The technology acceptance model: Past, present, and future

Communications of the Association for Information Systems Volume 12 Article 50 December 2003 The Technology Acceptance Model: Past, Present, and Future Younghwa Lee University of Colorado at Boulder, Younghwa.Lee@colorado.edu Kenneth A. Kozar University of Colorado at Boulder, Kozar@Spot.Colorado.edu Kai R.T. Larsen University of Colorado at Boulder, kai.larsen@colorado.edu Follow this and additional works at: https://aisel.aisnet.org/cais Recommended Citation Lee, Younghwa; Kozar, Kenneth A.; and Larsen, Kai R.T. (2003) "The Technology Acceptance Model: Past, Present, and Future," Communications of the Association for Information Systems: Vol. 12 , Article 50. DOI: 10.17705/1CAIS.01250 Available at: https://aisel.aisnet.org/cais/vol12/iss1/50 This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in Communications of the Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org. 752 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 THE TECHNOLOGY ACCEPTANCE MODEL: PAST, PRESENT, AND FUTURE Younghwa Lee Kenneth A. Kozar Kai R.T. Larsen Leeds School of Business University of Colorado at Boulder younghwa.lee@colorado.edu ABSTRACT While the technology acceptance model (TAM), introduced in 1986, continues to be the most widely applied theoretical model in the IS field, few previous efforts examined its accomplishments and limitations. This study traces TAM’s history, investigates its findings, and cautiously predicts its future trajectory. One hundred and one articles published by leading IS journals and conferences in the past eighteen years are examined and summarized. An openended survey of thirty-two leading IS researchers assisted in critically examining TAM and specifying future directions. Keywords: IT adoption, technology acceptance model, meta-analysis I. INTRODUCTION The prolific stream of research on information systems use takes a variety of theoretical perspectives. Of all the theories, the Technology Acceptance Model (TAM) is considered the most influential and commonly employed theory for describing an individual’s acceptance of information systems. TAM, adapted from the Theory of Reasoned Action [Ajzen and Fishbein, 1980] and originally proposed by Davis [1986], assumes that an individual’s information systems acceptance is determined by two major variables: • Perceived Usefulness (PU) and • Perceived Ease of Use (PEOU). During the past eighteen years, the information systems community considered TAM a parsimonious and powerful theory [Lucas and Spitler, 1999; Venkatesh and Davis, 2000]. Further supporting the notion of TAM’s popularity, Venkatesh and Davis [2000] found that the first two The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 753 TAM articles, by Davis [1989] and Davis et al. [1989] received 424 journal citations in the Social Science Citation Index (SSCI) by the beginning of 2000. Extending the citation search further, we found to 698 journal citation by 2003. TAM has been applied to different technologies (e.g. word processors, e-mail, WWW, GSS, Hospital Information Systems) under different situations (e.g., time and culture) with different control factors (e.g., gender, organizational type and size) and different subjects (e.g. undergraduate students, MBAs, and knowledge workers), leading its proponents to believe in its robustness. Currently, researchers in the IS field consider TAM one of the information systems fields’ own theories, and still put much effort into the study of research using the theory. Despite its great success, however, few previous systematic efforts trace its history or investigate and evaluate its findings, limitations, and future [e.g., Doll et al., 1998; Gefen and Straub, 2000; Legris et al., 2003]. Evaluation is crucial for the IS community in that it helps researchers of IS adoption understand TAM’s past research findings, identify possible research topics, and conduct future studies. In addition, it helps educate current IS doctoral students in examining how a wellknown IS-owned theory evolved. The present study goes back to 1986, traces the TAM research trajectory, and extensively investigates TAM’s findings. The research purpose of the study is to answer the following five questions: • • • • • How much progress did TAM make over the past eighteen years (1986-2003)? What are the findings and discoveries of TAM research? Who published what and where did they publish it? What do leading IS researchers currently think about TAM research? What are future directions for TAM research? In all, one hundred and one articles published in information systems journals during 1986-2003 and survey results from thirty-two leading IS researchers were analyzed. II. RESEARCH METHODS Both a meta-analysis of previous TAM literature and a survey were conducted. Meta-analysis is a research technique that uses statistical procedures to combine the results of independent studies [Glass, 1981; Hwang, 1996; Mahmood et al., 2001]. This analytical method is appropriate for the research goals of tracing the history of TAM studies and for investigating previous findings in a systematic manner. Using this methodology, previous studies can be effectively and quantitatively analyzed and the inconsistencies among their findings resolved [Hwang, 1996; Hwang and Wu, 1990]. In addition, meta-analysis can enhance the general validity of interpretations [Cook, 1991], and include studies taking place over a long period of time and with a large scope [Mahmood et al., 2001]. Meta-analysis is successfully applied in IS. [e.g., Dennis and Gallupe, 1993; Farhoomand and Drury, 1999]. This study includes TAM research conducted from 1986 to June, 2003. An exhaustive electronic search using Social Science Citation Index, ABI/INFORM, and Business Source Premier resulted in 101 papers. The papers were published in IS journals such as Data Base, Decision Sciences (DS), Information & Management, Information Systems Research (ISR), Journal of Management Information Systems (JMIS), Management Science (MS), and MIS Quarterly, rated as leading journals in IS and reflecting the major research stream of the IS field [Barki et al., 1993; Cheon et al., 1993; Claver et al., 2000; Farhoomand and Drury, 1999). In addition, papers published at two Information Systems conferences, namely the International Conference on Information Systems (ICIS) and the Hawaii International Conference on Systems Science (HICSS), and other papers published in interdisciplinary journals closely related to IS field were included (e.g., Decision Support Systems). The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 754 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Before starting the analysis, the authors jointly developed a protocol to ensure consistent analysis. Twenty-four MIS Quarterly, Information & Management, and ISR papers were first analyzed by two of the authors. To measure inter-rater reliability, the initial analysis results were compared to the analysis scales (e.g., major themes, type of IS, external variables). A 90% interrater reliability was found. Discrepancies were resolved through follow-up discussions. The second reliability test was performed after finishing the coding. Three doctoral students familiar with TAM participated in and performed the reliability test. For twenty randomly selected papers, a 93% consistency was found. To supplement the findings of the meta-analysis, a survey of thirty-two leading IS researchers was conducted. Nine survey questions in open-ended format were used. Example questions included: • In what ways has TAM added value to the IS field? • In what ways has TAM detracted from the IS field? • What do you feel is TAM’s future? We initially selected two groups of IS researchers: TAM researchers and non-TAM researchers. The selection was based on the publication productivity of researchers, especially in the MIS Quarterly and ISR during the 1990s. Twenty TAM researchers and twenty-four non-TAM researchers were selected. The participation was solicited through an invitation letter. A total of thirty-two researchers (16 of them TAM researchers and 16 Non-TAM researchers) completed the survey, a response rate of 76%. III. RESULTS The analysis in this section is divided into three parts. First, the chronological progress of TAM across four separate periods is presented. Second, findings and limitations of past TAM research are summarized. Finally, future directions are addressed. THE CHRONOLOGICAL PROGRESS OF TAM RESEARCH TAM did not maintain its original form. Like an organic being, TAM has ceaselessly evolved. We investigated how TAM has made progress by dividing the past 18 years into four periods: introduction, validation, extension, and elaboration, as shown in Figure 1. Model Introduction Period After introducing information systems into organizations, user technology acceptance received fairly extensive attention [Rogers, 1983; Kwon and Zmud, 1987; Swanson, 1988]. Researchers and practitioners expended substantial research effort determining what factors affect users’ beliefs and attitudes on the IS acceptance decision, and what factors contribute to user resistance [Lucas et al., 1990]. As an output from those streams of research, TAM evolved from Ajzen and Fishbein’s [1980] Theory of Reasoned Action (TRA) to “provide an explanation of the determinants of computer acceptance that is general, capable of explaining user behavior across a broad range of end-user computing technologies and user populations, while at the same time being both parsimonious and theoretically justified” [Davis et al. 1989, p. 985]. After the introduction, researchers in this period performed several TAM studies mainly focused on two streams. • The first attempted to replicate TAM with other technologies, longitudinal situations, and research settings, to verify whether it is a parsimonious model. • The other stream compared TAM and its origin, TRA, to investigate whether TAM can be differentiated from TRA, and whether TAM is superior to TRA. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Venkatesh & Davis (2000) 755 Venkatesh et al. (2003) Model Elaboration Gefen et al. (2003) Straub (1994) Model Extension Adams et al. (1992) Davis & Venkatesh (1996) Model Validation Taylor and Todd (1995) Davis (1986) Model Introduction 1986 1990 1995 2000 2003 Figure 1 Chronological Progress of TAM Research Replication Studies. Several replication studies appeared in this period. Adams et al. [1992] examined TAM in 5 different applications—word processors, graphics, spreadsheets, e-mail, and v-mail—and found that, in general, TAM maintained its consistency and validity in explaining users’ IS acceptance behavior. Davis [1993] replicated his previous study [Davis et al., 1989] using e-mail and a text editor with 112 knowledge workers, and found that TAM successfully explained the adoption of both technologies (R2 =0.36). Sambamurthy and Chin [1994] applied TAM to study group attitudes toward GDSS use, and found that the ratio PU/PEOU successfully predicted group attitude to GDSS use. Finally, Subramanian [1994] performed the replication of the original TAM with two mailing systems’ acceptance, and found that TAM variables showed results consistent with previous studies. Relation of TAM and TRA. In another stream of research, researchers tried to differentiate TAM from TRA. For example, Davis et al. [1989] compared TRA and TAM in how they measure an MBA student’s relative facility with a word processor across two time periods—immediately after introducing the system and 14 weeks later. They found that TAM (R2 = 0.47 at time 1, R2 = 0.51 at time 2) better explained the acceptance intention of the users than TRA (R2 = 0.32 at time 1, R2 = 0.26 at time 2). Hubona and Cheney [1994] compared both TAM and the Theory of Planned Behavior (TPB) model and found that TAM offers a slight empirical advantage and is a much simpler, easier to use, and more powerful model to explain users’ technology acceptance. Taylor and Todd [1995b] compared TAM, TPB, and Decomposed TPB through a longitudinal study of 786 students who used a computer information resource center (CIRC). They found that Decomposed TPB and TPB gave a fuller explanation than TAM. They asked for a cautious interpretation of the results because of the trade-off between explanatory power and complexity. TAM addressed use intention and use with slightly lower variances, while Decomposed TPB increased the explained variance up to only 2% of use, and to 8% of usage intention, paying the high cost by adding 7 more variables. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 756 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 In sum, through the studies in this period, it was found that TAM could successfully predict IS acceptance behavior under different technologies and different situations. In addition, it was found that TAM was a much simpler, easier to use, and more powerful model of the determinant of user acceptance of computer technology than TRA [Igbaria et al. 1997, p. 281]. Model Validation Period Similar to researchers who insisted that most IS instruments are in the early stage of development and thus require a rigorous validation of their measurement instruments [Jarvenpaa et al., 1985; Moore and Benbasat, 1991; Straub, 1989], researchers in the model validation period initiated validation studies of TAM’s original instruments. Supported by Bejar’s [1980] suggestion noting that robust instruments greatly enhance the value of research, researchers wanted to confirm that TAM truly uses an accurate measurement of the user’s acceptance behavior under different technologies, situations, and tasks. Adams et al. [1992] replicated and extended the Davis 1989 study and found both validity and reliability of measurement for both PU and PEOU across different settings and different information systems. Hendrickson et al. [1993, 1996] examined the test-retest reliability of the PU and PEOU scales and found the TAM instrument to be reliable and valid in terms of test-retest analysis. Segars and Grover [1993] found results contrary to the previous researchers. Through confirmatory factor analysis, they found that instead of the two-factor model (PU and PEOU), a three-factor model, including effectiveness as a new TAM variable, is more salient. They contended that the Adams et al. [1992] results could be attributed to its use of classical statistical techniques. Segars and Grover’s [1993] study earned both support and objections. Barki and Hartwick [1994] asserted that original PU consists of distinct constructs within it and can be measured with both items assessing perceived usefulness and perceived increase in productivity, effectiveness, and performance. However, Segars and Grover’s study was refuted by Chin and Todd [1995]. After performing a structural equation modeling (SEM) analysis, they concluded that a single factor PU measure has reasonable psychometric properties, thus there is no substantive rationale to separate PU into two dimensions (PU and effectiveness). They contend that the Segars and Grover findings resulted from the confounding effect of changing scales and constructs in an additive fashion to examine the overall model fit, and small numbers in the sample size. Szajna [1994] investigated the predictive validity of TAM measurements that identify whether the measurement can successfully predict future behavior. She found good predictive validity for PU and PEOU through discriminant analysis of DBMS selection behavior by 47 MBA students. Davis and Venkatesh [1996] examined whether item grouping generates bias when comparing intermixed items. They found item grouping vs. item intermixing had no significant effect. Therefore, Davis and Venkatesh concluded that original grouped items could be used for predicting IS acceptance. In sum, studies in this period extensively investigated whether TAM instruments were powerful, consistent, reliable, and valid and they found these properties to hold. Researchers checked for validation of the instruments every time, even when used in a different context, noting that “no absolute measures for those constructs exist across varying technological and organizational context…. Measurement models must be rigorously assessed and, if necessary, respecified” [Segars and Grover 1993, p. 525]. Model Extension Period After validation efforts confirmed the saliency of the measurement instruments, prolific expansion efforts began to introduce new variables postulating diversified relationships between constructs The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 757 and the search for antecedents (or external) variables of the major TAM constructs, PU and PEOU, in an attempt to identify boundary conditions. One distinctive feature of TAM studies in this period was to attempt model extension with external variables which include individual, organizational, and task characteristics. For instance, Agarwal and Prasad [1999] extended TAM with five kinds of individual difference variables as the external variables of PU and PEOU. They found that the relationship between participation in training and PU, between prior experiences, role with regard to technology, tenure in workplace, level of education, and prior experience and PEOU, were predicted successfully. Igbaria et al. [1995] investigated the effects of organizational factors and found that user training, computing support, and managerial support significantly affect both PU/PEOU and microcomputer usage. Karahanna and Limayem [2000] conducted a study with two technologies, e-mail and voice-mail, and found that the determinants of the system usage and those of PU and PEOU are different among the technologies. PU did not influence e-mail usage but social influence did, and the result was reversed in the case of voice-mail. Another effort in the extension period was to identify and investigate TAM’s boundary conditions. As suggested by Adams et al. [1992], the moderating effects for TAM variables such as culture, gender, task, user type, and IS type needed to be examined. For example, Straub [1994] applied the TAM model in two countries with different cultures, and found that culture played an important role in the attitude toward and choice of communication media. He found that Japanese workers perceived fax to be more useful than did U.S. workers, but in the case of e-mail, the perception was reversed. Gefen and Straub [1997] also investigated the effect of gender difference on IS acceptance, and determined that gender significantly moderates the effects of PU, PEOU, and social presence. They found that men are more affected by PU, while women are more affected by PEOU and Subjective Norm. The influence of task type was examined by Gefen and Straub [2000] who divided WWW usage tasks into information inquiry and product purchasing, and found that PEOU responded differently according to the task type. PEOU significantly predicted WWW usage for a purchasing task, but not for an inquiry task. Similarly, Moon and Kim [2001] applied TAM in the Internet context, differentiating tasks into entertainment and work–related task. They found that the significant factors affecting Internet usage depend on the task type. Perceived playfulness was most pivotal for an entertainment task and PU for a work-related task on the Internet. For different user types, Karahanna et al. [1999a] found a significant difference between potential adopters’ IS adoption and current users’ continuous IS adoption over time. Subjective norms significantly affect the adoption intention of potential adopters, while attitudes significantly affect current users. Finally, Ridings and Gefen [2000] applied TAM in a situation where both the old IS and new IS are used in parallel. The PU of the new IS increases the preference for the new IS adoption, while that of the old IS decreases it; and PEOU of the new and old IS is the significant determinant of PU of the new and old IS respectively. In sum, studies during this period made tremendous strides to develop a “greater understanding [that] may be garnered in explicating the causal relationships among beliefs and their antecedent factors” [Chin and Gopal 1995, p. 46]. Model Elaboration Period This period can be characterized as the elaboration of TAM in two key ways: to develop the next generation TAM that synthesizes the previous effects and to resolve the limitations raised by previous studies. First, in 2000, Venkatesh and Davis [2000] and Venkatesh [2000] introduced TAM II, a new millennium version of original TAM. TAM II synthesizes the previous efforts, and reflected the previous request for the model’s elaboration. It clearly defines the external variables of PU and PEOU, and provided a concrete means to advance the multi-level model. For example, Venkatesh and Davis [2000] define the external variables of PU, such as social influence (subjective norms) and cognitive instruments (job relevance, image, quality, and result demonstrability). Venkatesh [2000] provides the external variables of PEOU, such as anchor The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 758 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 (computer self-efficacy, perceptions of external control, computer anxiety, and computer playfulness) and adjustments (perceived enjoyment and objective usability). Through both efforts, the explained variance increases to 60% of PEOU (R2 = 0.6) and 40% to 60% of PU (R2 = 0.4 ~ 0.6), while at the same time the variance of intention increased to 60% (R2 = 0.6). Second, studies were performed to resolve several problems in TAM. For example, Venkatesh [2000] performed a TAM study considering both voluntary and mandatory situations. This longitudinal study, including Subjective Norm excluded by Davis [1989], used employees in a working environment and measured actual usage instead of self-reported usage. Venkatesh and Davis [2000] performed a longitudinal study with four different subject groups and information systems in a working environment considering both voluntary and mandatory situations. Jointly, the efforts of this period helped delineate uncovered determinants of PEOU and PU, and thus advanced TAM as a salient theory, laying the foundation for further research. In sum, with the inspection of the development of TAM studies across four periods, we find that TAM has evolved continually. It underwent a normal evolution through those years of efforts, culminating in the introduction of TAM II. FINDINGS OF PAST TAM RESEARCH TAM studies have been performed by many different researchers with different research purposes, subjects, information systems, and tasks applying diverse research methodology under different environments. Several new variables were incorporated into the original TAM, combined with other theoretical models, re-specifying their causal relationship with major TAM variables. These extensive research projects were published in the leading information systems journals, drawing interest from both researchers and practitioners alike. This section investigates the findings of these TAM studies examining a number of variables including • Types of Information Systems • Relationships Between Major TAM Variables • External Variables • Major Limitations, • Numbers of Publications by Year and by Journals • Most Published Authors • Characteristics of Research Subjects • Research Methodology Types of Information Systems Over 30 different types of information systems were used as target systems. We classified them into four major categories: • communication systems, • general-purpose systems, • office systems, and • specialized business systems. Each type of system was evenly applied in TAM studies (Table 1). General-purpose systems include Windows, personal computers, microcomputers, workstations, the Internet, and other computer facilities. More recently, the Internet was the most widely applied target technology in TAM studies. Communication systems included e-mail, v-mail, fax, dial-up systems, and other systems mainly used for communications. E-mail was the predominantly researched target system, especially during the early 1990s. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 759 Table 1. Summary of Information Systems Used in TAM Studies Type # of IS ISs of Each Category E-mail (13) Communication Systems 25 (20%) V-mail (6) FAX (1) Dial-up Systems (1) Others (e.g., cellular) (4) Windows (1) PC (or Microcomputer) (9) General Purpose Systems 34 (28%) WWW(or e-commerce) (17) Workstation (3) Computer Resource Center(2) Groupware (2) Word processor (16) Spreadsheet (7) Office Systems 33 (27%) Presentation S/W (6) Database programs (2) Groupware (2) Computerized Model (1) Case Tools (4) Specialized Business Systems 30 (25%) Hospital IS (Telemedicine) (5) DSS, GSS, GDSS (7) Experts support System (2) Others (e.g. MRP) (11) References Karahanna and Straub [1999], Straub [1994] Karahanna and Limayem [2000] Straub [1994] Subramanian [1994] Kwon and Chidambaram [2000] Karahanna et al. [1999] Igbaria et al.[1995], Agarwal & Prasad [1999] Gefen and Straub [2000] Lucas and Spitler [1999, 2000] Taylor and Todd [1995] Lou et al. [2000] Adams et al. [1992], Hubona and Geitz [1997] Methieson[1991],Venkatesh and Davis[1996] Doll et al. [1998], Hendrickson et al. [1993] Szajna [1994], Doll et al. [1998] Malhotra and Galletta [1999],Lou et al. [2000] Lu et al. [2001] Xia and Lee [2000], Dishaw and Strong [1999] Lu and Gustafson [1994], Rawstorne et al.[2000] Sambamuthy and Chin [1994], Vreede et al[1999] Gefen and Keil [1998], Keil et al. [1995] Gefen [2000] Office systems include word processors and spreadsheets, the most commonly used technologies in the office systems category. Specialized business system includes special usage purposes and company developed systems. Case tools, DSS, MRP II, and Expert Systems are some examples of this technology. Relationships between Major TAM Variables TAM’s four major variables are: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI), and Behavior (B). PU is used as both a dependent and independent variable since it is predicted by PEOU, and predicts BI and B at the same time. Behavior was usually measured using frequency of use, amount of time using, actual number of usages, and diversity of usage. As shown in Table 2, the relationship between PU and BI is strongly significant. 74 studies showed a significant relationship between the two variables. These studies stated that PU is a stronger determinant of BI (or B), noting that users willingly use the system that has a critically useful functionality [e.g. Davis, 1989]. However, only 58 studies showed a significant relationship between PEOU and dependent variables, indicating that PEOU is an unstable measure in predicting BI (or B). The results are similar to the studies of Gefen and Straub [2000] — raising the controversy of the role of PEOU in TAM — and Keil et al. [1995], who questioned the overall effects of PEOU in TAM, noting that “no amount of PEOU will compensate for low usefulness” [p. 89]. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 760 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Table 2. Relationships between Major TAM Variables PEOU Æ PU 69 13 19 101 Significant Non-significant Not Applicable Total PU Æ BI or B 74 10 17 101 PEOUÆ BI or B 58 24 19 101 BI Æ B 13 2 86 101 Many studies found reasons for that non-significance. For example, Subramanian [1994] asserted that, when systems used in studies are by their inherent nature relatively easy to use, PEOU has less or no impact on the IS acceptance decision. Igbaria et al. [1995a] explained that the hard reality of organizations might put priority on the usefulness of computer systems rather than the pleasure brought by them. Finally, PEOU was found as a significant antecedent of PU, rather than a parallel, direct determinant of acceptance, and thus it can affect indirectly the acceptance through PU [Davis et al., 1992]. As shown in Table 2, 69 studies showed a significant relationship between PEOU and PU. External Variables A number of external variables were introduced into TAM as suggested by Davis [1989]. Figure 2 and Table 3 show the most frequently referred external variables that affect PU, PEOU, BI, or B, and their relationships. The most frequently introduced variables are system quality [e.g., Igbaria et al., 1995b], training [e.g., Igbaria et al., 1995a], compatibility, computer anxiety, self-efficacy, enjoyment, computing support, and experience [e.g., Chau, 1996]. RELEV DEM ON + + * SO C PRES + AT T + * ANX IE * x x AC C * + C OM PL* x + + * SN/SI/SP VOL * +* * * IN NOV M GT SUP / EXP + + + ++ PU UI U PEOU * * USABIL IM G T RIAL + + PLAYF * V IS IB + + + ENJOY/ QUALI x x x FAC IL * * + + x SELF/ EUS C OM P + R ELAT •AC C : Accessibility, AN X IE : A nx iety, AT T : Attitude, C O M P: C om patib ility, CO M PL: C om plexity, D E M O N : R esu lt D em onstrability, EN JO Y : Perceived E njoym ent, EU S : E nd U ser Support, EX P : Experience, FAC IL : Facilitating C ond itions, IM G : Im ag e, REL EV : Job R elevance, M G T SU P: M anagerial Su pport, PLAY F : P layfulness, IN N O V : Personal Innovativeness, REL AT : R elative Advantage, SE LF : Self-E fficacy, SI/SN /SP: S ocial Influence, Subjective N orm s, and Social Pressure, SO C PRES: Social Presence, TR IAL : Trialability, U SABIL : U sab ility, V ISIB : V isibility, V O L : V oluntariness, *: mixed, +: significant, x: insignificant relationship Figure 2 Relationships between External Variables and Major TAM Variables The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 761 Table 3. Summary of Variables Used in TAM1 Variable Voluntariness Definition The degree to which use of the innovation is perceived as being voluntary, or of free will Origin Moore and Benbasat [1991] Relative Advantage The degree to which an innovation is perceived as being better than its precursor Rogers [1983] Compatibility The degree to which an innovation is perceived as being consistent with the existing values, needs, and past experiences of potential adopters The degree to which an innovation is perceived as being difficult to use Rogers [1983] The degree to which the results of an innovation are observable to others The degree to which an innovation may be experimented with before adoption Rogers [1983] Rogers [1983] Personal Innovativeness The degree to which use of an innovation is perceived to enhance one’s image or status in one’s social system The belief that one has the capability to perform a particular behavior High levels of support that promotes more favorable beliefs about the system among users as well as MIS staffs A construct that allows for a comparison of systems on the actual level of effect regarding to complete specific tasks An individual trait reflecting a willingness to try out any new technology Computer Playfulness The degree of cognitive spontaneity in microcomputer interactions Social Presence The degree to which a medium permits users to experience others as being psychologically present Person’s perception that most people who are important to him think he should or should not perform the behavior in question The degree to which the innovation is visible in the organization The capabilities of a system to enhance and individual’s job performance Webster and Martocchio [1992] Fulk et al. 1987 Complexity Observability Trialability Image Self efficacy End Support User Objective Usability Subjective Norms/ Social Influence Visibility Job Relevance Computer Attitude The degree to which a person likes or dislikes the object Rogers [1983] Rogers [1983] Bandura[1977] Igbaria [1995] Card [1980] et et al. al. Agarwal and Karahanna [2000] Fishbein and Ajzen [1975] Rogers [1983] Thompson et al. [1991] Ajzen and Fishbein[1980] Referred Articles Barki and Hartwick [1994]; Venkatesh and Davis [2000] Moore and Benbasat [1991]; Premkumar and Potter [1995] Chin and Gopal [1995]; Xia and Lee [2000] Premkumar and Potter [1995], Igbaria et al. [1996] Moore and Benbasat 1991 Moore and Benbasat[1991]; Karahanna et al. [1999] Karahanna et al. [1999]; Venkatesh & Davis [2000] Fenech [1998]; Venkatesh and Speier [2000] Igbaria et al. [1996]; Karahanna and Limayem [2000] Venkatesh and Davis [1996]; Venkatesh [2000] Agarwal and Prasad [1998]; Agarwal and Karahanna [2000] Moon and Kim [2001]; Agarwal and Karahanna [2000] Karahanna and Straub [1999]; Karahanna and Limayem [2000] Malhotra and Galletta [1999]; Venkatesh and Morris [2000] Xia and Lee [2000]; Karahanna et al. [1999] Venkatesh and Davis [2000]; Thompson et al. [1991] Chau [2001] 1 We did not analyze the magnitude of effects of each variable since each study was performed with different statistical methods, information systems, and subjects. Averaged values (e.g. coefficient, or correlation) will deliver contaminated interpretations. Instead, we analyzed consistency of the findings with respect to always significant, mixed, and insignificant relationship. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 762 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Accessibility Result Demonstrability Management Support Computer Anxiety Perceived Enjoyment System (Output or Information) Quality Facilitating Conditions Prior Experience - Physical accessibility: the extent to which someone has physical access to the hardware needed to use the system - Information accessibility: the ability to retrieve the desired information from the system The degree to which the results of adopting/using the IS innovation are observable and communicatable to others The degree of support from managers to ensure sufficient allocation of resources and act as a change agent to create a more conductive environment for IS success An individual’s apprehension, or even fear, when she/he is faced with the possibility of using computers The extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system usage The perception how well the system performs tasks that match with job goals Karahanna and Limayem [2000] Karahanna and Straub [1999]; Karahanna and Limayem [2000] Rogers [1983] Karahanna et al. [1999]; Venkatesh and Davis [2000] Igbaria et al. [1997]; Liao and Landry [2000] The control beliefs relating to resource factors such as time and money and IT compatibility issues that may constrain usage Experience gained Igbaria [1997] et al. Simonson et al. [1987] Montazemi et al. [1996]; Gopal et al. [1994] Davis [1992] al. Chin and Gopal [1995]; Teo et al. [1999] Venkatesh and Davis [2000] Lucas and Spitler [2000]; Lederer et al. [2000] Taylor and Todd [1995b] Taylor and Todd [1995b]; Karahanna and Straub [1999] Various Jackson et al. [1997]; Dishaw and Strong [1999] et Major Limitations of TAM studies Self-reported usage is the most commonly reported limitation. Instead of measuring actual usage, 36 studies relied mainly on self-reported use assuming that self-reported usage successfully reflects actual usage. However, self-reported usage is known to be subject to the common method bias, which distorts and exaggerates the causal relationship between independent and dependent variables [Agarwal and Karahanna, 2000; Podsakof and Organ, 1986]. The second most cited limitation of the studies is the tendency to examine only one information system with a homogeneous group of subjects on a single task at a single point of time, thus raising the generalization problem of any single study. The use of student subjects also deteriorates generalizability of the findings. The dominance of cross-sectional study is also an important limitation. Since the user’s perception and intention can change over time, it is important to measure these quantities at several points of time. However, only 13 studies performed a longitudinal comparison. The cross-sectional study’s major weakness is that it cannot infer the causality of the research results [Agarwal and Karahanna, 2000]. Low explanations of variance were referred to as a major problem of TAM studies. In general, 30-40% of the variance of the causal relationship was explained, but in some cases, only 25% was explained by the independent variables [e.g., Chin and Gopal, 1995; Gefen and Straub, 2000]. The majority of the studies with lower variance explanations did not consider external variables other than original TAM variables. Other suggested limitations of TAM studies included single measurement scales, relatively short exposure to the technology before testing, and self-selection biases of the subjects. The detailed limitations are summarized in Table 4. Numbers of Publications by Year and by Journals A total of one hundred one articles using TAM in the leading information systems (IS) journals and conferences were examined. As shown in Table 5, while there was no specific trend, the publication of TAM studies has increased steadily. Some years had a heavier concentration of The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 763 Table 4. Summary of Limitations in TAM Studies Limitations # of Papers Self-reported Usage 36 Did not measure the actual usage Venkatesh and Davis[2000] Single IS 18 Use only a single information system for the research Venkatesh[1999] 15 Inappropriate to reflect the real working environment Agarwal and Karahanna [2000] Only one organization, one department, MBA students Mainly performed based on cross-sectional study Low validity of newly developed measure, use single item scales Did not granulize the tasks, and test them with the target IS Did not adequately explain the causation of the model Did not classify mandatory and voluntary situation, or assume voluntary situation Small sample size, short exposure time to the new IS, few considerations of cultural difference, self-selection bias Karahanna and Straub [1999] Karahanna et al.[1999] Agarwal and Prasad [1998] Student Samples (or University Environment) Single Subject (or Restricted subjects) One Time Cross Sectional Study Measurement Problems 13 13 12 Single Task 9 Low Variance Scores Mandatory Situations Others 6 3 15 Explanation Examples Mathieson [1991] Igbaria et al. [1997] Jackson et al.[1997] Gefen and Straub[1997] Table 5. Publications by Years and Journals 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 Total MIS Quarterly 1 1 Information Systems Research 1 2 2 2 2 1 1 Journal of Management Information Systems 1 Decision Sciences 2 Management Science 3 1 1 Information & Management 1 1 1 Data Base 2 1 1 1 Total 2 0 3 1 1 2 2 3 7 10 10 1 1 1 2 19 2 1 10 1 1 10 7 1 2 1 Hawaii International Conference on System Sciences 1 2 2 1 Others 2 3 1 on 2 1 1 International Conference Information Systems 1 2 3 5 4 1 1 2 1 1 7 2 2 5 1 2 1 2 7 4 12 17 12 5 3 12 2 5 4 22 9 101 papers. In 2000 alone, 17 papers were published in the major IS journals. TAM studies were evenly published across all the leading IS journals with the MIS Quarterly the leader. A total of 19 articles were published in the MIS Quarterly. In all but 4 of the last 15 years, at least one TAM study was published in the MIS Quarterly. Over 10 articles each were published in ISR, JMIS, and I&M. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 764 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Considering that only 16-20 research articles are published in leading IS journals per year (e.g. ISR, MISQ), TAM studies occupy around 10% of total publications. The most prolific authors are listed in Table 6, including authors who published at least 4 papers. Those authors were shown as author or co-authors in 50 of the one hundred one articles, thus 50% of the papers included the dominant authors. Table 6. Most Prolific Researchers Based on Journals in Table 5 Authors Viswanath Venkatesh Fred D. Davis Detmar W. Straub Elena Karahanna David Gefen Patrick Y.K. Chau Magid Igbaria Peter A. Todd Anthony R. Hendrickson Wynne W. Chin Michael G. Morris University University of Maryland University of Arkansas Georgia State University University of Georgia Drexel University University of Hong Kong Claremont Graduate University University of Houston Iowa State University University of Houston Air Force Institute of Technology # of Articles 12 9 8 6 6 6 5 4 4 4 4 Characteristics of Research Subjects As shown in Table 7, the research subjects of TAM studies may be divided into two groups: students and real-world knowledge workers. 46 studies used student subjects and 60 used knowledge worker subjects.2 The average sample size of the studies was 211. Gender was fairly evenly distributed across TAM studies. Thirty one studies mentioning the gender proportion showed that 0.565 were male, and 0.435 were female. Average age of student subject was early 20’s and that of knowledge workers was early 30’s. Table 7. Summary of Research Subjects Subject Type Students Subcategory # of Studies of Each Type Undergraduate 28 MBA or Graduate Students 13 Merged 3 Knowledge Workers 60 Sample Size 211.2 (µ), 220.5 (σ) Gender Proportion Men: 56.5 %, Women: 43.5% Ages 21.23 (student subjects), 32.31 (knowledge workers) Research Methodology Used Our research yielded only 13 longitudinal studies out of the 101 TAM papers studied (Table 8). Most studies used a one-shot cross-sectional method after exposing the subjects to the new IS through hands-on sessions or training. The majority of research incorporated questionnairebased field study. Only three studies used qualitative data, such as participatory observations and 2 Some studies used both students and practitioner subjects. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 765 content analysis. Laboratory experiments were mainly conducted on students in a university environment. Data was analyzed using regression with software such as SPSS and SAS, and structural equation modeling, with Partial Least Square (PLS), LISREL, and AMOS. In recent studies, LISREL was the predominantly data analysis method used. Table 8. Summary of Research Methodology Methodology Longitudinal Study Methodology Details 13 (Yes) 88 (No) Field Study (86) Lab Experiment (12) Qualitative Study (3) PLS (18) LISREL (30) Analysis Method AMOS (7) Regression (32) Others (e.g., Conjoint Analysis ) (14) Venkatesh[2000];Venkatesh and Morris [2000] Straub [1994]; Taylor and Todd [1995] Igbaria et al. [1995]; Agarwal and Prasad [1999] Mathieson[1991]; Doll et al.[1998] Briggs et al.[1999] De Vreede et al.[1999] Sambamuthy and Chin [1994]; Agarwal and Karahanna[2000] Taylor and Todd[1995] Karahanna and Limayem [2000] Chin and Todd[1995] Fenech[1998] Lucas and Spitler[1999] Venkatesh[1999] Discriminate Analysis: Szajna [1994], Conjoint Analysis:Chin and Gopal [1995] LEADING IS RESEARCHERS’ PERSPECTIVE OF TAM RESEARCH To strengthen our observations and prognostications, we contacted leading IS researchers to identify their perception of TAM research. Thirty-two of forty-four queried responded to the study. As discussed earlier, the sample included TAM researchers based on an extended Table 6 and the authors with the most publications in ISR and MISQ from 1996 through 2001. A list of respondents is included in the acknowledgements at the end of this paper. In this section, we report their responses in summarized form. Value Added by TAM Research Question asked: “In what ways has TAM added value to the IS field?” Two major points were made: TAM provided a parsimonious model to examine factors leading to IS acceptance. It includes a systematic grounding for research and focuses previously scattered work. This standardization allows an examination of findings to bring greater meaning to mixed or inconclusive results, thus leading to further work. Building on prior IS research, TAM conceptualized usefulness and ease of use as important perceptions leading to intentions to adopt new systems. The IS field contains few such foundations for its research. “it has also provided a starting point for many extensions and elaborations, and has compared favorably to alternative or competing models of user acceptance.“ Fred Davis TAM provided a stream of research papers to aid and grow our knowledge about IS acceptance. TAM strengthened the IS field by its research rigor. It is a theory “owned” by the IS research community. In the IS field where theories are scarce, TAM served as an example for other areas of IS research. Growing and refining the theoretical foundation with tested measurement instruments will serve to legitimize the field in the eyes of other business disciplines. For example, some marketing studies use TAM as a theoretical foundation. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 766 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Shortcomings of TAM Research Question asked: “In what ways has TAM detracted from the IS field?” The responses from persons who felt TAM may have detracted from the field fall into four categories. TAM researchers may have fallen into the trap of following an incremental approach based on replicating previous studies with minor adjustments. Some people see researchers attempting to take advantage of the previous investment in this area and the broad appeal of TAM in the IS field. Other disciplines built on this “cumulative tradition,” but some respondents felt this idea may have been carried too far. TAM research may be overdone. However, it could be argued that although possible, it was necessary. “it has likely focused us too much on this one theory to the detriment of others.” Detmar Straub. “it has received disproportional amount of attention in IS research detracting research from more relevant research problems which may not be as easy to investigate rigorously.” Juhani Iivari TAM narrows what is included in studies of technology adoption. TAM’s narrow focus reduced attention on the role of technology and design. “it has acted as an inhibitor to more advanced theories of IS use in that people seem stuck or distracted by the model.” Anonymous TAM’s simplicity makes if difficult to put into practice. Practitioners may not be well served by TAM. “imagine talking to a manager and saying that to be adopted technology must be useful and easy to use. I imagine the reaction would be "Duh! The more important questions are what makes technology useful and easy to use.” Alan Dennis The following words are indicative of detractors of TAM: “TAM's simplicity and ease of operationalizability also appears to have attracted many researchers into conducting quick and easy studies by adding a variable or relationship to TAM and comparing the slightly modified versions of TAM with its original version. While most such studies don't get published because of lack of contribution, they still represent scarce research efforts being somewhat wasted.” Henri Barki Further Exploration Needed Question Asked: “Are there areas of TAM that need more exploration?” From conducting the meta-analysis and the survey, three major future directions for TAM came to the forefront. Incorporating More Variables and Exploring Boundary Conditions. Although TAM has aided the understanding of information systems acceptance, it was concluded that a deeper understanding of factors contributing to ease of use and usefulness is needed. One neglected area is examining different information systems and environments. Researchers including Venkatesh [1999] suggested studies on multi-user systems, team-level IS acceptance, and more complex technologies. Opportunities in study of the Internet may also exist. Previous TAM The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 767 studies investigated the effects of different environments and individual differences (e.g. cultural difference [Chau, 1996; Hu et al., 1999; Straub et al., 1997] and gender [Gefen and Straub, 1997]), but more efforts to examine the broader environmental factors including emotion, habit, personality difference, technology change, even going beyond individual acceptance to organizational and societal acceptance [Taylor and Todd, 1995] are necessary. Further, mandatory settings need further study [Davis et al., 1992; Davis, 1993; Taylor and Todd, 1995b; Venkatesh, 2000]. Social influence plays a crucial role in human behavior and decision making [Azjen, 1991; Barki and Hartwick, 1994; Taylor and Todd, 1995b]. While TAM studies attempted to investigate the effect of social influence on the technology acceptance decision, results were mixed. Davis [1989], Barki and Hartwick [1994], and Mathieson [1991] found weak associations between subjective norm and other variables. Lucas et al. [1999], Moore and Benbasat [1993], Taylor and Todd [1995], and Thompson et al. [1991] found a significant relationship. These questions still remain for future study. Some studies attempted to include social influence into TAM and to start finding the boundary conditions that affect the significance of social influence [e.g. Karahanna and Limayem, 2000; Lee et al., 2001; Venkatesh and Morris, 2000; Venkatesh and Davis, 2000]. Barki and Hartwick [1994] found that subjective norm is more important in the early stage of system development. Taylor and Todd [1995] found subjective norm is a better predictor of intention with inexperienced subjects. Venkatesh and Davis [2000] found that subjective norm significantly affected intention under mandatory situations, and that it weakened over time. However, this issue is still in the early stages of investigation, requiring more research to find the causal linkage between social influences and IT adoption and the incorporation of new socially influential factors. For example, social identity and norms as new social factors in social psychology fields are candidates for investigation [Corner and Armitage, 1998]. One of the major problems of TAM studies was that TAM was applied to tasks that were too broad. Previous studies were mainly performed by assigning a single task to a single IS. However, many studies of task-technology fit [Goodhue, 1995], revealed that perception of the technology varies according to task type. For example, Karahanna and Straub [1999] recognized that the research findings cannot be generalized under task-dependent situations. Heeding the warning by Goodhue and Thompson [1995] that the lack of task focus in evaluating IS caused the mixed results in IS acceptance, future TAM studies need to specify tasks more granularly. Finally, as shown in Table 8, TAM studies mainly focused on cross-sectional studies which may not find causal linkage between research variables [Doll and Ahmed, 1983; Igbaria et al., 1996; 1997]. Doll and Ahmed [1983] stressed the importance of longitudinal study, indicating that users’ expectations might change as they become more familiar with IS technology, and what was once acceptable may no longer be adequate. Qualitative study, another natural extension in method is a more useful alternative to determine richer information with a small number of subjects. IS researchers also recommended triangulation methods to uncover richer results than can be found using only a single method [Karahanna et al., 1999; Lee, 1991]. Investigation of Actual Usage and the Relationships Between Actual Usage and Objective Outcome Measures. The investigation of actual usage and the relationships between actual usage and objective outcome measures was another suggestion. Self-reported usage is widely used assuming that it is a reasonable predictor of actual system usage [e.g. Agarwal and Prasad, 1999; Jackson et al., 1997; Sheppard et al., 1988]. However, the risk of distorted research findings by using self-reported usage instead of actual objective usage was cautioned by several studies [Lederer et al., 2000; Karahanna and Straub, 1997; Rawstorne et al., 2000; Straub et al., 1995; Szajna, 1996]. For example, Straub et al. [1995] found that research based on selfreported usage shows distinctly different results with that of actual usage. Self-reported usage was also found to be the major reason for common method bias [Igbaria et al., 1997], and derives its socially desirable answers from the halo effect [Orne, 1979]. The problem was negatively interpreted by cognitive dissonance theory [Festinger, 1957] and self-perception theory [Bem, 1967]. While it is difficult to measure actual usage under diverse restrictions such as privacy consideration, research should continue to pursue measuring actual usage. The investigation of The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 768 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 the relationships between actual usage and objective outcome measures (e.g. performance, productivity, quality) is another issue to be examined. TAM studies were performed under the general assumption that relationship between IS usage and satisfaction, productivity, and quality is positive [e.g., Chau, 1996; Trice and Treacy, 1986]. For example, Bowen [1986] asserted that performance gains by using IS did not materialize if users did not willingly accept the systems. However, only a few studies attempted to verify those relationships [Igbaria et al., 1995; Lucas and Spitler, 1999]. Therefore, new studies are required to determine whether that assumption can be supported by empirical testing. This recommendation follows Davis et al.’s [1989] suggestion that “practitioners and researchers should not lose sight of the fact that usage is only a necessary but not sufficient, condition for realizing performance improvements due to information technology” [p. 1000]. That is, usage does not assure bottom line benefits. TAM will provides more insightful value if the model examines the causal chain between IS investment, IS use, and objective IS value. Significant Changes in TAM Research. Some of the IS scholars surveyed suggested major rather than incremental changes in TAM research. For example, “I think it will be well-used in future work, but that more studies of TAM per se will die out, unless someone can find a new addition to TAM and the paradigm shifts.” Alan Dennis TAM certainly made a contribution, but it may be time to address issues of concern to IS practitioners that can greatly impact their bottom line, and increase their longevity in IS management. Furthermore, a number of other theories that have been applied to the causal linkage of a user’s IS acceptance behavior may be aligned with TAM research. Social Cognitive Theory, Diffusion of Innovation Theory, the Theory of Reasoned Action/Theory of Planned Behavior, the Triandis Model, Human Computer Interaction research, the Technology Transition Model [Briggs et al., 1999], and Social Network Theory [Robertson, 1989] are representative examples. Integration efforts are required to obtain a better understanding of IT adoption [Hu et al., 1999]. Examples of such efforts include TAM II [Venkatesh and Davis, 2000] and the Unified Theory of Acceptance and Use of Technology [Venkatesh et al., 2003]. IV. CONCLUSIONS This study examined the progress of TAM and the findings of TAM research through the metaanalysis of 101 articles published between 1986 and 2003. This study found that TAM has progressed continually during that time and was elaborated by researchers, resolving its limitations, incorporating other theoretical models or introducing new external variables, and being applied to different environments, systems, tasks, and subjects. In addition, through a meta-analysis and a survey of IS researchers, this study identified many of TAM’s rich findings. and carefully predicted the future trajectory of TAM studies. TAM has come a long way. While there are still contradictory views on TAM research considering the previous and current research trends, many exciting directions remain for making future discoveries. ACKNOWLEDGMENT The authors thank Maryam Alavi, Henri Barki, Anitesh Barua, Wynne Chin, Vivek Choudhury, Fred Davis, Alan Dennis, David Gefen, Varun Grover, Tony Hendrickson, Sid Huff, Iivari Juhani, Elena Karahanna, Rob Kauffman, Dorothy Leidner, Hank Lucas, Vijay Mookerjee, Mike Morris, Barrie Nault, Jayesh Prasad, Blaize Reich, Dan Robey, Detmar Straub, Bernadette Szajna, Peter Todd, Joe Valacich, Betty Vandenbosch, Viswanath Venkatesh, Rick Watson, Ron Weber, The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Weidong Xia, and Bob Zmud for their participation and research. 769 comments on the survey for this Editor’s Note: This article was received on October 17,2003 and was published on December 28, 2003. It was with the authors one week for one revision. REFERENCES Adams, D. A., R.R. Nelson, and P.A. Todd (1992) “Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication” MIS Quarterly 16(2), pp. 227-247. Agarwal, R., and J. Prasad (1998) “A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology” Information Systems Research 9(2), pp. 204-215 Agarwal, R., and J. Prasad (1999) “Are Individual Differences Germane to The Acceptance of New Information Technologies?” Decision Sciences 30(2), pp. 361-391. Agarwal, R., and E. Karahanna (2000) “Time Flies When you’re Having Fun Cognitive Absorption and Beliefs about Information Technology Usage” MIS Quarterly 24(4), pp.665-694. Ajzen, I., and M. Fishbein (1980) Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: Prentice Hall. Atkinson, M., and C. Kydd (1997) “Individual Characteristics Associated with World Wide Web Use: An Empirical Study of Playfulness and Motivation” Data Base 28, pp. 53-62. Bandura, A. (1977) Social Learning Theory, Englewood Cliffs, NJ: Prentice Hall. Barki, H., and J. Hartwick (1994) “Measuring User Participation, User Involvement, and User Attitude” MIS Quarterly 18(1), pp. 59-82. Barki, H. S. Rivard, S., and J. Talbot (1993) “A Keyword Classification Scheme for IS Literature: an Update” MIS Quarterly (17) 2, pp. 209–226. Bejar, I. (1980) “Biased Assessment of Program Impact Due to Psychometric Artifacts” Psychological Bulletin (87), pp. 513-524. Bem, D.J. (1967) “Self-perception: an Alternative Interpretation of Cognitive Dissonance Phenomena” Psychological Review (74), pp.183-200. Bhattacherjee, A. (2001) “An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance” Decision Support Systems 32, pp. 201-214. Bhattacherjee, A. (2001) “Understanding Information Systems Continuance: An ExpectationConfirmation Model” MIS Quarterly 25(3), pp.351-370. Bowen, W. (1986) The Puny Payoff from Office Computers Fortune, May 26, 1986, pp. 20-24. Briggs, R.O., M. Adkins, D. Mittleman, J. Kruse, S. Miller, and J.F. Nunamaker (1999) ”A Technology Transition Model Derived From Field Investigation of GSS Use Abroad the U.S.S. CORONADO” Journal of Management Information Systems 15(3), pp. 151-195. Briggs, R.O., G. D. Vreede, and J.F. Nunamaker (2003) “Collaboration Engineering with ThinkLets to Pursue Sustained Success with Group Support Systems” Journal of Management Information Systems 19(4), pp. 31-64. Chau, P.Y.K. (1996a) “An Empirical Investigation on Factors Affecting the Acceptance of CASE by Systems Developers” Information and Management 30(6), pp. 269-280. Chau, P.Y.K. (1996b) “An Empirical Assessment of a Modified Technology Acceptance Model” Journal of Management Information Systems 13(2), pp.185-204. Chau, P.Y.K. (2001) “Influence of Computer Attitude and Self-efficacy on IT Usage Behavior” Journal of End User Computing 13(1), pp. 26-33. Chau, P.Y.K., and P.J. Hu (2002) “Examining a Model of Information Technology Acceptance by Individual Professionals: An Exploratory Study” Journal of Management Information Systems 18(4), pp. 191-229. Chau, P.Y.K., and P.J. Hu (2002) “Investigating Healthcare Professionals’ Decisions to Accept Telemedicine Technology: An Empirical Test of Competing Theories” Information and Management 39, pp. 297-311. Chen, L., M.L. Gillenson, and D.L. Sherrell (2002) “Enticing Online Consumers: An Extended Technology Acceptance Perspective” Information and Management 39, pp. 705-719. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 770 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Cheon, M.J., V. Grover, and R. Sabherwal (1993) “The Evolution of Empirical Research in IS Maturity” Information & Management (24) 3, pp.107-119. Chin, W.W., and P.A. Todd (1995) “On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution” MIS Quarterly 19(2), pp. 237-246. Chin, W.W., and A. Gopal (1995) “Adoption Intention in GSS Relative Importance of Beliefs” Data Base 26(2&3), pp. 42-63. Chismar, W.G., and S.W. Patton (2003) “Does the Extended Technology Acceptance Model Apply to Physicians“ Proceedings of the 36th Hawaii International Conference on System Sciences (HICSS). Claver, E., R. GonzaÂlez, and J. Llopis (2000) “An Analysis of Research in Information Systems (1981-1997)” Information & Management (37) 4, pp.181-195. Cook, T.D. (1991) “Meta-analysis: Its Potential for Causal Description and Causal Explanation within Program Evaluation” in G. Albrecht, H-U. Otto, S. Karstedt-Henke, and K. Bollert (eds.), (1991) Social Prevention and the Social Sciences Theoretical Controversies, Research Problems and Evaluation Strategies. New York, NY: Walter de Gruyter. Corner, M., and C. J. Armitage (1998) “Extending the Theory of Planned Behavior: A Review and Avenues for Further Research” Journal of Applied Social Psychology 28(15), pp.1429-1464. Davis, F.D. (1986) “Technology Acceptance Model for Empirically Testing New End-user Information Systems Theory and Results” Unpublished Doctoral Dissertation, MIT. Davis, F.D. (1989) “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology” MIS Quarterly 13(3), pp. 319-340. Davis, F.D. (1993) “User Acceptance of Information Technology System Characteristics, User Perceptions and Behavioral Impacts” International Journal of Man-Machine Studies 38(3), pp. 475-487. Davis, F.D., R.P. Bagozzi, and P.R. Warshaw (1989) “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models” Management Science 35(8), pp.982-1003. Davis, F.D., R.P. Bagozzi, and P.R. Warshaw (1992) “Extrinsic and Intrinsic Motivation to Use Computers in the Workplace” Journal of Applied Social Psychology 22(14), pp.1111-1132. Davis, F.D., and V. Venkatesh (1996) “A Critical Assessment of Potential Measurement Biases in the Technology Acceptance Model: Three Experiments” International Journal of HumanComputer Studies 45(1), pp.19-45. Dennis, A., and R. Gallupe (1993) “A History of Group Support Systems: Empirical Research Lessons Learned and Future Directions” in Jessup, L.M. and J. S. Valacich (eds.), Group Support Systems New Perspectives New York, NY: Macmillan, pp. 59-77. Devaraj, S., M. Fan, and R. Kohli (2002) “Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics” Information Systems Research 13(3), pp.316333. Dillon, A., and M.G. Morris (1996) “User Acceptance of Information Technology Theories and Models” Annual Review of Information Science and Technology 31, pp. 3-32. Dishaw, M.T., and D.M. Strong (1999) “Extending the Technology Acceptance Model with Tasktechnology Fit Constructs” Information and Management 36(1), pp. 9-21. Doll, W.J., and M.U. Ahmed (1983) “Managing User Expectations” Journal of Systems Management 34(6), pp. 6-11. Doll, W.J., A. Hendrickson, and X. Deng (1998) “Using Davis’s Perceived Usefulness and Easeof-use Instruments for Decision Making: A Confirmatory and Multigroup Invariance Analysis” Decision Sciences 29(4), pp. 839-869. Farhoomand, A.F., and D. H. Drury (1999) “A Historiographical Examination of Information Systems” Communications of the Association for Information Systems(1). Feldman, J.M., and J. Lynch (1988) “Self-generated Validity and Other Effects of Measurement on Belief, Attitude, Intention and Behavior” Journal of Applied Psychology 73(3), pp. 421435. Featherman,M., and M. Fuller (2003) “Applying TAM to E-services Adoption: the Moderating Role of Perceived Risk” Proceedings of the 36th Hawaii International Conference on System Sciences. Fenech, T. (1998) “Using Perceived Ease of Use and Perceived Usefulness to Predict Acceptance of the World Wide Web” Computer Networks and ISDN Systems 30, pp.629-630. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 771 Festinger, L. A. (1957) Theory of Cognitive Dissonance. London, UK: Travistock Publications. Franz, C.R., and D. Robey (1986) “Organizational Context, User Involvement, and The Usefulness of Information Systems” Decision Sciences (17) 3, pp. 329-356. Gefen, D. (2000) “It Is Not Enough to Be Responsive: The Role of Cooperative Intentions in MRP II Adoption” Data Base 31(2), pp.65-79. Gefen, D., E. Karahanna, and D. W. Straub (2003) “Trust and TAM in Online Shopping: An Integrated Model” MIS Quarterly 27(1). pp. 51-90. Gefen, D., and M. Keil (1998) “The Impact of Developer Responsiveness on Perceptions of Usefulness and Ease of Use: An Extension of the Technology Acceptance Model” Data Base 29(2), pp.35-49. Gefen, D., and D.W. Straub (1997) “Gender Difference in the Perception and Use of E-mail: An Extension to the Technology Acceptance Model” MIS Quarterly 21(4), pp.389-400. Gefen, D., and D.W. Straub (2000) “The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-commerce Adoption” Journal of the Association for Information Systems 1. Glass, G.V. (1981) Meta-analysis in Social Research, Beverly Hills, CA: Sage Publishing. Goodhue, D.L., and R. L. Thompson “Task-technology Fit and Individual Performance” MIS Quarterly 19(2), pp. 213-236. Gopal, A., S.M. Miranda, B. P. Robichaux, and R. P. Bostrom (1997) “Leveraging Diversity with Information Technology: Gender, Attitude, and Intervening Influences in The Use of Group Support Systems” Small Group Research 28(1), pp. 29-71. Gould, J.D., S.J. Boies, and C. Lewis (1991) “Making Usable, Useful, Productivity-enhancing Computer Applications” Communications of the ACM 34(1), pp.74-85. Heijden, H. (2003) “Factors Influencing the Usage of Websites: The Case of a Generic Portal in The Netherlands” Information and Management 40, pp.541-549 Hendrickson, A.R., and M.R. Collins (1996) “An Assessment of Structure and Causation of IS Usage” Data Base 27(2), pp.61-67. Hendrickson, A.R., and P.D. Latta (1996) “An Evaluation of the Reliability and Validity of Davis’s Perceived Usefulness and Perceived Ease of Use Instrument” Journal of Computer Information Systems 36(3), pp.77-82. Hendrickson, A.R., P.D. Massey, and T.P. Cronan (1993) ”On the Test-retest Reliability of Perceived Usefulness and Perceived Ease of Use Scales” MIS Quarterly 17(2), pp.227-230. Hong, W. J.Y.L. Thong, W. M. Wang, and K.Y. Tam (2001) ”Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics” Journal of Management Information Systems 18(3), pp.97-124 Hu, P.J., P.Y.K. Chau, O.R.L. Sheng, and K.Y. Tam (1999)”Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology” Journal of Management Information Systems 16(2), pp.91-112. Hubona, G.S., and P. H. Cheney (1994) “System Effectiveness of Knowledge-Based Technology: The Relationship of User Performance and Attitudinal Measures” Proceedings of the Twentyseventh Annual Hawaii International Conference on System Sciences (HICSS-27), pp. 532541. Hwang, M.I., and B.J.P. Wu (1990) “The Effectiveness of Computer Graphics for Decision Support: A Meta-analytical Integration of Research Findings” The Data Base for Advanced in Information Systems (21), pp. 11-20. Hwang, M.I. (1996) “The Use of Meta-analysis in MIS Research: Promises and Problems” The Data Base for Advanced in Information Systems(27) 3 , pp. 35-48. Igbaria, M., T. Guimaraes, and G.B. Davis (1995a) “Testing the Determinants of Microcomputer Usage Via a Structural Equation Model” Journal of Management Information Systems 11(4), pp. 87-114 Igbaria, M., and J. Iivari (1995) “The Effects of Self-efficacy on Computer Usage” Omega 23(6), pp.587-605. Igbaria, M., J. Iivari, and H. Maragahh (1995b) “Why do Individuals Use Computer Technology? A Finnish Case Study” Information & Management 29(5), pp. 227-238. Igbaria, M., S. Parasuraman, and J.J. Baroudi (1996) “A Motivational Model of Microcomputer Usage” Journal of Management Information Systems 13(1), pp.127-143 The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 772 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Igbaria, M., N. Zinatelli, P. Cragg, and A.L.M. Cavaye (1997)” Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model” MIS Quarterly 21(3), pp.279-305. Jackson, C.M., S. Chow, and R.A. Leitch (1997) “Toward an Understanding of the Behavioral Intention to Use an Information System” Decision Sciences 28(2), pp.357-389. Jarvenpaa, S. L., G.W. Dickson, and G. DeSanctis (1985) “Methodological Issues in Experimental IS Research Experiences and Recommendations” MIS Quarterly 9(2), pp.141156. Karahanna, E., D.W. Straub, and N.L. Chervany (1999a) “Information Technology Adoption across Time: A Cross-sectional Comparison of Pre-adoption and Post-adoption Beliefs” MIS Quarterly 23(2), pp.183-213. Karahanna, E., and D.W. Straub (1999b) “The Psychological Origins of Perceived Usefulness and Ease-of-use” Information and Management 35(4), pp.237-250. Karahanna, E., and M. Limayem (2000) “E-mail and V-mail usage: Generalizing across Technologies” Journal of Organizational Computing and Electronic Commerce 10(1), pp.4966. Keil, M., P.M., Beranek, and B.R. Konsynski (1995) “Usefulness and Ease of Use: Field Study Evidence Regarding Task Considerations” Decision Support Systems 13(1), pp.75-91. Koufaris, M. (2002) “Applying the Technology Acceptance Model and Flow Theory to Online Customer Behavior” Information Systems Research 13(2), pp.205-223. Kwon, H.S., and L. Chidambaram (2000) “A Test of the Technology Acceptance Model-The Case of Cellular Telephone Adoption” Proceedings of the 33rd Hawaii International Conference on System Sciences. Kwon, T.H., and R. W. Zmud (1987) “Unifying the Fragmented Models of Information Systems Implementation” in Boland, R.J. and R.A. Hirschheim (eds.), Critical Issues in Information Systems Research, New York, NY: John Wiley and Sons. Lederer, A.L., D.J. Maupin, M.P. Sena, and Y. Zhuang (2000) “The Technology Acceptance Model and the World Wide Web” Decision Support Systems 29(3), pp.269-282. Lee, A.S. (1991) “Integrating Positivist and Interpretive Approaches to Organizational Research” Organization Science (2) 4, pp. 342-365. Lee, D., J. Park, and J. Ahn (2001) “On the Explanation of Factors Affecting E-commerce Adoption” Proceedings of the 22nd International Conference on Information Systems, pp.109120. Lee, Y., Lee, J., and Lee, Z. (2001) “The Effect of Self-identity and Social-identify on Technology Acceptance” Proceedings of the International Conference on Information Systems (ICIS). Legris, P., J. Ingham, and P. Collerette (2003) “Why Do People Use Information Technology? A Critical Review of the Technology Acceptance Model” Information & Management (40), pp.191-204. Liao, Z., and R. Landry (2000) “An Empirical Study on Organizational Acceptance of New Information Systems in a Commercial Bank Environment” Proceedings of the 33rd Hawaii International Conference on System Sciences. Lipsey, M. W., and D. B. Wilson (2001) Practical Meta-analysis. Thousand Oaks: Sage. Lou, H., W. Luo, and D. Strong (2000) “Perceived Critical Mass Effect on Groupware Acceptance” European Journal of Information Systems 9(2), pp.91-103. Lu, H., and H. Gustafson (1994) “An Empirical Study of Perceived Usefulness and Perceived Ease of Use on Computerized Support System Use over Time” International Journal of Information Management 14(5), pp.317-329. Lu, H., H. Yu, and S.S.K. Lu (2001) “The Effects of Cognitive Style and Model Type on DSS Acceptance: An Empirical Study” European Journal of Operational Research 13(1), pp.649663. Lucas, H., R. Schultz, and M. Ginzberg (1990) Information Systems Implementation: Testing a Structural Model, Norwood, NJ: Ablex Publishing. Lucas, H.C., and V.K. Spitler (1999) “Technology Use and Performance: A Field Study of Broker Workstations” Decision Sciences 30(2), pp.291-311. Lucas, H.C., and V.K. Spitler (2000) “Implementation in a World of Workstations and Networks” Information and Management 38(2), pp.119-128. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 773 Mahmood, M.A., L. Hall, L., and D. L. Swanberg (2001) “Factors Affecting Information Technology Usage: A Meta-analysis of The Empirical Literature” Journal of Organizational Computing and Electronic Commerce, 11(2), pp.107-130. Malhotra, Y., and D.F. Galletta (1999) “Extending the Technology Acceptance Model to Account for Social Influence Theoretical Bases and Empirical Validation” Proceedings of the 32nd Hawaii International Conference on System Sciences. Markus, M., and N. Bjorn-Anderson (1987) “Power Over Users: Its Exercise by System Professionals” Communications of the ACM 30 (6), pp. 498-505. Martins,L.L, and F.W. Kellermanns (2001) “User Acceptance of a Web-based Information System in a Non-voluntary Context” Proceedings of the 22nd International Conference on Information Systems, pp. 607-612. Mathieson, K. (1991) “Predicting User Intentions Comparing the Technology Acceptance Model with the Theory of Planned Behavior“ Information Systems Research 2(3), pp.173-191. Mathieson,K., E. Peacock, and W.W. Chin (2001) “Extending the Technology Acceptance Model: The Influence of Perceived User Resources” Data Base 32(3), pp. 86-112. Montazemi, A.R., D.A. Cameron, and K.M. Gupta (1996) “An Empirical Study of Factors Affecting Software Package Selection” Journal of Management Information Systems 13(1), pp.89-105. Moon, J., and Y. Kim (2001) “Extending the TAM for a World-Wide-Web Context” Information and Management 38(4), pp.217-230. Moore, G.C., and I. Benbasat (1991) “Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation” Information Systems Research 2(3), pp.192-222. Orne, M.T. (1979) “On the Social Psychology of The Psychological Experiment” in Mowday, R. T. and R.M. Steers (eds.), Research in Organizations Issues and Controversies, Santa Monica, CA.: Goodyear Publishing Company, Inc. Plouffe,C.R., J.S. Hulland, and M. Vandenbosch (2001) “Richness versus Parsimony in Modeling Technology Adoption Decisions-Understanding Merchant Adoption of a Smart Card-based Payment Systems” Information Systems Research 12(2), pp.208-222. Podsakoff, P.M., and D. W. Organ (1986) “Self-reports in Organizational Research Problems and Prospects” Journal of Management Information Systems (12)4, pp. 531-544. Premkumar, G., and M. Potter (1995) “Adoption of Computer Aided Software Engineering (CASE) Technology: An Innovation Adoption Perspective” Data Base 26 (2&3), pp. 105-124. Rawstorne, P., R. Jayasuriya, and P. Caputi (2000) “Issues in Predicting and Explaining Usage Behaviors with the Technology Acceptance Model and the Theory of Planned Behavior: When Usage is Mandatory” Proceedings of the 21st International Conference on Information Systems, pp. 35-44. Ridings, C.M., and D. Gefen (2000) “Applying TAM to a Parallel Systems Conversation Strategy” Journal of Information Technology Theory and Application 2(2). Riemenschneider,C.K., D.A. Harrision, and P.P. Mykytyn (2003) ”Understanding IT Adoption Decisions in Small Business: Integrating Current Theories” Information and Management 40, pp.269-285. Roberston, D.S. (1989) “Social Determinants of Information System Use” Journal of Management Information Systems 5(4), pp. 55-71. Rogers, E.M. (1983) Diffusion of Innovations New York, NY: The Free Press. Sambamurthy, V., and W.W. Chin (1994) “The Effects of Group Attitudes toward Alternative GDSS Designs on the Decision-making Performance of Computer-Supported Groups” Decision Sciences 25(2), pp. 215-241. Segars, A.H., and V. Grover (1993) “Re-examining Perceived Ease of Use and Usefulness: A Confirmatory Factor Analysis” MIS Quarterly 17(4), pp.517-525. Sheppard, B.H., J. Hartwick, and P. R. Warshaw (1988) “The Theory of Reasoned Action: A Meta-analysis of Past Research with Recommendations for Modifications and Future Research” Journal of Consumer Research 15(3), pp. 325-343. Straub, D.W. (1989) “Validating Instruments in MIS Research” MIS Quarterly (13)2, pp.147-166. Straub, D.W. (1994) “The Effect of Culture on IT Diffusion E-mail and FAX in Japan and the U.S.” Information Systems Research 5(1), pp.23-47. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 774 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Straub, D.W., M. Keil, and W. Brenner (1997) “Testing the Technology Acceptance Model across Cultures: A Three Country Study” Information & Management 33(1), pp.1-11. Straub, D., M. Limayem, and E. Karahanna (1995) “Measuring System Usage Implications for IS Theory Testing” Management Science 41(8), pp.1328-1342. Subramanian, G.H. (1994) “A Replication of Perceived Usefulness and Perceived Ease of Use Measurement” Decision Sciences 25(5/6), pp.863-874. Suomi, R. (1993) “On the Nationality Balance of Authors and References in Selected MIS Journals” Information & Management 24(6), pp. 339-347. Sussman,S.W., and W.S. Siegal (2003) “Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption” Information Systems Research 14(1), pp. 47-65. Swanson, E.B. (1988) Information System Implementation Bridging the Gap between Design and Utilization, Homewood, IL: Irwin. Szajna, B. (1994) “Software Evaluation and Choice Predictive Validation of the Technology Acceptance Instrument” MIS Quarterly 18(3), pp.319-324. Szajna, B. (1996) “Empirical Evaluation of the Revised Technology Acceptance Model” Management Science 42(1), pp.85-92. Taylor, S., and P.A. Todd (1995a) “Assessing IT Usage: The Role of Prior Experience” MIS Quarterly 19(4), pp.561-570. Taylor, S., and P.A. Todd (1995b) “Understanding Information Technology Usage: A Test of Competing Models” Information Systems Research 6(2), pp.145-176. Teo, T.S.H., V.K.G. Lim, and R.Y.C. Lai(1999) “Intrinsic and Extrinsic Motivation in Internet Usage” Omega 27, pp.25-37. Thompson, R.L., C.A. Higgins, and J.M. Howell(1991) “Personal Computing Toward a Conceptual Model of Utilization” MIS Quarterly 15(1), pp.125-143. Trice, A.W., and M. E. Treacy (1986) “Utilization as a Dependent Variable in MIS Research” in the Proceedings of the 7th International Conference on Information Systems (ICIS), pp. 227239. Venkatesh, V. (1999) “Creation of Favorable User Perceptions Exploring the Role of Intrinsic Motivation” MIS Quarterly 23(2), pp.239-260. 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), pp.342-365. Venkatesh, V., and S. Brown (2001) “A Longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges” MIS Quarterly 25(1), pp.71-102. Venkatesh, V., and F.D. Davis (1994) “Modeling the Determinants of Perceived Ease of Use” Proceedings of the 15th International Conference on Information Systems, pp. 213-227. Venkatesh, V., and F.D. Davis (1996) “A Model of the Antecedents of Perceived Ease of Use Development and Test” Decision Sciences 27(3), pp.451-481. Venkatesh, V., and F.D. Davis (2000) “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies” Management Science 46(2), pp. 186-204. Venkatesh, V., and M.G. Morris (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), pp.115-139. Venkatesh, V., and C. Speier (1999) “Computer Technology Training in the Workplace: A Longitudinal Investigation of the Effect of Mood” Organizational Behavior and Human Decision Processes 79(1), pp.1-28. Venkatesh, V., and C. Speier (2000) “Creating an Effective Training Environment for Enhancing Telework” International Journal of Human Computer Studies 52(6), pp. 991-1005. Venkatesh, V., M.G. Morris, and P.L.Ackerman (2000) “A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision Making Processes” Organizational Behavior and Human Decision Processes 83(1), pp.33-60. Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis (2003) “User Acceptance of Information Technology: Toward a Unified View” MIS Quarterly 27(3), pp. 425-478. Vreede, G.D., N. Jones., and R.J. Mgaya (1999) “Exploring the Application and Acceptance of Group Support Systems in Africa” Journal of Management Information Systems 15(3), pp.197-234. The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 775 Xia, W., and G. Lee (2000) “The Influence of Persuasion, Training and Experience on User Perceptions and Acceptance of IT Innovation” Proceedings of the 21st International Conference on Information Systems, pp. 371-384. APPENDIX I. REFERENCES INCLUDED IN META-ANALYSIS Authors Year Adams, D. A., R.R. Nelson, P.A. Todd 1992 Agarwal, R., J. Prasad 1998 Agarwal, R., J. Prasad 1999 Agarwal, R., E. Karahanna 2000 Atkinson, M., C. Kydd 1997 Barki, H., J. Hartwick 1994 Bhattacherjee, A. 2001 Bhattacherjee, A. 2001 Briggs, R.O., M. Adkins, D. Mittleman, J. Kruse, S. Miller, J.F. Nunamaker Briggs, R.O., G. D. Vreede, J.F. Nunamaker Chau, P.Y.K. 1999 Chau, P.Y.K. 1996b Chau, P.Y.K. 2001 Chau, P.Y.K., P.J. Hu 2002 Chau, P.Y.K., P.J. Hu 2002 Chen,L., M.L. Gillenson, D.L. Sherrell Chin, W.W., P.A. Todd 2002 1995 Chin, W.W., A. Gopal 1995 Chismar,W.G., S.W. Patton Davis, F.D. 2003 2003 1996a 1989 Title Perceived usefulness, ease of use, and usage of information technology: A replication A conceptual and operational definition of personal innovativeness in the domain of information technology Are individual differences germane to the acceptance of new information technologies? Time flies when you're having fun cognitive absorption and beliefs about information technology usage Individual characteristics associated with world wide web use: an empirical study of playfulness and motivation Measuring user participation, user involvement, and user attitude An empirical analysis of the antecedents of electronic commerce service continuance Understanding information systems continuance: an expectation-confirmation model A technology transition model derived from field investigation of GSS use abroad the U.S.S. CORONADO Collaboration engineering with ThinkLets to pursue sustained success with group support systems An empirical investigation on factors affecting the acceptance of CASE by systems developers An empirical assessment of a modified technology acceptance model Influence of computer attitude and selfefficacy on IT usage behavior Examining a model of information technology acceptance by individual professionals: an exploratory study Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories Enticing online consumers: an extended technology acceptance perspective On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution Adoption intention in GSS relative importance of beliefs Does the extended technology acceptance model apply to physicians Perceived usefulness, perceived ease of use, and user acceptance of information technology Journal Page MISQ 16(2) 227-247 ISR 9(2) 204-215 DS 30(2) 361-391 MISQ 24(4) 665-694 Data Base 53-62 MISQ 18(1) 59-82 DSS (32) 201-214 MISQ 25(3) 351-370 JMIS 15(3) 151-195 JMIS 19(4) 31-64 I&M 30(6) 269-280 JMIS 13(2) 185-204 J. of End User Computing 13(1) JMIS 18(4) 26-33 I&M (39) 297-311 I&M (39) 705-719 MISQ 19(2) 237-246 Data Base 26(2&3) 42-63 191-229 HICSS-36 MISQ 13(3) 319-340 The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 776 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 475-487 User acceptance of information technology system characteristics, user perceptions and behavioral impacts User acceptance of computer technology: A comparison of two theoretical models Int’l Journal of ManMachine Studies 38(3) MS 35(8) 1992 Extrinsic and intrinsic motivation to use computers in the workplace J. of Applied Social Psychology 22(14) 11111132 1996 A critical assessment of potential measurement biases in the technology acceptance model: three experiments Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics User acceptance of information technology theories and models Int’l Journal of Human-Computer Studies 45(1) ISR 13(3) 19-45 Annual Review of Information Science and Technology 31 I&M 36(1) 3-32 DS 29(4) 839-869 Davis, F.D. 1993 Davis, F.D., R.P. Bagozzi, P.R. Warshaw Davis, F.D., R.P. Bagozzi, P.R. Warshaw Davis, F.D., V. Venkatesh 1989 Devaraj,S. M. Fan, R. Kohli 2002 Dillon, A., M.G. Morris 1996 Dishaw, M.T., D.M. Strong Doll, W.J., A. Hendrickson, X. Deng 1999 Featherman,M., M. Fuller Fenech, T. 2003 1998 1998 Gefen, D., E. Karahanna, and D.W. Straub Gefen, D., D.W. Straub 1997 Gefen, D., M. Keil 1998 Gefen, D., D.W. Straub 2000 Gefen, D. 2000 Heijden, H. 2003 Hendrickson, A.R., M.R. Collins Hendrickson, A.R., P.D. Latta 1996 Hendrickson, A.R., P.D. Massey, T.P. Cronan Hong,W. J.Y.L. Thong, W. M.Wang, K.Y. Tam 1993 Hu, P.J., P.Y.K. Chau, O.R.L. Sheng, K.Y. Tam 1999 2003 1996 2001 Extending the technology acceptance model with task-technology fit constructs Using Davis’s perceived usefulness and ease-of-use instruments for decision making: A confirmatory and multigroup Invariance Analysis Applying TAM to e-services adoption: the moderating role of perceived risk Using perceived ease of use and perceived usefulness to predict acceptance of the World Wide Web Trust and TAM in online shopping: an integrated model Gender difference in the perception and use of e-mail: An extension to the technology acceptance model The impact of developer responsiveness on perceptions of usefulness and ease of use: An extension of the technology acceptance model The relative importance of perceived ease of use in IS adoption: A study of ecommerce adoption It is not enough to be responsive: The role of cooperative intentions in MRP II adoption Factors influencing the usage of websites: the case of a generic portal in The Netherlands An assessment of structure and causation of IS usage An evaluation of the reliability and validity of Davis’s perceived usefulness and perceived ease of use instrument On the test-retest reliability of perceived usefulness and perceived ease of use scales Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics Examining the technology acceptance model using physician acceptance of telemedicine technology 9821003 316-333 9-21 HICSS-36 Computer Networks and ISDN Systems 30 MISQ 27(1) 629-630 MISQ 21(4) 389-400 Data Base 29(2) 35-49 J. of the Association for Information Systems 1 Data Base 31(2) 65-79 I&M 40 541-549 Data Base 27(2) 61-67 Journal of Computer Information Systems 36(3) MISQ 17(2) 77-82 JMIS 18(3) 97-124 JMIS 16(2) 91-112 51-90 227-230 The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 777 Testing the determinants of microcomputer usage via a structural equation model Why do individuals use computer technology? A Finnish case study The effects of self-efficacy on computer usage A motivational model of microcomputer usage JMIS 11(4) 87-114 I&M 29(5) 227-238 Omega 23(6) 587-605 JMIS 13(1) 127-143 1997 Personal computing acceptance factors in small firms: A structural equation model MISQ 21(3) 279-305 1997 Toward an understanding of the behavioral intention to use an information system Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs The psychological origins of perceived usefulness and ease-of-use E-Mail and v-Mail usage: Generalizing across technologies DS 28(2) 357-389 MISQ 23(2) 183-213 I&M 35(4) 237-250 J. of Organizational Computing and ECommerce 10(1) DSS 13(1) 49-66 ISR 13(2) 205-223 Igbaria, M., T. Guimaraes, G.B. Davis Igbaria, M., J. Iivari, H. Maragahh Igbaria, M., J. Iivari 1995a Igbaria, M., S. Parasuraman, J.J. Baroudi Igbaria, M., N. Zinatelli, P. Cragg, A.L.M. Cavaye Jackson, C.M., S. Chow, R.A. Leitch 1996 Karahanna, E., D.W. Straub, N.L. Chervany 1999a Karahanna, E., D.W. Straub Karahanna, E., M. Limayem 1999b Keil, M., P.M., Beranek, B.R. Konsynski Koufaris, M. 1995 Usefulness and ease of use: Field study evidence regarding task considerations 2002 Kwon, H.S., L. Chidambaram 2000 Lee,D., J. Park, J. Ahn 2001 Lederer, A.L., D.J. Maupin, M.P. Sena, Y. Zhuang Legris,P., J. Ingham, P. Collerette 2000 Applying the technology acceptance model and flow theory to online customer behavior A test of the technology acceptance model-The case of cellular telephone adoption On the explanation of factors affecting ecommerce adoption The technology acceptance model and the World Wide Web Liao, Z., R. Landry 2000 Lou, H., W. Luo, D. Strong 2000 Lu, H., H. Gustafson 1994 Lu, H., H. Yu, S.S.K. Lu 2001 Lucas, H.C., V.K. Spitler Lucas, H.C., V.K. Spitler Mahmood, M.A., L. Hall, D.L. Swanberg 1999 Malhotra, Y., D.F. Galletta 1999 1995b 1995 2000 2003 2000 2001 Why do people use information technology? A critical review of the technology acceptance model An empirical study on organizational acceptance of new information systems in a commercial bank environment Perceived critical mass effect on groupware acceptance An empirical study of perceived usefulness and perceived ease of use on computerized support system use over time The effects of cognitive style and model type on DSS acceptance: An empirical study Technology use and performance: A field study of broker workstations Implementation in a world of workstations and networks Factors affecting information technology usage: A meta-analysis of the empirical literature Extending the technology acceptance model to account for social influence theoretical bases and empirical validation 75-91 HICSS-33 ICIS-22 109-120 DSS 29(3) 269-282 I&M (40) 191-204 HICSS-33 European Journal of Information Systems 9(2) Int’l Journal of Information Management 14(5) 91-103 European Journal of Operational Research 13(1) DS 30(2) 649-663 291-311 I&M 38(2) 119-128 J. of Organizational Computing and ECommerce 11(2) HICSS-32 107-130 317-329 The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 778 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Martins,L.L, F.W. Kellermanns 2001 Mathieson, K. 1991 Mathieson,K., E. Peacock, W.W. Chin 2001 Montazemi, A.R., D.A. Cameron, K.M. Gupta Moon, J., Y. Kim 1996 Moore, G.C., I. Benbasat 1991 Plouffe,C.R., J.S. Hulland, M. Vandenbosch 2001 Premkumar, G., M. Potter 1995 Rawstorne, P., R. Jayasuriya, P. Caputi 2000 Ridings, C.M., D. Gefen 2000 Riemenschneider,C.K., D.A. Harrision, P.P. Mykytyn Sambamurthy, V., W.W. Chin 2003 Segars, A.H., V. Grover Straub, D.W. 1993 2001 1994 1994 Straub, D., M. Limayem, E. Karahanna Straub, D.W., M. Keil, W. Brenner Subramanian, G.H. 1995 Sussman,S.W., W.S. Siegal 2003 Szajna, B. 1994 Szajna, B. 1996 Taylor, S., P.A. Todd 1995a Taylor, S., P.A. Todd 1995b Teo, T.S.H., V.K.G. Lim, R.Y.C. Lai Thompson, R.L., C.A. Higgins, J.M. Howell 1999 1997 1994 1991 User acceptance of a web-based information system in a non-voluntary context Predicting user intentions comparing the technology acceptance model with the theory of planned behavior Extending the technology acceptance model: the influence of perceived user resources An empirical study of factors affecting software package selection Extending the TAM for a World-WideWeb context Development of an instrument to measure the perceptions of adopting an information technology innovation Richness versus parsimony in modeling technology adoption decisionsunderstanding merchant adoption of a smart card-based payment systems Adoption of computer aided software engineering (CASE) technology: An innovation adoption perspective Issues in predicting and explaining usage behaviors with the technology acceptance model and the theory of planned behavior: When usage is mandatory Applying TAM to a parallel systems conversation strategy Understanding IT adoption decisions in small business: integrating current theories The effects of group attitudes toward alternative GDSS designs on the decision-making performance of computer-supported groups Re-examining perceived ease of use and usefulness A confirmatory factor analysis The effect of culture on IT diffusion e-mail and FAX in Japan and the U.S. Measuring system usage implications for IS theory testing Testing the technology acceptance model across cultures: A three country study A replication of perceived usefulness and perceived ease of use measurement Informational influence in organizations: an integrated approach to knowledge adoption Software evaluation and choice predictive validation of the technology acceptance instrument Empirical evaluation of the revised technology acceptance model Assessing IT usage: The role of prior experience Understanding information technology usage: A test of competing models Intrinsic and extrinsic motivation in internet usage Personal computing toward a conceptual model of utilization ICIS-22 607-612 ISR 2(3) 173-191 Data Base 32(3) 86-112 JMIS 13(1) 89-105 I&M 38(4) 217-230 ISR 2(3) 192-222 ISR 12(2) 208-222 Data Base 26 (2&3) 105-124 ICIS-21 35-44 J. of Information Technology Theory and Application 2(2) I&M (40) 269-285 DS 25(2) 215-241 MISQ 17(4) 517-525 ISR 5(1) 23-47 MS 41(8) 13281342 I &M 33(1) 1-11 DS 25(5/6) 863-874 ISR 14(1) 47-65 MISQ 18(3) 319-324 MS 42(1) 85-92 MISQ 19(4) 561-570 ISR 6(2) 145-176 Omega 27 25-37 MISQ 15(1) 125-143 The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Venkatesh, V. 1999 Venkatesh, V. 2000 Venkatesh, V., S. Brown 2001 Venkatesh, V., F.D. Davis Venkatesh, V., F.D. Davis Venkatesh, V., F.D. Davis 1994 Venkatesh, V., M.G. Morris 2000 Venkatesh, V., C. Speier 1999 Venkatesh, V., C. Speier 2000 Venkatesh, V., M.G. Morris, P.L. Ackerman 2000 Venkatesh, V. , M. G. Morris, G. B. Davis, and F. D. Davis Vreede, G.D., N. Jones., R.J. Mgaya Xia, W., G. Lee 2003 1996 2000 1999 2000 Creation of favorable user perceptions exploring the role of intrinsic motivation Determinants of perceived ease of use integrating control, intrinsic motivation, and emotion into the technology acceptance model A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges Modeling the determinants of perceived ease of use A model of the antecedents of perceived ease of use development and test A theoretical extension of the technology acceptance model four longitudinal field studies Why don't men ever stop to ask for directions? gender, social influence, and their role in technology acceptance and usage behavior Computer technology training in the workplace: A longitudinal investigation of the effect of mood Creating an effective training environment for enhancing telework A longitudinal field investigation of gender differences in individual technology adoption decision making processes User Acceptance of Information Technology: Toward a Unified View Exploring the application and acceptance of group support systems in Africa The influence of persuasion, training and experience on user perceptions and acceptance of IT innovation 779 MISQ 23(2) 239-260 ISR 11(4) 342-365 MISQ 25(1) 71-102 ICIS-15 213-227 DS 27(3) 451-481 MS 46(2) 186-204 MISQ 24(1) 115-139 Org. Behavior and Human Decision Processes 79(1) Int. Journal of Human Computer Studies 52(6) Org. Behavior and Human Decision Processes 83(1) MISQ 27(3) 1-28 9911005 33-60 JMIS 15(3) 197-234 ICIS-21 371-384 LIST OF ACRONYMS ATT B BI Decomposed TPB DSS PEOU PU PE SN TAM TRA TPB Attitude Behavior Behavioral intention Decomposed theory of planned behavior Decision support systems Perceived ease of use Perceived usefulness Perceived enjoyment Subjective norm Technology acceptance model Theory of reasoned action Theory of planned behavior ABOUT THE AUTHORS Younghwa Lee is a doctoral candidate at the Leeds School of Business, University of Colorado at Boulder. His research interest is in web usability, technology acceptance, and IT security and ethics. He is an ICIS 2003 doctoral consortium fellow. His research is published in several journals including Communications of the ACM and Computers & Security, and in several conferences including International Conference on Information Systems and Academy of Management . The Technology Acceptance Model: Past, Present, and Future by Y. Lee, K.A. Kozar, and K.R.T. Larsen 780 Communications of the Association for Information Systems (Volume 12, Article 50) 752-780 Kenneth A. Kozar is Professor of Information Systems in the Leeds School of Business, the University of Colorado at Boulder. His interests are in human and organizational impacts of technology. He published in a number of journals, served two terms as an Associate Editor of the MIS Quarterly, and was the chair of the Society for Information Management's International Paper Award Competition. He is the author of "Humanized Information Systems Analysis and Design: People Building Systems for People" (McGraw-Hill, 1989). Kai R.T. Larsen is Assistant Professor of Information Systems in the Leeds School of Business, the University of Colorado at Boulder. His research interests center around interdisciplinary approaches to information systems implementation, and interorganizational networks. Copyright © 2003 by the Association for Information Systems. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and full citation on the first page. Copyright for components of this work owned by others than the Association for Information Systems must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists requires prior specific permission and/or fee. Request permission to publish from: AIS Administrative Office, P.O. 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