Technology Acceptance Model

TCC 2011 Proceedings

Understanding the Influence of the Technology Acceptance Model for Online Adult Education Barbara Lauridsen, MBA Capella University, PhD Learner, Information Technology Education Core Adjunct Faculty, National University, School of Engineering and Technology

Abstract How institutions make decisions to accept or reject technology innovation has been explored by academics with the assistance of the Technology Acceptance Model (TAM). Scenarios involving successful delivery of online learning from degree granting universities guide this literature review. It examines decision processes influenced by TAM methods combined with dominant research perspectives such as Self-efficacy Theory and Universal Technology Adoption and Use Theory. This paper analyzes which variables determine perceptions of usefulness, attitude and preferences and become frequent factors to influence typical TAM results. It identifies patterns about reliable predictors of outcomes (behaviors, aligning IT and preferences) for educational investments in learning environments, content delivery and teacher preferences. Adoption of technology is a complex, inherently social process guided by perceptions or misperception of value and ease of use. Thus, facilitating a decision to adopt devices, software or processes must address emotional, cognitive, and contextual concerns of all stakeholders. This paper focuses on theories appropriate to decisions made to increase use of virtual community environments for effective online learning, virtual communications, putting into perspective the contribution made by TAM when used in combination with other models. Keywords and Acronyms: Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Social Cognitive Theory (SCT), Web Enhanced Instruction (WEI), Virtual Communities (VCs), Universal Technology Adoption and Use Theory (UTAUT). Background of the Problem and Nature of the Study This paper adds value to the literature about using Technology Acceptance Model (TAM) as a framework for making decisions. It applies to learning to perform research studies that have criteria that is objective and quantifiable and as answers to surveys. Thus, TAM processes are about collecting opinions of human participates in the study. The first part of this paper is a survey of contributing sources. The second part analyzes the scholarly publications from the perspective by the author. A shortfall in literature for applying TAM to the industry’s decisions about technology investments is discussed by integrating an analysis about how decisions are made in academic and business arenas. To control the scope of this paper, only TAM project relevant to virtual communities (VCs) are included as they apply to learning and to higher education institutions especially for mobile learning technologies. 1


The original TAM diagram was published in a pioneering article by Davis, Bagozzi & Warshaw (1989), titled “User Acceptance of Computer Technology: Comparison of Two Theoretical Models”. This original work has guided many student projects and has been frequently cited by researchers, authors, scholars, PhD students but rarely by business practitioners.

Figure 1 Technology Acceptance Model (TAM) (Davis et al., 1989)

Figure 1 introduces the vocabulary of the method that has persisted for 20 years with slight variations of terms used for specific studies. 

Perceived Usefulness of Technology (PU)

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Perceived Ease of Use of Technology (PEOU)

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Attitude toward Using Technology (ATUT)

 Intention to Use Technology (IU) Review of Technology Acceptance Model (TAM) This section provides a review of literature from available sources on the topic of Technology Acceptance Model (TAM) selecting those that are most relevant to its application to institutions of higher learning. This lays out the strengths of using the TAM as a viable way to provide education about frameworks, to guide learners in quantitative, qualitative and mixed empirical research and also in using of case study scenarios that are either based on real or fictional but realistic scenarios set up as a teaching aid. Three coherent themes are highlighted by the keywords shown in bold italics: 1. Application of TAM to behaviors described as attitudes that focus on usage of technology or of methodologies for virtual communities of learners. 2. Using TAM to make changes that lead to aligning IT organizations with a business agenda. 3. Applying TAM for comparing preferences for technology (hardware devices or software). 2


The patterns seen in the literature are typical academic studies with data gathered from a homogeneous demographic of college students and which state a level of confidence that results can be generalized in a meaningful way to similar age groups of Internet users outside of campus (Hsiu-Fen, 2008; Min, Yan & Yuecheng, 2004; Huang, Lin, & Chuang, 2007; Baker-Eveleth, Eveleth, O’Neill, & Stone, 2006). Authors with small scale projects are often cited by authors reporting similar research. Attitudes - Behaviors, Virtual Communities, M-Learning Self-efficacy is defined as “the specific beliefs that an individual holds about his or her ability to complete a course of action (Straub, 2008). Self-efficacy is often discussed in conjunction with Social Cognitive Theory (SCT). It is also defined as “the belief that one has the capability to perform a particular behavior, [and] is an important concept in SCT” (Min, Yan & Yuecheng, 2004, p. 336) and offers a Computer Self-Efficacy framework for IT Alignment. The pattern in the TAM literature about Virtual Communities (VCs) is that studies feed upon each other and appear set up variables that lead to accepting most of the hypotheses and rejecting at least one statement for the exercise (Min, Yan & Yuecheng, 2004; Huang et al., 2007; Baker-Eveleth et al., 2006; Dong-Hee & Won-Young, 2008). From Taiwan, Hsiu-Fen’s (2008) empirical study offers a context of rapid growth of VCs. The study an integrated model, comparing “three models in terms of overall model fit, explanatory power, and path significance” (p. 138). The study briefly reveals findings from a study (not showing data tables) about member loyalty. The simple graphic model shows outcome using path significance as a data analysis method to measure VC loyalty. See Figures 2 and 3 “TAM framework for VCs”and “Resulting statistics for TAM” as typical illustrations of a construct model overlaid with summary statistics from the opinion surveys. The author’s self evident conclusion is that to “sustain a successful VC, tool providers need to focus on designing both useful and easy-to-use Web sites (2008, p. 143). The use of a hybrid approach fits the education industry for justifying investment in online computer mediated communication environments and tools. VCs are notorious for being dependent on Web-based communications for learning.

Figure 2 Theoretical Framework Virtual Communities (Hsiu-Fen, 2008, Figure 1, p. 139)

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Figure 3 Results of TAM Path Significance (Hsiu-Fen, 2008, Figure 2, p. 141)

VCs are fond of Mobile Learning (M-Learning). An empirical study by Min, Yan & Yuecheng (2004) set a context of contemporary education and SCT and offers a new framework which has substantial influence on the teachers’ technology acceptance and which has a strong direct effect on intention to use. All hypotheses were supported by a convenience population of survey data collected, displayed using the typical codes variable of the TAM constructs. Implications were discussed using an additional determinant from the SCT. Even with a coded list of measurement items for each variable, the “enhanced model” was analyzed to test its predictive power towards acceptance of a web based learning system called Interactive Learning Network, a communityoriented learning management system” (2004, p. 368). Likewise, Huang, Lin, & Chuang (2007) used the TAM approach to draw a conclusion that predicting user acceptance of M-learning, two external constructs are needed in data analysis. The variables perceived enjoyment and perceived mobility value are imperative to lead to a conclusion that the predictive power of the model. Further, regarding functionality of VCs and the preferences of participants for tools, BakerEveleth, Eveleth, O’Neill, & Stone (2006) collaborated on an empirical research on perceived ease of use for secure software that enables laptop exams for e-Learners. The authors seek additional explanation for factors that influenced the results for a population 107 students in two business classes. For the intent of the study, this sample is probably representative. As an academic exercise, it reported that faculty pointed out benefits of software over paper-based examinations “as well as the current diffusion of the software, and the ease of using the software itself” (p. 219). To analyze the data the Standardized Path Coefficients method was used. The authors considered a finding to be “interesting” when it showed a weak influence for a factor that technical support had on student attitudes and behavioral intentions (p. 419). Nevertheless their paper defended deployment into the education industry assessments of performance by students carrying laptops on campus or using them from home to connect to online courses. Other forms of mobile learning are of interest to the same benefactors of the mobility of laptops. Figure 3 resulting in a measurement of behavioral intention to use software and Figure 4 showing summary statistics and bolded arrows to emphasize strong correlations.

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Figure 4 TAM Framework including Faculty and Technical Support (Baker-Eveleth et al., 2006, p. 414)

Figure 5 TAM Estimate Acceptance, Standardized Path Coefficients (Baker-Eveleth et al., 2006, p. 418)

Regarding acceptance of online education by teachers and students, Gibson, Harris & Colaric’s (2008) concise article applies the TAM approach in the context of faculty acceptance of online education, a topic that fits the overall interest of virtual communities. An interesting artifact from this paper is list of survey items used because it is representative of the simplicity of a typical TAM opinion survey (see table 1).

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TCC 2011 Proceedings Table 1 Questions to Predict Ease of Use (Gibson et al., 2008, Table 1, p. 357)

To overcome an obvious limitation of using a convenience sample, the suggestion made is that “this exploratory study be replicated at other universities to allow for the comparison of results” (Gibson et al. 2008, p. 358). The Concerns-Based Adoption Model was used interpret a perspective about influences on individuals’ intention to integrate an innovation. A major concern by university administrators is IT Alignment. Administrators seek the most comprehensive yet convenient solution but in the end may outsource technology to a partner or to a consortium rather than support an internal initiative for online delivery. Walker and Johnson’s (208) study examined Web Enhanced Instruction (WEI) using a TAM approach for isolating the faculty’s perspective about factors of usefulness and effectiveness for virtual learning communities. Their research extended the dissertation research performed by Landry (2003) on the topic of student reactions to web enhanced instructional elements and measuring the preferences that influence a decision. Two scales for measuring WEI were used, namely effectiveness and job performance called “Usefulness-Effectiveness”. This conclusion drawn from the data gathered was highly dependent on the perceptions of the teacher that WEI was valuable and that teacher’s mastery of the new tools be engaged early and be convenient. Decision Making – Aligning IT with the Business of Education In the context of deployment of eLearning systems in educational institutions, discriminating the various stages of the implementation process to the final acceptance is critical. In a comprehensive empirical study from New Zealand the focus is on web-based eLearning 6


behaviors by VC membership. Applying the TAM approach to model managerial implications for intentional usage of an eLearning system to determine significance of perceived usefulness introduces the concept of flow. The initial structural model is more complex than other TAM studies with the familiar constructs being in place. In figure 6, Davis and Wong (2007) examine the influence of user participation and engagement and suggest that “Future research could also test this integrated model across a broader set of technologies (information systems) and user populations to determine its predictive robustness and generalizability” (p. 120). The Davis and Wong paper offers insights about data analysis. The researchers apply a lesson. “Rather than excluding individuals from participation through sampling criteria, variables that were considered to be liable to confound the results were measured and statistically controlled during data analysis” (p. 107). The researchers point out the implications for managing eLearning system, the intented usage for “is significantly affected by [the] perceived usefulness and flow” (2007, p. 119). The lesson is for practitioners interested in aligning eLearning systems into the business side of education should begin by emphasize relevance of known requirements with features of the tools being evaluated. This applies to the business of online learning with the customers being adult learners and aligning IT with the business virtual team incentives to retain online students because of the perceived value of working within virtual communities.

Figure 6 Integrated Conceptual TAM Constructs (Davis & Wong, 2007, Figure 1, p. 207)

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Research Methodology Components This section evaluates research methodologies used by the scholars and the relevancy to the outcome of the studies. This paper was drafted with an intention to share ideas with colleagues at National University to encourage activating projects that apply the principles of TAM for examination of preferences of diverse learners. In the culture on knowledge institutions such as universities, regardless of the nature of competition for enrollments in online programs there is a “recognition of the transactional aspect of knowledge, and an appreciation of the concepts outlined in the resource-based model” (Grover, Gokhale, & Narayanswamy, 2009). See Figure7 for a proposed decision quadrant defining resource-based implications for disciplinary strategy which reflects practitioner interest in action research.

Figure 7 Resource-based Implications for Disciplinary Strategy (Grover et al., 2009, p. 322)

The quadrant illustrates reputation (y axis) against heterogeneity (x axis) with the four cells labeled primary (for high-high), illusionary (high-low), concealed (low-high) and submissive (low-low). Grover et al (2009) articulate a perspective that resource allocation for knowledge markets, such as universities, needs to consider behaviors rather than merely opinions. Given questionable value added, a coupling strategy is to “forego abstract research with long term implications and instead focus on short term, highly context specific solutions that serve the needs of the practitioner” (p. 322) with a perspective on sustainability of the solution especially 8


when it involves intellectual capital. The relevance of resource based frameworks to online educational delivery to virtual communities is yet to be determined but is of serious interest for delivering online education. Dominant Research TAM Perspectives TAM’s Old Paradigm. Perceived usefulness is a common factor in many early TAM constructs when the technique is used to predict decisions shown to affect the intention and the attitude among goal-directed users, these individuals who will actually try web technology “even if they do not have a positive attitude towards using it - because it may provide productivity enhancement”. Goal-directed behavior can be shown to increase rewards without impacting the attitude of users (Sánchez-Franco, & Roldán, 2005), a study which provides a framework mapping TAM to several hypotheses of a typical empirical study. Figure 8 provides a framework for assessing constructs uses a statistical methods for structural equation modeling, and partial least squares.

Figure 8 Hypotheses mapped on TAM Framework (Sánchez-Franco & Roldán, 2005, Figure 2)

Gibson, Harris & Colaric’s (2008) concise article applies TAM to teachers’ preferences. Patterns of adoption of technology are analyzed around tradition demographics for evaluating student populations: age, gender, economic status, an affinity for technology and degree program in which they are enrolled. Likewise, preferences for types and functionality of media objects have often been examined (Liu, Liao & Peng, 2005; Hsiu-Fen, 2007). Interestingly, many later articles on TAM representing the old paradigm frequently cite Bagozzi’s earlier works. TAM’s New Paradigm. Bagozzi’s (2007) later work is essentially a critique of the predecessor TAM approach pointing out the legacy of the process, its strengths and the shortcomings and expands the thinking into IT Alignment. Bagozzi calls the TAM “a remarkable model” that has had a long track record for empirical research but considers its influence to be at a “turning point” …saying that a “[m]ethodology is important to consider because it so closely interfaces 9


with theory and theory testing and interpretation, and because how we study a phenomenon constrains how we think about it” (p. 252). Dong-Hee and Won-Young’s (2008) concise article has a context of using the “Cyworld” for applying the TAM approach, specifically examining Web2.0, a tool for navigating a virtual world. The resulting diagram is a map for the statistical facts, capturing data from a Web-based survey collecting data from a random sample of 950 potential respondents selected from a virtual community of learners. The study hypothesized that “the constructs of perceived usefulness and perceived enjoyment would mediate the relationship between perceived synchronicity and perceived involvement” (2008, p. 381). The authors claim that “modified TAM framework can be a good tool to understand market potential through an analysis of users’ needs and prototyping market profiles” (p. 382). This analysis maps to user factors for Web2.0 solutions which is coming into vogue for eLearning. It is essential to the new paradigm for e-learning. As a representative of the new paradigm, Bagozzi (2007) who has a long track record of old paradigm publications, defends the TAM model because it “consists first of a decision making core (goal desire → goal intention → action desire → action intention) that is grounded in basic decision making variables/processes of a universal nature” (p. 243). Figure 9 is a diagram of “Decision Making Core” representing Bagozzi’s road map offers a foundation for his suggested new paradigm for TAM models. Inclusion of Bagozzi’s 2007 critique of MAP balances with the many testimonies about the value of the TAM approach as being useful as a guide to empirical studies by academic programs without being challenged.

Legend: The heart of the decision making core consists of goal desire → goal intention → action desire → action intention. These processes and their causes or constraints (labeled A and B in the figure) and their effects (D) constitute fully deterministic processes. Figure 9 The Decision Making Core (Bagozzi, 2007, Figure 1, p. 250)

Components of TAM Methodology A typical TAM diagram shows the variables gender, educational background, self reported level of computer knowledge (Alshare et al., 2005; Liu et al., 2005; Dong-Hee & Won-Young, 2007). A TAM framework often illustrates a technique of plotting a set of hypothesizes along the 10


arrows joining the study’s constructs. This pattern of plotting factors on to a framework shows evidence that some TAM projects started with a simple structure and then refined it. How does this pattern fit extending a model to the industry of online education and virtual communities? The answer is to start with basic assumptions about demographics of learner/participants which may include explicit generalizations, then, when survey results from participants are assessed, acknowledge the subtle differences. An example is a thirty-something male student with economic stability and an affinity for technology devices is found to be open and accepting of online education with a library of media objects. When a variable of parenting status is included, success with exams might be isolated as factors measured as motivation, concentration, or preferences for simplicity. Then, the variables that do not impact the findings can be set aside. Outcome of Research for Effective Decision Making about Technology This section covers literature for articles reporting IT Managements use of TAM as a framework for decision and puts into perspective how TAM research studies has or has not influenced decisions in the field of IT business alignment, strategy planning and impact on the life cycle for deployment of solutions involving software or network architectures. Analysis of the TAM literature reveals that one-time-only published studies are often merely academic studies whereas a series of studies by a research team may involve research into an industry that is seeking confidence in an investment decision. The pattern for authors that have multiple publications is evidence that the TAM approach is being extended by repeating data gathering phases with new data and new variables for a larger population (Alshare et al., 2008). Content-based vs. functionally. A research model published by Hsiu-Fen (2007) hypothesized relationships drawing upon an extended TAM for data that was empirically tested using the structural equation modeling approach to suggest capability of understanding the “determinants of sustainability of virtual communities” (¶ 5). The authors note theoretical and managerial implications for improved effectiveness of strategies for expanding VCs. Whereas their report does not show a framework diagram nor tables of statistical data it does use a narrative to point out interpretations of the data that “information quality focuses on the content-based online feature, whereas system quality and service quality are related to functionally-based online features (¶ 26). For eLearning environments curriculum that satisfies established learning outcomes for an entire program compared to the perception of satisfaction by a random selection of students about their eLearning experiences when invited into a study, caution is needed to ensure that a sample is unbiased. Streaming Media to enhance eLearning. VCs favor streaming media for learning and for conversations. The context for Liu, Liao & Pratt’s (2009) study is that media rich e-learning technologies which reflect the sophistication by computer users especially for acceptance of streamed audio and video. The authors express a premise that the “use of just one theory or model, such as the technology acceptance model, is no longer sufficient to study the intended use of e-learning systems” (Abstract, p. 299). The authors applied the TAM approach to examine the perceived ease of use as a predictor of usefulness; and the attitude of the user correlated with predictors of intention to use for an audience interested in the practical implications of 11


integrating streaming media into eLearning. It is still a mystery which other approach would serve to predict acceptance of streaming media objects to achieve learning goals for virtual communities that operate within a well-defined culture of dialog. Limitations of this research paper Many authors openly point out the limitation of their own research as being based on a convenience sample and often suggest that their findings should be substantiated by further research with a difference sample population or by extending the set of variables and gathering additional data from different demographics for faculty of online education (Gibson, Harris & Colaric’s, 2008). For web-based learning (Min, Yan & Yuecheng, 2004; Huang, Lin, & Chuang, 2007). Some authors use objective rather than subjective case study data to set up the decision framework for measuring preferences. Taking an objective approach, Straub (2009) points out that “state standards, cost, available funds, security, and technical support”, all considered to be external forces may constrain an overall decision to deploy an innovation but inhibit adopting a particular technology. When teachers' views on internal factors like perceived ease of use for a specific technology are variable within a TAM study, opinions are not “the defining factor when making major technology decisions” (p. 644). Decisions for an entire enterprise involves risks. Decisions that anticipate acceptance of technology can reduce risk of the unknown, especially about human behaviors. TAM research findings do offer a method to isolate variables and reconstruct them into a framework of understanding that increases confidence in recommendations. For selection of and deployment of a virtual campus operation of online education and support of virtual communities will necessarily have a multi-vendor point of view for sponsored experiences by participants in a TAM study. This review did not attempt to discover and reveal the many times when TAM studies discussed finding that were of doubtful value in a business context.

Perspectives The perspective arrived at after analyzing the literature is that three dominant theories have viability when combined with use of TAM. One is Theory of Reasoned Action (TRA); the second is the Common Method Variance (CMV), the third is a compliment of Innovation Diffusion Theory, Concerns-Based Adoption Model (CBAM), the United Theory of Acceptance and Use of Technology (UTAUT). On objective study examines TRA to predict the acceptance of information or advice for inducing change in behaviors and set of trials where Silva (2007) applies principles of science to scrutinize the meta-theoretical and scientific foundations of TAM (p. 256). Silva distinguishes arguments of “logical connection” and positions TAM as a “normal science” and further examines a possible “paradigmatic crisis” in a discipline known as “Lakatos’ Concept of Scientific Research Programs” (pp. 258-262). Silva claims to challenge the legimacy of the TAM approach “through the lens of the post positivist philosophy of science with the purpose of providing a constructive critique” (p. 263). Silva integrates the perspectives of three postpositivist philosophers using historical publications and perspectives, to offer a “broader 12


perspective on how to evaluate our endeavors in any historical context” (p. 264). The idea challenges the TAM approach in the context of decision making from a practitioner’s position. This tie to a university’s operations is respected. Common Method Variance (CMV). Sharma, Yetton & Crawford (2009) published a comprehensive study applies a meta-analysis-based technique explaining “between-study variance in observed correlations as a function of the susceptibility to CMV of the methods employed in individual studies” … that may be perceived to be a “major potential validity threat in social sciences research” (p. 473).

Figure 10 Nomological Network of Relationship, Common Model Variance (Sharma et al., 2009, Figure 1, p. 475)

CMV that illustrates the author’s concern that observed correlations are inflated when common or similar methods are used to employ two variables. From a perspective of mixed methods for assessing data, Sharma et al. (2009) reviews literature about TAM approach and find that method effects impacts the observed correlations by a factor of 56% (p. 474). Sharma et al. (2009) chose the TAM domain as subject to extensive investigation and claims that findings are generally accepted as valid even when empirical support for TAM is subject to a validity threat on account of CMV. Special characteristics of TAM research make it simple to illustrate. These three reasons provide a deeper understanding to TAM and a scholarly perspective. Like wise, in his dissertation, without explicitly using the TAM approach, but illustrating his progressive analysis with constructs that resemble the TAM framework, Straub (2009) discusses emotions in response to a situation when technology fails expectations. His work models a dual primary appraisal process called “Content Specific Antecedent” (p. 48) and probes into the perceived important of technology under consideration as a differentiator for attitudes that increase negative emotions when the technology appears to fail. He credits selfefficacy theory for negative emotions during failure reduced confidence that a person can complete a task. Straub classifies three factors (trust, self-efficacy and technical affinity) as “generalized antecedents” representing “habitual patterns of actions and thoughts” that impact a decision (p. 51). Trust and an affinity for technology generate persistence on the part of people having sufficient self-efficacy to accept rather than resist a decision after it may have been made. 13


UTAUT, a Successor for TAM? The article by Straub (2009) includes an analysis of the integration of the TAM with Innovation Diffusion Theory, CBAM, and UTAUT. Straub’s comprehensive study focuses on use of multiple theories to predict adoption of technology and promotes UTAUT as successor for TAM. Addressing their close theoretical ties, Straub documents criticisms of TAM as “lack of acknowledgement of individual differences” and a weakness described as not accounting for “prior experience, age, gender, and many other characteristics that may influence attitudes about technology” (p. 638). Straub’s concern points out “Whereas the results of adoption theory are measured in terms of behavioral change, the predictors of mat behavioral change can be understood through contextual, cognitive, and affective factors. Existing theories deals independently with these factors but no one theory accounts for all three” (p. 627).

Figure 11 How Individual Adoptions Compose Diffusion (Straub, 2009, Figure 1, p. 627).

The pattern found in TAM data does not usually consider the influence of an individual acceptance of technology impacting results to follow and the bell curve style of statistics influenced by diffusion of knowledge. How this applies to educational institutions can be interpreted as pilots being a healthy way to deploy new programs and then letting the testimonies of satisfied students build up the momentum for those who follow. Figure 11 illustrates individuals making a decision to adopt early, mid-late in a graphic showing a threshold to the diffusion curve. Summary and Conclusions A summary of analysis of TAM findings acknowledges the value of the TAM approach as an academic exercise that does not yet have a credible track records for influencing decisions made in the industry of technology in which investments can be very large. The voice of the believers encourages persisting with the TAM approach to measure and then predict acceptance of specific technologies, often single products or processes. As a skeptic, Grover et al. (2009) accomplished 14


a project scoped as resource-based sustainability of Information Systems with a discussion defining TAM as appropriate to decisions regarding intent to use a single IT product for which an intent is predicted and that the model was fairly accurate. However, scenarios that reveal TAM’s shortcomings on the positioning criteria when networked products are within the domain of the study, Grover et al. (2009) state that TAM is “not operationally valid, since perceived usefulness and perceived ease of use are, by themselves, not operationally valid or actionable”. Grover et al. concludes that the TAM “does not capture use in a context in which IS executives are likely to be interested” (2009, p. 316) so that TAM in its basic form would not become a reliable decision tool. The voice of the skeptics is seeking a bigger picture, one which constrains any risk of an ill advised decision influenced by research into ordinary variables with a convenience sample of participants that does not represent those concerns of stakeholders in the long term impact of the near term decision. Bagozzi’s 2007 perspective is that TAM is “a remarkable model” with a long track record for empirical research which is now positioned at a turning point considering that TAM interfaces between academic theory and interpretations about how we study a phenomenon constrained by our own thinking. As an exercise for learners, the TAM approach still has much to offer as a structure and a process for worthwhile exercises in designing a scholarly research study. References Alshare, K., Grandon, E., and Miller, D., (2005a) Internet usage in the academic environment: the technology acceptance model perspective. Academy of Educational Leadership Journal, 9(2), 81-97. Alshare, K., & Alkhateeb, F. (2008). Predicting student usage of internet in two emerging economies using an extended technology acceptance model (TAM). Academy of Educational Leadership Journal, 12(2), 109-128. Bagozzi, R. (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. Baker-Eveleth, L., Eveleth, D., O’Neill, M., & Stone, R. (2006). Enabling laptop exams using secure software: Applying the technology acceptance model. Journal of Information Systems Education, 17(4), 413-420. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: Comparison of two theoretical models. Management Science, 35(8), 982-1003 Davis, R., & Wong, D. (2007). Conceptualizing and measuring the optimal experience of the eLearning environment. Decision Sciences Journal of Innovative Education, 5(1), 97126. doi:10.1111/j.1540-4609.2007.00129.x. Dong-Hee, S., & Won-Young, K. (2008). Applying the technology acceptance model and flow theory to cyworld user behavior: Implication of the web2.0 user acceptance. CyberPsychology & Behavior, 11(3), 378-382. doi:10.1089/cpb.2007.0117. Gibson, S., Harris, M., & Colaric, S. (2008). Technology acceptance in an academic context: Faculty acceptance of online education. Journal of Education for Business, 83(6), 355359. Grover, V., Gokhale, R., & Narayanswamy, R. (2009). Resource-based framework for IS research: Knowledge firms and sustainability in knowledge markets. Journal of the Association for Information Systems, 10(4), 306-332. (Document ID: 1702204581). 15


Huang, J., Lin, Y, & Chuang, S. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 586-599. (Document ID: 1373506001). Hsiu-Fen L. (2007). The role of online and offline features in sustaining virtual communities: an empirical study. Internet Research, 17(2), 119. . (Document ID: 1247980211). Hsiu-Fen, L. (2008). Antecedents of virtual community satisfaction and loyalty: An empirical test of competing theories. CyberPsychology & Behavior, 11(2), 138-144. doi:10.1089/cpb.2007.0003. Landry, B. J. L. (2003). Student reactions to web enhanced instructional elements. Dissertation Abstracts International, 64(03), 869. (UMI No. AAT 3080200). Liu, S., Liao, H., & Pratt, J. (2009, April 1). Impact of media richness and flow on e-learning technology acceptance. Computers & Education, 52(3), 599-607. (ERIC Document Reproduction Service No. EJ827658). Liu, S-H, Liao, H-L, Peng, C-H (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. Issues of Information Systems, IV(2). Retrieved from www.jcis-online.org/iis/2005_IIS/PDFs/Liu_Liao_Peng.pdf Min, G., Yan, X., & Yuecheng, Y. (2004). An enhanced technology acceptance model for webbased learning. Journal of Information Systems Education, 15(4), 365-374. Sánchez-Franco, M. J., & Roldán, J. L., (2005). Web acceptance and usage model: A comparison between goal-directed and experiential web users. Internet Research, 15(1), 21-48. ABI/INFORM Global. (Document ID: 805606891). Sharma, R., Yetton, P., & Crawford, J. (2009). Estimating the effects of common method variance: The method—method pair technique with illustration from TAM research. MIS Quarterly, 33(3), 473-A13. Silva, L. (2007). Post-positivist Review of Technology Acceptance Model. Journal of the Association for Information Systems, 8(4), 256-266. Straub, E. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649. (Document ID: 170241151). Walker, G., & Johnson, N.. (2008). Faculty intentions to use components for web-enhanced instruction. International Journal on ELearning, 7(1), 133-152. (Document ID: 1428128541).

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