Integrating technology readiness into the expectation-confirmation model: An empirical study of mobile services

Cyberpsychology, Behavior, and Social Networking

Volumn 16, Issue 8, 2013, Pages 604-612

  Chen, Shih Chih ^a,b   Liu, Ming Ling ^b   Lin, Chieh Peng ^b  

^a SOUTHERN TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ANTICIPATION; ARTICLE; ATTITUDE; FEMALE; HUMAN; MALE; MOBILE PHONE; PSYCHOLOGICAL MODEL; TECHNOLOGY; UTILIZATION REVIEW; YOUNG ADULT;

ADULT; ANTICIPATION, PSYCHOLOGICAL; ATTITUDE; CELLULAR PHONE; FEMALE; HUMANS; MALE; MODELS, PSYCHOLOGICAL; TECHNOLOGY; YOUNG ADULT;

EID: 84882624783     PISSN: 21522715     EISSN: 21522723     Source Type: Journal    
DOI: 10.1089/cyber.2012.0606     Document Type: Article

References (63)

1. Niculescu MF, Whang S. Codiffusion of wireless voice and data services: An empirical analysis of the Japanese mobile telecommunications market. Information Systems Research 2012; 23:260-79
2. Lopez-Nicolas C, Molina-Castillo FJ, Bouwman H. An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management 2008; 45:359-64
3. Bhattacherjee A. Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly 2001; 25:351-70 (Pubitemid 33738440)
4. Liao C, Chen JL, Yen DC. Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior 2007; 23:2804-22 (Pubitemid 46880140)
5. Kang YK, 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: 253-364
6. Recker J. Explaining usage of process modeling grammars: Comparing three theoretical models in the study of two grammars. Information & Management 2010; 47:316-24
7. Hung MC, Chang IC, Hwang HG. Exploring academic teachers' continuance toward the web-based learning system: The role of causal attributions. Computers & Education 2011; 57:1530-43
8. Kim B. Understanding antecedents of continuance intention in social-networking services. CyberPsychology, Behavior and Social Networking 2011; 14:199-205
9. Shin D, Shin Y, Choo H, Beom K. Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior 2011; 27:2207-14
10. Coursaris CK, Hassanein K, Head MM, Bontis N. The impact of distractions on the usability and intention to use mobile devices for wireless data services. Computers in Human Behavior 2012; 28:1439-49
11. Parasuraman A. Technology readiness index (TRI): A multipleitem scale to measure readiness to embrace new technologies. Journal of Service Research 2000; 2:307-20
12. Burke R. Technology and the customer interface: What consumers want in the physical and virtual store. Journal of Academy of Marketing Science 2002; 30:411-32 (Pubitemid 40815802)
13. Meuter ML, Ostrom AL, Bitner MJ, Roundtree R. The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research 2003; 56:899-907
14. Chen SC, Chen HH, Chen MF. Determinants of satisfaction and continuance intention towards self-service technologies. Industrial Management & Data Systems 2009; 109:1248-63
15. Picazo-Vela S, Chou SY, Meicher A, Pearson JM. Why provide an online review? An extended theory of planned behavior and the role of Big-Five personality traits. Computers in Human Behavior 2010; 26:685-96
16. Walczuch R, Lemmink J, Streukens S. The effect of service employees' technology readiness on technology acceptance. Information & Management 2007; 44:206-15
17. Limayem M, Cheung CMK. Understanding information systems continuance: The case of internet-based learning technologies. Information & Management 2008; 45:227-32 (Pubitemid 351615161)
18. Premkumar G, Bhattacherjee A. Explaining information technology usage: A test of competing models. Omega 2008; 36:64-75
19. Oliver RL. A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research 1980; 17:460-69
20. Hayashi A, Chen C, Ryan T, Wu J. The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education 2004; 15:139-54
21. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 1989; 13:319-40
22. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991; 50:179-211
23. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS Quarterly 2003; 27:425-78
24. Bhattacherjee A, Barfar A. Information technology continuance research: Current state and future directions. Asia Pacific Journal of Information Systems 2011; 21:1-18
25. Lin CH, Shih HY, Sher PJ. Integrating technology readiness into technology acceptance: The TRAM model. Psychology and Marketing 2007; 24:641-57
26. Lin JC, Hsieh PL. The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies. Computers in Human Behavior 2007; 23:1597-615
27. Parasuraman A, Colby CL. (2001) Techno-ready marketing: How and why your customers adopt technology. New York: Free Press
28. Yen HR. An attribute-based model of quality satisfaction for Internet self-service technology. The Service Industries Journal 2005; 25:641-59 (Pubitemid 40975188)
29. Massey AP, Khatri V, Montoya-Weiss MM. Usability of online services: The role of technology readiness and context. Decision Sciences 2007; 38:277-308
30. Liljander V, Gillberg F, Gummerus J, van Riel A. Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services 2006; 13:177-91 (Pubitemid 43137120)
31. Lin JC, Hsieh PL. The role of technology readiness in customers, perception and adoption of self-service technologies. International Journal of Service Industry Management 2006; 17:497-517
32. Thong JYL, Hong S, Tam KY. The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human- Computer Studies 2006; 64:799-810 (Pubitemid 43977890)
33. Lee MC. Explaining and predicting users' continuance intention toward e-learning: An extension of the Expectation Confirmation Model. Computers & Education 2010; 54:506-16
34. KimDJ, FerrinDL, RaoHR. Trust and satisfaction, two stepping stones for successful e-commerce relationships: A longitudinal exploration. Information Systems Research 2009; 20:237-57
35. Hung MC, Hwang HG, Hsieh TC. An exploratory study on the continuance of mobile commerce: An extended expectationconfirmation model of information system use. International Journal of Mobile Communications 2007; 5:409-22
36. Au N, Ngai EWT, Cheng TCE. Extending the understanding of end user information system satisfaction formation: An equitable needs fulfillment model approach. MIS Quarterly 2008; 32:43-66 (Pubitemid 351378749)
37. Hong SJ, Thong JYL, Tam KY. Understanding continued information technology usage behavior: A comparison of three models in the context of mobile Internet. Decision Support Systems 2006; 42:1819-34 (Pubitemid 44648452)
38. Bhattacherjee A, Perols J, Sanford C. Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems 2008; 49:17-26
39. Chen SC, Yen DC, Hwang MI. Factors influencing the continuance intention to the usage of Web 2.0: An empirical study. Computers in Human Behavior 2012; 28:933-41
40. Limayem M, Hirt SG, Cheung CMK. How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly 2007; 31:705-37 (Pubitemid 351153309)
41. Brown SA, Venkatesh V, Goyal S. Expectation confirmation in technology use. Information Systems Research 2012; 23:474-87
42. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: A comparison of two theoretical models. Management Science 1989; 35:982-1003
43. Tan M, Teo TSH. Factors influencing the adoption of Internet banking. Journal of the Association for Information Systems 2000; 1:1-42
44. Anderson JC, Gerbing DW. Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin 1988; 103:411-23
45. Little TD, Cunningham WA, Widaman KF, Shahar G. To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling 2002; 9:151-73
46. Nunnally JC, Bernstein IH. (1994) Psychometric theory (3rd ed.). New York: McGraw-Hill
47. Loo R, Thorpe K. Confirmatory factor analyses of the full and short versions of the Marlowe-Crowne Social Desirablity Scale. The Journal of Social Psychology 2000; 140:628-35
48. Schumacker RE, Lomax RG. (2004) A beginner's guide to structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates
49. Doll WJ, Xia W, Torkzadeh G. A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly 1994; 18:453-61
50. Hair JF, Black WC, Babin BJ, Anderson RE. (2010) Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River, NJ: Pearson Prentice Hall
51. Hu L, Bentler PM. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling 1999; 6:1-55
52. Bentler PM. (1989) EQS structural equations program manual. Los Angeles: BMDP Statistical Software
53. Bentler PM, Bonett DG. Significance tests and goodness-offit in the analysis of covariance structures. Psychological Bulletin 1980; 88:588-606
54. Bentler PM. Comparative fit indices in structural models. Psychological Bulletin 1990; 107:238-46
55. Henry JW, Stone RW. A structural equation model of enduser satisfaction with a computer-based medical information systems. Information Resources Management Journal 1994; 7:21-33
56. Fornell CD, Larcker F. Evaluating structural equation models with unobservable variables and measurement errors. Journal of Marketing Research 1981; 18:39-50
57. Hatcher L. (1994) A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: The SAS Institute
58. Podsakoff PM, Organ DW. Self-reports in organizational research: Problems and prospects. Journal of Management 1986; 12:531-44
59. Mossholder KW, Bennett N, Kemery ER, Wesolowski MA. Relationships between bases of power and work reactions: The mediational role of procedural justice. Journal of Management 1998; 24:533-52 (Pubitemid 128180016)
60. Sobel ME. Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology 1982; 13:290-312
61. MacKinnon DP, Fritz MS, Williams J, Lockwood CM. Distribution of the product confidence limits for the indirect effect: Program PRODCLIN. Behavior Research Methods 2007; 39:384-89
62. Chiu CM, Wang ETG. Understanding web-based learning continuance intention: The role of subjective task value. Information & Management 2008; 45:194-201 (Pubitemid 351491532)
63. Wang YS, Shih YW. Why do people use information kiosks? A validation of the unified theory of acceptance and use of technology. Government Information Quarterly 2009; 26: 158-65