ISECON 2006 Sharp

Information about ISECON 2006 Sharp

Published on January 13, 2008

Author: Sigfrid

Source: authorstream.com

Content

Development, Extension, and Application: A Review of the Technology Acceptance Model:  Development, Extension, and Application: A Review of the Technology Acceptance Model Jason Sharp Computer Information Systems Tarleton State University Stephenville, Texas, USA [email protected] Introduction:  Introduction Question: Why do people accept or reject technology? Technology Acceptance Model (TAM) Geared specifically toward information technology Strong reliability and validity of instruments Extensive research: 147 articles between 1990 and 2003 Good example of how a model is extended and applied Purpose: To examine the development, extension, and application of TAM in order to identify potential areas of research for future study To provide IS educators with a foundation for guiding students in regard to the TAM literature To provide a starting point for evaluating educational technologies To serve as a general reference for those interested in technology acceptance Methodology:  Methodology Keyword search of ABI Inform, Academic Search Premier, and IEEE Express Criteria: Extension of Legris, Ingham, and Collerette (2003) Prior analysis of articles from 1980 to 2001 Current analysis of articles from 2001 to 2005 Compared articles utilizing a quantitative research method PLS, LISREL, path or regression analysis Broader range of journals than Legris et al. (2003) Prior analysis included only six IT related journals Articles grouped on logical categories chosen by the author (Strauss & Corbin, 1998) A Review of the Technology Acceptance Model:  A Review of the Technology Acceptance Model Development Development: TAM (Original):  Development: TAM (Original) Perceived Usefulness Perceived Ease of Use Attitude Intention to Use Usage Behavior Davis (1989) Perceived ease of use – “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p. 320) Perceived usefulness – “the degree to which a person believes that using a particular system would enhance his or her job performance” (p. 320) Development: TAM (Original):  Development: TAM (Original) Study 1 Technology: PROFS electronic mail and XEDIT editor Sample Size: 120 users employed by IBM Study 2 Technology: Chart-Master and Pendraw Sample Size: 40 MBA students Overall Findings: Perceived Usefulness significant determinant of Usage Perceived Ease of Use significant determinant of Usage Effect of Perceived Usefulness significantly greater than Perceived Ease of Use Attitude does not fully mediate effect of Perceived Usefulness and Perceived Ease of Use on Behavior Perceived Ease of Use as an antecedent of Perceived Usefulness Development: TAM (Parsimonious):  Development: TAM (Parsimonious) Study (Davis, Bagozzi, and Warshaw, 1989) Technology: WriteOne, word processor Sample Size: 107 MBA students Overall Findings Perceived Usefulness strong significant determinant of Usage Perceived Ease of Use significant determinant of Usage, but significantly weaker than Perceived Usefulness Attitude only partially mediated effects of Perceived Usefulness and Perceived Ease of Use on Usage Development: TAM (Parsimonious):  Development: TAM (Parsimonious) Perceived Usefulness Perceived Ease of Use Intention to Use Usage Behavior Davis, Bagozzi, and Warshaw (1989) Development: TAM2:  Development: TAM2 Perceived Usefulness Perceived Ease of Use Intention to Use Usage Behavior Result Demonstrability Output Quality Job Relevance Image Subjective Norm Experience Voluntariness Venkatesh and Davis (2000) Development: TAM2:  Development: TAM2 Subjective Norm – influence of others on user’s decision to use or not use Image – maintaining a favorable standing Job Relevance – degree to which the target system is applicable Output Quality – how well the system performs tasks Result Demonstrability – tangible results Experience – with the system Voluntariness – perception of voluntary/mandatory use Development: TAM2:  Development: TAM2 Study 1 (voluntary): Technology: Proprietary system Sample Size: 38 floor supervisors Study 2 (voluntary): Technology: Migration to Windows-based environment Sample Size: 39 personal financial services employees Study 3 (mandatory): Technology: Windows-based account management system Sample Size: 43 accounting firm services employees Study 4 (mandatory): Technology: Stock portfolio analysis system Sample Size: 36 investment banking employees Development: TAM2:  Development: TAM2 Perceived Usefulness Perceived Ease of Use Intention to Use Usage Behavior Result Demonstrability Output Quality Job Relevance Image Subjective Norm Experience Voluntariness Venkatesh and Davis (2000) Development: Antecedents of Perceived Ease of Use:  Development: Antecedents of Perceived Ease of Use Perceived Usefulness Perceived Ease of Use Intention to Use Usage Behavior Computer Self-Efficacy Objective Usability Direct Experience Venkatesh and Davis (1996) Computer Self-efficacy – how does the user feel about their ability to use technology Objective Usability – objective system measures, e.g., keystroke model, expert to novice performance comparison Development: Antecedents of Perceived Ease of Use:  Development: Antecedents of Perceived Ease of Use Study 1: Technology: Chartmaster and Pendraw Sample Size: 40 MBA students Study 2: Technology: WordPerfect and Lotus Sample Size: 36 undergraduate students Study 3: Pine (electronic mail) and Gopher (information access) Sample Size: 32 part-time MBA students Overall Findings Before hands-on experience, Computer Self-efficacy was a significant determinant of Perceive Ease of Use, Objective Usability was not After direct experience, both Computer self-efficacy and Objective Usability were significant determinants of Perceived Ease of Use Development: Antecedents Revised:  Development: Antecedents Revised Perceived Usefulness Perceived Ease of Use Intention to Use Usage Behavior Objective Usability Perceived Enjoyment Computer Playfulness Computer Anxiety Perception of External Control Computer Self-Efficacy Venkatesh (2000) Perception of External Control - availability of support staff Computer Anxiety – apprehension or fear Computer Playfulness – desire to explore and play Perceived Enjoyment – enjoyable apart from performance consequences Development: Antecedents of Perceived Ease of Use:  Development: Antecedents of Perceived Ease of Use Three studies measured three times over three months Study 1: Technology: Interactive online help desk system Sample Size: 58 retail electronic store employees Study 2: Technology: Multimedia system for property management Sample Size: 145 real estate agency employees Study 3: Technology: Migration to PC-based environment Sample Size: 43 financial services employees Pooled Results T1: Perceived Enjoyment and Objective Usability not significant T2: All antecedents significant T3: Computer Playfulness not significant A review of the Technology Acceptance Model:  A review of the Technology Acceptance Model Extension Extension: Determinants of Intention to Use :  Extension: Determinants of Intention to Use Extension: Determinants of Attitude:  Extension: Determinants of Attitude Extension: External Variables of Usefulness:  Extension: External Variables of Usefulness Extension: External Variables of Ease of Use:  Extension: External Variables of Ease of Use A review of the Technology Acceptance Model:  A review of the Technology Acceptance Model Application Application: Original TAM (Supporting):  Application: Original TAM (Supporting) Perceived Usefulness a stronger determinant than Perceived Ease of Use Application: Original TAM (Opposing):  Application: Original TAM (Opposing) Perceived Ease of Use a stronger determinant than Perceived Usefulness Application: Influence of Attitude on Intention:  Application: Influence of Attitude on Intention Application: Parsimonious TAM (Supporting):  Application: Parsimonious TAM (Supporting) Perceived Usefulness a stronger determinant than Perceived Ease of Use Application: Parsimonious TAM (Opposing):  Application: Parsimonious TAM (Opposing) Perceived Ease of Use a stronger determinant than Perceived Usefulness Application: TAM2 (Mixed results):  Application: TAM2 (Mixed results) Subjective Norm and Image significant determinant of Perceived Usefulness Results Demonstrability not a significant determinant of Perceived Usefulness Perceived Ease of Use significant determinant of Perceived Usefulness, but not of Intention to Use Perceived Usefulness significant determinant of Intention to Use Application: Environment:  Application: Environment Research Potential:  Research Potential Mixed results of Perceived Usefulness and Perceived Ease of Use as the stronger determinant Ten studies supported Perceived Usefulness Six studies supported Perceived Ease of Use How does the type of technology of affect the results? Volitional versus mandatory use environments Fifteen studies conducted in volitional environments Two studies conducted in mandatory environments How does the environment affect the results? The role of Attitude Seven studies indicated Attitude as a direct determinant Two studies indicated Attitude is not a direct determinant Does attitude play a greater role than previously thought? Importance to Information Systems Educators:  Importance to Information Systems Educators Provides a foundation for assisting faculty to guide students about the history of TAM Provides a quick summary of statistical significance of various determinants and external variables Provides a starting point for evaluating educational technologies Provides a ready reference of current technologies evaluated with TAM Conclusion:  Conclusion Examined the development, extension, and application of TAM Identified three specific areas for future research Constructed a ready reference for IS educators Developed a general overview of TAM for those interested in technology acceptance Development, Extension, and Application: A Review of the Technology Acceptance Model:  Development, Extension, and Application: A Review of the Technology Acceptance Model Jason Sharp Computer Information Systems Tarleton State University Stephenville, Texas, USA [email protected] References:  References Amoako-Gyampah, K., & Salam, A. F. (2004). An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41(6), 731-745. Chan, S., & Lu, M. (2004). Understanding internet banking adoption and use behavior: A Hong Kong perspective. Journal of Global Information Management, 12(3), 21-43. Chau, P. Y. K. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of End User Computing, 13(1), 26-33. Chau, P. Y. K., & Hu, P. (2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39(4), 297-311. Brown, S. A., Massey, A. P., Montoya-Weiss, M. M., & Burkman, J. R. (2002). Do I really have to? User acceptance of mandated technology. European Journal of Information Systems, 11(4), 283-295. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. Delone, W. H., & McLean, E. R. (1992). Information systems successes: The quest for the dependent variable. Information Systems Research, 3(1), 60-95. Delone, W. H., & McLean, E. R. (2003). The Delone and McLean model of information systems Success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Gong, M., Xu, Y., Yu, Y. (2004). An enhanced technology acceptance model for web-based Learning. Journal of Information Systems Education, 15(4), 365-374. Hong, W., Thong, J. Y. L., Wong, W., & Tam, K. (2001-2002). Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. Journal of Management Information Systems, 18(3), 97-124. References:  References Horton, R. P., Buck, T., Waterson, P. E., & Clegg, C. W. (2001). Explaining intranet use with the technology acceptance model. Journal of Information Technology, 16(4), 237-249. Hu, P. J., Lin, C., & Chen, H. (2005). User acceptance of intelligence and security informatics technology: A study of COPLINK. Journal of the American Society for Information Science and Technology, 56(3), 235-244. Huang, E. (2005). The acceptance of women-centric websites. The Journal of Computer Information Systems, 45(4), 75-83. Liaw, S. S., & Huang, H. M. (2003). An investigation of user attitudes toward search engines as an information retrieval tool. Computers in Human Behavior, 19(6), 751-765. Lin, F., & Wu, J. (2004). An empirical study of end-user computing acceptance factors in small and medium enterprises in Taiwan: Analyzed by structural equation modeling. Journal of Computer Information Systems, 44(3), 98-108. Mathieson, K., Peacock, E., & Chinn, W. C. (2001). Extending the technology acceptance model: The influence of perceived user resources. The Data Base for Advances in Information Systems, 32(3), 86-112. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world-wide-web context. Information & Management, 38(4) 217-230. Shih, H. (2004). Extended technology acceptance model of internet utilization behavior. Information & Management, 41(6), 719-729. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365. Venkatesh, V., & Davis, F. D. (1996). A model of antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. Wixom, B. H., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. Yi, M. Y., & Hwang, Y. (2004). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449.

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