they will be inclined to use it more.  Effort expectancy could focus on four areas: the effort to: operate the system, start and stop the video-conferencing session in the facility, schedule a videoconferencing meeting, or attend a videoconferencing meeting. 


 

Figure 5. Effort expectancy. Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh et al., 2003, MIS Quarterly, 27(3), p. 451. (see Appendix A for permission to use)

 

 

Empirical results from technology utilizations generally support a positive relationship between effort expectancy and videoconferencing utilization.  For example, Agnor (2012) examined factors affecting use of telepresence technology in a global technology company.  However, empirical support for a significant positive influence of effort expectancy on videoconferencing utilization is not universal (Agnor, 2012).  This study found that participants’ expectation of effort was summarized in one quote:

As mobile as our workers are, for me it does not make sense to have a facilities-based solution.  If it’s not on your desktop, I don’t think it’s going to be used a lot.  If everyone had a video camera on top of their PC I think everybody would be thrilled and would use it. (Agnor, 2012, p. 80)

The primary concern regarding effort expectancy lies in the user’s training and awareness of videoconferencing.

Social influence. Venkatesh et al. (2003) stated that social influence is also a direct determinant of behavioral intention to use technology (videoconferencing): employees’ behavior is influenced by how others perceive their actions when they are using videoconferencing (Figure 6).  This factor assesses if a supervisor is supportive of his subordinates when they are using videoconferencing.  Furthermore, it assesses if the organization supports its employees to use technology (videoconferencing); hence, management sponsorship to use videoconferencing is a derivative of this social-influence factor.  According to some (Venkatesh & Davis, 2000a; Warshaw, 1980), social influence in technology-acceptance decisions has an impact on employees’ compliance with organizational policy to use technology.  This means that organization policies cause an individual to change his or her intention in response to management pressure.  For example, when a manager sets clear expectations for subordinates about using videoconferencing, the outcome could be more utilization.  French and Raven (1959) and Warshaw (1980) suggested that employees are more likely to comply with management expectations when management has the ability to reward the desired behavior.


 

Figure 6. Social expectancy. Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh et al., 2003, MIS Quarterly, 27(3), p. 452. (see Appendix A for permission to use)

 

Facilitating conditions. Venkatesh et al. (2003) defined facilitating conditions as the means of the organizational and technical infrastructure and whether they have presence to support use of the system.  Similarly, Thompson et al. (1991) defined facilitating conditions (Figure 7) as “objective factors in the environment that observers agree make an act easy to accomplish” (p. 129).  Infrastructure is an element of facilitating-condition factors. For example, if videoconferencing is not available to all employees within the organization, there will be less usage.  Provision of technical and professional support for users of videoconferencing systems can also influence system utilization (Thompson et al., 1991).  For this study, it would include professional support for videoconferencing, in which could be attributed to professional training.

Figure 7. Facilitating conditions. Adapted from “User Acceptance of Information Technology: Toward a Unified View,” by V. Venkatesh et al., 2003, MIS Quarterly, 27(3), p. 454. (see Appendix A for permission to use)

 

 

The DPSS research and development team has also aided the literature review.  This related literature was helpful to build the case for assessing utilization and usability in an organization interested in benchmarking growth in the area of videoconferencing.  The literature showed that other public service agencies have invested in videoconferencing equipment but had limited success.  For instance, Lim and Freed (2009) reported, “We have the Videoconference Equipment Installed, Now What?”  Their study examined challenges faced by educators currently using videoconferencing.  These challenges were classified as educator challenges and administrative support.  Educators lacked professional development to use videoconferencing and were never given clear expectations how to use videoconferencing.  Administrative support issues included placement of the equipment, scheduling, and budgeting.  In a nutshell, this study concluded that successful implementation of videoconferencing “requires the cooperation of administrators as they listen to the needs of teachers” (Lim & Freed, 2009).  In the end, teamwork is needed to ensure successful implementation of videoconferencing.

Education is key to the growth of videoconferencing.  As the workforce grows education may reduce boundaries.  Integration of videoconferencing in the classrooms enables teachers to deliver course content to remote students and allows schools to offer education to those in remote areas.  However, teachers need professional development to ensure ease of use and encourage use video of conferencing (Greenberg, 2009).

Many states have invested in building videoconferencing infrastructure; states like California, Texas, New York, Florida, and Michigan are continuously adopting and using videoconferencing in K-12 (Greenberg, 2009).  The researcher’s assumption is that as students exposed to video-conferencing-enabled classrooms enter the workforce, usability will increase.  Theory development in the field of videoconferencing is also on the rise.  In development of the TAM model, Davis (1986) was able to look at videotape measures.  Johnson (2013) suggested communication technologies will mature within five years.  Technology has advanced and a need to assess video-conferencing utilization within organizations is recognized in the literature.  According to Davis (1986):

In the present context, if we can establish that attitude confidence enables us to tell how well video based ratings will predict hands-on based ratings, the confidence variable may serve as an important diagnostic in the future contexts where only video-based measurements are taken. (p. 131)

Videotapes have a number of attractive advantages relative to hands-on demonstration of prototype systems for user-acceptance testing:

1.      Videotapes enable testers to present hypothetical systems which may not physically exist, by simulating the system interface.  The outcome of a video-based acceptance test can then be used as an input to decisions regarding which of the hypothetical system capabilities should be built.

2.      In cases where prototypes actually do exist, they may not be in enough of a “final product” form to obtain untainted user judgments.  For example, if software precautions necessary to trap certain classes of user errors are planned, but have not been implemented, experimental participants may inadvertently get stuck in undesired states from which they cannot proceed toward completing their task without external help.

                  This could be perceived as negative perceptions regarding ease of use which may not be reflective of the final product.  (Conversely, however, videotapes may unknowingly disguise interface flaws that go unnoticed by designers, and do not get conveyed to subjects viewing a video demonstration).

3.      Videotapes are more portable, making it easier to run acceptance tests at remote sites.

4.      Videotapes make it more feasible to run larger numbers of subjects in parallel, being constrained not by the number of test systems, but rather by the number of subjects that can view a projection screen.

5.      Less time appears to be required to cover the same amount of information by video than by live demonstration, which reduces the testing time per subject.

6.      Due to reduced time requirements, more system versions can be shown to each subject, thereby generating more useful information for the same number of subjects. (Davis, 1986, p. 129)

Davis (1986) simulated the use of videoconferencing using videotaping for study measurements.  He admitted that a major potential disadvantage was that passive viewing of a system and its operation via videotape may be less realistic than active hands-on use of the same system, resulting in less accurate predictions of ultimate user adoption of the system.  System advances have eliminated some disadvantages of early research.

The Four Determinant Factors of Technology Use

Globalization has created a need for extended collaboration across geographic boundaries for all business activities (Carmel, 1999; Friedman, 2006).  In researching methods to measure Enterprise 2.0 social networks/media technologies’ use, acceptance, and evaluation, a review and analysis of the Technology Acceptance Model (TAM) provides an opportunity for exploring the possibility of using the same model and the use of quantitative methods to evaluate its use and acceptance.  The TAM is a well-known and practical tool for anticipating the ability of people to accept new technology (Venkatesh & Davis, 2000a).  By January 2000, 424 journal citations were listed in the Institute for Scientific Information's Social Science Citation Index that used introductory journal articles for TAMing (as cited in Venkatesh & Davis, 2000a; see also Davis 1989; Davis et al., 1989a).

Since the 2000 study, TAM has been developed and extended within different populations and various technology systems (Agarwal & Prasad, 2000; Chau & Hu, 2001; Cheung & Huang, 2005; Drennan, Kennedy, & Pisarki, 2005; Hong, Thong, Wong, & Tam, 2002; Moon & Kim, 2001; Riemenschneider, Harrison, & Mykytyn, 2003; Venkatesh, 2000; Venkatesh & Davis, 2000a, 2000b; Venkatesh & Morris, 2000).  The model continues to be used worldwide in business, information technology, and educational settings to help predict and assess an individual's acceptance of technology.  Therefore, the TAM is applicable when studying the theoretical framework for technology use and acceptance.

Pragmatically, understanding determinants of technology use should help predict technology adoption while use determines intended technology success (Borthick, 1988). The literature review indicated several determinants of the behavioral intention to use and utilization of videoconferencing: performance expectancy, effort expectancy, social influence, and facilitating conditions.  These constructs were designed into the videoconferencing TAM model as an extension of the TAM based on other research (Davis et al., 1989a; Mathieson, 1991; Mathieson, Peacock; & Chin, 2001; Taylor & Todd, 1995; Venkatesh, 2000).

Performance Expectancy

Video-conferencing usage and acceptance are important to support collaboration. By considering previous use of the TAM in corporations, this study brought to light a use that is becoming a trend in the IT industry.  In looking for more specific models that focused on particular applications and usage, the TAM that Davis (1986) developed seemed to be more adequate for this study's objective because it was developed to perform these specific tasks.

The TAM assumed that performance expectancy is the primary factor that affects usage of technology (Davis et al., 1989b).  An attitude toward technology acceptance and its use is composed of a belief component and the perception of attainability.  Davis (1989) defined performance expectancy as “the degree to which a person believes that using a particular system would enhance his or her job performance” (p. 320) or “the degree to which a person believes that using a particular system would be free of effort” (p. 320).  For example, according to Agarwal and Prasad (1997), graphic-user interfaces shield users from technical complexities.  Venkatesh and Davis (2000a) further noted that employees were more likely to use a technology if they believed it would be useful in their jobs.  During a study of 217 subjects in a free simulation experiment, acceptance was found to happen only when the task was deemed an integral part of an IT interface (Gefen & Straub, 2000).

Extending this concept, the task of collaborating occurs within organizations that leverage videoconferencing.  Morris and Dillon (1997) applied the TAM to account for why systems’ usefulness has a strong bearing on user acceptance.  Research to analyze and enhance cooperation using Microsoft Access as a tie-in to database systems was considered a typical example of use of the TAM (Leong, 2001).  In this study, Leong (2001) found relationships between user acceptance of a database-application interface and four antecedents: perceived usefulness, perceived ease of use, management support, and system quality.  Other studies of software usage supported these findings (Babar, Winkler, & Biffl, 2007; Gefen & Straub, 1997; Kim, Kim, Aiken, & Park, 2006; Mathieson et al., 2001; Morris & Dillon, 1997; Pijpers, Bemelmans, Heemstra, & van Montfort, 2001; Rangarajon, 2003).

Fitzgerald and Kiel (2001) applied TAM to study consumers’ adoption of online purchasing.  They extended the concepts found in IT research using consumer-technology acceptance and applied it to a marketing context.  In a survey of 128 respondents, the TAM was identified as having explanatory power for end-user acceptance of online- purchasing technologies.  Once online shopping was accepted, continued use resulted not only from perceived usefulness and perceived ease of use, but also from habit (Gefen, 2003).

Aladwani (2002) noted that in electronic commerce the interaction between buyers and sellers has shifted from a face-to-face model to a face-to-screen model.  The TAM was used to prove the importance of organizations  considering the Web attributes needed to attract users.  The findings showed that perceptions of usefulness and ease of use are among the primary determinants of acceptance of information technology.

Pavlou (2003) utilized the TAM as a predictor for consumer acceptance of electronic commerce because electronic commerce operates in a technology-driven environment.  Two studies, an exploratory study with three scenarios consisting of 103 students and a confirmatory study of 155 online consumers, validated that perceived usefulness and ease of use are key drivers of electronic commerce acceptance.  Klopping and McKinney (2004) also supported use of the traditional TAM to predict e-commerce online shopping activity but refuted the notion of a link between perceived ease of use and perceived usefulness or that perceived usefulness was directly linked to use.

Adams, Nelson, and Todd (1992) studied the perceived usefulness, ease of use, and usage of information technology.  A survey of 118 participants from 10 organizations identified their attitudes toward voice and e-mail messaging technologies.  The results supported TAM, and indicated that amount of use was tied to performance expectancy.  Lepervanche’s (2006) TAM study for TV-network Web-based news sites also linked Web-site usage with perceived usefulness and ease of use.

Pijpers et al. (2001) constructed a research model from the TAM to assess the factors that influence senior executives' use of information technology.  A theoretical research tool based on TAM was constructed, utilizing an executive information system to investigate the acceptance of information technology based on managerial belief, attitude, and usage.  The study was administered among 87 executives across 21 European multinational corporations.  It provided an understanding of an executive's inclination to use information technology and determined that beliefs and attitudes did not influence the external variables.

In Pijpers’ and von Montfort’s (2006) collaboration, the authors continued their study of executive acceptance as a predictor of the successfulness and effectiveness of information-technology innovations while corroborating the original TAM foundational understanding of technology acceptance.  Perceived fun and enjoyment also played a key role as an external variable influencing beliefs, attitude, and usage.

Chau and Hu (2001) studied technology acceptance by surveying 400 Hong Kong physicians using telemedicine technology.  Using the TAM, Chau and Hu modeled use by tracking perceived usefulness and perceived ease of use, which are fundamental determinants of user acceptance.  They utilized system usage as a dependent variable with the same independent variables, methods of research, and tools as Davis et al. (1989b) had used.  The findings indicated that individual health-care professionals’ decisions were based on usefulness of the technology rather than on ease of use (see also Keil, Beranek, & Konsynski, 1995; Venkatesh, 2000).

The TAM was further validated by Walter (2004) and Walker and Johnson (2008), who studied faculty objectives to use web-enhanced instructional components.  Walter determined that instructor take on usefulness and ease of use of web-enhanced instruction correlated with actual use.  Walker and Johnson also determined that usefulness and effectiveness probably predicted acceptance of web-enhanced instruction.

Pavlou, Dimoka, and Housel (2008) studied how collaborative IT tools can be successfully used to improve group work.  Collaborative IT tools are those that facilitate communication and sharing of information across an organization.  The tool-set included workspace sharing, conferencing, file sharing, scheduling, chat, and e-mail.  The researchers used the TAM to investigate the group's perceptions of usefulness and ease of use of collaborative IT tools, as well as group members’ trust, environmental uncertainty, and propensity of using collaborative IT tools.  Although the survey results from 365 group managers corroborated the TAM determinants for usability and ease of use, the findings implied that the extent of using collaborative IT tools did not have a role in predicting group performance or facilitating effective use of collaborative IT tools.

Social Influence

Social influence is defined as the person's reaction to peer pressure to conform to and exercise a given action (Ajzen, 1991).  In the context of videoconferencing, people might adopt collaboration technologies if the people they need to contact are already using it.  Although adoption sometimes occurs voluntarily, it can also be mandated by a superior (Davis et al., 1989a).  Social approval and image frequently have been recognized as key to a person's normative belief structure and are reasons for the adoption of new technologies (Moore & Bendasat, 1991).  Hossain and Silva (2009) determined that weak and strong social ties influence technology acceptance.  Because social influences are not mentioned in the TAM, Davis et al. (1989a) included them and found they had no effect on behavioral intent.  Having omitted them from the original TAM, the authors did acknowledge the need for additional research to “investigate the conditions and mechanisms governing the impact of social influences on usage behavior” (p. 999). Green (1998) performed a field study considering software usage in 10 intact work groups and identified that normative influence explained a significant portion of the usage variance.  Other research including social norms has shown mixed results. Mathieson (1991) found no significant effect on behavioral intention, whereas Taylor and Todd (1995) and Harrison, Mykytyn, and Riemenschneider (1997) found the opposite. Venkatesh and Davis (2000b) found a significant effect on user acceptance.  In a more recent study, 106 U.S. registered nurses indicated that social influences strongly predicted behavioral intention to use radio-frequency identification (Norten, 2012).

Green (1998) performed a field study by comparing software usage in 10 intact work groups to the mechanisms of normative influence, and evaluated measurement techniques that applied to other IT acceptance studies.  The results suggested that normative pressures might be more influential in technology acceptance compared to earlier research findings.  Group norms might have had a direct impact on the approval or disapproval of an action that a user might have taken in a particular situation.

Chau and Hu (2001) empirically compared TAM and TPB models using responses from more than 400 physicians practicing in Hong Kong public hospitals. Results from the study regarding social networking concurred with Davis et al. (1989a).  A Fitzgerald and Kiel (2001) study of Internet social influences for adopters of online purchasing indicated that significant variance was explained by future-use intentions and attitudes.  The results of a survey of Fortune 500 companies demonstrated that social influences could be a predictor of groupware usage based on the 409 South Korean companies that responded (Kim et al., 2006).

However, Lin (2007) studied membership needs of virtual communities that form on the Internet and found that social influences had little impact on member behavioral intentions.  Hsu and Lin (2008) found that subjective norms did not significantly change or influence the person’s intention to blog.  In addition, social influence did not statistically predict intention or perceived usefulness in engineering and technical students’ acceptance of network virtualization (Yousif, 2010).

Fiato’s (2012) study examined the relationship between peer-support implementation techniques and nurses’ perceptions of the usefulness and ease of use of an emergency medical room.  Based on results from an online survey of 153 registered nurses, peer support enhanced nurses’ perceptions regarding emergency medical rooms; social influence had a strong impact on the interaction between peer support and perceived usefulness.

Effort Expectancy

Bandura (1982) defined effort expectancy as “concerned with judgments of how well one can execute courses of action required to deal with prospective situations” (p. 122).  Effort expectancy is associated with beliefs and behavior; it also has a crucial influence as internal control in technology adoption (Bandura, 1982; Venkatesh, 2000).  In IT research, perceived ease of use has been identified as a determinant of attitude, while internal control has been tied to perceived behavioral control (Davis et al., 1989a; 1989b; Taylor & Todd, 1995; Venkatesh & Davis, 1996).  Previous research has confirmed that adoption of new technology relies on self-efficacy (Igbaria & Iivarari, 1995).

Studies reflecting the adoption of a computer or concerning computer usage have been conducted by Compeau and Higgins (1995), Igbaria and Iivari (1995), and Johnson and Marakas (2000).  In the Igbaria and Iivari survey of 450 microcomputer users, it was found that effort expectancy had an indirect and direct impact on usage and a strong direct impact on ease of use, but only an indirect impact on perceived usefulness.

Compeau and Higgins (1995) also found effort expectancy to exert a significant influence on computer use and individual expectations of outcomes from using computers.  The Johnson and Marakas study, which extended and replicated the work of Compeau and Higgins, refuted those findings, but supported Venkatesh and Davis (1996).  This suggested that performance and effort expectancy improved after manipulation, signifying training’s importance.

Venkatesh and Davis (1996) also indicated that effort expectancy is a good determinant of perceived ease of use and usage.  They suggested that lack of systems acceptance has been associated with low effort expectancy and hinted that training could have improved the outcome.  Lopez and Manson (1997) also found effort expectancy to be significant, but that it had less direct influence on usage and, indirectly, on perceived usefulness.  In Venkatesh’s (2000) longitudinal study, self-efficacy was identified as an anchor employed to form perceived ease of use of a new system.  The research team of Hong et al. (2002) determined that digital libraries were easier to use for users with higher effort expectancy and greater knowledge of the search domain.

Studies have represented effort expectancy with respect to externally facing or consumer-based social computing (Hsu & Chiu, 2004; Luarn & Lin; 2005; Vijayasarathy, 2004).  Luarn and Lin (2005) determined that perceived ease of use and behavioral intention to use were affected by effort expectancy for users’ acceptance of online banking.  A study of e-commerce use identified that effort expectancy had a positive impact on both behavioral intention to use and actual use (Hsu & Chiu, 2004). 

In Vijayasarathy’s (2004) study of online consumer intentions, one's intention to shop was strongly influenced by self-efficacy. Lin (2007) determined that member effort expectancy as related to virtual community participation clearly affected perceived behavioral control, which in turn was found to affect behavioral intent.

In a questionnaire study, Koufaris (2002) examined acceptance of online consumer behavior as a shopper and computer user; he tested constraints from an integrated theoretical framework based on information systems TAM, marketing (consumer behavior), and psychology (flow and environment psychology).  His findings did not demonstrate support for the perceived behavioral-control hypotheses.

In a health-care setting, Chau and Hu (2001) empirically examined 400 professionals’ responses to telemedicine technology acceptance.  Their findings were consistent with previous studies in which perceived behavioral control was found to have a significant direct impact on behavioral intention.  However, the effect on perceived behavioral control was weaker than that of perceived usefulness and attitude.  In another study of health-care professionals, researchers Yi, Jackson, Park, and Probst (2006) determined that perceived behavioral control had a positive effect on both behavioral intention and perceived ease of use in the context of acceptance of a personal digital assistant.  Lin (2007) corroborated this when she determined that perceived behavioral control of members in virtual communities clearly affects behavioral intention with respect to participation.

Facilitating Conditions

Ajzen (1985) identified that external control has an important function in forming intention and behavior in diverse fields.  In technology, the external control exerts its influence in the form of having the proper tools and connectivity available conveniently to access videoconferencing technologies (Taylor & Todd, 1995).  Mathieson (1991) suggested that control beliefs can be situational (e.g., having access to the Internet) as well as personal (e.g., being able to use the Internet).  In the context of videoconferencing while organizations provide Internet access, they sometimes block or restrict access to social-networking sites (Murugesan, 2008).  Moreover, Bhattacherjee (2000) determined that the convenience of the Internet affected users’ perception of behavioral control regarding externally facing e-commerce services.  Thus, the videoconferencing TAM model proposed that a facilitating condition was an important anchor of the perceived behavioral control determinant.

Demographic Factors in Technology Use

Demographic factors have also been studied with regard to how they affect use of technology.  Gefen and Straub (1997) tested gender differences in modes of communication.  In a cross-sectional survey of 392 female and male respondents, it was determined that there is a difference between men and women's perceptions but not in their use of communication media.  This study did not investigate the impact of gender as a variable.  Researchers Teo, Lim, and Lai (1999) also investigated the impact of gender, age, and educational level on perceived enjoyment and perceived usefulness as motivators for Internet usage.  Their findings indicated that perceived usefulness was significantly negatively associated with female gender but significant positively associated with education; age and female gender correlated negatively with perceived enjoyment.

Colvin’s (2008) study of domestic technologies, such as ambient computing devices or smart phones, utilized standard TAM measures along with gender.  A survey was administered to 113 participants and path analysis determined that gender might have an impact on behavioral intention to use the technology.  Cha (2009) studied the impact of factors affecting shopping attitudes on social-networking sites for real and virtual products.  The results suggested that age was critical in establishing a favorable attitude toward shopping for real items, while for virtual items, gender-related perceptions of ease of use might have influenced attitude.

Burton-Jones and Hubona (2005) included staff seniority, age, and education as determinants for perceived usefulness and perceived ease of use for e-mail and word processing adoption.  Staff seniority and education, but not age, correlated with perceiving e-mail and word processing as useful.  Staff seniority and education level were not associated with the perception that it was easy to use, but younger age was.

When Pijpers and von Montfort (2006) investigated factors that influence senior-executive acceptance of information-technology innovations, they included age, gender, education, managerial and IT knowledge, professional experience, computer experience, computer training, personality of the manager, cognitive style (brain-orientation), and individual culture in the scenario.  Although age was not a factor in acceptance, men were more accepting than women.  Acceptance was also positively related to education, culture, and managerial and IT knowledge.  The researchers also distinguished between brain and orientation as constructs for analytical and directive cognitive styles. 

They determined that both cognitive styles were positively associated with acceptance, which meant the brain construct more so than the conceptual and behavioral cognitive style, and the orientation construct more so than the directive and behavioral cognitive style. 

Areas Needing Additional Investigation

Based on the literature review, it appears that several areas need further investigation.  Drawing on previous research, Ajzen and Madden (1986) linked perceived behavioral control to behavioral intent; however, other sources did not make this link.  These mixed results warranted further investigation with respect to the impact this constraint has on the adoption of videoconferencing.  Using the concept of performance expectancy, this study incorporated perceived behavioral control into the conceptual videoconferencing TAM model.  These findings might suggest that perceived behavioral control, or performance expectancy, affects people’s behavioral intent to utilize videoconferencing.  Table 2 suggests some ways in which that could be the case.

 

 

 

 

 

 

 

 


 

Table 2

Proposed Instrument Factors and Justification for Hypotheses

 

Unified Theory of Acceptance and Use of Technology model (UTAUT)

Factors effecting Videoconferencing  Utilization

 

 

 

Test Construct Items Sample

Performance expectancy

1-       Using videoconferencing contributes to improve employees’ job performance, because if they use videoconferencing they will be capable to conduct more meetings (Virtual meetings) as opposed to travelling to attend face-to-face meetings. This will result in more employee output in terms of quality and quantity resulting in an increase in the employees’ performance.

2-       Using videoconferencing contributes to improving employees’ productivity, as the case of steelworkers, where remote workers are able to produce more output leveraging videoconferencing utilizations.

3-       Using videoconferencing enhances employees' effectiveness, for example, it helps managers and project managers to make decisions fast.

 

Effort expectancy

1-       It is easy to learn to use a videoconferencing system.

2-       Employees find it easy to become skillful using the system.

3-       User training could improve videoconferencing utilization.

 

Social Influence

Management Subject norm

1-       DPSS management believes employees should use

videoconferencing.

2-       Supervisor believes subordinates should use videoconferencing.

3-       For an employee, it is important to do what his/her division management wants him or her to do.

Organizational Relative Advantage

1-       Videoconferencing system allows the organization to be more productive (Produce more output and quality and quantity)

2-       Videoconferencing system allows the organization to be more efficient (Produce more output with less resources)

3-       Videoconferencing system makes the organization more responsive to customer needs (e.g., Live chat and integration of Ebay and Skype)

Facilitating

Conditions

Technology Facilitating Conditions

1-       If videoconferencing is available to all employees, it could influence videoconferencing utilization.

2-       Quality of videoconferencing could influence videoconferencing utilization.

3-       Video infrastructure is key to increasing videoconferencing utilization.

4-       Videoconferencing accessible to all DPSS could increase videoconferencing utilization.

5-       Professional support training could improve videoconferencing utilization.

Additionally, previous research has explored effort expectancy with respect to the internal-facing concerns of the workplace (see Pijpers & van Montfort, 2006; Shen & Eder, 2009).  Pijpers and van Montfort (2006) investigated factors influencing senior management’s acceptance of executive information systems, and identified self efficacy’s direct impact on perceived ease of use and usage; more importantly, the study also found such acceptance improved an executive’s self-efficacy.  However, not yet analyzed in a public-institutions’ context, the influence of this factor on videoconferencing utilization is ignored in most time-series studies.  In a Shen and Eder (2009) study exploring usage intentions of virtual worlds in business, effort expectancy was identified as an important predictor of perceived ease of use, which indirectly supported perceived usefulness and behavioral intent.  In contrast, Yousif (2010) examined engineering and technical students’ acceptance of network virtualization and determined that computer effort expectancy did not demonstrate correlation with the model’s variables.

Using these arguments, this study incorporated effort expectancy into the conceptual videoconferencing TAM model.  Table 2 suggests some ways in which effort expectancy could affect people’s behavioral intent to use videoconferencing.

This chapter also investigated social influence as a factor affecting the adoption of videoconferencing.  Because mixed results have been observed throughout the theoretical and empirical literature, additional research was warranted to determine the impact that social influence has on behavioral intent.  Table 2 shows ways that social influence could affect people’s behavioral intent to use videoconferencing.

This research also included facilitating conditions as they related to perceived behavioral control in the conceptual videoconferencing TAM model.  The literature suggests that facilitating conditions affect peoples’ perceived behavioral control and their intention to use (see Table 2 for some ways in which that might be the case).

Summary

This literature review addressed the current state of videoconferencing and its value in helping organizations to communicate and interact.  Chapter 2 also focuses on the use of an extended model of the TAM in understanding acceptance of technology.

In summary, global businesses have expanded their business systems to reach more users by implementing enterprise business systems and customized applications.  Utilization of videoconferencing was expected to be affected by important determinants found in the TAM and its extensions: performance expectancy, effort expectancy, social influence, and facilitating conditions.  In order to utilize the same analytical techniques Davis et al. (1989b) used, many studies in the reviewed literature have used linear regression analysis to identify the contribution of each independent determinant to the outcome variables, behavioral intention to use and system usage.  The methodology used in this literature also used descriptive statistics and correlation matrices for all the variables.

The reviewed publications provided background information about leveraging technology to communicate and interact within organizational structures and using the TAM to predict videoconferencing adoption based on defined determinants.  This section also exposed gaps in the literature and discussed how research should be tested in different organizational contexts (e.g., public or government institutions; see Venkatesh et al., 2003). 

For this study, based on the use of an extended model of the TAM, utilization of videoconferencing was expected to be affected by performance expectancy, effort expectancy, social influence, and facilitating conditions.  The next chapter, Methodology, describes the research design, including the instrument used to collect data on the studied variables.


CHAPTER THREE: METHODOLOGY

The review of literature covered in Chapter 2 provided a framework for videoconferencing utilization and the determinant factors that could predict high or low utilization and acceptance of such technology; these factors were (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions. Furthermore, it has shown that no studies have examined videoconferencing utilization and usage within a psychosocial framework of behavior or how a social service organizational context influences relevant managers and employees’ perceptions and behavior.

In this study the principle investigator addressed these research gaps by examining videoconferencing utilization within a social services organization, using an extended Unified Theory of Acceptance and Use of Technology model (UTAUT).  This model was a consolidation of eight technology acceptance models, including the technology acceptance (TAM) model created by Davis (1998), theory of reasoned action, theory of planned behavior, and social cognitive theory.  Kok et al. (2014) conducted a study where they examined psychosocial and organizational factors relevant to teleconference use; they concluded that most teleconference-related principles depended on the organizational sectors, such as private and public. 

In this chapter the detailed plan for conducting an investigation to acquire information to assess videoconferencing technology acceptance in a Southern California social service organization was described.  Authors have agreed that the research design constitutes a directional map for collection, measurement and analysis of data.

Chapter 3 focuses on the research approach: statistical tools, with a description of the research method, research design; participants; materials and survey instruments; procedures, and analysis. In addition, this chapter describes how many actual participants were needed for the study and how data were collected.

The aim of this quantitative study was to examine relationships between variables.  They are presented as psychosocial and social factors that could influence acceptance and use of videoconferencing technology.  The final section contains methodological assumptions, limitations, and delimitations, as well as ethical assurances given for the protection of human participants.

Restatement of the Purpose

The purpose of this study was to assess obstacles to videoconferencing implementation and usage within a southern California social service organization.  In other words, this study entails an analysis of incongruent patterns of videoconferencing utilization and usability arising from a real world need for a social services organization to understand videoconferencing utilization trends and to discover factors and settings that contribute to the acceptance and use of videoconferencing or which limit acceptance and use.

This research was conducted within the Los Angeles County Department of Public Social Services (DPSS); it established a baseline of videoconferencing utilization and usability.  The knowledge derived from this study could allow DPSS to take measures to effectively leverage and influence positive factors that improve utilization and usability while correcting any deficiencies of utilization and usability of videoconferencing.

The findings from the research survey and interviews will establish a conceptual understanding of efficient usage and utilization of videoconferencing. A quantitative survey methodology was used to collect and analyze data on four key variables: (a) performance expectancy, (b) effort expectancy, (c) social influence, (d) and facilitating conditions, and to assess their association with videoconferencing utilization.

Results from the data analysis were used to answer the research questions and test the following hypotheses:  

Research Question 1 asked, Does performance expectancy of employees influence videoconferencing utilization?

H10:      There is no significant relationship between performance expectancy of employees and videoconferencing utilization.

H1A:    There is a significant relationship between performance expectancy of employees and videoconferencing utilization.

Research Question 2 asked, Does effort expectancy of employees influence videoconferencing utilization?

H20:     There is no significant relationship between effort expectancy of employees and videoconferencing utilization.

H2A:    There is a significant relationship between effort expectancy of employees and videoconferencing utilization.

Research Question 3 asked, Does social influence promote videoconferencing utilization?

H30:     There is no significant relationship between social influence and employees’ intention to use videoconferencing.

H3A:    There is a significant relationship between social influence and employees’ intention to use videoconferencing.

Research Question 4 asked, Do facilitating conditions influence videoconferencing utilization?

H40:     There is no significant relationship between facilitating conditions and videoconferencing utilization.

H4A:    There is a significant relationship between facilitating conditions and videoconferencing utilization.

Research Question 5 asked, Do demographic variables (position, age, gender, education) influence performance expectancy, effort expectancy, social influence, or facilitating conditions?

H50:     There is no significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

H5A:    There is a significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

Research Design

This study utilized a quantitative cross-sectional survey design.  In quantitative studies, standardized dependent measures allow for study replications and cross-study comparisons (Garrity & Sanders, 1999) and make it possible to determine patterns of relationships and trends of a predictive nature.  The analytical research technique utilized in most studies in the literature review represents a number of independent units, using statistical sampling theory to collect and analyze data from finite populations.  A quantitative replication approach was used to establish commonality of factors for baseline analysis.  This investigation also used the analytic approach.  An extended TAM isolated the variables from their context to identify variable characteristics.  The goal was to determine how useful videoconferencing was perceived to be, the intent to use, and its ease of use rather than the precision or sensitivity of the results.

Surveys can be effective in gathering data to understand participants’ thoughts, feelings, and behavior (Aiman-Smith & Markham, 2004).  This design was also considered appropriate because, in cross-sectional designs, a researcher can “examine current attitudes, beliefs, opinions, or practices” (Creswell, 2012, p. 237) towards a system, such as a communication system, in a series of systematic steps or procedures.  These steps are described in this chapter. 

In order to utilize the same analytical techniques Davis et al. (1989b) used, many studies have used linear regression analysis to identify the contribution of each independent determinant to the outcome variables, behavioral intention to use, and system usage.  Due to the multiple independent variables involved in this study, a multivariate approach was also employed to analyze data, following the views of researchers (e.g., Aliaga & Gunderson, 1999; Anderman, 2009; Charles & Mertler, 2002; Creswell, 2003).  These multivariate analytical tools were used to analyze multiple relations between the four determinant factors and videoconferencing utilization. 

Subjects

Population and Sampling

It is important to answer the question of why an organization might use videoconferencing (Olaniran, 2009).  Questioning leads to a free flow of ideas and information that is so important in today’s changing organizations (Daft, 2007).  Meeting organizational communication challenges requires input and collaboration from all levels of the organization structure.  The population for this study consisted of current employees of a Southern California social service organization, regardless of their position or rank, age, gender, or educational qualification or training in the organization. 

Sampling estimates a property of the entire population based on data collected from a subset of the elements (Mendes & Reed, 2004).  A non-random but targeted single-stage sampling method was used purposefully to select this study’s sample.  The targeted sample size was 300, which was deemed acceptable as it represented more than 5% of the total population (Copper & Schindler, 2008).  From the target population of all employees of the service organization, the sample was selected as follows: 203 DPSS staff located at DPSS administrative office headquarters, who possessed a WebEx account to host videoconferencing sessions, and who had access to room-based video conferencing systems.  Same employees were also drawn from each of the DPSS’ five bureaus: Bureau of Contract and Technical Services (BCTS), Bureau of Special Operations (BSO), Bureau of Administrative Services (BAS), Bureau of Workforce Services (BWS), and Bureau of Program and Policy (BPP).  In addition, other possible study participants included administrative staff, managers, and general employees who did not have a WebEx account but had access to room-based videoconferencing.  There were also 97 videoconferencing support staff across 11 regions representing all 11 regional offices where videoconferencing had initially been deployed.  Combining the groups of 203 centrally located staff and 97 regional staff gave a total of 300 DPSS employees selected for this study, and who received emails in the organization’s internal email system asking them to participate.  However, due to IRB constraints, it was only possible to conduct research for one week, which limited participation.  A total of 126 participants completed the survey; representing 42% of the 300 potential participants targeted and greater than the 30% projected to participate in the study.

Characteristics of the Sample

Table 3 shows demographic characteristics of the 122 participants in the sample.  The majority of participants were male, in their 30s and 40s, and had completed a bachelor’s degree.  About half of the participants were from the Bureau of Contract and Technical Services, and about 40% of participants were technical staff.

Table 3

Demographic and Employee Characteristics of Sample

____________________________________________________________

 

Variable                                                           N                          %   

____________________________________________________________

 

Gender

Male                                                          71                    58.68

Female                                                       50                    41.32

 

Age group

18-29                                                           3                      2.46

20-29                                                         20                    16.39

30-39                                                         41                    33.61

40-49                                                         43                    35.25

50-59                                                         12                      9.84

60-65                                                           3                      2.46

____________________________________________________________

Table 3 (continued)

Demographic and Employee Characteristics of Sample

____________________________________________________________

 

Variable                                                           N                          %    

____________________________________________________________

Education

High school                                               16                      13.22

AA                                                             17                      14.05

BA/BS                                                       62                      51.24

MBA/MS/MPA                                         24                      19.83

PhD                                                              2                        1.65

 

Departmental Bureau

Contract and technical services                  61                      51.69

Special operations                                       17                      14.41

Administrative services                              12                      10.17

Workforce services                                       4                        3.39

Program and policy                                     24                      20.34

 

Position

ASM1/HSA1                                             27                      22.13

ASM2/HSA2                                               6                        4.92

ASM3/HSA3                                             13                      10.66

Chief                                                            5                        4.10

Executive staff                                            1                        0.82

Secretary                                                     5                        4.10

Technical supervisor                                 12                        9.84

Technical staff                                           41                      33.61

  Other administrative staff                         12                        9.84

____________________________________________________________

 

Instrumentation

A web-based survey consisting of 22 questions (Appendix B) was administered to the participants; it allowed the researcher to collect information for particular purposes that were determined by the research question (Oishi, 2003).  This survey, the videoconferencing utilization assessment questionnaire, has been used and validated in various studies (Benedictus, 2011; Saouli, 2004; Taylor & Todd, 1995; Venkatesh & Davis, 2000b).  The survey contained multiple-choice questions that had 5-point Likert-scale response choices or solicited a yes or no response.

Survey Questionnaire Sections

Questions 1 through 8 of the instrument were designed to collect information about general characteristics and the demographic background of DPSS employees, including age, gender, position in the organization, and level of education.  Demographic variables are very important in accounting for statistically significant variance (Kangasharju, 2000).  Demographic constructs were measured using questions that had closed-choice single responses.  Questions 9 through 12 addressed facilitating conditions as a factor affecting videoconferencing utilization.  Questions 13 through 16 addressed social factors affecting videoconferencing utilization, and Questions 17 through 20 addressed the role of effort expectancy in videoconferencing utilization within the Department of Public Social Service. 

In keeping with the spirit of the original TAM, the survey results were measured and analyzed using a 5-point Likert scale (Davis, 1989b).  Likert-scale response choices for Questions 9 through 22 were as follows: 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, and 5 = strongly agree. 

The four independent variables in this study included performance expectancy, effort expectancy, social influence, and facilitating conditions (see Figure 4).  There were two dependent variables: any videoconferencing utilization and frequency of utilization.  Demographic variables were also included as possible covariates in multivariate analyses.

Figure 8. Independent variables, possible covariates and dependent variables.

 

 

Reliability and Validity

Cronbach’s alpha coefficient was used to estimate the reliability or the internal consistency reliability of the intercorrelations of the items making up each of the scales that were used as independent variables.  Cronbach's alpha coefficient varies between 0 and 1, α ≥ 0.9 is considered excellent; 0.7 ≤ α < 0.9 good; 0.6 ≤ α < 0.7 is acceptable (Crocker & Algina, 1986; DeVellis, 1991; Gregory, 1992; Henson, 2001).  All scales had acceptable internal reliability: Facilitating conditions (Cronbach’s alpha = .68), Performance expectancy (alpha = .88), Social influence (alpha = .80), and Effort expectancy (alpha = .59), and so items for each scale were averaged for use in analyses.

Construct validity is a test of the degree to which a test measures what it claims to measure.  As mentioned above, this survey, the videoconferencing utilization assessment questionnaire, has been used and validated in various studies (Benedictus, 2011; Saouli, 2004; Taylor & Todd, 1995; Venkatesh & Davis, 2000b).  

Methodological Assumptions, Limitations, and Delimitations

Methodological Assumptions

Three assumptions were made in this study.  First, it was assumed that the population from which the sample was drawn had characteristics that were normally distributed and had equal variances.  Secondly, it was assumed that the respondents were able to answer questions related to how their managers would perceive them when using videoconferencing and whether employees believed their management supported their using videoconferencing.  Lastly, it was assumed that there might be significant differences by gender, age, position in the company, role in the organization, and education level when using videoconferencing.

Limitations

In the survey proceedings there was a risk of non-response error; this problem could have been because of differences between those who responded and those who did not in the event of low response rate (Dillman, 2000).  The study offered a one-time collection of data for participants to address questions.  The researcher distributed the questionnaire online to DPSS employees.  The participants may have had unique interpretations of some words or phrases within the instruments that may have influenced their responses.  Because this research was conducted in the United States, study findings may be most relevant to the American culture.  The study also used self-reports on a survey, so the element of subjectivity could not be eliminated completely.

Delimitations

This study was confined to the Los Angeles County Department of Public Social Services only.  Uniqueness of the study within a specific context makes it difficult to replicate exactly into another context (Creswell, 2003).  Thus, the scope of this study was limited to the Los Angeles County Department of Public Social Services.  This study was delimited to a public organization and findings may not be generalized to private organizations.  The study did not account for private organizations that are considered profit-driven companies.  The study only accounted for those employees who responded to the questionnaires within the Los Angeles County Department of Public and Social Services.

Procedures

There were three general procedures for the study: (a) data collection involving sampling, securing informed consents, meeting all IRB protocols and requirements, and administering the survey through email; (b) data coding, grouping, and scoring so the appropriate statistical tests could be run; and (c) testing the hypotheses, answering the research questions, and reporting the results (as described in the data analysis section).

First, IRB approval was requested from the DPSS (Appendix C) to conduct research and distribute the survey (Appendix B).  It took about four weeks to gain access and approvals, to obtain emails of participants, and to prepare emails individually for each participant as directed by DPSS’s internal IIRB.  Once DPSS approved the research, an email invitation to participate in the study (Appendix D) and consent forms (Appendix E) were sent to all subjects along with a link to the Qualtrics survey platform, where the web-survey (Appendix C) was hosted.  The consent form stated the purpose of the study, what participants were expected to do and the time it would take, that responses would be confidential, that participation was voluntary, and the possibility of withdrawal from the study at any time.  The email also included the researcher’s contact information.  

On the third day, follow-up reminders were sent to those who did not respond.  On the fourth day, the investigator called DPSS employees who had not completed the survey.  It is DPSS policy to allow students to survey and collect data in only one week, due to the disruption this process causes DPSS employees.  Therefore, the researcher had only one week to collect the data.  Subsequently, the researcher collected, cleaned, and prepared the data for analysis in Statistical Package for Social Sciences (SPSS) software, Version 12.  Thank-you responses were also sent to all participants in the study.

Data Processing and Analysis

The objective of the data analysis was to validate the hypotheses and to demonstrate the acceptance of social-networking technologies within the business enterprise.  The methodological goal was to determine the explanatory capability of the videoconferencing TAM tool to predict usage within the domain of videoconferencing.

First, frequencies were presented for (a) variables representing reported use of and perceptions of videoconferencing and (b) the four main independent variables indicating participants’ attitudes about the importance of various conditions for improving use of videoconferencing.  Correlations of the scales with each other were also presented.

To test the hypotheses, the relationship between each of the four independent variables and, respectively, any use of videoconferencing and frequency of use of videoconferencing were examined using respectively two-tailed Spearman’s correlations and two-tailed Pearson’s correlations.  Analyses were also run to ask if any demographic or employee variables (e.g., age, gender, educational qualification, or training in the use of videoconferencing) were significantly associated with reporting any use of videoconferencing or more frequent use.  Any significant predictors of any videoconferencing use from the analyses above were entered together into a multiple logistic regression predicting any videoconferencing use.  Similarly, any significant predictors of reported frequency of videoconferencing use from the analyses above were entered together into a multiple linear regression predicting reported frequency of videoconferencing use.  Only variables that were significant were retained in the final regression models.  Alpha was set at .05, as appropriate for social-science research (Aliaga & Gunderson; 1999; Anderman, 2009; Charles & Mertler, 2002; Creswell, 2003).

Summary

The purpose of this quantitative study was to investigate the influence of factors affecting videoconferencing utilization within a social services organization.  This research aimed not only to extend the Venkatesh et al. (2003) UTAUT model, but also to add to the existing research in the field and to offer recommendations to improve videoconferencing utilization within an organization.  As defined by the UTAUT model (Venkatesh et al., 2003), factors studied were (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions, which have all been proven to have an impact on how technology is utilized in organizations.  A quantitative methodology involving cross-sectional survey research was used to collect and analyze data to answer four research questions as to whether videoconferencing utilization was associated respectively with performance expectancy, effort expectancy, social influence, or facilitating conditions.  

This chapter details the study’s methodology strategy and protocols used to conduct the study.  It presents the study’s research method, research design, participants, materials and survey instruments, and data collection, processing, and analysis.  The aim of this quantitative study was to examine relationships between variables—specifically, to ask how psychosocial and social factors might influence acceptance and use of videoconferencing technology.  The final section of this chapter describes methodological assumptions, limitations, and delimitations, as well as ethical assurances given for the protection of human participants.  Chapter 4 reports the findings.  Chapter 5 presents recommendations based on the findings and addresses their implications for future research directions.


 

CHAPTER FOUR: RESULTS

Restatement of the Purpose of the Study

The purpose of this study was (a) to identify barriers to the implementation and effective usage of videoconferencing as an alternative to face-to-face meetings within the Los Angeles County Department Public Social Services and (b) to establish a conceptual understanding of efficient utilization of videoconferencing and identify a basis for developing a Videoconferencing Acceptance Model that could help social services organizations achieve a high level of videoconferencing utilization.  A survey was sent to DPSS employees regarding their perceptions, attitudes, and beliefs about their acceptance and utilization of videoconferencing technology within the social services organization.  Chapter 4 presents statistical analysis of the data collected from a sample of DPSS employees to address the following research questions and hypotheses.

Research Questions and Hypotheses

Research Question 1 asked, Does performance expectancy of employees influence videoconferencing utilization?

H10:      There is no significant relationship between performance expectancy of employees and videoconferencing utilization.

H1A:    There is a significant relationship between performance expectancy of employees and videoconferencing utilization.

Research Question 2 asked, Does effort expectancy of employees influence videoconferencing utilization?

H20:     There is no significant relationship between effort expectancy of employees and videoconferencing utilization.

H2A:    There is a significant relationship between effort expectancy of employees and videoconferencing utilization.

Research Question 3 asked, Does social influence promote videoconferencing utilization?

H30:     There is no significant relationship between social influence and employees’ intention to use videoconferencing.

H3A:    There is a significant relationship between social influence and employees’ intention to use videoconferencing.

Research Question 4 asked, Do facilitating conditions influence videoconferencing utilization?

H40:     There is no significant relationship between facilitating conditions and videoconferencing utilization.

H4A:    There is a significant relationship between facilitating conditions and videoconferencing utilization.

Research Question 5 asked, Do demographic variables (position, age, gender, education) influence performance expectancy, effort expectancy, social influence, or facilitating conditions?

H50:     There is no significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

H5A:    There is a significant relationship between demographic variables and performance expectancy, effort expectancy, social influence, or facilitating conditions.

Reported Use of and Perceptions of Videoconferencing

Table 4 shows that the majority of study participants reported that they thought videoconferencing would be useful for communication and collaboration, while using videoconferencing.  A little under one third of respondents (32%) reported that they never used videoconferencing, a little over one third of respondents reported that they used it less than one a month, and the remainder of participants reported that they used it once a month or more.  With regard to the positive factor most perceived to influence use of videoconferencing, facilitating conditions and performance expectancy were the most commonly mentioned factors.  With regard to the factor most perceived to hinder use of videoconferencing, facilitating conditions and effort expectancy were the most commonly mentioned factors.


Table 4

Reported Use of and Perceptions of Videoconferencing

                                                                                                                      

 

Variable                                                           N                        %     

                                                                                                                      

 

Videoconferencing useful for communication/collaboration

Yes                                                          112                    92.56

No                                                               9                       7.44

 

Currently use videoconferencing

Yes                                                            70                     57.85

No                                                             51                     42.15

                                                                 

How often use videoconferencing

Never                                                        39                     31.97

< Once a month                                        46                     37.70

Once a month                                            23                     18.85

2-3 times a month                                     10                       8.20

Once a week                                               1                       0.82

2-3 times a week                                         3                       2.46

 

Positive factor that most affects use

Facilitating condition                                51                     43.22

Performance expectancy                           44                     37.29

Effort expectancy                                     12                     10.17

Social influence                                        11                       9.32

 

Hindering factor that most affects use

Facilitating condition                                50                     43.48

Effort expectancy                                     41                     35.65

Social influence                                        13                     11.30

Performance expectancy                           11                       9.57

                                                                                                                      

 

 

Next, analyses examined scales indicating participants’ attitudes about the importance of various conditions for improving use of videoconferencing.  Table 5 shows the scales were generally significantly correlated with each other, but not multicollinear (r > .70), so they could be used together in regression analyses.  Facilitating condition was especially highly correlated with Performance expectancy and Social influence.  Effort expectancy and Social influence were not significantly correlated. 

Table 5

Correlations of Scales with Each Other

                                                                                                                                        

 

                                                                              Social      Facilitating      Performance

                                                                           influence     condition         expectancy

                                                                                                                                        

 

Facilitating condition                          r                  .53                                   

                                                            p                  .00                                   

                                                            N                 121                                  

 

Performance expectancy                     r                  .33                  .65            

                                                            p                  .00                  .00            

                                                            N                 120                 120          

 

Effort expectancy                                r                0.10                0.18                   0.25

                                                            p                0.29                0.05                   0.01

                                                            N                 119                 120                    119

 

                                                                                                                                        

Note: Correlation coefficients from Pearson’s two-tailed correlations.

 

Table 6 shows the means and standard deviations for these scales indicating participants’ attitudes about the importance of various conditions that would improve use of videoconferencing.  On average, participants agreed that improving facilitating conditions, performance expectancy about using videoconferencing, social influence, and effort expectancy would improve use of videoconferencing. 


 

Table 6

 

Attitudes About Videoconferencing and Correlations With Videoconferencing Use

 

 

Videoconferencing use

Scale

N

M

SD

Range

Any

Frequency

Importance of conditions to improve videoconferencing use

 

Facilitating conditions

122

4.15

0.49

3, 5

.78

.04

.30

.00

 

Performance expectancy

121

3.86

0.75

2, 5

.74

.01

.38

.00

 

Social influence

121

3.81

0.76

1, 5

.29

.24

.20

.03

 

Effort expectancy

120

3.51

0.64

1.67, 5

.71

.02

.17

.07

 

Note: Scales scored: 1= strongly disagree; 2 = disagree; 3 = neither agree nor disagree;
4 = agree;5 = strongly agree. Frequency of videoconferencing use recoded 1 = never,

2 = < once a month, 3 = once a month or more.

 

 

Tests of Hypotheses

To test the hypotheses, the scales above were also entered into two-tailed Spearman’s correlations with reported use of any videoconferencing, and entered into two-tailed Pearson’s correlations with reported frequency of use (Table 6).  Frequency of videoconferencing use was recoded 1 = never, 2 = < once a month, 3 = once a month or more to ensure a normal distribution of the dependent variable and meet the assumption of normality of the dependent variable for the linear regression analysis that follows.

Hypothesis 1A predicted that there would be a significant relationship between perceived importance of performance expectancy of employees and videoconferencing utilization.  The alternative hypothesis was accepted; there was a significant relationship between performance expectancy of employees and videoconferencing utilization.  Table 6 shows the perceived importance of performance expectancy of employees was significantly positively associated both with reporting any use of videoconferencing (p = .01) and reporting more frequent use (p = .00; Table 6).

Hypothesis 2A predicted that there would be a significant relationship between effort expectancy of employees and videoconferencing utilization.  The null hypothesis was rejected, and H2A was accepted: there was a significant relationship between effort expectancy of employees and videoconferencing utilization.  Perceived importance of effort expectancy was significantly positively associated with reporting any use of videoconferencing (p = .02), although it was only marginally significantly associated (= .07) with reporting more frequent use (Table 6).

Hypothesis 3A predicted that there would be a significant relationship between perceived importance of social influence and videoconferencing utilization.  The null hypothesis was rejected, and H3A was accepted; there was a significant relationship between social influence and employees’ intention to use videoconferencing.  Table 6 shows that perceived importance of social influence was not significantly associated with reporting any use of videoconferencing (p = .24) but was significantly positively associated with reporting more frequent use (p = .03).

Hypothesis 4A predicted that there would be a significant relationship between perceived importance of facilitating conditions and videoconferencing utilization.  The null hypothesis was rejected, and H4A was accepted; there was a significant relationship between facilitating conditions and videoconferencing utilization.  Table 6 shows the perceived importance of facilitating conditions was significantly positively associated both with reporting any use of videoconferencing (p = .04) and with reporting more frequent use (p = .00).

Demographic Correlates of Independent Variables

Hypothesis 5A predicted that there would be a significant relationship between demographic variables (position, gender, age group, and education level) and respectively performance expectancy, effort expectancy, social expectancy, and facilitating conditions.  Age, gender, education, and technical position were entered into multiple regressions as predictors of the study’s independent variables.  Table 5 shows the F‑test for each variable, to test the null hypothesis that there was no linear relationship between the demographic variables and each of the study’s independent variables (R2 = 0) versus the alternate hypothesis that there was a linear relationship between these variables (R2 ≠ 0). With regard to performance expectancy, given that the significant value of .44 was greater than .05, the null hypothesis was not rejected (Table 7).  Thus, there was no linear relationship between the demographic variables (position, gender, age group, and education level) and performance expectancy in this model.  With regard to effort expectancy, the significant value of .02 was less than .05, so the null hypothesis was rejected that there was no linear relationship between the demographic variables in the model and effort expectancy.  Having a technical job was a significant predictor of effort expectancy.  With regard to social expectancy, the significant value of .18 was greater than .05, so the null hypothesis was not rejected in that there was no linear relationship between the demographic variables in the model and social expectancy.  With regard to facilitating conditions, the significant value of .24 was greater than .05, so the null hypothesis was not rejected in that there was no linear relationship between the demographic variables in the model and facilitating conditions. 

Table 7

Demographic Factors as Predictors of Independent Variables

 

 

Sum of

squares

 df

Mean

square

F

p

b

SE

b*

t

p

Performance expectancy

Regression

2.10

4

.52

.94

.44

 

 

 

 

 

Residual

61.32

110

.56

 

 

 

 

 

 

 

Total

63.42

114

 

 

 

 

 

 

 

 

Gender

 

 

 

 

 

–.02

.16

–.01 

–.11

.91

Education

 

 

 

 

 

–.06

.08

–.07

–.72

.47

Age

 

 

 

 

 

–.11

.07

–.15

–1.57

.12

Technical job Adjusted R2 = .00, p =.44

 

 

–.03

.16

–.02 

–.19

.85

Effort expectancy

Regression

5.03

4

1.26

3.21

.02

 

 

 

 

 

Residual

43.04

110

.39

 

 

 

 

 

 

 

Total

48.06

114

 

 

 

 

 

 

 

 

Gender

 

 

 

 

 

–.02

.16

–.01 

–.11

.91

Gender

 

 

 

 

 

–.03

.13

–.02 

–.22

.83

Education

 

 

 

 

 

–.01

.07

–.01

–.13

.90

Age

 

 

 

 

 

–.04

.06

–.06

–.65

.52

Technical job Adjusted R2 = .07, p =.02

 

 

.40

.14

.31 

2.94

.00

Social expectancy

Regression

3.71 

4

.93

1.61

.18

 

 

 

 

 

Residual

63.24

110

.57

 

 

 

 

 

 

 

Total

66.95

114

 

 

 

 

 

 

 

 

Gender

 

 

 

 

 

.30

 .16

.19

1.85

 .07

Education

 

 

 

 

 

–.13

 .08

–.15

–1.58

.12

Age

 

 

 

 

 

–.07

.07

–.09

 –.90

.37

Technical job Adjusted R2 = .02

 

 

   .23

.16

.15

1.42

.16

 

 


 

Table 7 (continued)

Demographic Factors as Predictors of Independent Variables

 

 

Sum of

squares

 df

Mean

square

F

p

b

SE

b*

t

p

Facilitating condition

Regression

1.28

4

.32

1.41

.24

 

 

 

 

 

Residual

25.26

111

 .23

 

 

 

 

 

 

 

Total

26.54

115

 

 

 

 

 

 

 

 

Gender

 

 

 

 

 

.05

.10

.05 

.51     

.61

Education

 

 

 

 

 

–.10

.05

–.19

–1.92      

.06

Age

 

 

 

 

 

–.05

.05

–.09

–1.00     

.32

Technical job Adjusted R2 = .01

 

 

 

.05

.10

.05 

.48     

.63

 

 

Demographic Correlates of Use of Videoconferencing

Analyses were also run to ask if any demographic or employee variables were significantly associated with reporting any use of videoconferencing or more frequent use.  Participants employed by the Bureau of Contracts and Technical Services were more likely to report using this technology (66.67%) than those employed in other bureaus (49.12%), X2(1) = 3.70, p =.05.  Participants were less likely to report using this technology if they had a high school degree (33.33%) or an A.A. degree (41.18%) as compared to a bachelor’s degree (69.35%) or a graduate degree (57.69%), X2(3) = 9.20, p =.03.  Participants who reported any use of videoconferencing were also more likely to report valuing it for communication and collaboration (61.6%) than those who did not report using it (11.11%), X2(1) = 8.93, p =.00.  With regard to reported frequency of use of videoconferencing, having a bachelor’s degree correlated with more frequent reported use, r(N = 121) = .20, p =.03, as did valuing use of videoconferencing for communication and collaboration, r(N = 121) = .27, p =.00.

Multiple Regression Analyses

Next, any significant predictors of any videoconferencing use from the analyses above were entered together into a multiple logistic regression predicting any videoconferencing use.  In this regression analysis, only the significant variables were retained in the final model.  Only variables that were significant were retained in the final model.  Table 8 shows that participants were significantly more likely to report any videoconferencing use if they expected it to improve their performance.  These participants valued using it for communication and collaboration, or were employed by the Bureau of Contracts and Technical Services.

Table 8

Multiple Logistic Regression Analysis Predicting Any Videoconferencing Use

________________________________________________________________________

                                                                                                Any videoconferencing use

Predictors of any videoconferencing use (N = 114)               b               SE        p     Exp(b)

________________________________________________________________________

 

Performance expectancy                                                         0.63     0.30     .04       1.88

Value videoconferencing for communication/collaboration   2.75     1.14     .02      15.68

In Bureau of Contracts and Technical Services                      1.08     0.43     .01       2.94

________________________________________________________________________

Adjusted R2 = .23

 

 

Next, any significant predictors of reported frequency of videoconferencing use from the analyses above were entered together into a multiple linear regression predicting reported frequency of videoconferencing use.  The only variables were retained in the final model.  With regard to reporting more frequent use of videoconferencing, the significant value of .00 was less than .05, so the null hypothesis was rejected in that there was no linear relationship between the independent variables in the model and reporting more frequent use of videoconferencing.  Table 9 shows that these variables were significantly and positively predicted by performance expectancy and valuing videoconferencing for communication and collaboration.

Table 9

Multiple Linear Regression Analysis Predicting Frequency of Videoconferencing Use

 

 

Sum of

squares

 df

Mean

square

F

p

b

SE

b*

T

p

Frequency of videoconferencing use

Regression

13.77

2

6 .89

13.05

.00

 

 

 

 

 

Residual

61.22

116

6.53

 

 

 

 

 

 

 

Total

74.99

118

 

 

 

 

 

 

 

 

Performance expectancy

 

 

0.36

0.09

0.34

3.90

.00

Value videoconferencing for communication collaboration

 

0.61

0.26

0.20

2.35

.02

Adjusted R2 = .17

 

 

 

 

 

 

 

 

 

 

Summary

Chapter 4 presents the results built on the foundation laid in Chapters 1 through 3.  In this chapter, the descriptive statistics, correlational analysis, and tests of relationships were presented in response to the research questions.

Hypothesis 1A predicted that there would be a significant relationship between perceived importance of performance expectancy of employees and videoconferencing utilization.  The alternative hypothesis was confirmed, because the perceived importance of performance expectancy of employees was significantly positively associated both with reporting any use of videoconferencing and reporting more frequent use.  Hypothesis 2 A predicted that there would be a significant relationship between effort expectancy of employees and videoconferencing utilization.  The hypothesis was confirmed.  The perceived importance of effort expectancy was significantly positively associated with reporting any use of videoconferencing; it was marginally significant and associated with reporting more frequent use.

Hypothesis 3 A predicted that there would be a significant relationship between perceived importance of social influence and videoconferencing utilization.  The hypothesis was confirmed.  Perceived importance of social influence was significantly positively associated with reporting more frequent use.

Hypothesis 4 A predicted that there would be a significant relationship between perceived importance of facilitating conditions and videoconferencing utilization.  The hypothesis was confirmed.  Perceived importance of facilitating conditions was significantly positive and was associated both with reporting any use of videoconferencing and with reporting more frequent use.

Additionally, performance expectancy for using videoconferencing technology was reported as a positive factor that commonly affects videoconferencing use, whereas effort expectancy was reported as a common factor hindering use.  Facilitating conditions were reported both as common positive and hindering factors that affect use. 

The perceived importance of facilitating conditions, performance expectancy, social influence, and effort expectancy all predicted reported use of videoconferencing technology, confirming this study’s hypotheses.  However, performance expectancy and valuing videoconferencing for communication and collaboration were the strongest predictors of reported use after controlling for other demographic or employee variables that were associated with reported videoconferencing use.


 

CHAPTER 5: DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS

Restatement of the Purpose of the Study

In recent years, to address problems in communicating globally, businesses have implemented new and efficient systems that are cost-saving, such as videoconferencing.  The use of videoconferencing has increased globally because of travel expenses and security concerns.  Videoconferencing has provided a new and unique platform through which firms and people can interact with each other on a regular basis.  Use of this interactive communication may be effectively achieved by motivating and encouraging people to share information, emotions, feelings, opinions, and experiences using new media communication techniques, such as videoconferencing.  However, lack of skills in operating equipment and low motivation are reasons the technology is not fully utilized.

This research focused on identifying incentives and barriers to implementing the practical use of video conferencing within a southern California social service organization.  The research focused on analyzing the degree and direction of any relationship that existed between the reported use and frequency of video conferencing and employees’ reports of performance expectancy, effort expectancy, social influence, and facilitating conditions. 

The purpose of this study was to examine the relationship of four predominant factors from the UTAUT model: (a) performance expectancy, (b) effort expectancy, (c) social influence, (d) facilitating conditions and their association with videoconferencing utilization.

In summary, Chapter 1 included the study’s problem background, statement of the problem, purpose of the study, research questions, definitions, significance of study, and theoretical framework.  Chapter 2 reviewed relevant theory, empirical findings, and related literature on videoconferencing utilization and the impact of the TAM/UTAUT factors on these technologies’ acceptance and utilizations.  Based on the literature review, it appeared that several areas need further investigation.  Drawing on previous research, Ajzen and Madden (1986) linked perceived behavioral control to behavioral intent; however, other sources did not make this link.  These mixed results warranted further investigation with respect to the impact these constraints had on the adoption of videoconferencing.  Using the concept of performance expectancy, this study incorporated perceived behavioral control into the conceptual videoconferencing TAM model.

Chapter 3 detailed the study’s methodology strategy and protocols used to conduct the study.  It presented the study’s research method, research design, participants, materials and survey instruments, and data collection, processing, and analysis.  The aim of this quantitative study was to examine relationships between variables, specifically, to ask how psychosocial and social factors might influence acceptance and use of videoconferencing technology.

A quantitative cross-sectional survey design was used.  Data were collected from 122 employees of the Los Angeles County Department of Social Services.  The results indicated a significant relationship between all four factors and videoconferencing utilization, with performance expectancy and valuing videoconferencing for communication and collaboration being the best predictors of reported utilization as indicated in the multiple regressions performed. 

Performance expectancy for using videoconferencing technology was reported as a positive factor that commonly affects videoconferencing use, whereas effort expectancy was reported as a common factor hindering use.  Facilitating conditions were reported both as being both positive and hindering factors that affect use.  The final section in Chapter  3 also describes methodological assumptions, limitations, and delimitations, as well as ethical assurances given for the protection of human participants.

Chapter 4 provided reports of data collection and findings.  The chapter presents the results built on the foundation laid in Chapters 1 through 3.  The descriptive statistics, correlational analysis, and tests of relationships were presented to answer the research questions.

Discussion

This chapter provides discussion on the summary of the results pertaining to relationship of four predominant factors from the UTAUT model: (a) performance expectancy, (b) effort expectancy, (c) social influence, and (d) facilitating conditions and their association with videoconferencing utilization.  The chapter also addresses the conclusions of the five research questions by explaining the level of significance among the four factors, including demographics and videoconferencing utilization.  Lastly, the implications for practices and recommendations for future research are addressed as they apply to this study. This study was implemented to address the five research questions. The statistical analyses from the results gathered from 122 respondents were used to interpret the following research questions:

Research Question 1

The first research question asked if employees’ self-reported performance expectancy was associated with self-reported videoconferencing utilization.  The findings showed that employees’ reports of performance expectancy were significantly associated with reports of utilization of video conferencing, such that the null hypothesis was rejected and the alternative hypothesis was accepted.  One of the greatest benefits of videoconferencing is that it encourages autonomy and reduces travelling cost, which allows employees to save time, increase productivity, enhance experiences, and make complex decisions.  Utilization of video conferencing also generally increases productivity so employees can work with greater personal flexibility.

Videoconferencing also encourages collaboration among team members, contributing to increased morale, better decision-making, and relief of stress on managers because they do not have to micromanage all aspects of employees’ work.  This may help explain why valuing videoconferencing for collaboration was also associated with greater reported use of videoconferencing.

Furthermore, communication through video conferencing can significantly affect the productivity and performance of employees.  Videoconferencing can help employees communicate with each other on a regular basis so that they are able to track each other’s productivity level and can share their experiences with each other.  It is important to have a proper communication medium, so that goals and objectives are clearly communicated. Otherwise, it will create conflicts among the employees and their performance will be affected.  

Use of videoconferencing for meetings should also help in developing trust and motivating team members; it can help employees feel comfortable that their ideas are heard and that other team members are pulling their own weight on the team.  Employees also need to communicate the objective and benefits of the videoconferencing usage and encourage others to do so. 

Research Question 2

The second research question sought to identify whether self-reported effort expectancy of employees was associated with self-reported video conferencing utilization.  The findings showed the null hypothesis was rejected and the alternative hypothesis was accepted.  The effort expectancy was significantly positive and associated with reporting any use of videoconferencing.  The findings also showed that reports of effort expectancy were related to working in the technical bureau.  This means that the implementation of video conferencing may be directly affected by whether employees believe it is easy to use videoconferencing or not.  Venkatesh (2000) and Bandura (1982) also stated that effort expectancy was associated with beliefs and behavior; it also has a crucial influence as an internal control in technology adoption.

Research Question 3

The third research question asked about the relationship between self-reported social influence and self-reported utilization of videoconferencing.  The results showed that social influence was significantly positive and associated with reporting more frequent use of videoconferencing. Thus, the null hypothesis was rejected and the alternative hypothesis was accepted.  Using a UTAUT model, Hui-Yi, Luh-Wang, and Hsiu-Chuan (2010) also found that social influence plays an important role as a behavioral factor in using technology.  Hossain and Silva (2009) also determined that weak and strong social ties influence technology acceptance.  The relationship between social influence and videoconferencing utilization also supported the Teng and Calhoun (1996) model suggesting a correlation between decision-making and the use of communication technologies. 

In particular, Graetz (2000) examined the role of leadership in managing the challenge of large-scale change.  Studies have also pinpointed the pivotal role top management plays in ensuring the long-term effectiveness of corporate transformation.  Top management inspires, engages, and promotes action-driven results.  When leaders distribute their authority, they create a climate for change (Beer & Walton, 1990; Pettigrew & Whipp, 1993).  Leaders tend to set clear goals and encourage staff at all levels to share ideas and be involved in decision making.  Kotter (1996) also documented the importance of connecting the support and commitment of key leaders through the organization who would help these messages across, down, and  through the organization.

Research Question 4

The fourth research question asked if self-reported facilitating conditions are associated with self-reported videoconferencing utilization.  The results showed that perceived importance of facilitating conditions was significantly positive. This was associated with both with reporting any use of videoconferencing and with reporting more frequent use.  In other words, the null hypothesis was rejected and the alternative hypothesis was accepted.  This suggests that improving facilitating conditions could promote more videoconferencing usage; whereas, if the facilitating conditions were not good there could be a negative impact on the utilization of videoconferencing.  Employees are likely to be less willing to adopt the use of video conferencing if they are not aware of the use of the technology.  Before using technology, employees should have good knowledge of it, so that they are aware of the various uses of that technology, including videoconferencing and being able to make maximum use of the technology.  This will also help in resolving issues that are encountered during communication with team members. 

The findings also showed that performance expectancy of employees was perceived to have a positive effect on use, whereas effort expectancy was reported as a common factor hindering use.  Facilitation conditions were reported both as common positive and hindering factors that affect use.  Empowering employees to use technology not only reduces barriers but also increases motivation and morale in using it. 

Conclusions

Videoconferencing technology utilization has increased because of globalization and travel security concerns.  The problem addressed in this research is that in spite of government mandates, there are inconsistent levels of videoconferencing utilization and usability experienced in this domain within a southern California social services organization.

Government agencies have been mandating the use of videoconferencing as it will cut the cost of the business meetings by reducing hours spent travelling and other costs funded by the government (Tuutti, 2012).  The Los Angeles Country Department of Public Social Services has spent a large sum of money to continue to build and deploy infrastructure for videoconferencing.  Yet the level of utilization of videoconferencing has remained unknown.  The purpose of this study was to identify barriers to implementing videoconferencing in organizations and to assess videoconferencing utilization and the factors that may affect its usage.  This research also extended TAM models and the UTAUT model to a new setting, a social services organization.  The UTAUT was used as the framework to examine the relationship of four predominant factors from the UTAUT model: (a) performance expectancy, (b) effort expectancy, (c) social influence, (d) facilitating conditions and their association with videoconferencing utilization.  

The literature review for this study addressed the current state of videoconferencing to help organizations communicate and interact.  Utilization of videoconferencing was expected to be affected by important determinants found in the TAM and its extensions: performance expectancy, effort expectancy, social influence, and facilitating conditions.  Research on issues and barriers faced by organizations in adopting the utilization of videoconferencing suggested that meeting the communication challenges of organizations required input and collaboration from all levels of organizational communication.  TAM theories about technology acceptance (e.g., Lee et., 2003) were applied for organizational use specifically with regard to videoconferencing acceptance and utilization.

Through the literature research it was observed that utilization of videoconferencing generally increases productivity so employees can individually use their time in a more flexible manner.  From an organizational perspective, companies have people across the globe working 24 hours; therefore, companies never see an off-hour.  This helps organizations take a faster approach to reach the market through this innovative technology.  A diverse task force or focus group can help in keeping an eye on diverse opportunities for the technology.  As employees are spread across the globe, firms have an advantage in creating links with their customers worldwide.  This provides an opportunity for the firms to compete at the global level.  Utilization of videoconferencing is increasing to manage work throughout the globe and to develop a global customer base.  In order to reach customers globally, organizations adopt this technology so that traveling costs are reduced, the performance of employees are increased, and customers are catered to according to the norms of that particular country.

This quantitative cross-sectional study was a correlational study.  A survey with 22 questions was used to collect data from 122 respondents working at a social services organization in southern California.  Correlations were used to test hypotheses about the relationships between each of the four independent variables and, respectively, any use of videoconferencing and frequency of use of videoconferencing, as well as between the independent variables and demographic or employee variables (e.g., age, gender, educational qualification, or training on the use of videoconferencing).  The results supported previous studies by Venkatesh et al. (2003) in that there is a significant relationship between all four factors and videoconferencing utilization.

Additionally this study aimed to provide recommendations about how to promote videoconferencing utilization throughout a social services organization, focusing on the seven steps of the Concerns-Based Adoption Model (Hall & Hord, 1987).  Finding a relationship to other measures of technology acceptance may provide support for propositions that were not supported in this study: that is, using more specific measures of videoconferencing utilization may help more clearly define the factors that lead to greater videoconferencing utilization. This is definitely an area for followup and future inquiry.  Further study is also recommended to extend the study to all other 21 Los Angeles County departments, to use a mixed-method approach for evaluating the impact of a concerns-based adoption model on videoconferencing utilization, and to test and validate the Videoconferencing Acceptance Model.

In summary, this research based on the UTAUT model (Venkatesh et al., 2003) sought to establish a baseline for videoconferencing utilization and to assess its current state within a social services organization.  The TAM model was also considered relevant to videoconferencing acceptance; the key role of reports of performance expectancy in predicting reports of videoconferencing utilization is congruent with the TAM emphasis on perceived usefulness of the technology in its acceptance.  The key findings from the study demonstrated that important concepts in the UTAUT model—the perceived importance of facilitating conditions, performance expectancy, social influence, and effort expectancy—all predicted reported use of videoconferencing technology, confirming this study’s hypotheses and the value of the UTAUT model.  This study thus contributed to a conceptual understanding of efficient utilization of videoconferencing as a basis for developing a videoconferencing acceptance model that can help organizations achieve a high level of utilization and usability of videoconferencing. 

Implications for Practice

The statistical analyses produced from the 122 respondents’ responses presented suggestions for relevant implications.  This section addressed the implications as they related to improve use of videoconferencing and recommendations for implementation of videoconferencing.

According to Johnson (2013), the use of videoconferencing could improve business productivity and efficiency.  Organizations using videoconferencing can reduce their travel expenses; economies of scale have contributed to making the technology affordable.  However, Gallion (2000) stated that systems and organizational factors influence user attitudes and beliefs about technology and acceptance or rejection of it.  Some barriers include lack of videoconferencing solutions available to employees, cost concerns, infrastructure limitations, low level of general use, organizational cultural barriers, lack of awareness of benefits, and lack of managerial buy-in (Telework Exchange, 2012).  This research provided preliminary information that could assist DPSS in developing policies, incentives, and measures to boost use of videoconferencing so as to allow co-workers to come together visually and reduce travelling costs.

Improve Technology Protocols

Communication is the contextual framework within which the researcher planned to develop the utilization of videoconferencing.  Innovations in technology usage within communication systems are rapidly developing in order to improve employee productivity.  Most companies have incorporated the use of the internet in their business models so they can earn more profit through this new market.  Firms are also working to change their method of interaction from face-to-face meetings to virtual meetings through videoconferencing technology, such as WebEx from Cisco, Inc.  However, some employees who are unfamiliar with the technology have faced difficulty in operating it.  According to Holzinger et al. (2011), employees have to trust and accept the technology to adopt the use of the technology to its full capacity. 

Tuutti (2012b) stated that previously employees using video teleconferencing faced many difficulties in operating the technology, such as problems of poor videostreaming connections.  The Videoconferencing Report of Los Angeles Country (Tindal & Sanchez, 2012) provided some directives, such as the potential benefits of videoconferencing managed in the central system. 

Prior to any equipment implementation, assessing the network and bandwidth utilization for videoconferencing for each country should also ensure usability of the service.  Using International Telecommunication Union Standards for videoconferencing protocols should help in maintaining compatibility with systems worldwide.  From an organizational perspective, companies have people across the globe working 24 hours a day for them, hence companies never see an off hour, providing a faster approach to reaching the market.  As employees are spread across the globe, firms have an advantage in creating links with their worldwide customers so they can compete at the global level.  Utilization of videoconferencing is increasing to manage work throughout the globe and developing a global customer base.  In order to reach customers globally, organizations need to adopt this technology so that traveling costs are reduced and organizations can cater to customers according to the technical and social norms of that particular country.

Improve Training and Assessment in Technology

Employees also need to develop skills in operating the technology; they should receive adequate training and user-friendly training material that help them operate the technology easily.  Organizational leaders should also implement training courses that help team members understand the unique dynamics associated with using videoconferencing as a form of dispersed collaboration.  

Managers and technical staff should also carefully assess the characteristics and knowledge of employees to ensure they possess the relevant qualities and information to adopt the use of video conferencing and succeed in a dispersed atmosphere.  An adoption model may be used to identify manager training needs in terms of job classification, management level, race, education level, gender, and ethnic background.  This will help leaders to determine when management training is needed. 

Technical support staff should ensure that norms of video conferencing use are shared by team members.  Reducing barriers to using video conferencing can also include implementing strategies to facilitate sharing information and understanding the task to be accomplished.  Staff should also assess use of the technology to ensure that it helps increase output quality, individual learning, creativity and innovation, and reduces travelling cost.  DPSS managers also need to collect data before and after training on videoconferencing utilization trends. 

Improve Managerial Communication about Technology

Because social influence is related to frequency of reported use of videoconferencing, management strategies are needed to target utilization of videoconferencing, delegate responsibilities, and establish policies.  Leaders need to meet with all levels of management in order to address ways to increase videoconferencing utilization.  Managers often under-communicate the organizational vision, because there are multiple levels in the organization hierarchy, the message tends to get lost in transmission.  Organizational leaders devote precious time to produce a strategic plan, yet little is done to communicate strategic plan objectives to produce the desired change outcome.  Managers are encouraged to send consistent messages to alter behavior about the urgency of change.

Managers should also increase employee involvement with videoconferencing technology by empowering employees to use videoconferencing so they can perform certain tasks.  Leaders should ensure that goals and objectives are clearly communicated; that all team members have understood the use of video conferencing technology.  Therefore, team members are not having difficulties communicating with each other, but instead have proper interactions through video conferencing and trust each other. 

Decision makers will need to continue effectively collaborating with their business units, human-resource management, staff, and other stakeholders to use videoconferencing technology to improve organizational practices and performance, as well as to address low videoconferencing utilization.  Managers and team leaders will need to gather preliminary information about content to be used for collaboration, form partnerships, develop a shared vision, and assess current strengths, concerns, and conditions of staff.  Survey data collected from managers and staff can suggest measures to be taken to improve utilization of technology (Guthrie & Schuermann., 2009).

Managers should also ensure that new media communication (e.g., videoconferencing) empowers customers, leading them towards enormous shifts in the business world.  Kotter (1996) stated,

For people who have been trained only to be managers, communication of vision can be particularly difficult.  Managers tend to think in terms of their immediate subordinates and boss, not the broader constituencies that need to buy into a vision.  They tend to be most comfortable with routine factual communication, not future-oriented strategizing and dreaming.  Of course, they can learn.  But that requires time, effort, and perhaps most of all, a clear sense of what the problem is and how it can be resolved. (p. 87)

Recommendations for Future Research

Future videoconferencing studies should collect larger samples.  Future studies can also focus on using focus groups and observational methods for obtaining more comprehensive information about employees’ strategies and techniques for utilizing video conferencing. 

Use Different Methodologies and Samples

Focusing on the limitations of this study, future videoconferencing studies should collect larger samples.  Future studies can also focus on using focus groups and observational methods for obtaining more comprehensive information about employees’ strategies and techniques for utilizing video conferencing.  Furthermore, researchers can use interviews from a targeted sample to conduct in-depth analysis so as to gain more knowledge about employee experiences.  Future studies can also conduct case studies of other departments within Los Angeles County, other social service organizations, or other government agencies.  Research on utilization of videoconferencing could also inquire about communication barriers related to differences in organizational culture, language, and time zones that have significant impact on the performance of team members.

Cost-Effectiveness Studies

Another area of research is to evaluate videoconferencing usage to justify past and future expenditures on videoconferencing to be in compliance with cost-saving means within government agencies.  Government agencies have been mandating the use of video conferencing to cut the cost of business meetings by reducing productive and efficient time spent travelling and other costs funded by the government (Tuutti, 2012a).  Los Angeles County has also found it costly and environmentally unfriendly to host face-to-face meetings and prompt training sessions for DPSS staff at 36 district offices, spread throughout the county.  DPSS has faced significant mileage reimbursement costs for staff to attend meetings at various locations.  As a result, the Los Angeles Country DPSS has continued to build and deploy infrastructure for videoconferencing but the utilization level of video conferencing remains unknown, even though a large sum of money has been used to adopt video conferencing.

Study Videoconferencing and High Performance

Studies on utilization of video conferencing can also ask how high performance can be achieved.  They could focus on high-quality work of employees or on the communication structure of employees.  Studies could also ask about the relationship between technology utilization and social influence.  Schmitz and Fulk (1991) conducted a study where they concluded that current technology has presented opportunities for managers to make decisions at any level within a business.  Huber’s (1990) theory suggested that advanced information technologies enabled organizations to achieve higher level decision-making competencies when employees utilized technology to its full capacity (Newman, 2010).  Key areas of interest are strategic planning and change process.

Strategic planning. Weingarten, Humphreys, Cao, and McHugh (2013) found that organizations might be able to gain significant performance improvements when their information technology solutions were aligned with organizational strategy and organizational process.  The same research also suggested that organizational culture plays a big role in adapting to new technology.  Melville, Kraemer, and Gurbaxani (2004) also found that information technology solutions are welcomed by organizations, because they provide logical solutions, and yet adoption of such solutions is dependent upon internal and external factors, including management practices, organizational structure, and organizational policies. It is important to understand more about these factors.

Change process. Hall and Hord (1987) defined a concern as “the composite representation of feelings, preoccupation, thought, and consideration given to a particular issue or task is called concern” (p. 61); Hall and Hord went on to formulate a concerns-based adoption model (Figure 14) concerning (technology) change agents based on the following assumptions: (a) For employees to change there must be appropriate and promising practices and procedures; (b) it is imperative to understand employees’ attitudes and skills so that support activities can be directly related to what employees perceive they need; (c) technology specialists must have a well-developed understanding of what and how to provide support to users; and (d) most important, technology change agents must assist others in ways that are relevant to the concerns of users so that users become more effective and skilled in using new technology. 

Table 10

Stages of Concern about Technology Use From Concerns-Based Adoption Model

 

Stages

Description

Stages to Guide Change

Stage 0: Awareness

·   Employees may not know about videoconferencing

·   Employees may not be ready to use technology

 

·   Involve employees in discussions about videoconferencing usage

·   Share enough information to stir interest, but not to overwhelm

·   Provide open environment where all questions are allowed and lack of awareness is accepted

·   Share information about videoconferencing presence

Stage 1: Informational

·   Want to learn more about the technology

·   Curious how technology can be used to improve business processes

·   Share information through all forms of media

·   Find those that are using the technology on- and off-site and have them share their experiences using videoconferencing

·   Be enthusiastic about all who are using technology

Stage 2: Personal

 

·   How will using IT affect me?

Stage 3: Management

·   Want practical suggestions on how to use videoconferencing for specific purposes

 

·   Explain components of technology and share “how-tos”

·   Share how technology can be used in small attainable steps

Stage 4: Consequence

·   Uses technology but not sure how to use it within a business processes 

·   Give employees positive feedback and access to resources

Stage 5: Collaboration

·   Would like to share lessons with other employees

·   Offers technical support to others

·   Provide opportunities to develop technology coordinators’ skill-sets to provide support

Stage 6: Refocusing

·   Looks for ways to improve utilization of videoconferencing

·   Serves on technology committees

·   Thinks “outside the box”

·   Provide access to all resources so employees can refine their idea and put into practice

·   Let employees take risks

·   Encourage employees to test new ideas and technology

Note. Adapted from Change in Schools: Facilitating the Process, by G. Hall and S. Hord, 1987, p. 60, State University of New York Press, Albany, New York.

 

 

Administrators could use this concerns-based adoption model (Hall & Hord, 1987) to identify concerns and then act on them (see Hord, Rutherford, Hurling-Austin, & Hall, 1999).  This framework could also be used in research to provide a deeper understanding of how administrators and stakeholders react to change related to technology, and the way that all management levels in a workplace collaborate to build teamwork through empowerment, educational equities, and open communication lines among employees (see Hall., George, & Rutherford, 1979).

Application of Videoconferencing Acceptance Model.  This proposed model was synthesized and based on the key findings of this research.  It is meant to serve as an extension of the TAM, TAM2, and UTAUT models but to focus solely on creating a new technology-acceptance model targeted for videoconferencing technology.  This proposed model needs to be validated and tested to ensure all variables have direct effect on increasing videoconferencing utilization within an organization.  The four stages of the Videoconferencing Acceptance Model are:

Stage 1: Full deployment

Stage 2: Measure videoconferencing utilization through policy

Stage 3: Continuous training

Stage 4: Incentive for videoconferencing utilization

Figure 5 presents these stages and the inputs, processes, and outputs related to them.

 


 

 

 

 

 

 

 

 

 

 

 

Figure 9. Videoconferencing acceptance model

 

                                           


During the first stage of the proposed videoconferencing acceptance model, organizations should focus on the full deployment of videoconferencing across all remote offices of the organization.  This step is vital to the success of users’ experience, because granting employees full access to videoconferencing resources will aid in increasing awareness of videoconferencing among employees.  Secondly, measuring videoconferencing utilization through policy will enforce utilization throughout the organization.  Thirdly, providing continuous training to employees and support staff will encourage more utilization and eliminate fear of use.  Lastly, organization and management should provide employees with a reward or an incentive for utilizing videoconferencing.  A suggested future study would be researching a comprehensive model of the factors affecting user acceptance of videoconferencing in an organization, which would involve testing and validation of the Videoconferencing Acceptance Model.


 

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APPENDICES


 

 

 

 

 

 

 

 

 

 

APPENDIX A

Permission to Use MIS Quarterly


 

APPENDIX A

Permission to Use MIS Quarterly

 


 

 

 

 

 

 

 

 

 

 

APPENDIX B

Department of Public Social Services Employees Survey


 

APPENDIX B

Department of Public Social Services Employees Survey

Argosy University, Southern California

 

The purpose of this survey is to determine how social influence, effort expectancy, performance expectancy and facilitating conditions may affect the success or failure of videoconferencing utilization. 

 

Please complete all sections of this questionnaire.  For any questions, please call Rachid Ameur 562-301-2860

 

Sincerely,

Rachid Ameur

 

Date_____________________

 

 

VIDEOCONFERENCING UTILIZATION ASSESMENT QUESTIONAIRE

 

Q1-Q8 General information about yourself, demographics, frequency of videoconferencing utilization.

 

  1. Please check the appropriate box to indicate your position.

 

ASM I, HAS I

ASM II, HAS II

ASM III, HAS III

Chief

Executive Staff

Secretary

Technical Supervisor

Technical Staff

Other Administrative Staff

 


 

 

  1. Where do you work? (Mark your selection in the box to indicate your Departmental Bureau)

Bureau of Contract and Technical Services

 Bureau of Special Operations

 Bureau of Administrative Services

 Bureau of Workforce Services

 Bureau of Program and Policy

 

  1. Are you currently using videoconferencing (room based system Polycom or videoconferencing cart or WebEx)?

Yes

No

 

  1. What is your gender?

Male

Female

 

  1. What is your age group? 

18-29

30-39

40-49

50-59

60-65

66+

 

  1. Do you think the Department’ Videoconferencing as a communication and collaboration tool is beneficial to you?

Yes

No


 

 

  1. What is your education level? 

Ph.D.

M.S/M.P.A/MBA

BS/BA

AA/AS

High School

  1. How often do you utilize Videoconferencing to conduct or attend: meetings, training, or share content? 

Never

Less than once a month

Once a month

2-3 times a month

Once a week

2-3 times a week

Daily

 

Q9-Q12 – Address the influence if any of Facilitating Conditions affecting videoconferencing utilization.

 

  1. Generally speaking, Videoconferencing system (hardware and software) is reliable and meet my technical expectations, which could impact videoconferencing utilization.

Strongly agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. Training technical support staff could improve Videoconferencing utilization.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

  1. Making videoconferencing accessible to all employees could improve employees’ intention to use videoconferencing.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. Easy access to technical support for assistance with videoconferencing difficulties could improve videoconferencing utilization.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

Q13- Q14 are designed to address influence if any, of social factors affecting videoconferencing utilization.

 

  1. Management believes employees should utilize videoconferencing.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. The senior management is supportive of videoconferencing utilization for my job.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 


 

Q15- Q16 are designed to address influence if any, of performance expectancy affecting videoconferencing utilization.

 

  1. Utilizing Videoconferencing system allows me to be more efficient.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. Utilizing Videoconferencing system allows me to more productive would enhance my job performance.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

Q17-Q20 address the influence if any of effort expectancy affecting videoconferencing utilization in the Department of Public and Social Service.

 

  1. User training to host a videoconferencing productivity session (meeting, sharing content, and conducting training with videoconferencing) could improve videoconferencing utilization.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree


 

 

  1. It is easy to learn to use videoconferencing system (room-based like Polycom system installed in conference rooms or videoconferencing cart or desktop WebEx).

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. Working with videoconferencing systems is so complicated; it is difficult to understand what is going on.

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

  1. I believe that it is easy to get the videoconferencing to do what I want it to do. 

Strongly Agree

Agree

Neither Agree nor Disagree

Disagree

Strongly Disagree

 

Q21-Q22 – Address the factor with most influence if any affecting videoconferencing utilization.  Given the definitions of concepts: Effort Expectancy, Performance Expectancy, Social Influence and Facilitating Conditions as listed in the questions, answer the following questions:

 

  1. In your opinion what is the most important factor positively influencing videoconferencing utilization within your organization?

Effort Expectancy: The degree to which the videoconferencing is perceived as relatively difficult to understand and use

Performance Expectancy: The degree to which a person believes that using videoconferencing would enhance his or her job performance

Social Influence: The degree to which use of videoconferencing is perceived to enhance one’s image or status in the Department of Public Social Service

Facilitating Condition: The degree to which Videoconferencing is accessible to all DPSS employees and specific group is available for assistance with system difficulties.

 

  1. In your opinion what is the most important factor hindering videoconferencing utilization?

Effort Expectancy: The degree to which the videoconferencing is perceived as relatively difficult to understand and use

Performance Expectancy: The degree to which a person believes that using videoconferencing would enhance his or her job performance

Social Influence: The degree to which use of videoconferencing is perceived to enhance one’s image or status in the Department of Public Social Service

Facilitating Condition: The degree to which Videoconferencing is accessible to all DPSS employees and specific group is available for assistance with system difficulties.

 

End of Survey

Thank you for taking

VIDEOCONFERENCING UTILIZATION ASSESMENT SURVEY

Rachid Ameur


 

 

 

 

 

 

 

 

 

 

APPENDIX C

Permission to Conduct Research by Department of Public Social Services

 


 

APPENDIX C

Permission to Conduct Research by Department of Public Social Services

 


 

 

 

 

 

 

 

 

 

 

APPENDIX D

Email Invitation to Department of Public Social Services Employees

 

 

 

 


 

APPENDIX D

Email Invitation for Department of Public Social Services Employees

Dear _DPSS employees                    ,

My name is Rachid Ameur a doctoral student at Argosy University Orange County, California seeking participants in my research study concerning the Videoconferencing Utilization. In addition, I will assess the influence of factors; performance expectancy, effort expectancy, social influence and facilities conditions effecting videoconferencing within a southern California social services organization.
 
I’m currently working under the supervision of my Dissertation Chair, Dr. Kambiz Moghaddam. Your participation is appreciated, as well as making a difference in helping to understand impacts and influence of factors effecting videoconferencing utilization. If you wish to take the survey, please read the informed consent letter, acknowledge the agreement, click “yes” then proceed to complete the survey.


Your identity and responses will be anonymous. Please respond by the date    1-29-2015. You will be asked to complete a questionnaire. This involves answering a series of questions. The study will take approximately 5-7 minutes to complete. There are minimal risks for participation in this study. This research study is designed to measure levels of videoconferencing utilization and usability experienced within a Southern California Social Services organization. And you are an employee of this organization. There are no direct benefits to participants. However, it is hoped that your participation will help Information Technology professionals and researchers look at the comprehensive model to improve videoconferencing utilization within a Social Services Organization.

 

This study is conducted under the supervision of Dr. Kambiz Moghaddam, from the Argosy University, Orange County Campus, and Education Department. He can be contacted at (714) 620-3700 or kmoghaddam@argosy.edu . You can also contact the Chair of Institutional Review Board of Argosy University, Southern California Campuses; Dr. Diana Siganoff. She can be contacted at (714)-620-3662 or dsiganoff@argosy.edu. And at the mailing address: Argosy University Orange County Campus, 601 South Lewis Street, Orange, CA, 92868.

 

You may follow the link below to begin the survey.

 

https://qtrial2014az1.az1.qualtrics.com/SE/?SID=SV_cM8LfXGPPxJvvbD  

 

Thank you for your support

Rachid Ameur

(562)301-2860 or e-mail: rachidameur_edd@atu.argosy.edu

 


 

 

 

 

 

 

 

 

 

 

APPENDIX E

Consent Form for Department of Public Social Services Employees

 


 

APPENDIX E

Consent Form for Department of Public Social Services Employees

Argosy University Orange County

Informed Consent Letter

 

Initial _______ Date__________

 

Dear DPSS employees,

 

You have been invited to participate in a study being conducted by Rachid Ameur at Argosy University Orange County working on a dissertation. This study is a requirement to fulfill the researcher’s degree and will not be used for decision-making by any organization. The title of the study is: FACTORS AFFECTING VIDEO CONFERENCING UTILIZATION AND USAGE IN A SOCIAL SERVICES ORGANIZATION.

 

The purpose of the study is to assess levels of videoconferencing utilization and usability experienced within a Southern California Social Services organization. It will further present a detailed correlational analysis between psychosocial factors and their influence effecting video conferencing utilization. A total of 300 employees have been asked to participate in this study. You were asked to participate in this study because your office location offers videoconferencing capabilities.

 

If you agree to be in the study: You will be asked to complete a questionnaire. This involves answering a series of questions. Questions will include details about the four determinant factors; effort expectancy, performance expectancy, social influence, and facilitating conditions and video conferencing utilization. There will multiple choice questions using Likert scale and yes/no questions with demographics and your own personal views and feelings about how to improve video conferencing utilization.

 

Time required: The study will take approximately 5-7 minutes to complete.

 

Risks: There are minimal risks for participation in this study; this research study is designed to measure levels of videoconferencing utilization and usability experienced within a Southern California Social Services organization. It aims to apply Technology Acceptance Model Theories (TAM), in effort to determine the best recommendations to improve video conferencing utilization.

 

Benefits: There are no direct benefits to participants. However, the findings from the research survey will establish a conceptual understanding of efficient usage and utilization of video conferencing and in identifying a basis for developing a recommendation for full implementation and to increase utilization of video conferencing to improve productivity and increase efficiencies.

Confidentiality: The records of this study will be kept private. No words linking you to the study will be included in any sort of report that might be published. All information provided will remain confidential and will only be reported as group data with no identifying information. All the information gathered from the study, will be kept in a secure location and only those directly involved with the research will have access to them. After the research is completed, the information will be destroyed after a period of a year. You have the right to get a summary of the results of this study if you would like to have them. You can get the summary by April 15, 2015 by contacting

rachidameur_edd@atu.argosy.edu

 

Participation and withdrawal: Your participation in this study is strictly voluntary. If you do not participate, it will not harm your relationship with Argosy University Orange County. If you decide to participate, you can refuse to answer any of the questions that may make you uncomfortable. You can quit at any time without your relations with the university, job, benefits, etc., being affected.

 

Researcher Contact: If you have any questions about the study, please contact the researcher: Mr. Rachid Ameur, at (562) 301-2860 or rachidameur_edd@atu.argosy.edu or at the mailing address: 2120 Petaluma Ave Long Beach CA, 90815.

 

Whom to contact about your rights in this experiment: This study is conducted under the supervision of Dr. Kambiz Moghaddam, from the Argosy University, Orange County Campus. and Education Department. He can be contacted at (714) 620-3700 or kmoghaddam@argosy.edu You can also contact the Chair of Institutional Review Board of Argosy University, Southern California Campuses; Dr. Diana Siganoff. She can be contacted at (714)-620-3662 or dsiganoff@argosy.edu. And at the mailing address: Argosy University Orange County Campus, 601 South Lewis Street, Orange, CA, 92868.

 

Agreement:

I understand that this study has been reviewed and Certified by the Institutional Review Board, Argosy University Orange, located at 601 South Lewis Street, Orange, CA, 92868. For problems or questions regarding participants' rights, you can contact the Institutional Review Board Chair; Dr. Diana Siganoff. She can be contacted at (714)-620-3662 or dsiganoff@argosy.edu.

 

I have read and understand the explanation provided to me. I have had all my questions answered to my satisfaction, and I voluntarily agree to participate in this study. I have been given a copy of this consent form. By signing this document, I consent to participate in the study.

 

Name of Participant (printed) _____________________

Signature: __________________________ Date: _____________________

Signature of Principal Investigator:_____________________ Date: ________________

Contact Investigator: Rachid Ameur, (562) 301-2860, 2120 Petaluma Ave Long Beach CA, 90815

Initial _______ Date__________