year 6, Issue 1 (Spring 2018)
Ann Appl Sport Sci 2018, 6(1): 75-86 |
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Mohammadi S, Isanejad O. Presentation of the Extended Technology Acceptance Model in Sports Organizations. Ann Appl Sport Sci 2018; 6 (1) :75-86
URL: http://aassjournal.com/article-1-503-en.html
URL: http://aassjournal.com/article-1-503-en.html
1- Department of Sport Management, Faculty of Sport Sciences, University of Kurdistan, Sanandaj, Iran , sardarmohammadii@gmail.com
2- Department of Counseling, Faculty of Humanistic Sciences, University of Kurdistan, Sanandaj, Iran
2- Department of Counseling, Faculty of Humanistic Sciences, University of Kurdistan, Sanandaj, Iran
Abstract: (10841 Views)
Background. In recent decades, information technology has become a vital component of various aspects of our lives. The use of information technology in different domains has made the analysis of the level of its acceptance/rejection a significant factor in organizations.
Objectives. The aim of this study was to illustrate the application of the extended technology acceptance model in sports organizations.
Methods. The participants consisted of 350 employees selected from across various Iranian sports organizations. The data were collected according to the perceived ease of use, perceived usefulness, attitude (AT), intention to use (IU), technology self-efficacy (TSE), technology anxiety, perceived enjoyment, and user satisfaction for each variable in the study model. A panel of experts determined the face and content validity of the experiment. The Cranach’s alpha coefficient was used to determine the validity.
Results. The results showed that AT, self-efficacy, PU, EOU, and user satisfaction have a meaningful effect on the intention to use information technology. The highest effect was related to AT, and the lowest to user satisfaction.
Conclusion. It can be claimed that when people have a more positive AT toward the use of information technology, they will exhibit beliefs or excitements, which makes it more viable for them to perceive information technology positively, and consequently increase the intention to use it.
Objectives. The aim of this study was to illustrate the application of the extended technology acceptance model in sports organizations.
Methods. The participants consisted of 350 employees selected from across various Iranian sports organizations. The data were collected according to the perceived ease of use, perceived usefulness, attitude (AT), intention to use (IU), technology self-efficacy (TSE), technology anxiety, perceived enjoyment, and user satisfaction for each variable in the study model. A panel of experts determined the face and content validity of the experiment. The Cranach’s alpha coefficient was used to determine the validity.
Results. The results showed that AT, self-efficacy, PU, EOU, and user satisfaction have a meaningful effect on the intention to use information technology. The highest effect was related to AT, and the lowest to user satisfaction.
Conclusion. It can be claimed that when people have a more positive AT toward the use of information technology, they will exhibit beliefs or excitements, which makes it more viable for them to perceive information technology positively, and consequently increase the intention to use it.
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APPLICABLE REMARKS
APPLICABLE REMARKS
• The AT, self-efficacy, PU, and EOU have the most and user satisfaction have the least effect on the decision to use the information technology.
• It is suggested that sports organization for increasing its productivity and efficiency not only emphasize to information technology but make it proportional to organization's need.
• Because of the most effect of AT on intention to use IT, it suggests that these organizations do their plans for creating a positive attitude on acceptance of information technology. This leads to make it easy and user satisfaction's reinforcement information technology in the organization.
• It is suggested that sports organization for increasing its productivity and efficiency not only emphasize to information technology but make it proportional to organization's need.
• Because of the most effect of AT on intention to use IT, it suggests that these organizations do their plans for creating a positive attitude on acceptance of information technology. This leads to make it easy and user satisfaction's reinforcement information technology in the organization.
Type of Study: Original Article |
Subject:
Sport Management and its related branches
Received: 2017/04/5 | Accepted: 2018/01/15
Received: 2017/04/5 | Accepted: 2018/01/15
References
1. Wallace LG, Sheetz SD. The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management. 2014;51(2):249-59. [DOI:10.1016/j.im.2013.12.003]
2. Davis FD. User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies. 1993;38(3):475-87. [DOI:10.1006/imms.1993.1022]
3. Lee Y-C. An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review. 2006;30(5):517-41. [DOI:10.1108/14684520610706406]
4. Bagozzi RP. The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the association for information systems. 2007;8(4):3. [DOI:10.17705/1jais.00122]
5. Lu Y, Zhou T, Wang B. Exploring Chinese users' acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior. 2009;25(1):29-39. [DOI:10.1016/j.chb.2008.06.002]
6. Chang S-H, Chou C-H, Yang J-M, editors. The Literature Review of Technology Acceptance Model: A Study of the Bibliometric Distributions. PACIS; 2010.
7. Sánchez-Prieto JC, Olmos-Miguelá-ez S, García-Pe-alvo FJ. Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior. 2016;55:519-28. [DOI:10.1016/j.chb.2015.07.002]
8. Huijts NM, Molin E, Steg L. Psychological factors influencing sustainable energy technology acceptance: A review-based comprehensive framework. Renewable and Sustainable Energy Reviews. 2012;16(1):525-31. [DOI:10.1016/j.rser.2011.08.018]
9. Holden H, Rada R. Understanding the influence of perceived usability and technology self-efficacy on teachers' technology acceptance. Journal of Research on Technology in Education. 2011;43(4):343-67. [DOI:10.1080/15391523.2011.10782576]
10. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Management science. 1989;35(8):982-1003. [DOI:10.1287/mnsc.35.8.982]
11. Surendran P. Technology acceptance model: A survey of literature. International Journal of Business and Social Research. 2012;2(4):175-8.
12. Leonard LN, Cronan TP, Kreie J. What influences IT ethical behavior intentions—planned behavior, reasoned action, perceived importance, or individual characteristics? Information & Management. 2004;42(1):143-58. [DOI:10.1016/j.im.2003.12.008]
13. Gumussoy C, Calisir F, Bayram A, editors. Understanding the behavioral intention to use ERP systems: An extended technology acceptance model. 2007 IEEE International Conference on Industrial Engineering and Engineering Management; 2007: IEEE. [DOI:10.1109/IEEM.2007.4419547]
14. Guritno S, Siringoringo H. Perceived usefulness, ease of use, and attitude towards online shopping usefulness towards online airlines ticket purchase. Procedia-Social and Behavioral Sciences. 2013;81:212-6. [DOI:10.1016/j.sbspro.2013.06.415]
15. Park E, Baek S, Ohm J, Chang HJ. Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics. 2014;31(1):3-15. [DOI:10.1016/j.tele.2013.07.001]
16. Ratten V. A cross-cultural comparison of online behavioural advertising knowledge, online privacy concerns and social networking using the technology acceptance model and social cognitive theory. Journal of Science & Technology Policy Management. 2015;6(1):25-36. [DOI:10.1108/JSTPM-06-2014-0029]
17. Hsiao C-H, Tang K-Y. Investigating factors affecting the acceptance of self-service technology in libraries: The moderating effect of gender. Library Hi Tech. 2015;33(1):114-33. [DOI:10.1108/LHT-09-2014-0087]
18. Erasmus E, Rothmann S, Van Eeden C. A structural model of technology acceptance. SA Journal of Industrial Psychology. 2015;41(1):01-12. [DOI:10.4102/sajip.v41i1.1222]
19. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science. 2000;46(2):186-204. [DOI:10.1287/mnsc.46.2.186.11926]
20. Saadé RG, Kira D. The emotional state of technology acceptance. Issues in informing science and information technology. 2006;3:529-39. [DOI:10.28945/913]
21. Chang PV-C. The validity of an extended technology acceptance model (TAM) for predicting intranet/portal usage. 2004.
22. Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychological review. 1977;84(2):191. [DOI:10.1037/0033-295X.84.2.191] [PMID]
23. Pauli KP, Gilson RL, May DR. Anxiety and avoidance: The mediating effects of computer self-efficacy on computer anxiety and intention to use computers. Review of Business Information Systems (RBIS). 2011;11(1):57-64. [DOI:10.19030/rbis.v11i1.4431]
24. Ramayah T, Aafaqi B, Ignatius J. Role of self-efficacy in e-library usage among students of a public university in Malaysia. Malaysian Journal of Library and Information Science. 2004;9:39-58.
25. Kripanont N. Using a technology acceptance model to investigate academic acceptance of the internet. Journal of Business Systems, Governance, and Ethics. 2006;1(2):13-28. [DOI:10.15209/jbsge.v1i2.72]
26. Saadé RG, Kira D. Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education. 2009;8(1):177-91. [DOI:10.28945/166]
27. Alenezi AR, Karim AMA, Veloo A. An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: A case study from Saudi Arabian governmental universities. TOJET: The Turkish Online Journal of Educational Technology. 2010;9(4).
28. Compeau D, Higgins CA, Huff S. Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS quarterly. 1999:145-58. [DOI:10.2307/249749]
29. Abubakar D, Adetimirin A. INFLUENCE OF COMPUTER LITERACY ON POSTGRADUATES'USE OF E-RESOURCES IN NIGERIAN UNIVERSITY LIBRARIES. Library Philosophy and Practice. 2015:1.
30. Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology. 1992;22(14):1111-32. [DOI:10.1111/j.1559-1816.1992.tb00945.x]
31. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly. 2003:425-78. [DOI:10.2307/30036540]
32. Mun YY, Hwang Y. 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. 2003;59(4):431-49. [DOI:10.1016/S1071-5819(03)00114-9]
33. Teo T, Noyes J. An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education. 2011;57(2):1645-53. [DOI:10.1016/j.compedu.2011.03.002]
34. Venkatesh V, Speier C, Morris MG. User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences. 2002;33(2):297-316. [DOI:10.1111/j.1540-5915.2002.tb01646.x]
35. Nah FF-H, Zhao F, Zhu W. Factors influencing users' adoption of mobile computing. Managing E-Commerce and Mobile Computing Technologies Book. 2003:260-71.
36. Bandura A. Self-efficacy mechanism in human agency. American psychologist. 1982;37(2):122. [DOI:10.1037/0003-066X.37.2.122]
37. Doll WJ, Torkzadeh G. The measurement of end-user computing satisfaction. MIS quarterly. 1988:259-74. [DOI:10.2307/248851]
38. Seddon P, Kiew M-Y. A partial test and development of DeLone and McLean's model of IS success. Australasian Journal of Information Systems. 1996;4(1). [DOI:10.3127/ajis.v4i1.379]
39. Negash S, Ryan T, Igbaria M. Quality and effectiveness in web-based customer support systems. Information & Management. 2003;40(8):757-68. [DOI:10.1016/S0378-7206(02)00101-5]
40. Yoon Y, Guimaraes T, O'Neal Q. Exploring the factors associated with expert systems success. MIS quarterly. 1995:83-106. [DOI:10.2307/249712]
41. Delone WH, McLean ER. The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems. 2003;19(4):9-30. [DOI:10.1080/07421222.2003.11045748]
42. Mathieson K. Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research. 1991;2(3):173-91. [DOI:10.1287/isre.2.3.173]
43. Bhattacherjee A. An empirical analysis of the antecedents of electronic commerce service continuance. Decision support systems. 2001;32(2):201-14. [DOI:10.1016/S0167-9236(01)00111-7]
44. Cenfetelli R, Benbasat I, Al-Natour S. Information technology mediated customer service: A functional perspective. ICIS 2005 Proceedings. 2005:58.
45. Konradt U, Christophersen T, Schaeffer-Kuelz U. Predicting user satisfaction, strain and system usage of employee self-services. International Journal of Human-Computer Studies. 2006;64(11):1141-53. [DOI:10.1016/j.ijhcs.2006.07.001]
46. Kim Y, Lee HS. Quality, perceived usefulness, user satisfaction, and intention to use: An empirical study of ubiquitous personal robot service. Asian Social Science. 2014;10(11):1. [DOI:10.5539/ass.v10n11p1]
47. Chen L, Gillenson L, Sherrell L. Enticing online consumers: an extended technology acceptance perspective, 39 (8), 709–719. doi: 10.1016. S0378-7206 (01). 2002:00127-6.
48. Gefen D, Karahanna E, Straub DW. Trust and TAM in online shopping: an integrated model. MIS quarterly. 2003;27(1):51-90. [DOI:10.2307/30036519]
49. Wu J-H, Wang S-C. What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management. 2005;42(5):719-29. [DOI:10.1016/j.im.2004.07.001]
50. Hsu C-L, Lu H-P. Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management. 2004;41(7):853-68. [DOI:10.1016/j.im.2003.08.014]
51. Yang Z, Cai S, Zhou Z, Zhou N. Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & Management. 2005;42(4):575-89. [DOI:10.1016/S0378-7206(04)00073-4]
52. Persico D, Manca S, Pozzi F. Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior. 2014;30:614-22. [DOI:10.1016/j.chb.2013.07.045]
53. Stantchev V, Colomo-Palacios R, Soto-Acosta P, Misra S. Learning management systems and cloud file hosting services: A study on students' acceptance. Computers in Human Behavior. 2014;31:612-9. [DOI:10.1016/j.chb.2013.07.002]
54. Sánchez RA, Hueros AD. Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior. 2010;26(6):1632-40. [DOI:10.1016/j.chb.2010.06.011]
55. Malhotra Y, Galletta DF, editors. Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. Systems sciences, 1999 HICSS-32 Proceedings of the 32nd annual Hawaii international conference on; 1999: IEEE.
56. Yi-Cheng C, Yi-Chien LV, Ron-Chuen Y. Examining factors influencing behavioral intentions to use asynchronous web-based language learning. PACIS 2006 Proceedings. 2006:65.
57. Kline RB. Principles and practice of structural equation modeling: Guilford publications; 2015.
58. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal. 1999;6(1):1-55. [DOI:10.1080/10705519909540118]
59. Tang K-Y. Investigating factors affecting the acceptance of self-service technology in libraries. Library Hi Tech. 2015;33(1):114-33. [DOI:10.1108/LHT-09-2014-0087]
60. Taylor S, Todd PA. Understanding information technology usage: A test of competing models. Information systems research. 1995;6(2):144-76. [DOI:10.1287/isre.6.2.144]
61. Koufaris M. Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research. 2002;13(2):205-23. [DOI:10.1287/isre.13.2.205.83]
62. Lee W, Xiong L, Hu C. The effect of Facebook users' arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management. 2012;31(3):819-27. [DOI:10.1016/j.ijhm.2011.09.018]
63. Lee KC, Chung N. Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean's model perspective. Interacting with computers. 2009;21(5-6):385-92. [DOI:10.1016/j.intcom.2009.06.004]
64. Park E, Del Pobil AP. Modeling the user acceptance of long-term evolution (LTE) services. annals of telecommunications-annales des télécommunications. 2013;68(5-6):307-15.
65. Jeong HI, Kim Y. The acceptance of computer technology by teachers in early childhood education. Interactive Learning Environments. 2016:1-17.
66. McFarland DJ. The Role of Age and Efficacy on Technology Acceptance: Implications for E-Learning. 2001.
67. Lopez DA, Manson DP. A study of individual computer self-efficacy and perceived usefulness of the empowered desktop information system. 1997.
68. Rezaei M, Mohammadi HM, Asadi A, Kalantary K. Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education. 2008;9(1).
69. Liaw S-S, Chang W-C, Hung W-H, Huang H-M. Attitudes toward search engines as a learning assisted tool: approach of Liaw and Huang's research model. Computers in Human Behavior. 2006;22(2):177-90. [DOI:10.1016/j.chb.2004.09.003]
70. ahmadi dehghotbi m, moshkani m, mohammad khani A. The Impact of Computer Self-efficacy and Anxiety on the Structures of Davis' TAM. The New Prospects of Social Psychology. 2010;13(1):51- 71.
71. Teo T. Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education. 2009;52(2):302-12. [DOI:10.1016/j.compedu.2008.08.006]
72. Sun H, Zhang P. Applying markus and Robey's causal structure to examine user technology acceptance research: a new approach. JITTA: Journal of Information Technology Theory and Application. 2006;8(2):21.
73. Olmos Miguelá-ez S, García-Pe-alvo FJ, Sánchez Prieto JC. Informal Tools in Formal Contexts: Development of a Model to Assess the Acceptance of Mobile Technologies among Teachers. 2016.
74. Al-Debei MM. The quality and acceptance of websites: an empirical investigation in the context of higher education. International Journal of Business Information Systems. 2014;15(2):170-88. [DOI:10.1504/IJBIS.2014.059252]
75. Adetimirin A. An Empirical Study of Online Discussion Forums by Library and Information Science Postgraduate Students using Technology Acceptance Model 3. Journal of Information Technology Education: Research. 2015;14:257-69. [DOI:10.28945/2269]
76. Ifinedo P. Acceptance and continuance intention of web-based learning technologies (WLT) use among university students in a Baltic country. The Electronic Journal of Information Systems in Developing Countries. 2006;23. [DOI:10.1002/j.1681-4835.2006.tb00151.x]
77. Hadji B, Degoulet P. Information system end-user satisfaction and continuance intention: A unified modeling approach. Journal of biomedical informatics. 2016;61:185-93. [DOI:10.1016/j.jbi.2016.03.021] [PMID]
78. Kang YS, Lee H. Understanding the role of an IT artifact in online service continuance: An extended perspective of user satisfaction. Computers in Human Behavior. 2010;26(3):353-64. [DOI:10.1016/j.chb.2009.11.006]
79. Mohammadi H. Investigating users' perspectives on e-learning: an integration of TAM and IS success model. Computers in Human Behavior. 2015;45:359-74. [DOI:10.1016/j.chb.2014.07.044]
80. Ofori KS, Larbi-Siaw O, Fianu E, Gladjah RE, Boateng EOY. Factors Influencing the Continuance Use of Mobile Social Media: The Effect of Privacy Concerns. Journal of Cyber Security.4:105-24.
81. Lee H, Kim J, Kim J. Determinants of success for application service provider: An empirical test in small businesses. International Journal of Human-Computer Studies. 2007;65(9):796-815. [DOI:10.1016/j.ijhcs.2007.04.004]
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