Predicting Continuance-Findings From a Longitudinal Study of Older Adults Using an eHealth Newsletter

Health Communication

Volumn 29, Issue 9, 2014, Pages 937-946

  Forquer, Heather A ^a   Christensen, John L ^b   Tan, Andy S L ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF CONNECTICUT   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGED; ATTITUDE TO HEALTH; BEHAVIOR; FEMALE; HEALTH BEHAVIOR; HUMAN; INTERNET; LONGITUDINAL STUDY; MALE; MEDICAL INFORMATION; MIDDLE AGED; PROCEDURES; PUBLICATION; UNITED STATES; UTILIZATION;

AGED; FEMALE; HEALTH BEHAVIOR; HEALTH COMMUNICATION; HEALTH KNOWLEDGE, ATTITUDES, PRACTICE; HUMANS; INTENTION; INTERNET; LONGITUDINAL STUDIES; MALE; MIDDLE AGED; PERIODICALS AS TOPIC; UNITED STATES;

EID: 84901621538     PISSN: 10410236     EISSN: 15327027     Source Type: Journal    
DOI: 10.1080/10410236.2013.833580     Document Type: Article

References (47)

1. Bajaj, A. & Nidumolu, S. R. (1998). A feedback model to understand information system usage. Information & Management, 33, 213-224.
2. Bem, D. J. (1972). Self-perception theory. Advances in Experimental Social Psychology, 6, 1-62.
3. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351-370.
4. Bhattacherjee, A., & Barfar, A. (2011). Information technology continuance research: Current state and future directions. Asia Pacific Journal of Information Systems, 21(2), 1-18.
5. Bolton, R. N., & Drew, J. H. (1991). A longitudinal analysis of the impact of service changes on customer attitudes. Journal of Marketing, 55(1), 1-9.
6. Cline, R. J. W., & Haynes, K. M. (2001). Consumer health information seeking on the Internet: The state of the art. Health Education Research, 16, 671-692.
7. Cole, C., Laurent, G., Drolet, A., Ebert, J., Gutchess, A., Lambert-Pandraud, R., Peters, E. (2008). Decision making and brand choice by older consumers. Marketing Letters, 19, 355-365.
8. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.
9. Enders, C. K. (2001). The performance of the full information maximum likelihood estimator in multiple regression models with missing data. Educational and Psychological Measurement, 61, 713-740.
10. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York, NY: Psychology Press.
11. Fox, S. (2011a). Health topics. Internet & American Life Project. Retrieved from http://pewinternet.org/Reports/2011/HealthTopics.aspx
12. Fox, S. (2011b). The social life of health information. Internet & American Life Project. Retrieved from http://pewinternet.org/Reports/2011/Social-Life-of-Health-Info.aspx
13. Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24, 1-55.
14. Hornik, R. C., Mello, S., Forquer, H., Tan, A. S. L., Johnson, M., Rusko, J., & Schwartz, J. S. (2012, November). Results from a randomized controlled trial testing the effects of routine health information exposure on cancer prevention and screening behaviors. Paper presented at the 98th Annual Convention of the National Communication Association, Orlando, FL.
15. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
16. Jasperson, J. S., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization of post-adoptive behaviors associated with information technology enabled work systems. MIS Quarterly, 29, 525-557.
17. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23,183-213S.
18. Kim, B., & Oh, J. (2011). The difference of determinants of acceptance and continuance of mobile data services: A value perspective. Expert Systems with Applications, 38, 1798-1804.
19. Kim, D., & Chang, H. (2007). Key functional characteristics in designing and operating health information websites for user satisfaction: An application of the extended technology acceptance model. International Journal of Medical Informatics, 76, 790-800.
20. Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science, 51, 741-755.
21. Kline, R. B. (2010). Estimation. In Principles and practice of structural equation modeling (3rd ed., pp. 115-177). New York, NY: Guilford Press.
22. Legris, P., Ingham, J., & Collerette, P. (2003). Why do we people use information technology? A critical review of the technology acceptance model. Information & Management, 40, 191-204.
23. Lemire, M., Pare, G., Sicotte, C., & Harvey, C. (2008). Determinants of Internet use as a preferred source of information on personal health. International Journal of Medical Informatics, 77, 723-734.
24. Leslie, E., Marshall, A. L., Owen, N., & Bauman, A. (2005). Engagement and retention of participants in a physical activity website. Preventive Medicine, 40, 54-59.
25. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31, 705-737.
26. Luo, M. M., Chea, S., & Chen, J. S. (2011). Web-based information service adoption: A comparison of the motivational model and the uses and gratifications theory. Decision Support Systems, 51, 21-30.
27. Muthén, L. K., & Muthén, B. O. (1998-2012). Mplus user's guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
28. Newman, D. A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6, 328-362.
29. Or, C. K., & Karsh, B. T. (2009). A systematic review of patient acceptance of consumer health information technology. Journal of the American Medical Informatics Association, 16, 550-560.
30. Ouelette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124, 54-74.
31. Peters, E. (2010). Aging-related changes in decision making. In A. Drolet, N. Schwarz, & C. Yoon (Eds.), The aging consumer: Perspectives from psychology and economics (pp. 75-101). New York, NY: Psychology Press.
32. Robroek, S. J. W., Lindeboom, D. E. M., & Burdorf, A. (2012). Initial and sustained participation in an Internet-delivered long-term worksite health promotion program on physical activity and nutrition. Journal of Medical Internet Research, 14(2), e43.
33. Ryan, E. B., & Butler, R. N. (1996). Communication, aging, and health: Toward understanding health provider relationships with older clients. Health Communication, 8, 191-197.
34. Silvestre, A. L., Sue, V. M., & Allen, J. Y. (2009). If you build it, will they come? The Kaiser Permanente model of online health care. Health Affairs (Millwood), 28, 334-344.
35. Southwell, B. G. (2010). On the need for a life-span approach to health campaign evaluation. Health Communication, 25, 525-528.
36. Sparks, L. (2003). An introduction to cancer communication and aging: Theoretical and research insights. Health Communication, 15, 123-131.
37. Tojib, D., & Tsarenko, Y. (2012). Post-adoption modeling of advanced mobile service use. Journal of Business Research, 65, 922-928.
38. Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52, 463-479.
39. U.S. Census Bureau. (2010). DataFerrett, Current Population Survey, July. Retrieved from http://dataferrett.census.gov/
40. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: adoption determinants and emerging challenges. MIS Quarterly, 25, 71-102.
41. Venkatesh, V., Morris, M. G., & Ackerman, P. I. (2000). A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organizational Behavior and Human Decision Processes, 83, 33-60.
42. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
43. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36,157-178.
44. Verheijden, M. (2007). Rates and determinants of repeated participation in a Web-based behavior change program for healthy body weight and healthy lifestyle. Journal of Medical Internet Research, 9(1), e1.
45. Vorderer, P., Klimmt, C., & Ritterfeld, U. (2004). Enjoyment: At the heart of media entertainment. Communication Theory, 4, 388-408.
46. World Health Organization. (2012). eHealth. Retrieved from http://www.who.int/topics/ehealth/en
47. Yoon, C., Cole, C. A., & Lee, M. P. (2009). Consumer decision making and aging: Current knowledge and future directions. Journal of Consumer Psychology, 19, 2-16.