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ORGANIZATIONAL INTERVENTION EFFECTIVENESS ON END USER INFORMATION TECHNOLOGY ACCEPTANCEbyBrian D. OtteJOHN C. HANNON, DBA, Faculty Mentor and ChairHENRY J. LINDBORG, PhD, Committee MemberBERNARD J. SHARUM, PhD, Committee MemberRaja K. Iyer, PhD, Dean, School of Business and TechnologyA Dissertation Presented in Partial Fulfillmentof the Requirements for the DegreeDoctor of PhilosophyCapella UniversityAdd Month Year (of conference approval)? Brian D. Otte, 2010AbstractInformation technology (IT) enables productivity, defines part of the culture for a social group, and enables both IT strategy and business strategy. IT end users who choose not to accept and utilize the available IT within an organization fail to take advantage of the productivity that technology offers. Businesses have multiple organizational interventions that address end user IT acceptance. Multiple field experiments will be conducted to measure end user perception changes in IT ease of use and IT usefulness enabled through training and incentivizing IT system utilization. A survey that contains scales on perceived ease of use, perceived usefulness, and communication perceptions will capture the training and incentive effectiveness. Training and incentive effectiveness at adjusting end user IT acceptance will be determined by comparing pretreatment and posttreatment survey results. Organizations may look to the results of this research effort when selecting specific preimplementation or postimplementation organizational interventions. Measuring the effectiveness of two specific interventions in a practitioner environment lays the groundwork for additional research surrounding organizational intervention effectiveness. The intervention effectiveness informs the practitioner concerning intervention selection and timing when implementing IT within a business environment.DedicationTo be added.AcknowledgmentsI would like to thank Barb Elwert for detailed editorial suggestions.Table of Contents TOC \o "1-2" \h \z \u Acknowledgments PAGEREF _Toc282610222 \h ivList of Tables PAGEREF _Toc282610223 \h viiiList of Figures PAGEREF _Toc282610224 \h ixCHAPTER 1. INTRODUCTION PAGEREF _Toc282610225 \h 1Introduction to the Problem PAGEREF _Toc282610226 \h 1Background of the Study PAGEREF _Toc282610227 \h 5Statement of the Problem PAGEREF _Toc282610228 \h 8Purpose of the Study PAGEREF _Toc282610229 \h 9Rationale PAGEREF _Toc282610230 \h 10Research Questions and Hypotheses PAGEREF _Toc282610231 \h 11Significance of the Study PAGEREF _Toc282610232 \h 12Definitions of Terms PAGEREF _Toc282610233 \h 14Assumptions and Limitations PAGEREF _Toc282610234 \h 15Nature of the Study PAGEREF _Toc282610235 \h 16Summary PAGEREF _Toc282610236 \h 16Organization of the Remainder of the Study PAGEREF _Toc282610237 \h 17CHAPTER 2. LITERATURE REVIEW PAGEREF _Toc282610238 \h 18Introduction PAGEREF _Toc282610239 \h 18Business Strategy PAGEREF _Toc282610240 \h 18Institutional Culture PAGEREF _Toc282610241 \h 25Interventions and IT System Implementation PAGEREF _Toc282610242 \h 32Preimplementation Interventions PAGEREF _Toc282610243 \h 35Postimplementation Intervention PAGEREF _Toc282610244 \h 42End User IT Acceptance PAGEREF _Toc282610245 \h 45Summary PAGEREF _Toc282610246 \h 48CHAPTER 3. METHODOLOGY PAGEREF _Toc282610247 \h 50Introduction PAGEREF _Toc282610248 \h 50Research Design PAGEREF _Toc282610249 \h 51Sample PAGEREF _Toc282610250 \h 53Intervention Effectiveness PAGEREF _Toc282610251 \h 56Access to Site PAGEREF _Toc282610252 \h 56Setting PAGEREF _Toc282610253 \h 58Instrumentation and Measures PAGEREF _Toc282610254 \h 59Data Collection PAGEREF _Toc282610255 \h 61Organizational Intervention Training: Phase 1 PAGEREF _Toc282610256 \h 62Organizational Intervention Incentives: Phase 2 PAGEREF _Toc282610257 \h 63Data Analysis PAGEREF _Toc282610258 \h 64Validity and Reliability PAGEREF _Toc282610259 \h 67Limitations PAGEREF _Toc282610260 \h 72Ethical Considerations PAGEREF _Toc282610261 \h 73REFERENCES PAGEREF _Toc282610262 \h 75APPENDIX A. interventions for Pre-IT system implementation PAGEREF _Toc282610263 \h 82APPENDIX B. interventions for Post-IT SYSTEM IMPLEMENTATION PAGEREF _Toc282610264 \h 83appendix c. technology acceptance model PAGEREF _Toc282610265 \h 84appendix D. Technology Acceptance model 3 PAGEREF _Toc282610266 \h 85APPENDIX E. Survey Instrument PAGEREF _Toc282610267 \h 86APPENDIX F. Survey Instrument Scales PAGEREF _Toc282610268 \h 90List of Tables TOC \f A \t "TOC Heading" \c Table 1. Perceived Usefulness Determinants Presented in TAM-3 PAGEREF _Toc282610269 \h 15Table 2. Perceived Ease of Use Determinants Presented in TAM-3 PAGEREF _Toc282610270 \h 16Table 3. Facility Name and Employee E-Mail Status PAGEREF _Toc282610271 \h 54Table 4. Phase 1: Training PAGEREF _Toc282610272 \h 63Table 5. Phase 2: Incentives PAGEREF _Toc282610273 \h 64Table 6. Survey Instrument Measurement Scales PAGEREF _Toc282610274 \h 66Table A1. Determinants of Perceived Usefulness PAGEREF _Toc282610275 \h 82Table A2. Determinants of Perceived Ease of Use PAGEREF _Toc282610276 \h 82Table B1. Determinants of Perceived Usefulness PAGEREF _Toc282610277 \h 83Table B1. Determinants of Perceived Ease of Use PAGEREF _Toc282610278 \h 83Table F1. Perceived Usefulness Scales PAGEREF _Toc282610279 \h 90Table F2. Perceived Ease of Use Scales PAGEREF _Toc282610280 \h 90List of Figures TOC \f B \t "TOC Heading" \c Figure 1. SDLC phases with implementation phase highlighted. PAGEREF _Toc282610281 \h 3Figure 2. Conceptual framework used during literature review. PAGEREF _Toc282610282 \h 19Figure 3. Strategy relationships enabled through organizational interventions. PAGEREF _Toc282610283 \h 48Figure 4. Project phases, facility sequencing, and experimental framework. PAGEREF _Toc282610284 \h 52Figure C1. Technology acceptance model. PAGEREF _Toc282610285 \h 84Figure D1. TAM–3. PAGEREF _Toc282610286 \h 85CHAPTER 1. INTRODUCTIONIntroduction to the ProblemBusinesses worldwide increased their percentage of revenue spent on information technology (IT) from 3.5% in 2000 to 3.54% in 2001 and 3.57% in 2002 (David, Schuff, & St. Louis, 2002). The increasing investments in IT have fostered short-term productivity growth by introducing IT and long-term productivity growth through strategic organizational change (Brynjolfsson & Hitt, 2003). As Lucas (1999) pointed out, although IT provides tools to increase data management productivity and efficiency, productivity can be realized only when the deployed IT actually is used. Increases in efficiency and productivity expected from deploying an IT system cannot be realized if end users choose not to use the IT system. As Mathieson (1991) asserted, voluntary IT systems are particularly prone to this problem. Organizations that implement IT, even if the user community views the IT system as effective, will not realize full IT system benefits if the user community does not use the IT system (Bierstaker, Brody, & Pacini, 2006).Organizational leadership faced with an unaccepted or underused IT system presents a problem for organizational management regarding ways to modify end user community perspectives targeting IT system acceptance. Schein (2004) asserted that an organization might purpose the IT implementation process itself as a mechanism to enable cultural change. Organizational interventions during IT system implementation enable efficiency and increase productivity through IT system use (Lucas, 1999) while enabling cultural changes within the organization (Schein, 2004). Venkatesh and Bala (2008) postulated that introducing organizational interventions designed to address specific end user IT acceptance determinants (see Appendices A & B) can increase IT system use. Increased end user IT acceptance facilitates an increase in productivity while achieving beneficial cultural changes by fostering IT system use.Hoffer, George, and Valacich (2008) described multiple implementation methodologies (IMs) that organizations can employ to install and replace information systems (ISs). One such commonly used IM is the systems development life cycle (SDLC; see REF _Ref277847559 \h Figure 1). The SDLC is characterized by multiple stages in which an IT system phases into or out of an organization (Hoffer et al., 2008). Ward and Peppard (2002) explained that organizational leadership choices, computing system, culture, and business process must align with an overarching business strategy. A business strategy aligns actions with the desired results and requires that organizational leadership be mindful of the organizational environment.An analysis by organizational leadership of user expectations, project commitment, and change commitment may determine that end user IT acceptance is less than acceptable. These organization-wide issues need enterprise-wide activities that address identified shortcomings in end user IT acceptance throughout the IT system implementation phase. The quantitative effectiveness of organizational intervention options before and after the IT system implementation phase provides organizational leadership with insight during selection of the best organizational interventions. Because the chosen IM depends on the implementation purpose and function, organizations need multiple SDLC-based organizational interventions to best align end user IT acceptance (Venkatesh & Bala, 2008). This study will focus attention on the SDLC implementation phase by empirically testing two organizational interventions.Figure SEQ Figure \* ARABIC 1. SDLC phases with implementation phase highlighted. TC "Figure 1. SDLC phases with implementation phase highlighted." \f B \l "1" Gulliksen et al. (2003); Martinko, Henry, and Zmud (1996); McAllister (2006); and Schenk, Vitalari, and Davis (1998) attested to the importance of involving the end users early in the SDLC phases, highlighting the importance of involving end users throughout the SDLC process. Early end user involvement in the SDLC phases enables the system implementers and end users to form a common understanding surrounding IT that controls implementation cost and time (McAllister, 2006). Other factors critical to successful IT implementations are user expectations (Ginzberg, 1981a) and change commitment (Ginzberg, 1981b). Training and incentives acting as organizational interventions involve and inform the end user community of the importance of the IT system while informing organizational leadership about end user IT acceptance. Organizational interventions before system implementation inform the end user community and prepare it for changes that the IT system will impart to the enterprise (Venkatesh & Bala, 2008). Organizational interventions after implementation address unanticipated results and offer the organization the opportunity to address end user acceptance results that do not meet organizational leadership expectations.Training is one frequently used intervention to introduce new information to a community, but Kang and Santhanam (2003) indicated that more than preimplementation training is required. For example, an organization that has completed the implementation of an IT system and is faced with end user acceptance issues cannot offer preimplementation training. Organizational leadership determining less than acceptable end user IT acceptance need enterprise-wide actions that address end user IT acceptance surrounding system implementation. Businesses need additional options beyond interventions applicable only during specific SDLC phases that address end user IT acceptance.In lieu of only a training option, Venkatesh and Bala (2008) suggested multiple interventions that adjust end user IT acceptance and ensure increased system usage. Though the topic has received little attention, Venkatesh and Bala called for research that determines the effectiveness of and optimal timing for interventions. Two organizational interventions that are the focus of this study are training the end user community about the IT system and offering incentives to use the IT system. Training informs the end user community about the IT system within the organizational context, and incentives require actual IT system use over time. Both training and incentives involve the end user community early and throughout the IT implementation phase. Introducing training and utilizing incentives as well as measuring the resulting effect on end user IT acceptance through perceived ease of use and perceived usefulness variables may address the void in the literature.Background of the StudyRoyce (1998) and Ward and Peppard (2002) suggested that the metrics that gauge IT implementation success include on-time system delivery and within-budget installation. However, truly successful IT system implementations involve more than on-time access and within-budget installation. End user IT acceptance and actual IT system use are vital because obtaining results from an unused resource is difficult (Mathieson, 1991). Organizations invest in IT to increase productivity and enable within-business change (Brynjolfsson & Hitt, 2003), yet the pressure to deliver IT projects on time and installed within budget has led to such temporary measures as implementation outsourcing and third-party contracting (Ward & Peppard, 2002). The basic premise for this study is that what is needed to truly assess successful IT implementation are metrics beyond time and budget that include end user IT acceptance.Determining IT implementation success via time and budget metrics alone does not consider other IT implementation benefits. IT implementations are approved and funded based upon realizable increased organizational efficiency (Ward & Peppard, 2002); productivity gains (Brynjolfsson & Hitt, 2003); and their ability to enable positive organizational/cultural changes (Cameron & Quinn, 2006; Schein, 2004). Taylor-Cummings (1998) indicated that positive social change requires a positive socialization environment that reinforces areas among organizational and work group arrangements, which then results in improved IT system integration and group effectiveness.IT system implementations that concentrate on end user community acceptance and use attend to the end user community and foster the reinforcing environment that Taylor-Cummings (1998) advocated. Why? Businesses require actual IT system use to achieve desired results from the IT installation, and as Mathieson (1991) contended, obtaining results from an unused resource is difficult. Metrics that focus on time and budget need to be augmented with metrics that focus the IT system implementation on end user IT acceptance. Forging the path toward this area of inquisition included research by Davis, Bagozzi, and Warshaw (1989) on end user IT acceptance that was extended by Venkatesh and Bala (2008), who looked at the interaction of preimplementation and postimplementation organizational interventions and end user IT acceptance. These foundational studies pointed to increasing the role of end users in the IT implementation process.Sharma and Yetton (2003, 2007) suggested an increased role for end users during the implementation process. Mathieson (1991) argued that the implementation team IT acceptance level is relevant to IT acceptance levels with the broader institutional users, signifying that if the system implementers themselves do not accept the system, the organization may abandon hope that the end users accept the system. If the organizational leadership determine that IT system acceptance is unacceptable, they can implement initiatives within an organization that influence IT system acceptance (Cohen, 2005). A need for interventions might occur anywhere within the SDLC, including before and after system implementation.The extant social group culture informs administration of the levers and implementation modes that align with the current organization (Cameron & Quinn, 2006), yet the intervention that an organization uses is its choice, derived from its own metrics and informed by research. A business interested in productivity and efficiency might look to research that details organizational intervention effectiveness within specific phases of the SDLC to make its decisions.Collaborative IT systems, in which system use is not mandated, benefit from increases in end user acceptance and actual system use because the increasing work performed through the IT system intrinsically enforces business rules and processes by using the IT system organizationally (Kang & Santhanam, 2003). End users who do not accept or use a collaborative system add little interaction value with other end users; they do not increase adoption rates, and they minimize any adaptation that is required (Kang & Santhanam, 2003). Restated simply, an IT system that is not used cannot be realized (Mathieson, 1991). Lack of end user IT acceptance, especially in a collaborative environment, means that the system becomes increasingly nonrelevant to the users in the performance of their jobs.The technology acceptance model (TAM; see Appendix C) indicates that perceived ease of use and perceived usefulness of technology predict actual system utilization (Davis, 1986, 1989; Venkatesh & Morris, 2000). Venkatesh and Bala (2008) extended the TAM with the TAM-3 by identifying and linking perceived ease of use and perceived usefulness to specific organizational interventions (see Appendix D). However, TAM-3 literature has provided little evidence on the effectiveness of organizational interventions.Statement of the ProblemThe problem that this study will address is the lack of empirical evidence indicating the effectiveness of TAM-3-based organizational interventions at adjusting end user IT acceptance during specific SDLC phases. At the core of the TAM-3 is the TAM. Davis (1986) introduced the TAM to enhance the understanding of end user IT acceptance. Davis et al. (1989) found that the TAM can predict and explain IT use by measuring the variables of perceived ease of use and perceived usefulness, which can then predict end user IT acceptance (Davis, 1986). The benefit of increased end user IT acceptance is that it enables greater technology utilization (Adams, Nelson, & Todd, 1992) and increased IT integration within the implementing organization (Kang & Santhanam, 2003). The TAM has received much research attention, and Venkatesh and Bala (2008) extended the TAM by introducing the TAM-3.Venkatesh and Bala (2008) extended the TAM by defining and testing the determinants of perceived ease of use and perceived usefulness through the TAM-3. Their study consisted of a literature review and four longitudinal field studies structured to identify antecedent factors that adjusted the TAM’s perceived ease of use and perceived usefulness variables. However, the effectiveness of different TAM-3 organizational interventions during specific SDLC phases has received little attention. The problem is that empirical evidence supporting use of the TAM-3 model in specific SDLC phases has been lacking. This research will add to the body of knowledge that TAM-3 practitioners can use to select the best intervention and the best timing to apply an intervention within the SDLC implementation phase to address end user IT acceptance by measuring different organizational interventions and varying the timing of a TAM-3 organizational intervention.Purpose of the StudyThis study addresses the lack of empirical evidence for TAM-3 practitioners detailing TAM-3 organizational intervention effectiveness. The study has four purposes:Determine within the systems installation environment the effectiveness of different IT-based TAM-3 organizational interventions.Determine whether one TAM-3 intervention (i.e., IT training) is more effective than another IT TAM-3 intervention (i.e., incentives).Determine whether perceived ease of use and perceived usefulness vary based upon the timing of IT training.Add to the body of TAM-3 knowledge about IT organizational interventions as they relate to perceived ease of use and perceived usefulness.RationaleMultiple organizational interventions can adjust end user perceptions toward IT acceptance. This study will inform the organizational intervention selection process. An organizational intervention may occur at any time. This study will inform the timing for an organizational intervention. Multiple phases exist within the SDLC, so it is the intent of this study to indicate whether one of two organizational interventions, namely, training or incentives, has a greater effect than the other.Beyond the empirical evidence that organizational leadership and IT system implementers will receive from this research, past experiences, such as bad system implementations, can influence future expectations from end users (Ramlall, 2004). Increasing practitioner understanding of the effectiveness of an IT implementation can improve the intervention selection process. Understanding intervention effectiveness allows businesses to focus on specific interventions that have been shown to be effective, which then facilitates user acceptance of the current implementation and may benefit future implementations.Finally, one option available to organizational leadership is to manage end user IT acceptance through mandates because top-management support is critical to IS success (Sabherwal, Jeyaraj, & Chowa, 2006). Mandates are an option for organizational leadership that reflect management support (Sharma & Yetton, 2003; Venkatesh & Bala, 2008). Taylor-Cummings (1998) pointed out that end user compliance with mandates does not increase organizational support for an IS from its members. Overt coercion such as mandates may facilitate short-term system utilization, but individual usage will vary (Martins & Kellermanns, 2004). End users who voluntarily choose to use a collaborative system exploit the efficiencies offered through its use and embrace the changes in process and culture that the system enables (Kang & Santhanam, 2003).Research Questions and HypothesesThe study will be guided by eight research questions and hypotheses:Is an end user’s IT system ease of use perception dependent on the training timing and IT system availability?H01: IT system perceived ease of use is not dependent on the training timing and IT system availability.Ha1: IT system perceived ease of use is dependent on the training timing and IT system availability.Is an end user’s IT system usefulness perception dependent on the training timing and IT system availability?H02: IT system perceived usefulness perception is not dependent on the training timing and IT system availability.Ha2: IT system perceived usefulness perception is dependent on the training timing and IT system availability.Does training increase end user IT system ease of use perception?H03: Training does not increase IT system perceived ease of use.Ha3: Training increases IT system perceived ease of use.Does training increase end user IT system usefulness perception?H04: Training does not increase IT system perceived usefulness.Ha4: Training increases IT system perceived usefulness.Do incentives increase end user IT system ease of use perception?H05: Incentives do not increase IT system perceived ease of use.Ha5: Incentives increase IT system perceived ease of use.Do incentives increase end user IT system usefulness perception?H06: Incentives do not increase IT system perceived usefulness.Ha6: Incentives increase IT system perceived usefulness.Does training increase end user IT system perceived ease of use more than incentives?H07: Training does not increase IT system perceived ease of use more than incentives.Ha7: Training increases IT system perceived ease of use more than incentives.Does training increase end user IT system perceived usefulness more than incentives?H08: Training does not increase IT system perceived usefulness more than incentives.Ha8: Training increases IT system perceived usefulness more than incentives.Significance of the StudyThis study will provide evidence for practitioners interested in the effectiveness of two interventions within an organization. The study also will inform the practitioners about the optimal timing to deliver training to harness the maximum intervention effect. A system implementer may add an additional system implementation tool that enables successful IT system implementation by involving the end user community. The end user community will then gain from increased collaborative system use and related productivity and efficiency because organizational value increases as each member increases system use. Additional IT system utilization increases end user information sharing and enables the end user community to exploit additional IT system features. Increasing system utilization by getting users to engage the system increases productivity, enables cultural changes, and expands these benefits for the organization, subsequently enabling strategic benefits beyond using the IT system.The study context surrounds the IT system implementation process and is significant because the study creates knowledge within the IT discipline for TAM-3 practitioners and scholars. The results of this study also will provide business management with mechanisms that facilitate business strategies and cultural changes within the organizational environment. Scholars may benefit from linking theory to practice by testing and measuring the results previously postulated by Venkatesh and Bala (2008). Practitioners who are implementing IT, as well as business executives, administrators, and management, may benefit from the quantitative results that indicate the effectiveness of specific interventions. The results may inform the decision makers who are responsible for constructing and selecting specific interventions.Definitions of TermsIncentive. “A thing that motivates or encourages one to do something” (Oxford American College Dictionary, 2002, p. 676).Perceived ease of use. “The degree to which the prospective user expects the target system to be free of effort” (Davis et al., 1989, p. 985).Perceived usefulness. “The prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context” (Davis et al., 1989, p. 985).Systems development life cycle (SDLC). “A common methodology for system development in many organizations; it features several phases that mark the progress of the system analysis and design effort” (Hoffer et al., 2008, p. 9).Technology acceptance model (TAM). Davis sought to apply the TAM to predict IT system user acceptance through two variables, namely, perceived ease of use and perceived usefulness (as cited in Davis et al., 1989). Davis (1986) adapted the theory of reasoned action introduced by Fishbein and Ajzen in 1975.Technology acceptance model 3 (TAM-3). Venkatesh and Bala (2008) extended TAM research detailing perceived ease of use determinants and perceived usefulness determinants, and they linked the identified determinants to specific organizational interventions believed effective in adjusting perceived ease of use and perceived usefulness variables (see REF _Ref275349033 \h Table 1 & REF _Ref275349037 \h Table 2). TAM-3 guides the organizational intervention design in this study that will address end user IT acceptance.Assumptions and LimitationsThis study will measure the effectiveness of two interventions captured through the variables of perceived ease of use and perceived usefulness. The TAM indicates that perceived ease of use and perceived usefulness of IT predict actual system usage (Davis, 1986, 1989; Venkatesh & Morris, 2000). This study will draw upon previously published research indicating that perceived ease of use and perceived usefulness are antecedents of IT end user acceptance and actual IT system usage (Venkatesh & Bala, 2008). Additional variables may be involved.Table SEQ Table \* ARABIC 1. Perceived Usefulness Determinants Presented in TAM-3 TC "Table 1. Perceived Usefulness Determinants Presented in TAM-3" \f A \l "1" DeterminantsDefinitionsPerceived ease of useThe degree to which the end user “expects the target system to be free of effort” (Davis et al., 1989, p. 985).Subjective normThe degree to which the end user perceives “that most people who are important to him think he should or should not perform the behavior in question” (Fishbein & Ajzen, 1975, p. 302).ImageThe degree to which “use of an innovation is perceived to enhance one’s image or status in one’s social system” (Moore & Benbasat, 1992, p. 195).Job relevance“An individual’s perception regarding the degree to which the target system is applicable to his or her job” (Venkatesh & Davis, 2000, p. 191).Output quality“People will take into consideration how well the system performs those tasks, which we refer to as perceptions of output quality” (Venkatesh & Davis, 2000, p. 191).Result demonstrability“The degree to which an individual believes that the results of using a system are tangible, observable, and communicable” (Venkatesh & Bala, 2008, p. 277).*Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 277.Table SEQ Table \* ARABIC 2. Perceived Ease of Use Determinants Presented in TAM-3 TC "Table 2. Perceived Ease of Use Determinants Presented in TAM-3" \f A \l "1" DeterminantsDefinitionsComputer anxiety“The fear or apprehension felt by an individual when using computers, or when considering the possibility of computer utilization” (Maurer & Simonson, 1984, p. 321).Computer playfulness“The degree of cognitive spontaneity in microcomputer interactions” (Webster & Martocchio, 1992, p. 204).Computer self-efficacy“An individual's perception of his or her ability to use a computer in the accomplishment of a job task” (Compeau & Higgins, 1995, p. 193).Objective usabilityA “comparison of systems based on the actual level (rather than perceptions) of effort required to completing specific tasks” (Venkatesh, 2000, pp. 350-351).Perceived enjoyment“The activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use” (Venkatesh, 2000, p. 351).Perception of external control“Perceptions of external control are related to individuals’ control beliefs regarding the availability of organizational resources and support structure to facilitate the use of a system” (Venkatesh & Bala, 2008, p.278)*Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 279.Nature of the StudyThis exploratory, quantitative study will use a field experiment to determine intervention effectiveness on an end user community surrounding an IT system implementation. End user IT system acceptance will be determined by measuring the perceived ease of use and perceived usefulness variables, which predict end user technology acceptance detailed in the TAM (Davis, 1986, 1989; Venkatesh & Bala, 2008).SummaryThis chapter introduced a problem that exists in a business environment, where either the end user community underutilizes an available IT system or the business seeks to maximize end user community productivity through increased IT usage. The context of this problem surrounds the implementation phase within the SDLC and presents two organizational interventions that Venkatesh and Bala (2008) posited address end user acceptance. Organizations seeking to increase end user IT system acceptance may look to specific organizational interventions that facilitate increased IT acceptance by the end user community. TAM-3-based organizational interventions theorized to increase end user IT acceptance have received little research attention, even though the research results may directly relate to the practitioner conducting IT system implementation and business management within any anization of the Remainder of the StudyThe comprehensive literature review in chapter 2 frames and contextualizes the study. Chapter 3 details the methodology and the rationale for using the field experiment methodology. Chapter 4 details the results of the study, followed by a discussion of the results, implications, and recommendations in chapter 5.CHAPTER 2. LITERATURE REVIEWIntroductionThe literature review includes a discussion of research on business strategy, derived from institutional executives, as well as institutional culture, which defines potential organizational interventions that address end user IT acceptance. REF _Ref275424136 \h Figure 2 depicts the narrowing focus of the literature review from the business strategy outer ring to the central ring, which is end user IT acceptance. The literature review provides an understanding of the influences that guide decision makers when implementing IT and formulating actions that enable successful IT implementation. The literature review indicated that the business strategy and culture of the participating organization favored specific interventions. The literature review results in two organizational interventions that adjust end user IT acceptance during IT system implementation.Business StrategyWard and Peppard (2002) conceptualized three IT eras, each with different objectives within the computing discipline that include data processing, management ISs, and strategic ISs. Ward and Peppard asserted that the computing discipline functionality has changed, suggesting that IT selection offers the business mechanisms that enable competitive forces when they strategically select, implement, and use IT. How IT is pressed into service within an organization and the reasons for selecting IT have changed from simply using IT to increase efficiencies to increasing management effectiveness to improve competitiveness (Ward & Peppard, 2002). Ward and Peppard also predicted a fourth IT era, where focused IT investments enable a business strategy through the organizational change that the IT investments create through the IT implementation process and actual IT system utilization. The business strategically aligns the changes inherent in implementing and using IT with the institutional strategy. Through continual strategic IT investments and an eye on the business strategy, the institution gains a competitive advantage that derives value.Figure SEQ Figure \* ARABIC 2. Conceptual framework used during literature review. TC "Figure 2. Conceptual framework used during literature review." \f B \l "1" IT is a broad discipline that encompasses networks, hardware, software, and data (O’Brien & Marakas, 2008). The IT system central to this study is e-mail, delivered from the manufacturer and bundled as a collaboration application suite that includes calendaring, contact manager, task manager, note manager, personal journal manager, and e-mail application. The literature review focused on the e-mail application. End users use software to process data (O’Brien & Marakas, 2008). Application software enables computer end users to get data into a computer, facilitates data retrieval from the computer, stores the data for later retrieval, and facilitates data movement from one individual to others who can use the data. The e-mail application facilitates communication through a computer and is the most fundamental and ubiquitous application system on a computer within a business context.Businesses generally improve efficiency through implementing IT, but calculating the actual return on investment is less clearly understood (Ward & Peppard, 2002). Much of the literature has focused on the difficulty associated with implementing complex systems within the organization. Depending on the IT system customization levels, the organization might expend significant effort to implement and require business process redesigns (Hoffer et al., 2008). The customization occurs within the IT system software and also requires changes within the business and social group concerning procedures and processes (Ward & Peppard, 2002).During IT system implementations, complex systems like Enterprise Resource Planning (ERP) may receive customization, the business process may change, and the employees may need to adapt their interactions within the social group and business. ERP implementations are so large and complex that significant customization and budgeting occur, and organizations typically augment their staff during implementations with consultant experts (Lucas, 1999). Basic systems that require minimal customization may not require an outside consultant or additional budgeting for implementation, yet they offer organizational leadership mechanisms that enable change.Implementing an IT system within an organization has various complexities that influence the cost associated with implementing the system. Implementing an IT system has some degree of cost and benefit, but the IT implementation also facilitates organizational changes (Ward & Peppard, 2002). The changes that occur by implementing an IT system offer business leadership the ability to facilitate strategic change. The end user community may choose not to accept the IT system because its members do not understand the system and may not understand how their individual actions disrupt the organization (Kang & Santhanam, 2003). Teo and Ang (2001) found that “people at all levels must understand and accept the change process” (p. 462). The strategic changes desired by introducing the IT system within the business cannot occur if the end users do not accept and use the installed system.Decision makers may specifically purpose IT system implementation to enable change within their organizations (Glen, 2003; Lucas, 1999; Schein, 2004, 2009; Senge, 2006; Ward & Peppard, 2002). Business executives may confront barriers to their strategic plans during IT system implementation. Teo and Ang (2001) separated IS planning into three implementation phases and indicated that the greatest problems surrounding IS strategy involve top management support, involvement, and commitment. A barrier exists for successfully implementing an IT system if organizational leaders refuse to embrace and accept such an implementation. Furthermore, any business process redesign that accompanies IT system implementation adds an additional barrier if the organizational leaders are not supportive, involved, or committed to such an implementation (Teo & Ang, 2001).A benefit exists for organizations to use system implementers who are familiar with the system implementation process (Lucas, 1999). Business use metrics that indicate IT system implementation success (Freedman, 2003). Time and budget are metrics used during system implementation (Taylor-Cummings, 1998), but these metrics fail to capture other quantifiable metrics available during system implementation. Aligning IT system changes, the organization changes, and the social group processes during IT system implementation enables a business strategy (Ward & Peppard, 2002). Yet the metrics upon which the system implementation experts are measured are on-time system delivery and within-budget installation (Taylor-Cummings, 1998), both of which indicate system implementation success (Freedman, 2003). Research has supported including end users throughout the IT system implementation process (David et al., 2002; Freedman, 2003; Martinko et al., 1996; Sabherwal et al., 2006; Sharma & Yetton, 2003), yet end user involvement is not captured through on-time system delivery and within-budget installation metrics.Other hurdles exist for IT system implementers, including incorrect job perceptions or job idealizations that result in misaligned organization changes from misaligned interactions between users and system implementers (Lyytinen & Hirschheim, 1987). Strategic IT use involves aligning continued IT use with the business (Ward & Peppard, 2002). Metrics such as on-time system delivery and within-budget installation marginalize concerns such as individual acceptance issues and training during system implementation for the user community. On-time system delivery and within-budget installation overlook the end user community, not capturing end user metrics such as the end user perceptions surrounding the technology ease of use or usefulness while implementing a system, even with overwhelming evidence indicating early and continuous end user involvement (Gulliksen et al., 2003; Martinko et al., 1996; McAllister, 2006; Schenk et al., 1998).Viewing system implementation from the end user perspective, it appears that multiple little problems with IT are associated with the IT system and are not separate problems. Ward and Peppard (2002) expanded on system implementation failures within the user domain, indicating that many system implementations result in progressive system use decreases because of systemic inadequacies in end user training. Ward and Peppard posited that organizations receive additional value through increased productivity as each individual increases system use, while simultaneously enabling change.Factors crucial during IT system implementation are system modification to fit the organization (Kang & Santhanam, 2003); business processes adaptation (Ward & Peppard, 2002); and installed system end user acceptance and utilization (Adams et al., 1992). On-time system delivery and within-budget installation do not capture the metrics associated with end user involvement, even though they are vital to IT system implementation. Metrics such as on-time system delivery and within-budget installation only measure and reward successful timing and budgets, not the desired business and strategic changes that result from actual IT system utilization.Lacking individual end user acceptance threatens the business changes and strategic changes enabled through implementation of the IT system. As more organization members accept and engage the IT system, the institutional body of knowledge increases, but with limited or no actual IT system utilization, there is no body of knowledge or common understanding of how the implemented IT system fits within the business (Ward & Peppard, 2002). IT implementations are successful if they meet implementation metrics within an environment, but organizationally, the system implementation is a failure if end users within the organization fail to understand the relationship between organizational procedures and the installed system (Ward & Peppard, 2002). Lacking system use by the end user community limits the results sought by a strategic IT system implementation.Bartlett, Ghoshal, and Birkinshaw (2004) noted that “the influence of culture can also be seen in organizational processes such as the nature of policies and procedures, planning and control, information processing and communication, and decision-making” (p. 168). The business strategy manifests within the social group culture as policies and procedures. Implementing an IT system within an organization initiates change, just as planning an intervention initiates change. Either using or not using an IT system reflects the organizational culture. If business management choose an IT system to enable a competitive or a business advantage, as Ward and Peppard (2002) suggested, system utilization is required because without actual system utilization, the IT can add little value.When the business characteristics change or when the current business strategy is no longer the best fit for the business, the institution can adopt a new business strategy. The cultural maturities of an organization inform organizational leadership about the malleability of culture and firm performance. Adaptable cultures exist in high-performing organizations, but strong cultured organizations exist in low-performing firms (Heskett, Sasser, & Schlesinger, 1997). Ward and Peppard (2002) argued that the business IT strategy extends beyond determining what technology can do for the organization and that the business strategy includes more than aligning IT and business objectives. Ward and Peppard suggested that strategic IT use includes aligning business and IT, as well as defining, communicating, and understanding the reasons specific IT deployment choices and methods are used. Delivering value through IT requires coupling the business strategy with strategic changes that an IT implementation enables, delivering value through strategic IT selection, implementation, and utilization (Lucas, 1999; Ward & Peppard, 2002; Schein, 2009).Institutional CultureImplicit in an intervention is that change will occur in a measurable way. The desired effect is that the change enables movement from a less desirable state toward a more desirable state. How organizational leadership present and implement change depends on the existing culture. This means that when organizational leadership select an intervention, they reflect on the people involved and where the organization has been, and they also consider the current culture while determining a new directed vision where the organization will move.The business strategy that the institution embraces defines and influences the culture because different business strategies employ different mechanisms that influence behavior. Ward and Peppard (2002) detailed three general business strategies: low cost, differentiation, and niche focused. An institution that embraces a differentiation business strategy focuses on people and their creativity or market orientation rather than on management controls, and it also utilizes incentive schemes that are not production based (Ward & Peppard, 2002). Within a differentiation business strategy, an incentive reinforces preferred activities, such as creativity, or deemphasizes other activities that are not preferred, such as production quotes. Conversely, within a low-cost business strategy, an incentive reinforces production quotas as a preferred activity and deemphasizes creativity. An organizational executive who is contemplating business strategy changes or cultural changes must consider the current business strategy and current culture (Ward & Peppard, 2002) while favoring change activities that achieve an alignment between business strategy and institutional culture.The message that makes change possible requires communication, but within an organization, barriers or boundaries can impede communication (Schein, 2009). Executives might communicate desired change by imposing rules and regulations on IT system utilization. Mandating IT system utilization is an option, but Schein (2009) indicated that before change can occur, the driving force for change must be greater than the restraining force against change and the organizational intervention selected must fit the organizational culture. Ward and Peppard (2002) noted that a business strategy that pursues a low-cost strategy seeks structure and conformity, and in such an environment, mandates are culturally agreeable. Conversely, an organization whose business strategy embraces differentiation or a niche-focused strategy may encourage workforce creativity and may find that mandates are not aligned with their culture and business strategy.Administration might select mandates as the best choice for their business environment. Mandating IT system utilization, where IT system use is up to end user discretion, is problematic and points to management issues. Baum and Kling (2004) stressed that mandating IT system utilization not based upon real and meaningful values has implications for corporate government effectiveness. Administration need additional methods to enable cultural change within their environment beyond mandates. Business executives who attempt to achieve strategic alignment between culture and strategy need mechanisms to change the culture by instilling meaning and meaningful values in its members.The business strategy and the organizational culture are executive responsibilities. Organizational leadership create the business strategy, and the business strategy informs the business culture (Baum & Kling, 2004; Cameron & Quinn, 2006; Schein, 2004, 2009). Before implementing any change, organizational management must understand what will work within their environment. The leaders need to know their constituents’ thoughts and feelings (Kouzes & Posner, 2007). Change through strategic IT utilization allows administration to shape change through IT implementation within the business (Ward & Peppard, 2002). Strategic change is possible if organizational leadership are aware of the current culture, understand what change means to the constituents who comprise the social group, and maintain a clear view of the resulting culture and business strategy.Different mechanisms that enable cultural change are available for different organizational maturities (Schein, 2004). If a misalignment exists between culture and the organization’s mission and goals, a cultural change process could be implemented (Knowles, Holton, & Swanson, 2005). As an example, Schein (2004) cited technological seduction as a cultural change mechanism, explaining that the organization introduces technology and develops the organization through an educational intervention. Cultural change mechanisms are effective for midlife and early growth organizations, but not for stabilized cultures (Schein, 2004). Multiple cultural change mechanisms exist, even though they may not be effective. Schein indicated that culture ranges from malleable to fixed, depending on the organization’s evolutionary stage.Using IT to enable change at an organization that is mature or in decline may result in a reaction from the end user community that is not the desired effect. Schein (2004) described the results from introducing IT on silo cultures where the implemented system met end user resistance, subversion, and refusal to engage the system. Positive cultural change or any other desired change is unlikely if the end user community meets the change with resistance, subversion, and refusal. Organizational leadership faced with these issues need methods to mitigate the issues and create a positive environment for the envisioned change.IT enables a business strategy when the end user community uses the IT system because the technology emplaces a common frame of reference throughout the organization (Schein, 2004). Low acceptance or low utilization by the end user community is problematic for the business if organizational leadership have strategically chosen IT implementation to enable change. Obtaining results from an unused resource is difficult (Mathieson, 1991). Cultural changes occur only when the end user community adopts the common reference and assumptions using IT (Schein, 2004). Action is required if end user acceptance and system utilization are less than desired, suggesting that the resulting cultural change envisioned through IT system implementation is in jeopardy.Senge (2006) developed the learning organization concept, which includes systems thinking, personal mastery, mental models, a shared vision, and team learning, all suggesting business strategy and cultural alignment. Senge described systems thinking in an organization as “bound by invisible fabrics of interrelated actions” (p. 7); personal mastery that infers “dominance over people or things” (p. 7); and mental models of “deeply ingrained assumptions, generalizations, or even pictures or images that influence how we understand the world and how we take action” (p. 8). Senge and Schein (2004) viewed culture from similar perspectives. Senge believed that organizational learning occurs through teams, not individuals. This view advocates that although individuals comprise the team and each individual retains knowledge, the institutional knowledge itself comprises culture and exists within individual team members.IT system implementation offers an organization the ability to incorporate a “managed change program” (Schein, 2009, p. 152) that enables cultural change. Central to enabling change and successful IT system implementation is that necessity for the end user community to accept and utilize the implemented IT to enable the envisioned changes and strategy. Schein (2004) indicated that educational interventions enable cultural change. Team learning (Senge, 2006) is one organizational intervention that may address end user IT acceptance. Education communicates information about the IT system. Training provided to the end user community addresses perceived ease of use and perceived usefulness determinates (Venkatesh & Bala, 2008).Organizations with adaptable cultures outperform organizations with strong cultures (Heskett et al., 1997), but confronted with either culture, administration must select and implement an intervention appropriate for the culture by weighing the business strategy and understanding possible changes within the social group culture and the desired effects of introducing the IT within the environment.Cultural InterventionsWithin a business context, Daft (2004) argued that some resistance to change is good. Daft suggested using strategies that overcome resistance, including training and communication, participation and involvement, creation of a psychological safe environment, and forcing and coercion. Open communication is required for people to understand the reasons for change, and coping, participation, employee involvement, and a psychologically safe environment imply that a culture of trust exists between employees and leadership (Daft, 2004).Daft (2004) noted that even though force and coercion have been used successfully to adjust business processes, these strategies to overcome resistance may have unforeseen and deleterious effects such as employee interpretations and sabotage; consequently, they are not advised. Open communication, participation, and employee involvement, while creating a psychologically safe environment, are positive intervention mechanisms. Force and coercion are not intervention mechanisms considered useful at the target anizational InterventionsThe interventions that are selected create a psychologically safe environment while ensuring employee involvement and participation. Training and incentives foster open communication. The training intervention design introduces the IT system, informs the user community about possible uses of the IT system, and communicates how the IT system addresses the strategic needs of the organization. The participating association maintains a culture that is familiar with training.Another intervention that fosters open communication, and addresses employee involvement and participation while creating a psychologically safe environment utilizes incentives to engage the installed IT system. The culture of the participating association is familiar with competition and rewards as an intervention mechanism. Determining system implementation success and linking system implementation success to the two selected interventions occurs next.Interventions and IT System ImplementationDuring IT system implementation, the system implementers integrate the IT within the environment. Metrics used to determine successful system implementation include on-time system delivery and within-budget installation (Royce, 1998), which has led to increased pressure to deliver IT on time and within budget (Ward & Peppard, 2002). On-time system delivery and within-budget installation metrics do not capture metrics surrounding the end user community acceptance or intention to utilize the IT system.IT implementation failures are normally attributable to unresolved organizational or cultural issues (Ward & Peppard, 2002). Implementing an IT system within an environment merges the organizational processes, the IT, and the application system end users within the environment. The implementation processes and changes required suggest that the metrics used to determine implementation success involve including the end user community. Hoffer et al. (2008) contended that a common and serious problem in IT system development is limited user involvement. Applying interventions and then measuring end user IT acceptance levels offers the organization a mechanism that measures and adjusts end user IT acceptance. The process keeps the end user involved throughout the SDLC and results in quantifiable information that informs the implementation process.End user IT acceptance, increased technology utilization, and increased IT integration within the implementing organization result from technology being perceived as aligned with the business needs. Venkatesh and Bala (2008) suggested specific organizational interventions in the TAM-3, including training and incentives, which they posited adjust end user IT acceptance and increase IT system use. Research has not indicated whether one TAM-3 intervention is more effective at addressing end user IT acceptance. This study will address that void by measuring two TAM-3 interventions and measuring their effectiveness in addressing end user IT acceptance.Segmenting of Organizational Interventions Within the SDLCInterventions can occur whenever organizational leadership determine that they are required, including events surrounding the SDLC implementation phase. The SDLC has five phases that cycle from one phase to the next phase. The context of this research effort surrounds the implementation phase in the SDLC, including before, during, and after the implementation phase.Research consistently has called for the involvement of end users in multiple functions and perspectives during all SDLC phases (Adams et al., 1992; David et al., 2002; Sharma & Yetton, 2003; Venkatesh & Bala, 2008). The five basic SDLC phases include planning, analysis, design, implementation, and maintenance (Hoffer et al., 2008). Specific interventions have an affinity for either preimplementation or postimplementation based upon administrative considerations or current implementation status (Venkatesh & Bala, 2008). This choice is arbitrary and assigned to the business executive, but measuring one intervention against another intervention informs the decision process.Throughout the SDLC, different interventions have different effects on the end users’ IT system perceptions because some interventions are less effective during specific SDLC phases. As an example, introducing an IT system through a live demonstration preview or introducing the system with a hands-on, live demonstration does not make sense postimplementation because the end user already has had extensive exposure to the IT system. Grouping interventions as preimplementation or postimplementation (Venkatesh & Bala, 2008) separates the interventions within the SDLC and enables specific IT system implementation milestones while giving the end user community a real stake in the system implementation process.Venkatesh and Bala (2008) listed organizational interventions associated with preimplementation, including design characteristics, user participation, management support, and incentive alignment. Interventions associated with postimplementation includes training, organizational support, and peer support. The TAM-3 links perceived usefulness determinates with subjective norm, image, job relevance, output quality, and result demonstrability, while linking perceived ease of use determinates with computer self-efficacy, perceptions of external control, computer anxiety, computer playfulness, perceived enjoyment, and objective usability (Venkatesh & Bala, 2008).The TAM-3 extended the TAM. Although the TAM is widely accepted within the technology adoption field (Bagozzi, 2007), it is not without its detractors. As Bagozzi (2007) indicated, most TAM research has extended perceived ease of use or perceived usefulness antecedents, but it has not detailed how perceived ease of use or perceived usefulness produce their effects. Venkatesh and Bala (2008) indicated the organizational interventions act on perceived ease of use and perceived usefulness. These determinates are used to construct interventions that attempt to adjust end user perceptions about an IT system. Within this research effort, the changes in perceived ease of use and perceived usefulness indicate the intervention effectiveness.Bagozzi (2007) called for research on specific linkages detailing how perceived ease of use or perceived usefulness produce effects. When designing interventions to employ the determinants of perceived ease of use and perceived usefulness, as identified by Venkatesh and Bala (2008), the results begin to address the questions presented by Bagozzi, and begin informing how perceived ease of use or perceived usefulness produce their effects. Intervention effectiveness speaks to how specific organizational interventions produce their effects, which was the larger dialogue posited by Bagozzi.Preimplementation InterventionsVenkatesh and Bala (2008) indicated that training occurs as either a preimplementation or a postimplementation intervention. Several factors rationalize using training as a preimplementation intervention. End users with varied computer proficiency levels comprise the target association, ranging from computer inexperienced to expert. Preimplementation is appropriate to adjust perceptions before the end users have a chance to form their own perceptions within the organizational context (Martinko et al., 1996). The organizational training focuses on training adults and presenting the application system functional aspects, including how the existing culture will change. The training communicates how each person plays a part in those changes. After the training, the end users gain application system access and begin using the new system. Adult learners want to know “how the learning will be conducted, what will be learned, and why it will be valuable” (Knowles et al., 2005, p. 201). The preceding factors indicated that preimplementation training is the appropriate time for training.Training Focus and DesignDaft (2004) listed training and communication, participation and involvement, and a psychologically safe environment as ways to overcome resistance. The training design in this research effort includes addressing computer functionality for the end user community and communicating envisioned business process changes as well as the resulting cultural changes through implementing the IT system. The training also includes informing the end user community why the changes are occurring and the rationale for the choices. Within a training or classroom environment, Brookfield (2006) advocated communicating the rationale for the decisions so that the learners understand that the decisions are based upon experience. Conveying information to the end user community about the IT system implementation and the strategic rationale for installing the system informs the end user community why the changes are occurring, thus relaying the business need for the changes.Knowles et al. (2005) espoused a view of teaching as “the management of procedures that will assure specified behavioral changes” (p. 84). Through training, the organization conveys to the end user community how the new system enables changes and how the changes are dependent on end user acceptance and system utilization. Within the target association, adults comprise the end user community, and adults are motivated to learn when they can link the learning tasks to problems in their lives (Knowles et al., 2005). Tailoring the instruction design with the recommendations from Daft (2004) and Knowles et al. relays the training information to the learner in a way that overcomes resistance to change in the business context and presents the adults in the organization with training that relates to their daily lives by contextualizing the IT system in their task performance.Using training to overcome change resistance (Daft, 2004) informs the end user community and orients the learners, thus enabling an examination of biases, habits, and new approaches (Knowles et al., 2005). Adult learning is motivated through the perception that the learning will help adults deal with real-life situations by using the IT system (Knowles et al., 2005). Training as a preimplementation intervention paves the way for implementing an IT system and informs those involved about the business changes that accompany the implementation.Training Within the SDLCHoffer et al. (2008) described an SDLC as having five phases: planning, analysis, design, implementation, and maintenance. Training is considered as supporting the IT system implementation (Hoffer et al., 2008). Training enables the end user community to understand the business purpose and cultural changes (Schein, 2004) facilitated through system implementation. Training is a form a communication (Kouzes & Posner, 2007; Senge, 2006) that informs and relates how the IT system enables the end user community to accomplish tasks (Knowles et al., 2005).Lacking organizational support such as training for an IT system requires that end users look for answers from available resources, such as coworkers, who may know how to complete a process within a system (Hoffer et al., 2008). End users who are not informed harbor negative IT system perceptions; those who entertain incorrect perspectives about the appropriate method to complete a task may communicate information that perpetuates mental models that are incorrect (Senge, 2007). Presenting the IT system accurately is important during preimplementation so that end users hold accurate IT system perceptions and minimize initial resistance (Venkatesh & Bala, 2008). Training is a preimplementation intervention.Training ComponentsAs an intervention, the training design requires that the training target what the organization wants to change. The IT system central to this study is e-mail that is delivered from the manufacturer and bundled as a collaboration application suite that includes calendaring, contact manager, task manager, note manager, personal journal manager, and e-mail application. Kang and Santhanam (2003) found that during a collaboration system implementation, task-focused training resulted in end users not having the information they needed to engage the system, requiring information beyond system task functionality. Knowles et al. (2005) commented that because adults perceive learning as life centered, they orient learning to their own lives and experiences. This finding indicated that task-oriented information by itself is inadequate. Training for adult learners presents the end users with information about how their system utilization fits into their lives and within their social group and the organization. The training includes conveying the business rationale with the reasons supporting any process changes (Kang & Santhanam, 2003).Adult education, or andragogy, has a different approach from educating children, as Knowles et al. (2005) indicated. Adults resist situations of imposed will from others. Rather than lecture-based training, the training involves a dialogue that involves the end user community. Training is designed to link the IT benefits to the end user interaction.Kouzes and Posner (2007) showed that an above-average investment on training benefits organizations through higher employee involvement, more commitment, and increased customer service levels. The investment in employees fosters increased end user community involvement. Leaders are responsible for creating an environment where all organizational members are empowered to voice their views and take initiatives (Kouzes & Posner, 2007). Involving end users during the SDLC extends beyond performing tasks during development and system implementation; it also includes full end user involvement in the project, including participation and involvement (Sabherwal et al., 2006). Freedman (2003) found that a lack of end user involvement during the IT implementation project “is the key cause of resistance, sniping, rumor, and sabotage” (p. 106). Training facilitates a psychologically safe environment where difficult topics such as change and what the change means for the individual and the organization receives adequate attention. Training gives the end user community a voice in the implementation process.Delivering information on functional tasks while fostering an environment where the adult end user community relates the IT system to their daily lives informs the end user community about the stakes involved and gives the adult learners a stake in the process. Adult learners maintain shared experiences that enable learning (Knowles et al., 2005). An open dialogue environment gives the training attendees the ability to ask questions that lead to a better IT system understanding, more participation in the implementation process, and an increase in their stake in the implementation success.Kouzes and Posner (2007) asserted that people “want to know what they do matters” (p. 134) and what they do has an impact to others in their lives. The end users should feel free to participate, ask questions, and receive realistic answers. Training enables the end users to contextualize their role with respect to other end users, the social group, the organization, and the IT system. Through training, the end users gain a real stake in the IT munication is not a one-way operation. Glen (2003) stated, “Effective communication occurs when a thought of one person is translated into words, expressed, heard, and translated back into an identical thought in the mind of another” (p. 35). After communication, the next logical step is taking action based upon the communicated message. Follow-through processes must accompany any communication of change, which end users can witness through evidence of money and materials (Kouzes & Posner, 2007). The training orients the end user community with IT system tasks, and informs the process changes within the environment through the IT system implementation. The end user community can rationalize the changes with the changes supported by observed institutional support for the IT system implementation within the organization. The learners thus receive the information they need to contextualize the information, understand what it means for them within their job, and perceive the institutional commitment from the institutional hierarchy within their anizations might use IT implementation as a mechanism that enables change within the environment (Schein, 2004). Surprisingly, even as organizations invest more in IT systems (David et al., 2002), organizational members are unable to explain the business benefits that the IT system offers (Faguet, 2003). If the institutional power structure choose to implement the new IT system without understanding why they are implementing it, communicating the reasons for implementation cogently to the end user community is unlikely. Leaders should communicate with their employees (Kouzes & Posner, 2007). From a strategic perspective, leaders must know their environment and create a dialogue with members by connecting with information sources (Kouzes & Posner, 2007). Through communication, organizational leadership builds culture, which then informs and conveys the existing business culture (Baum & Kling, 2004).“Communication plays a major role in promoting a transparent culture, and doing it frequently is important” (Baum & Kling, 2004, p. 20). Similar to resistance to change within organizations, Brookfield (2006) discussed resistance to learning, suggesting that teachers determine resistance sources. Resistance to change in the organizational context (Baum & Kling, 2004) or the training context (Brookfield, 2006) requires action to reduce the effect of the sources of resistance. Protecting desirable traits within the corporate culture means that organizational leaders can neutralize existing cultural threats (Baum & Kling, 2004). Communication that details organizational leadership vision informs the end user community about the changes required during IT system implementation and addresses change resistance.Postimplementation InterventionVenkatesh and Bala (2008) suggested that different interventions are applicable during preimplementation and postimplementation. Venkatesh and Bala summarized postimplementation organizational interventions that address design characteristics, user participation, management support, incentive alignment, training, institutional support, and peer support. The postimplementation intervention selected utilizes incentives.IncentivesChange by aligning the business process and the organizational culture requires coordination. Coordination requires communication and motivation (D. E. Campbell, 2006). One way to motivate individuals is by offering incentives. Incentives have such a profound impact on motivation that Martins and Kellermanns (2004), who investigated acceptance of a web-based application, found that just the perception of incentives was a motivating factor. Not all incentives are monetary; some incentives include public recognition of an accomplishment. Ramlall (2004) referenced Champagne and McAfee, who indicated that praise and awards can address employees’ esteem and their need for psychological security. Lindborg (1997) indicated that within the team environment, it is important to recognize the contributions of individual team members. Utilizing rewards and praise incentives influence employees’ actions in a positive way.Other literature on incentive effectiveness has been conflicting. Todd and Benbasat (1999) analyzed strategy selection choices and incentives, finding that employees who were incentivized were not influenced, hinting that offering incentives alone is not entirely effective. Just as training requires communication to convey the message, incentives must use communication so that the incentive intent is understood (D. E. Campbell, 2006). This means that the end user community must understand the incentives purposes so that they can align actions with incentive. Communication in conjunction with incentives fosters coordinated action and creates motivation (D. E. Campbell, 2006). The information communicated to the end user community guides the choices surrounding desirable actions, but the end user community must have the capability to choose from and possess knowledge about the available options surrounding the incentivized action (Todd & Benbasat, 1999). Training enables the employees by informing and presenting options that give the employee task choices to enable change.Research on incentives has indicated that rewards can be used to increase and decrease interest in tasks (Eisenberger & Cameron, 1996). Eisenberger and Cameron (1996) found that task interest increases with quality dependent rewards and that completion-dependent rewards and performance-independent rewards are unreliable. Increasing the end users’ interest in IT system use is the goal of the incentive, suggesting that quality dependent rewards are prudent.Businesses goals, the recognition and rewards that the business embraces, and the methods businesses use to measure employee performance are reflected in their business culture (Baum & Kling, 2004). Designing incentives that overcome resistance requires careful consideration (Cameron & Quinn, 2006), and the incentives used should be central to reinforcing the culture selected (Kouzes & Posner, 2007). Communication to the end user community about the new culture and the incentives ensures that the new methods and the rewards for embracing the new methods are common knowledge within the environment.Eisenhardt (1985) merged organizational literature and agency theory, explicitly adding reward as a control mechanism for organizations. Eisenhardt found that when organizations apply rewards as a form of control, the rewards are more effective as the task becomes simpler and the measurement is easier and cheaper. Conversely, Eisenhardt also noted that reward effectiveness decreases as the task becomes more complex and measurement becomes increasingly difficult with associated higher costs. Reward effectiveness is optimal when rewards are comprised of simple tasks that are easy and low cost to measure.The intent of this study is to determine incentive effectiveness at addressing end user IT acceptance, as measured through perceived ease of use and perceived usefulness variables. Davis (1989) theorized in the TAM that end user IT system use is predicted by two variables, namely, perceived ease of use and perceived usefulness. Incentives offer organizations a venue to both communicate the desired changes and reward desirable actions, which reinforces the new culture (Cameron & Quinn, 2006). Eisenhardt (1985) suggested designing an incentive system that is easy to observe and rewards employees based upon behaviors. A postimplementation incentive requiring actual e-mail system interaction by the end user community includes the incentive design suggestions from Eisenhardt: requires simple behavior, is easy to observe, and can be measured through low cost. The incentive will offer a voluntary multipart quiz delivered through e-mail and responded through e-mail over multiple days. The users who voluntarily choose to take part in the multipart quiz will engage the IT system and interact with the system to answer the questions. The reward taking part in the research is a chance to win a $50.00 gift card prize.Research Informing Incentives From Prior ResearchIncentives research has spanned different disciplines, including management for control (Eisenhardt, 1995); management support (Sharma & Yetton, 2003); and IS management used as coordination mechanisms (Sherif, Zmud, & Browne, 2006). Applying incentives within these management support activities will enable IT system acceptance and cultural changes at the target environment. Schein (2009) indicated that determining rewards and punishments is difficult. This research effort will use a monetary reward as an incentive to enable IT system utilization. The incentive enables IT system use because it requires an e-mail-delivered quiz and e-mail-delivered response. The incentive is a game designed with content that informs the end user community about e-mail security and e-mail features while requiring interaction with the e-mail system.End User IT AcceptanceDavis (1986) introduced the TAM, which adapts the theory of reasoned action to explain computer usage behavior (Davis et al., 1989). New models have extended the TAM to include the TAM-2 (Venkatesh & Davis, 2000) and the TAM-3 (Venkatesh & Bala, 2008). TAM constructs are perceived ease of use and perceived usefulness (Davis et al., 1989). Determinates of perceived ease of use and perceived usefulness detailed in the TAM-3 are the basis for selecting the two organizational interventions employed in this study.Venkatesh and Bala (2008) presented a view of end user IT system acceptance, including specific attributes associated with the IT system implementation that influence end user acceptance. The organizational interventions, highlighted in Venkatesh and Bala, are designed to modify end user IT acceptance. The intervention effectiveness is unknown at addressing end user IT acceptance. The two interventions used in this research are training and incentives. Training is a preimplementation organizational intervention and incentives occur postimplementation. This study will measure changes in perceived ease of use and perceived usefulness to indicate the effectiveness of the intervention. Measuring different interventions effectiveness originated from suggestions by Venkatesh and Bala (2008).End User IT Acceptance Cost and BenefitDholakia, Bagozzi, and Gopinath (2007) suggested that experiences derived from past system implementations are easier to envision than new implementation plans. Dholakia et al. suggested that past failed IT system implementations influence current or future implementations and also limit or interfere with decisions about current or future implementations. Integrating interventions throughout the IT system implementation enables mechanisms to measure end user acceptance throughout the IT system implementation process. The measures, mechanisms, and metrics quantify end user acceptance and giving organizational leadership the tools to monitor and react to end user IT acceptance.MandatesMandates are an option for organizational leadership to enable IT system utilization as well as negotiation, persuasion, motivation, and support (Sharma & Yetton, 2003). Venkatesh and Davis (2000) indicated that over time, mandates become less effective. Carlson (2000) believed that top down-mandates create workforce resistance. Venkatesh and Davis suggested that alternatives to mandates are more effective as work environments move toward worker empowerment. Maintaining good morale and creating a new culture through voluntary IT system usage are incongruent with institutional mandates. Even though mandates are an option available to businesses, research has indicated that mandates are not preferred and do not enable a positive working environment when addressing end user IT acceptance.McAllister (2006) stated, “A system delivered on time by developers but that fails to do what is needed by the users is a failed system, providing no value to the business” (p. 156). End users who are given access to an IT system that they do not accept may resist the new IT, but through increasing end user involvement during the implementation, the resistance can be addressed (O’Brien & Marakas, 2008). User resistance to the IT system limits the IT system’s effectiveness within the organization. Top challenges involved in developing and implementing systems include getting the employees to use it and making it easy to use (O’Brien & Marakas, 2008). Once the organization realizes that it has issues with end user IT acceptance, it must take action to adjust end user IT perceptions.SummaryThis literature review presented a path through the existing literature on management strategy, business strategy, and IT strategy. An IT system implementation that is introduced to enable change requires IT system utilization by the end user community (see Figure 3).Figure SEQ Figure \* ARABIC 3. Strategy relationships enabled through organizational interventions. TC "Figure 3. Strategy relationships enabled through organizational interventions." \f B \l "1" End user IT acceptance is important and relevant in determining actual IT system utilization (Joshi, 2005). Organizational interventions occur throughout the IT system implementation phase. Determining intervention effectiveness and determining which interventions are more effective at addressing end user IT acceptance informs the practitioner when selecting and implementing organizational interventions. Chapter 3 details the methodology and the rationale for using the field experiment methodology.CHAPTER 3. METHODOLOGYIntroductionSiponen and Oinas-Kukkonen (2007) extended the framework presented by J?rvinen (2000) that maps the research design to the research question within IS research. This study will use the framework detailed by Siponen and Oinas-Kukkonen with an approach that centers on artifact utility. This study will focus on the artifact of organizational interventions, and the intervention effectiveness on positively adjusting end user IT acceptance is the artifact evaluation, as measured through surveys that capture end user perceptions on perceived ease of use and perceived usefulness. The TAM indicates that perceived ease of use and perceived usefulness of an IT system predict actual system utilization (Davis, 1986, 1989; Venkatesh & Morris, 2000). Venkatesh and Bala (2008) extended the TAM with the TAM-3, which identifies and links the determinants of perceived ease of use and perceived usefulness with specific organizational interventions. TAM-3 literature has provided little evidence of the effectiveness of organizational interventions on end user IT system acceptance. This study seeks to determine the organizational intervention effectiveness on end user IT acceptance.J?rvinen (2008) identified the links between IS research questions and research methods. Yin (2009) indicated that an exploratory experiment and an exploratory survey are both appropriate for testing, noting that survey methods are beneficial when capturing the “prevalence of a phenomenon” (p. 9). Surveys and field experiments are theory-testing devices (J?rvinen, 2008), and this study is interested in determining the intervention effectiveness on end user IT acceptance. Measuring the intervention effectiveness will be done using a survey instrument.Research DesignThe research design for this study is a field experiment. A pre- and posttreatment survey will measure the treatment effectiveness. D. T. Campbell and Stanley (1963) presented multiple designs for experimental and quasi-experimental designs. This research effort adopts the conventions presented by D. T. Campbell and Stanley for depicting an experiment: X for exposure to treatment, O for observation through measurement, and R for random assignment.The target association in this research is comprised of eight separate facilities. Two experiment phases will occur (see REF _Ref267993882 \h Figure 4). Phase 1 involves conducting one experiment at each facility before IT system implementation. The Phase 1 experiments will be conducted sequentially at each of the eight facilities. Phase 2 will involve one association-wide experiment occurring postimplementation. An experiment determines the treatment effect by manipulating variables and measuring the effect on other variables (D. T. Campbell & Stanley, 1963).R is an individual random assignment predetermined using specific methods described in the Sample section. During Phase 1, the X is training. End users in the treatment group will receive training designed to focus on addressing perceived ease of use and perceived usefulness of an e-mail system, which is the target IT system. During Phase 2, the X offers an incentive to utilize the IT system. End users in the treatment group will take part in a 5-part quiz that will be delivered through e-mail each day, with a chance to win a prize for correctly answering the most questions. The prize is either a $50 gift card to a national submarine sandwich chain or a national coffeehouse chain. End users who return the top scoring quizzes will be entered into a draw for the gift card. One winner will receive the incentive gift card.TimeTimeTraining (Phase I) FinishedFigure SEQ Figure \* ARABIC 4. Project phases, facility sequencing, and experimental framework. TC "Figure 4. Project phases, facility sequencing, and experimental framework." \f B \l "1" Creswell (2009) recommended a process for designing quantitative research that is accomplished through a survey and experimental research. The experiment captures the treatment effect on end user IT acceptance, and the survey captures comparable information across the derived sample (Cooper & Schindler, 2008). Based upon the data collected, the measured difference between the treatment group and the control group is the treatment effect.A survey instrument can employ multiple modes, including the Internet and paper to deliver the questions (Dillman, Smyth, & Christian, 2009). In this study, two data collection modes will be used because the treatment will enable computer access within the organization. Before the first treatment, not all individuals at the target association will have access to computers; some individuals will have minimal computer experience. After the new IT system is introduced, all members will have access to computers and will have received preliminary training on basic computer functions, gaining access and the skills required to take a computer survey.SampleA true experiment requires random assignment of the participants to a control group or a treatment group (Creswell, 2009). Before randomly assigning individuals to either the control group or the treatment group, the organizational landscape will be detailed. Each participant in this study is assigned to one of eight facilities. It is where the participant primarily works. All eight facilities, when combined, comprise the target organization. REF _Ref254871125 \h Table 3 presents the facility initials, the research name, the number of assigned employees, and the number of organizationally provided e-mail assignments at each facility. Two field experiment phases will occur to capture information. During Phase 1, each facility will participate in its own experiment. The sample frame will be one facility. During Phase 2, the association as a whole will receive one experiment, with all facilities combined as the sample frame.Table SEQ Table \* ARABIC 3. Facility Name and Employee E-Mail Status TC "Table 3. Facility Name and Employee E-Mail Status" \f A \l "1" Real facility nameCLCACY*CORP*NABS*FWHVYNMYAPYResearch facility nameField Test AField Test B* or Facility (A)Field Test C* or Facility (A)Field Test D* or Facility (A)Facility BFacility CFacility DFacility EEmployee numbers6039228384120358629Employees with association-provided e-mail357117201931Employees with some e-mail address203322332580250434Note. * Facility scheduled as field test site if required. If not required, the remaining field test facilities will be combined as one facility and the data used as research data.During Phase 1, the participants in the sample will be from the target population of all employees at the target facility. Each employee assigned to the target facility will have an opportunity to participate in the study. During Phase 2, the participants in the sample will be from the target population of all employees at the participating organization. All employees will have an opportunity to participate in each phase of the study. The whole association employee total is approximately 1,600. Random assignment to the treatment group or the control group will be based upon the suggestions from Noru?is (2008), who supported assigning random numbers to the individuals, sorting the random numbers, and assigning the top half to one group and the bottom half to another group. A sort from smallest to largest on the random number assigned to each employee results in a random employee arrangement. The top half of the list becomes Group Zero, and the bottom half of the list becomes Group 1. Kerlinger and Lee (2000) suggested tossing a coin when assigning individuals to groups. A coin toss assigns the group attributed with a zero to either treatment or control. The group attributed with a 1 fills the other role.During Phase 1, all facility employees will have a nonzero chance of assignment to the control group or the treatment group. Random assignment to the treatment group or the control group will be determined before the research begins. An announcement mailed through the post office to all employees at the target organization will introduce the e-mail access project to them. The mailings will occur at one facility at a time in sequence. The same events will occur at each facility, but the intervention timing will change relative to IT system implementation. In addition to announcing the e-mail access project, the message will convey that before e-mail system access, mandatory training must occur, which the user must sign up for. The research consists of a pre- and posttreatment survey. Each end user will be given the same survey at two points in time. Those members in the treatment group receive training between the surveys, and those in the control group do not receive training.During Phase 2, all organization employees have a nonzero chance for assignment to the control group or the treatment group. Random assignment to either group will be determined before Phase 2 research begins. The research consists of a pre- and posttreatment survey. Each end user will be given the same survey at two points in time, with those members in the treatment group receiving e-mailed quizzes for 1 week, and if they are among those who successfully answer the most questions correctly, also being eligible for a reward incentive. Those in the control group will not receive the e-mailed quizzes and will not be eligible for a reward incentive.Intervention EffectivenessAn experiment methodology will be used to understand the effectiveness of an intervention. The experiment will measure the observed difference between a treatment group that received training or incentives and a control group that did not receive the treatment. During Phase 1, the sample frame will be one facility. During Phase 2, the sample frame will be the whole organization. The sample methods or the selection criteria for participants will be different, depending on the research phase.During Phase 1, the training lead time relative to IT system implementation will be altered to determine whether there is an optimal effective training lead time. System access time is controlled by username and password assignment by the organization. The facilities are numbered by e-mail rollout order, that is, the first facility is Facility A, the second facility is Facility B, and so on. The resulting facility research names are A, B, C, D, and E. Facility A is the first rollout facility, and Facility E is the last rollout facility.Access to SiteDetermining candidate organizations interested in participating in this study occurred by sending letters to geographically local organizations meeting specific researcher-defined attributes. Initial contact with 259 candidate sites occurred through mailing a one-page letter to the chief executive officer, the human resources officer, any training or development personnel, and key IT individuals identified at businesses that employ more than 250 people. A starting point identified one city in Wisconsin where the researcher lives. Then a selection of counties occurred by drawing successively larger circles on a Wisconsin county map surrounding the origin. If a circle touched a county, that county was included in the weekly mailing.With the target county list, identifying institutions that meet the 250-employee research requirement occurred by combining two methods. The Wisconsin Department of Workforce Development (2007) detailed the largest employers in each county in Wisconsin. This list details the employer name, the industry type, and the employee size range. The selection criteria included organizations that employed more than 250 employees. This resulted in an institution list within one Wisconsin county that employs more than 250 employees. With this institution list, a search in the LexisNexis database using the company name derived from the Wisconsin Department of Workforce Development revealed the mailing addresses, executive names, and other personnel positions targeted. Organizations with multiple locations had their company headquarters selected to limit the selection.This methodology resulted in a list that did not include companies known to the researcher that fit the selection criteria in the local area. Therefore, an additional step bolstered the results. Consulting the information published by the Wisconsin Department of Workforce Development Office of Economic Advisors (2008), this researcher selected targeted county profiles that revealed the 10 largest towns in each county. Using these town names, a LexisNexis database search for all employers that employ 250 or more employees resulted in a satisfactory institution list when coupled with the first method.A letter mailed through the post office facilitated initial contact. Stage 1 included 163 initial letters. Stage 2 included 273 initial letters. Stage 3 included 173 letters. And Stage 4 included 36 letters. A total of 645 letters were mailed to 259 companies in 14 counties. Letter mailing lasted 4 weeks. On average, each company contacted received nearly 2.5 letters of invitation. However, the researcher received 18 letters from the post office as undeliverable. Ten institutions responded and indicated interest in participating in this study. After a phone interview with each interested responding organization, an ideal candidate emerged that fit the research requirements.SettingThe target organization is a nonprofit that provides various hobby and sporting activities for individuals and families. The target organization has eight separate facilities located in southeastern Wisconsin. The facilities comprise one organization, which itself is a subsidiary of a nationwide entity, the Young Men’s Christian Association (YMCA). The target organization has approximately 1,600 staff. During the data collection phase, the organization will implement e-mail at each facility. Before implementing e-mail at the target organization, the organization must use expensive post office mailings and make announcements on community bulletin boards. They cannot use e-mail as the primary vehicle because few staff members have an institutionally provided e-mail service.Instrumentation and MeasuresVenkatesh and Bala (2008) validated the scales that measure TAM constructs, which include perceived ease of use and perceived usefulness. Both Venkatesh and Bala gave this researcher permission to use the survey scales used in their research. Beyond perceived ease of use and perceived usefulness information, the survey also captures demographic and communication metrics that this researcher incorporated into the survey instrument. The scales presented by Venkatesh and Bala are not changed, but the instrument has demographic information merged and communication scales added, which requires a field test for the instrument at the first few smaller facilities. A scale internal consistency assessment will occur, and the derived Cronbach’s alpha will be reported. During the field tests, if the survey instrument requires changes, the researcher will make the changes before administering the survey beyond the field test environment. Because this instrument requires validation and a field test, the scale internal consistency findings will be reported within the Results section.The changes in perceived ease of use and perceived usefulness from pretreatment and posttreatment surveys will be measured. The same survey administered pretreatment and posttreatment will capture perceived communication levels and demographic information. The demographic information includes age, gender, education, years using computers, and self-reported proficiency with computers. Survey instrument facilitation will occur through paper or an online medium. Perceived ease of use and perceived usefulness are fundamental to the TAM and will help to predict and explain IT use (Davis et al., 1989).During Phase 1, participants in the treatment group and the control group will receive the same instrument twice. During Phase 2, participants in the treatment group and the control group will receive the same instrument twice. In both research phases, the survey will be delivered pretreatment to participants in the treatment group and the control group. Then the treatment group will receive the treatment, but the control group will not. Then the delivery of the same instrument will occur to participants in the treatment group and the control group.The TAM, which was developed to predict actual system use, suggests that perceived ease of use and perceived usefulness determine end user behavioral intention to utilize an IT system (Venkatesh & Bala, 2008). Perceived ease of use and perceived usefulness changes will be measured by comparing before and after surveys using previously validated scales that measure perceived ease of use and perceived usefulness variables from Venkatesh and Bala (2008). The scales validated by Venkatesh and Bala are included in the survey instrument. Fowler (2009) indicated that a survey instrument requires validation through a field pretest before implementation of the actual data collection.The target organization has no organizational supplied e-mail, but is interested in the effect of organizational supplied e-mail on enabling communication within each facility, between facilities, and with overall association communication. An additional scale that measures end user communication perception before and after system implementation will be added to the survey. Capturing information surrounding communication aids in determining the changes in communication perceptions within the organizational context and highlights any communication enhancements attributable to IT system implementation.In Phase 1, the first facility scheduled is relatively small and will serve as the field test location for the survey instrument. The next facilities also are available as potential field test locations if required. If no changes to the instrument are required, and if the internal consistency of the instrument is higher than .7, they will participate with Facility A, and their data will be used within the research. The development of the survey instrument scales and their reported reliability and validity are discussed (Creswell, 2009). Data CollectionData collection will occur through various methods. The first survey, which will be a paper copy, will be delivered via the mail service. The rationale for manual survey and data entry is because some respondents have limited access to computers. The surveys will be collected, hand entered into SPSS, and verified that the data have been entered correctly. The posttest survey will be administered electronically because the treatment enables computer access to all end users. This survey will utilize a survey services such as SurveyMonkey, Zoomerang, or another survey provider that the YMCA prefers. Data analysis will be conducted using anizational Intervention Training: Phase 1The treatment in Phase 1 is training. Two training modes give the end users a choice of preferred training mode. In Phase 1, all individuals at one facility will receive a random assignment to either the treatment group or the control group. During Phase 1, an instrument will measure perceived ease of use and perceived usefulness. The instrument also will measure end user perspectives of organizational communication levels and demographic information.The treatment group will select one of two training modes, each of which has the same information but a different delivery mechanism. One mode will be face-to-face training delivered by an instructor in a classroom setting; the other mode will include training recordings, including video and audio presentations, that will be available online. The material covered in either training mode is the same, but one is a recording and the other is instructor led. The control group will receive no training. Each individual has an equal chance of being assigned to either the treatment group or the control group (see REF _Ref274641713 \h Table 4). This sampling method is simple random sampling (Trochim & Donnelly, 2008).The training design addresses two TAM constructs: perceived ease of use and perceived usefulness. In a true experiment, measuring the control and treatment group is the same, but the treatment group gets something that the control group does not; therefore, if everything else is controlled, the only difference between the control group and the treatment group is the treatment (D. T. Campbell & Stanley, 1963). In Phase 1, an independent variable is the variation in training time relative to IT system implementation. Measuring the effect of training lead time occurs by measuring the change in perceived ease of use and perceived usefulness variables because the training lead time varies per facility.Table SEQ Table \* ARABIC 4. Phase 1: Training TC "Table 4. Phase 1: Training" \f A \l "1" End user community (facility wide) completes survey(O1 treatment and O3 control)(R)Research variablesEnd user selects preferred training methodTraining is derived from focusing on perceived ease of use and perceived usefulness(X)End user randomly assigned to treatment or control groupIndependent VariablesLess than 14 days but greater than 7 days before implementationOnline recorded presentationTrainingLess than 7 days before implementationInstructor facilitated classroom presentationSame day as implementationControl group-No trainingEnd user community (facility wide) completes survey(O2 treatment and O4 control)Organizational Intervention Incentives: Phase 2Postimplementation, all employees will have e-mail system access during Phase 2. Incentives will be the intervention in Phase 2. In this phase, all individuals in the organization will be randomly assigned to either the treatment group or the control group. During Phase 2, the same instrument used in Phase 1 will be employed to again measure end user IT system perceived ease of use and perceived usefulness. The instrument also will capture demographic information and measure end user conceptions of existing organizational communication levels.The treatment group will receive a daily quiz through e-mail. The e-mail system facilitates delivery and completed quiz return, requiring interaction with the system for quiz delivery and response. Those employees who successfully return and answer the treatment quizzes with the highest correct answers receive a chance to receive a $50.00 gift card to either a national submarine sandwich shop or a national coffeehouse chain (see REF _Ref253998156 \h Table 5).Table SEQ Table \* ARABIC 5. Phase 2: Incentives TC "Table 5. Phase 2: Incentives" \f A \l "1" End user community (organization wide) completes survey(O1 treatment and O3 control)(R)Research variablesEnd user community participates in a 5 day, once daily online quiz on computer security and e-mail use(X)End user randomly assigned to treatment or control groupIndependent VariablesIncentive to utilize systemChance to win a $50.00 gift card to a national submarine shop or a national coffee houseIncentive to utilize systemControl group-No incentives to utilize systemEnd user community (organization wide) completes survey(O2 treatment and O4 control)Data AnalysisCreswell (2009) guided the researcher in the development of plans for the data analysis. Creswell suggested reporting descriptive statistics about the survey response rates, indicating that graphs are appropriate. Creswell also suggested determining whether response bias exists. The survey instrument uses scales from previously published research. The instrument has not been validated, and Fowler (2009) noted that the instrument requires field testing. The scale internal consistency for this instrument will be determined using Cronbach’s alpha (Creswell, 2009). The survey instrument contains two sections. The first part collects demographic information, and the second part collects scales on perceived ease of use, perceived usefulness, and communication perceptions on a 5-point Likert scale (see Appendix E). Creswell offered suggestions on statistical tests that depend on data normality, and the statistical choices used depend on the results received.The researcher will use SPSS to process the data. A preimplementation intervention trains the end user community, whereas varying the lead time for training with the actual IT system implementation adjusts the time between training and IT system implementation. A postimplementation intervention incentivizes the end user community to use the implemented system. Measuring the results occurs through surveys administered pre- and posttreatment.Phase 1 and Phase 2 data will determine the amount of change that is attributable to treatment. The instrument will capture changes in perceived ease of use, perceived usefulness, and communication perceptions between pretreatment and posttreatment that will indicate the treatment effect. A data analysis through descriptive means will occur, and depending on the data distribution, either parametric analysis or nonparametric analysis will occur. Statistical resources will aid the specific method used depending on the nature of the data (Cooper & Schindler, 2008; Creswell, 2009; Kerlinger & Lee, 2000; Noru?is, 2008).A nonparametric test for Research Questions 1 and 2 will test the hypotheses using chi-square (Noru?is, 2008). The data used to test these hypotheses will be from the treatment group. The test variable list includes Questions 17 to 20 for Research Question 1 and Questions 13 to 16 for Research Question 2 (see REF _Ref254851541 \h Table 6). The grouping variables used within SPSS are the different training groups whose training timing varies with system implementation.Table SEQ Table \* ARABIC 6. Survey Instrument Measurement Scales TC "Table 6. Survey Instrument Measurement Scales" \f A \l "1" QuestionMeasurement scale type1,7,8,9,28Scale2Nominal3-6;10-27Ordinal1-12Demographic3-4Computer skill perceptions9-11Educational attributes13-16Perceived usefulness scale (PU1;PU2;PU3;PU4)17-20Perceived ease of use scale (PEOU1; PEOU2; PEOU3; PEOU4)21,23,25,27E-mail communication enable scale (EC1, EC2, EC3, EC4)22;24;26Association awareness (AA1,AAE 2, AAE 3)28Self-reported system utilization (USE1)A parametric test for Research Questions 3 to 6 will test the hypothesis using one-way ANOVA (Noru?is, 2008). The dependent list for Research Questions 3 and 5 includes Questions 17 to 20, and the dependent list for Research Questions 4 and 6 includes Questions 13 to 16. Research Questions 3 and 4 are interested in comparing the amount of change end users experience from training. For Phase 1, two groups comprise the training, that is, participants in the treatment group or those in the control group. For Phase 2, the incentive phase has two groups, namely, those in the treatment group or those in the control group. The result will be four total groupings, two from Phase 1 and two from Phase 2. A value comparison derived from this procedure using ANOVA will answer Research Questions 3 to 6. Post hoc analysis using the Bonferroni test (Noru?is, 2008) will address Research Questions 7 and 8. The procedures used to test these hypotheses will follow and reference Noru?is as a guide.Validity and ReliabilityReliability deals with consistency and dependability, and validity refers to the standards to judge research quality (Trochim & Donnelly, 2008). The research design will embrace a positivistic research paradigm (Gephart, 1999). Validity and reliability have threats associated with them, which the research design will attempt to address. Taken together, validity and reliability define the quality and accuracy of the procedure used in the research. The research method used is a true experiment. Cooper and Schindler (2008) discussed validity in experimentation and indicated that there are two major validity varieties: internal and external. An experimental design addresses internal validity (Trochim & Donnelly, 2008). Controlling internal and external validity increases the research value.Internal Validity and Research DesignCreswell (2009) categorized internal validity threats as threats that involve participants, researcher manipulations, and procedures used in the experiment. Threats to internal validity are history, maturation, testing, instrumentation, regression, selection, mortality, selection interaction and maturation (Cooper & Schindler, 2008; D. T. Campbell & Stanley, 1963). Russ-Eft and Hoover (2005) suggested three methods to address internal validity as individual random assignment, management of confounding factors, and use of multiple measurement methods. However, each method has a cost associated with addressing the threat. Cooper and Schindler (2008) suggested seeking internal validity and then external validity to balance the ideal scientific method with what the target environment allows.A true experimental design, such as presented in the research of D. T. Campbell and Stanley (1963), allows the researcher to control for internal validity threats. The research method uses a pretest-posttest control group design, which D. T. Campbell and Stanley indentified as a true experimental design. Individual random assignment to the treatment and control group then occurs. Russ-Eft and Hoover (2005) suggested identifying, measuring, and controlling confounding factors. Reporting any relevant confounding factors identified during the research effort informs future research. Utilizing one instrument means that multiple measurement methods (Russ-Eft & Hoover, 2005) are not employed, which is a threat to internal validity.There are costs associated with exercising too much control to increase internal validity, including loss of external validity (Cone & Foster, 2006; Russ-Eft & Hoover, 2005). The ideal research design is strong in internal and external validity (D. T. Campbell & Stanley, 1963). Cone and Foster (2006) offered the researcher advice when they stated, “Remember that no research is perfect” (p. 280).Data collection will occur through one instrument, but survey delivery will occur through paper or an online survey delivery service. Using the same survey instrument to measure the treatment effectiveness fails to offer converging evidence (Russ-Eft & Hoover, 2005), which is a threat to internal validity. However, the survey instrument uses scales to measure the variables, which address the same question in different forms (Fowler, 2009). Checking each scale for internal consistency post hoc addresses instrument internal validity.External ValidityGeneralizability refers to the ability to extend the research to other locations and other venues. D. T. Campbell and Stanley (1963) detailed four threats to external validity in an experiment, which are interaction of testing and X, interaction of selection and X, reactive arrangements, and multiple X interference. Kerlinger and Lee (2000) asserted, “The fact that one is participating in an experimental study may alter one’s normal behavior” (pp. 477-478). Random assignment places the participants in either the treatment group or the control group. The experiment will not overwhelm the end user community, will sample the entire end user community, and will avoid obtrusive measures (Russ-Eft & Hoover, 2005) by focusing the research on the e-mail system installation process. All end users have an equal chance of assignment to either the control group or the treatment group.Creswell (2009) detailed three threats to external validity, namely, selection interaction, setting interaction, and history interaction. D. T. Campbell and Stanley (1963) called for greater external validity by maximizing experimental similarity. Addressing the external validity threats that Creswell presented requires conducting multiple experiments at different settings and different times, which will be accomplished during Phase 1.Generalizability to other places and other contexts require framing the current context and describing the current organization, thus enabling the reader to determine similar generalizability to other places and contexts. Trochim and Donnelly (2008) discussed two methods to address threats to external validity: sampling model and proximal similarity. Trochim and Donnelly noted that through proximal similarity, the researcher presents similarity gradients with different contexts in terms of similarities. Trochim and Donnelly suggested using proximal similarity by “describing the ways your contexts differ from others by providing data about the degree of similarity between various groups of people, places, and even times” (p. 36). Through proximal similarity, detailing organizational traits present in the current study allows the reader to determine other similar and comparable contexts that have similar traits. This presents the reader an opportunity to find similar attributes within the research context that are applicable to other contexts, external to the current research effort. Trochim and Donnelly suggested conducting the study “in a variety of places, with different people, and at different times” (p. 36). The Phase 1 experiment will occur across different facilities, at different times, and with different individuals. Cooper and Schindler (2008) suggested, “Secure as much external validity as is compatible with the internal validity requirements by making experimental conditions as similar as possible to conditions under which the results will apply” (p. 256). Threats to ReliabilityThreats to reliability include the survey questions, survey instrument, and research methods. Reliability threats identified and the steps used to address the reliability issues are discussed. Fowler (2009) stated, “Reducing measurement error through better question design is one of the least costly ways to improve survey estimates” (p. 112). An instrument field test determines internal consistencies. The questions focus on demographic attributes, measure communication perceptions, perceived ease of use, or perceived usefulness. Fowler reminded the researcher to be cognizant of educational and cultural backgrounds of the population. Question design focuses on asking one question of the respondent and keeping questions simple (Fowler, 2009).Cooper and Schindler (2008) advocated survey instrument internal validity to add to overall research reliability. Perceived ease of use and perceived usefulness survey questions used exist in other previously published research. Venkatesh and Bala (2008) reported internal consistencies of .92 for perceived ease of use and .93 for perceived ease of use (see Appendix F). Cameron and Quinn (2006) cited similar results.Reliability estimating is the degree or the confidence in the reliabilities (Trochim & Donnelly, 2008). Perceived ease of use and perceived usefulness scales exist within the survey, and the rollout plan incorporates a research field test at the first facilities. Hinkin (2005) stressed the importance of reporting internal consistency reliability. Evidence has not supported one specific value for reliability, but researchers have used the .7 cutoff (Kerlinger & Lee, 2000). The cutoff for reliability used here is .7. During the field test, the survey instrument scale internal consistency reliabilities will be reported.The survey instrument is reliable if it consistently measures something precisely, but Kerlinger and Lee (2000) indicated that if it is not measuring what it is supposed to be measuring, it is not valid. The survey focus is to determine end user community perceptions of the IT system’s perceived ease of use and perceived usefulness. According to Venkatesh and Bala (2008), the scales have high internal consistency, which are the same scales used here.Phase 1 will be conducted preimplementation to measure the effectiveness of training to address end user IT acceptance, which will be assessed through perceived ease of use and perceived usefulness. Phase 2 will be conducted postimplementation with the whole organization to measure the incentive effectiveness to enable IT system utilization, which will be assessed through perceived ease of use and perceived usefulness. Gephart (1999) indicated that reliability is a criterion for assessing positivism research. Crow, Davis, and Maxfield (1960) discussed experimental conclusions and stated that the “reliability of experimental conclusions can also be increased by refining the experimental technique” (p. 110). An instrument gathers pretreatment and posttreatment end user perceptions on end user IT acceptance. Holton and Burnett (2005) discussed reliable measures, noting “[a] reliable measure is one that yields consistent results” (p. 35). Through iterating the first experiment five times, any experimental refinements techniques incorporated will be included in the methodology section, and anomalies found in the data that require explanation receive attention in the results section.LimitationsTwo phases will comprise the research effort. During Phase 1, each facility will receive training as an intervention and delivered in an experiment. After completing the posttest data collection for one facility, each control group at the target facility will receive training previously offered to the treatment group so that all members will receive training on the e-mail application. Also during Phase 1, multiple training modes and methods are available for members in the treatment group so that they can choose their preferred training method. The training presented to respondents includes handouts, with the training delivered through different venues such as self-paced, online, and face to face. The training design addresses e-mail ease of use and e-mail usefulness. During Phase 2, measuring incentive effectiveness at adjusting end user IT acceptance captured through perceived ease of use and perceived usefulness variables will occur after all individuals have completed Phase 1 training. Incentives have different effects on different populations, and the incentive selected may not appeal to all people or appeal to all people equally.Ethical ConsiderationsThere are minimal potential risks involved in participating in this study. The survey captures demographic information, which presents a very small risk that the questionnaire can link the respondents to specific survey responses. The researcher will keep the paper surveys in a secure location external to the organization. After entering the data into the computer, the researcher will keep the data in a locked, fireproof cabinet. A biometrically secured laptop will maintain data file security. Endpoint security exists on the laptop, and both the operating system and the endpoint security regularly receive updates. A locked, fireproof cabinet also houses the data file backups. After publishing the results, the researcher will destroy all paper surveys, data files, and backup data.The survey captures demographic information, communication perceptions within the organization, IT perceived ease of use, and IT perceived usefulness. These measures contain no inherent concerns or risks associated with answering the survey questions. Maintaining confidentiality surrounding respondents, surveys, and respondent anonymity is highly valued, and data reporting will occur through aggregated results only.Utilizing the Belmont Report as a guide will help the researcher to address and remediate ethical concerns. The Belmont Report is concerned with respect for persons, beneficence, and justice (National Institutes of Health, 1979). The principles within the Belmont Report will guide the research by focusing on the participants’ well-being throughout any activities.All participants will receive an informed consent form, which they must sign before taking part in any research. It provides information about the study and the voluntary nature of participation. A locked, fireproof cabinet will hold the informed consent forms. There are no foreseeable risks inherent with participating in this research for the participants, each facility, or the target organization. Benefits exist for the individual, facility, and the target organization from the results derived from this research.REFERENCESAdams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247.Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244-254.Bartlett, C. A., Ghoshal, S., & Birkinshaw, J. (2004). Transnational management: Texts, cases, and readings in cross-border management (4th ed.). New York, NY: McGraw-Hill Irwin.Baum, H., & Kling, T. (2004). The transparent leader: How to build a great company through straight talk, openness, and accountability. New York, NY: HarperCollins.Bierstaker, J. L., Brody, R. G., & Pacini, C. (2006). Accountants’ perceptions regarding fraud detection and prevention methods. Managerial Auditing Journal, 21(5), 520.Brookfield, S. D. (2006). The skillful teacher: On technique, trust, and responsiveness in the classroom (2nd ed.). San Francisco, CA: Jossey-Bass.Brynjolfsson, E., & Hitt, L. M. (2003). Computing productivity: Firm-level evidence. Review of Economics & Statistics, 85(4), 793-808.Cameron, K. S., & Quinn, R. E. (2006). Diagnosing and changing organizational culture: Based on the competing values framework (Rev. ed.). San Francisco, CA: Jossey-Bass.Campbell, D. E. (2006). Incentives: Motivation and the economics of information (2nd ed.). New York, NY: Cambridge University Press.Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research on teaching. In N. L. Gage (Ed.), Handbook of research on teaching (pp. 171-246). Chicago, IL: Rand McNally.Carlson, P. A. (2000). Information technology and the emergence of a worker-centered organization. ACM Journal of Computer Documentation, 24(4), 204-212. doi:10.1145/353927.353930Cohen, D. S. (2005). Why change is an affair of the heart ; In the drama of change, emotions, not logic, impel people to cast off the old and embrace the new. Chief Information Officer, 19(5), peau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.Cone, J. D., & Foster, S. L. (2006). Dissertations and theses from start to finish: Psychology and related fields (2nd ed.). Washington, DC: American Psychological Association.Cooper, D. R., & Schindler, P. S. (2008). Business research methods (10th ed.). New York, NY: Irwin/McGraw-Hill.Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Thousand Oaks, CA: Sage.Crow, E. L., Davis, F. A., & Maxfield, M. W. (1960). Statistics manual: With examples taken from ordnance development. New York, NY: Dover.Daft, R. L. (2004). Organization theory and design (8th ed.). Mason, OH: South-Western.David, J. S., Schuff, D., & St. Louis, R. (2002). Managing your total IT cost of ownership. Communications of the ACM, 45(1), 101-106. doi:10.1145/502269.502273Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation). doi:1721.1/15192Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.Dholakia, U. M., Bagozzi, R. P., & Gopinath, M. (2007). How formulating implementation plans and remembering past actions facilitate the enactment of effortful decisions. Journal of Behavioral Decision Making, 20(4), 343-364. doi:10.1002/bdm.562Dillman, D. A., Smyth, J. D., & Christian, L. M. (2009). Internet, mail and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: John Wiley & Sons.Eisenberger, R., & Cameron, J. (1996). Detrimental effects of reward: Reality or myth? American Psychologist, 51(11), 1153-1166. doi:10.1037/0003-066x.51.11.1153Eisenhardt, K. M. (1985). Control: Organizational and economic approaches. Management Science, 31(2), 134-149.Faguet, D. (2003). Practical financial management: A guide for today’s manager. Hoboken, NJ: John Wiley & Sons.Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Boston, MA: Addison-Wesley.Fowler, F. J. (2009). Survey research methods (Vol. 1., 4th ed.). Los Angeles, CA: Sage.Freedman, R. (2003). Building the IT consulting practice. San Francisco CA: Jossey-Bass.Gephart, R. (1999). Paradigms and research methods. Retrieved from . rm/1999_RMD_Forum_Paradigms_and_Research_Methods.htmGinzberg, M. J. (1981a). Early diagnosis of MIS implementation failure: Promising results and unanswered questions. Management Science, 27(4), 459-478.Ginzberg, M. J. (1981b). Key recurrent issues in the MIS implementation process. MIS Quarterly, 5(2), 47-59.Glen, P. (2003). Leading geeks: How to manage and lead people who deliver technology. San Francisco, CA: Jossey-Bass.Gulliksen, J., G?ransson, B., Boivie, I., Blomkvist, S., Persson, J., & Cajander, ?. (2003). Key principles for user-centred systems design. Behaviour & Information Technology, 22(6), 397-409.Heskett, J. L., Sasser, W. E., & Schlesinger, L. A. (1997). The service profit chain: How leading companies link profit and growth to loyalty, satisfaction, and value. New York, NY: Free Press.Hinkin, T. R. (2005). Scale development principles and practices. In R. A. Swanson & E. F. Holton III (Eds.), Research in organizations: Foundations and methods of inquiry (pp. 161-179). San Francisco CA: Berrett-Koehler.Holton, E. F., & Burnett, M. F. (2005). The Basics of Quantitative Research. In R. A. Swanson & E. F. Holton III (Eds.), Research in organizations: Foundations and methods of inquiry (pp. 29-44). San Francisco CA: Berrett-Koehler Publishers, Inc.Hoffer, J. A., George, J. F., & Valacich, J. S. (2008). Modern systems analysis and design (5th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.J?rvinen, P. (2000). Research questions guiding selection of an appropriate research method. In H. R. Hansen, M. Bichler, & H. Mahrer (Eds.), Proceedings of the European Conference on Information Systems 2000, 3-5 July (pp. 124-131). Vienna, Austria: Vienna University of Economics and Business Administration.J?rvinen, P. (2008). Mapping research questions to research methods. In D. Avison, G. Kasper, B. Pernici, I. Ramos, & D. Roode (Eds.), Advances in information systems research, education & practice (pp. 29-41). Boston, MA: Springer. doi:10.1007/978-0-387-09682-7-9_3Joshi, K. (2005). Understanding user resistance and acceptance during the implementation of an order management system: A case study using the equity implementation model. Journal of Information Technology Case and Application Research, 7(1), 6-20. Retrieved from ABI/INFORM Global database. (Document ID: 874990641)Kang, D., & Santhanam, R. (2003). A longitudinal field study of training practices in a collaborative application environment. Journal of Management Information Systems, 20(3), 257-281.Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research (4th ed.). London, England: Thompson Learning.Knowles, M. S., Holton, E. F., & Swanson, R. A. (2005). The adult learner: The definitive classic in adult education and human resource development (6th ed.). Burlington, MA: Elsevier.Kouzes, J. M., & Posner, B. Z. (2007). The leadership challenge (4th ed.). San Francisco, CA: Jossey-Bass.Lindborg, H. J. (1997). The basics of cross-functional teams. New York, NY: Quality Resources.Lucas, H. C. (1999). Information technology and the productivity paradox: Assessing the value of investing in IT. New York, NY: Oxford University Press.Lyytinen, K., & Hirschheim, R. (1987). Information systems failures: A survey and classification of the empirical literature Oxford Surveys in Information Technology (pp. 257-309). Oxford: Oxford University Press.Martinko, M. J., Henry, J. W., & Zmud, R. W. (1996). An attributional explanation of individual resistance to the introduction of information technologies in the workplace. Behaviour & Information Technology, 15(5), 313-330.Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.Martins, L. L., & Kellermanns, F. W. (2004). A model of business school students’ acceptance of a web-based course management system. Academy of Management Learning & Education, 3(1), 7-26.Maurer, M. M., & Simonson, M. R. (1984). Development and validation of a measure of computer anxiety. Paper presented at the annual meeting of the Association for Educational Communication and Technology, Dallas, TX. Retrieved from , C. A. (2006). Requirements determination of information systems: User and developer perceptions of factors contributing to misunderstandings. (Doctoral dissertation). Retrieved from ProQuest Dissertation & Theses database. (AAT 3226800)Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192-222.National Institutes of Health. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. Retrieved from , M. J. (2008). SPSS 16.0 guide to data analysis. Upper Saddle River, NJ: Prentice Hall.O’Brien, J. A., & Marakas, G. M. (2008). Introduction to information systems (14th ed.). New York, NY: McGraw-Hill/Irwin.Oxford American college dictionary. (2002). New York, NY: G. P. Putnam & Sons.Ramlall, S. (2004). A review of employee motivation theories and their implications for employee retention within organizations. Journal of American Academy of Business, Cambridge, 5(1/2), 52.Royce, W. (1998). Software project management: A unified framework. Reading, MA: Addison Wesley Longman.Russ-Eft, D., & Hoover, A. L. (2005). Experimental and quasi-experimental designs. In R. A. Swanson & E. F. Holton III (Eds.), Research in organizations: Foundations and methods of inquiry (pp. 75-95). San Francisco CA: Berrett-Koehler.Sabherwal, R., Jeyaraj, A., & Chowa, C. (2006). Information system success: Individual and organizational determinants. Management Science, 52(12), 1849-1864.Schein, E. H. (2004). Organizational culture and leadership (3rd ed.). San Francisco, CA: Jossey-Bass.Schein, E. H. (2009). The corporate culture survival guide (Rev. ed.). San Francisco, CA: Jossey-Bass.Schenk, K. D., Vitalari, N. P., & Davis, K. S. (1998). Differences between novice and expert systems analysts: What do we know and what do we do? Journal of Management Information Systems, 15(1), 9-50.Senge, P. M. (2006). The fifth discipline: The art & practice of the learning organization. New York, NY: Currency Doubleday.Sharma, R., & Yetton, P. (2003). The contingent effects of management support and task interdependence on successful information systems implementation. MIS Quarterly, 27(4), 533-555.Sharma, R., & Yetton, P. (2007). The contingent effects of training, technical complexity, and task interdependence on successful information systems implementation. MIS Quarterly, 31(2), 219-238.Sherif, K., Zmud, R. W., & Browne, G. J. (2006). Managing peer-to-peer conflicts in disruptive information technology innovations: The case of software reuse. MIS Quarterly, 30(2), 339-356.Siponen, M. T., & Oinas-Kukkonen, H. (2007). A review of information security issues and respective research contributions. SIGMIS Database, 38(1), 60-80. doi:10.1145/1216218.1216224Taylor-Cummings, A. (1998). Bridging the user-IS gap: A study of major information systems projects. Journal of Information Technology, 13(1), 29-54.Teo, T. S. H., & Ang, J. S. K. (2001). An examination of major IS planning problems. International Journal of Information Management, 21(6), 457-470. doi:10.1016/ s0268-4012(01)00036-6Todd, P., & Benbasat, I. (1999). Evaluating the impact of DSS, cognitive effort, and incentives on strategy selection. Information Systems Research, 10(4), 356-374.Trochim, W. M. K., & Donnelly, J. P. (2008). The research methods knowledge base (3rd ed.). Mason, OH: Atomic Dog.Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342.Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. doi:10.1111/j.1540-5915.2008.00192.xVenkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186.Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115.Ward, J., & Peppard, J. (2002). Strategic planning for information systems (3rd ed.). New York, NY: John Wiley & Sons.Webster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly, 16(2), 201-226.Wisconsin Department of Workforce Development. (2007). Private-sector and public-sector establishments. Retrieved from largest_employers/largest_employers_march_2007_all_ownership.xlsWisconsin Department of Workforce Development Office of Economic Advisors. (2008). County workforce profiles in Wisconsin 2008. Retrieved from . oea/county_profiles/current.htmYin, R. K. (2009). Case study research: Design and methods (4th ed.). Thousand Oaks, CA: Sage.APPENDIX A. interventions for Pre-IT system implementationTable A1. Determinants of Perceived Usefulness TC "Table A1. Determinants of Perceived Usefulness" \f A \l "1" Determinates of perceived usefulnessDesign characteristicsUser participationManagement supportIncentive alignmentSubjective norm?X*XXImage??XXJob relevanceXXXXOutput qualityXXXXResult demonstrabilityXXXX*X indicates a particular intervention can potentially influence a particular determinant of perceived usefulness or perceived ease of use.**Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 293.Table A2. Determinants of Perceived Ease of Use TC "Table A2. Determinants of Perceived Ease of Use" \f A \l "1" Determinants of perceived ease of useDesign characteristicsUser participationManagement supportIncentive alignmentComputer self-efficacy????Perceptions of external control?X*X?Computer anxiety?X??Computer playfulness?X??Perceived enjoymentXX?XObjective usabilityXX??*X indicates a particular intervention can potentially influence a particular determinant of perceived usefulness or perceived ease of use.**Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 293.APPENDIX B. interventions for Post-IT SYSTEM IMPLEMENTATIONTable B1. Determinants of Perceived Usefulness TC "Table B1. Determinants of Perceived Usefulness" \f A \l "1" Determinates of Perceived UsefulnessTrainingOrganizational supportPeer supportSubjective norm??X*Image??XJob relevanceXXXOutput qualityXXXResult demonstrabilityXXX*X indicates a particular intervention can potentially influence a particular determinant of perceived usefulness or perceived ease of use.**Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 293.Table B1. Determinants of Perceived Ease of Use TC "Table B1. Determinants of Perceived Ease of Use" \f A \l "1" Determinants of perceived ease of useTrainingOrganizational supportPeer supportComputer self-efficacyX*??Perceptions of external control?XXComputer anxietyXX?Computer playfulnessX??Perceived enjoymentX??Objective usabilityX??*X indicates a particular intervention can potentially influence a particular determinant of perceived usefulness or perceived ease of use.**Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 293.appendix c. technology acceptance modelExternal VariablesPerceived UsefulnessPerceived Ease of UseBehavioral IntentionUse BehaviorFigure C1. Technology acceptance model. TC "Figure C1. Technology acceptance model." \f B \l "1" Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 276.appendix D. Technology Acceptance model 3Figure D1. TAM–3. TC "Figure D1. TAM–3." \f B \l "1" Note. Adapted from “Technology Acceptance Model 3 and a Research Agenda on Interventions,” by V. Venkatesh and H. Bala, 2008, Decision Sciences, 39(2), p. 280.APPENDIX E. Survey InstrumentBefore completing this survey, I would like to thank you for taking the time to respond to this survey. This survey is important for understanding how effective events that the association organizes are at addressing end user IT acceptance. Your help is greatly appreciated.SURVEY INSTRUCTIONS:Do not write any personally identifiable information on the survey, as your answers are anonymous and confidential.There is no right or wrong question answers on this survey. This survey is exploring your computer application perceptions. Based on your experience with computer applications, choose the appropriate response.Be sure to mark your answers carefully. Most questions ask you to circle one value, which best matches the description of how you feel about the item. As an example, if you were asked how much you agree with the statement, “Winters are cold in Wisconsin”, and you feel you agree, you would circle the item “Agree” like this:Winters are cold in Wisconsin.1Strongly Disagree2Disagree3Neutral4Agree5Strongly AgreeNote that throughout the survey the scale descriptions change. As an example, the sample asked whether you agree or disagree. In another question, the question may ask how useful something is. The special instructions will highlight these. Please read the instructions.This survey should take less than 7 minutes to complete and is voluntary. Thank you for your time and cooperation.Section 1: Demographics. This information will allow comparisons among different groups of people. Your responses are anonymous and confidential. Please answer below each question the answer that best describes you.What was your age (in years) on your last birthday?Are you (circle one).MaleFemaleDescribed your computer skills (circle one).Extremely usefulOf considerable useOf useNot very usefulOf no useDescribe how people who know you would rate your computer skills (circle one).Very satisfactorySatisfactoryBorderlineUnsatisfactoryVery UnsatisfactoryDescribe your proficiency with computers (circle one).Very satisfactorySatisfactoryBorderlineUnsatisfactoryVery UnsatisfactoryDescribe your comfort level with computers (circle one).Very goodGoodBorderlinePoorVery PoorRoughly, how many years have you have been using computers?As of January 1, 2011 the number of years you have worked at the YMCA of the Fox cities. (Estimations are o.k.).Total years receiving formal education. (Only count complete years).Select highest degree completed (circle one).Did not graduateGrade SchoolHigh SchoolAssociate degreeBachelor’s degreeMaster’s or higher degreeSelect highest degree started (circle one).Grade SchoolHigh SchoolAssociate degreeBachelor’s degreeMaster’s degreePhDIndicate your job level, which best describes you (circle one).EmployeeTeam leader/supervisorMid-level managerManagerExecutiveNot employedSection 2: Using your perceptions of e-mail, answer the following questions. Circle one response that best describes your answer.Using e-mail improves my performance in my job.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeUsing e-mail in my job increases my productivity.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeUsing e-mail enhances my effectiveness in my job.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI find e-mail to be useful in my job.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeMy interaction with e-mail is clear and understandable.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeInteracting with e-mail does not require a lot of my mental effort.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI find e-mail to be easy to use.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI find it easy to get e-mail to do what I want it to do.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeE-mail increases my ability to communicate1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI know what is going on within the association.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI believe that e-mail enables communication within the organization.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI know what is going on at my branch facility1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI believe that e-mail enables communication at my branch facility.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI know what is going on within my department.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeI believe that e-mail enables communication within my department.1Strongly disagree2Disagree3Neutral4Agree5Strongly agreeOn average, how much time do you spend using e-mail each day?APPENDIX F. Survey Instrument ScalesScales used with permission from Venkatesh and Bala.Table F1. Perceived Usefulness Scales TC "Table F1. Perceived Usefulness Scales" \f A \l "1" Using the system improves my performance in my job.Using the system in my job increases my productivity.Using the system enhances my effectiveness in my job.I find the system to be useful in my job.Table F2. Perceived Ease of Use Scales TC "Table F2. Perceived Ease of Use Scales" \f A \l "1" My interaction with the system is clear and understandable.Interacting with the system does not require a lot of my mental effort.I find the system to be easy to use.I find it easy to get the system to do what I want it to do. ................
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