Assessing the performance of long-term care information systems and the continued use intention of users

Telematics and Informatics

Volumn 32, Issue 2, 2015, Pages 273-281

  Chang, I Chiu ^a   Chang, Chia Hui ^a,b   Wu, Jen Wei ^a   Huang, Tony Cheng Kui ^a  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b Taichung General Hospital   (Taiwan)

Author keywords

Continued use intention; Long term care (LTC); Long term care information system (LTCIS); Performance evaluation

Indexed keywords

BIOMEDICAL ENGINEERING; INFORMATION SYSTEMS; POPULATION STATISTICS;

AGING POPULATION; CONTINUED USE; LONG TERM CARE; MEDICAL TECHNOLOGIES; PERFORMANCE EVALUATION; SYSTEM SATISFACTION; TASK TECHNOLOGY FIT; USER SATISFACTION;

INFORMATION USE;

EID: 84912571848     PISSN: 07365853     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tele.2014.08.006     Document Type: Conference Paper

References (25)

1. V.P. Aggelidis, and P.D. Chatzoglou Hospital information systems: measuring end user computing satisfaction (EUCS) J. Biomed. Inform. 45 3 2012 566 579
2. A. Bhattacherjee Understanding information systems continuance: an expectation-confirmation model MIS Quarterly 25 3 2001 351 370
3. A. Bhattacherjee, and N. Hikmet Physicians' resistance toward healthcare information technology: a theoretical model and empirical test Eur. J. Inf. Syst. 16 6 2007 725 737
4. C.C. Chang Examining users' intention to continue using social network games: a flow experience perspective Telematics Inform. 30 4 2013 311 321
5. W.H. DeLone, and E.R. McLean The DeLone and McLean model of information systems success: a ten-year update J. Manage. Inf. Syst. 19 4 2003 9 30
6. W.J. Doll, and G. Torkzadeh The measurement of end-user computing satisfaction MIS Quarterly 12 2 1988 259 274
7. O.F. El-Gayar, A.V. Deokar, and M.J. WillS Evaluating task-technology fit and user performance for an electronic health record system Int. J. Healthc. Technol. Manage. 11 1/2 2010 50 65
8. C. Fornell, and D.F. Larcker Structural equation models with unobservable variables and measurement error - algebra and statistics J. Mark. Res. 18 3 1981 382 388
9. D.L. Goodhue, and R.L. Thompson Task-technology fit and individual performance MIS Quarterly 19 2 1995 213 236
10. A. Hassanzadeh, F. Kanaani, and S. Elahi A model for measuring e-learning systems success in universities Expert Syst. Appl. 32 19 2012 10959 10966
11. I. Junglas, C. Abraham, and B. Ives Mobile technology at the frontlines of patient care: understanding fit and human drives in utilization decisions and performance Decis. Support Syst. 46 3 2009 634 647
12. S. Jarupathirun, and F.M. Zahedi Exploring the influence of perceptual factors in the success of web-based spatial DSS Decis. Support Syst. 43 3 2007 933 951
13. T.J. Larsen, A.M. Sorebo, and O. Sorebo The role of task-technology fit as users' motivation to continue information system use Comput. Hum. Behav. 25 3 2009 778 784
14. L. Lepanto, C. Sicotte, and P. Lehoux Assessing task-technology fit in a PACS upgrade: do users' and developers' appraisals converge? J. Digit. Imaging 24 6 2011 951 958
15. C.C. Liao, P. Palvia, and J.L. Chen Information technology adoption behavior life cycle: toward a technology continuance theory (TCT) Int. J. Inf. Manage. 29 4 2009 309 320
16. I.C. Li, S.L. Fann, and H.T. Kuo Predictors of the utilization of long-term care (LTC) services among residents in community-based LTC facilities in Taiwan Arch. Gerontol. Geriatr. 53 3 2011 303 308
17. W.S. Lin, and C.H. Wang Antecedences to continued intentions of adopting e-learning system in blended learning instruction: a contingency framework based on models of information system success and task-technology fit Comput. Educ. 58 1 2012 88 99
18. W.S. Lin Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives Int. J. Hum. Comput. Stud. 70 7 2012 498 507
19. J.C. Nunnally Psychometric Theory 1978 McGraw-Hill New York
20. R.L. Oliver A cognitive model of the antecedents and consequences of satisfaction decisions J. Mark. Res. 17 4 1980 460 469
21. E. Park, S. Baek, J. Ohm, and H.J. Chang Determinants of player acceptance of mobile social network games: an application of extended technology acceptance model Telematics Inform. 31 1 2014 3 15
22. Y.S. Wang Assessing e-commerce systems success: a respecification and validation of the DeLone and McLean model of IS success Inf. Syst. J. 18 5 2008 529 557
23. Wittenberg, R., Pickard, L., Malley, J., King, D., Comas-Herrera, A., Darton, R., 2008. Future demand for social care, 2005 to 2041: projections of demand for social care for older people in England: report to the Strategy Unit (Cabinet Office) and the Department of Health. PSSRU discussion paper, 2514. PSSRU, London, UK.
24. Waseda University International e-Government Ranking, 2013. Available at < http://www.e-gov.waseda.ac.jp/pdf/Press-Released-on-e-Gov-ranking-2013.pdf > (accessed 27.1.2014).
25. P. Yu, H.C. Li, and M.P. Gagnon Health IT acceptance factors in long-term care facilities: a cross-sectional survey Int. J. Med. Inform. 78 4 2009 219 229