臺灣博碩士論文加值系統

In recent years, gamification apps have attracted increasing attention, and more and more companies are engaged in their development. These apps have their own functional demands, but one thing they have in common is they are embedded with game elements.
Due to profitability potential, homogeneous gamification apps compete with one another in the market, causing users to switch to other homogenous gamification apps for various reasons and leading to the investments of providers and users becoming sunk costs. Although it is understood users may switch due to certain factors, there is limited research on the significant factors that ultimately affect users' switching behavior. To ascertain the reasons, this study conducts an empirical survey and develops a dual-driver SEM model to dissect the reasons for the user’s intention to switch the gamification app. This research model is based on the viewpoint of individual exterior power and interior power. A total of 142 valid online responses were received as our research samples. The structural equation model (SEM) was applied for the data analysis. The reasons for using the PLS-SEM statistical analysis model and the related reports required will be described in detail. In addition, the three highlights of the structural model that show robustness in PLS-SEM, nonlinear effects, endogeneity and unobserved heterogeneity will be checked in detail.
The results of data analysis show these exogenous variables can explain up to 46.8% of the variance of endogenous variables. We re-distributed the questionnaire in March 2023 to see if there were any differences in respondents' perceptions of the same research issue after a few years. In addition, in the research model, two new concepts, perceived enjoyment and privacy concerns, are added to the positive-driven and negative-driven mechanisms respectively, and the results of the statistical analysis are used to examine the causality of the entire structural model path to understand the effect of each construct on intention to switch other gamification apps.
This research result is expected to provide app designers, product manufacturers, and sales managers several constructive suggestions. In the view of designers’ concerns, quality optimization and diversified services are offered to increase the internal value of the product, hoping that in addition to effectively retaining existing customers, it can also become a competitive advantage to attract customers to use their switch products.

Contents
1. Introduction .......................................................................................................... 16
2. Literature Review................................................................................................. 19
2.1 Gamification .................................................................................................. 19
2.2 Attractive Alternatives ................................................................................... 22
2.3 Social influence .............................................................................................. 22
2.4 Procedural Switching Costs ........................................................................... 24
2.5 Satisfaction ..................................................................................................... 25
2.6 Habits ............................................................................................................. 28
2.7 Perceived Enjoyment ..................................................................................... 29
2.8 Privacy Concerns ........................................................................................... 30
2.9 Common Method Bias (Common Method Variance) .................................... 32
2.10 PLS-SEM ..................................................................................................... 33
2.10.1 Introduction and Differences between CB-SEM and PLS-SEM ...... 33
2.10.2 Statistical power ................................................................................ 35
2.10.3 Goodness-of-fit ................................................................................. 35
2.10.4 Principle of statistical calculation of PLS-SEM ............................... 35
2.10.5 When to use and how to deal with the results of PLS-SEM ............. 35
2.11 Structural Model Robustness Checks in PLS-SEM ..................................... 36
2.11.1 Nonlinear effects ............................................................................... 36
2.11.2 Endogeneity ...................................................................................... 37
2.12.3 Unobserved Heterogeneity................................................................ 37
3. Research Methodology ........................................................................................ 38
3.1 Research model and hypotheses .................................................................... 38
3.2 Evaluation of PLS-SEM modeling results ..................................................... 49
3.2.1 Assessing reflective measurement models .......................................... 49
3.2.2 Assessing Collinearity ......................................................................... 50
3.2.3 Assessing structure models ................................................................. 50
3.3 Common method bias (common method variance) ....................................... 52
3.4 Nonlinearity ................................................................................................... 54
3.5 Endogeneity ................................................................................................... 55
3.6 Unobserved Heterogeneity............................................................................. 55
3.7 Questionnaire development and study design................................................ 56
3.8 Sample and data collection ............................................................................ 57
4. Data Analysis and Results .................................................................................... 58
4.1Data analysis and results in 2017 .................................................................... 58
4.1.1 Demographic characteristics of the qualified respondents and brief
introduction of PLS-SEM analysis method ................................................. 58
4.1.2 Detecting CMV issues ........................................................................ 69
4.1.3Examine the measurement model ........................................................ 72
4.1.4Examine the structural model .............................................................. 77
4.1.5 Nonlinearity Effects ............................................................................ 82
4.1.6 Unobserved Heterogeneity.................................................................. 83
4.2 Data analysis and results in 2023_Original Questionnaire Scale .................. 84
4.2.1 Demographic characteristics of the qualified respondents ................. 85
4.2.2 Detecting CMV issues ........................................................................ 96
4.2.3 Examine the measurement model ..................................................... 100
4.2.4 Examine the structure model ............................................................ 104
4.2.5 Nonlinearity Effects .......................................................................... 109
4.2.6 Endogeneity ...................................................................................... 110
4.2.7 Unobserved Heterogeneity................................................................ 110
4.3 Data analysis and results in 2023_ Re-issue the questionnaire.................... 112
4.3.1 Demographic characteristics of the qualified respondents and brief
introduction of PLS-SEM analysis method ............................................... 112
4.3.2 Detect CMV issues ........................................................................... 124
4.3.3 Examine the measurement model ..................................................... 128
4.3.4 Examine the structure model ............................................................ 133
4.3.5 Nonlinearity effects ........................................................................... 139
4.3.6 Endogeneity ...................................................................................... 139
4.3.7 Unobserved Heterogeneity................................................................ 140
4.4 Data analysis and results in 2023_ Re-issue the questionnaire and add two
driving constructs (CLC) ................................................................................... 142
4.4.1 Demographic characteristics of the qualified respondents ............... 142
4.4.2 Detect CMV issues ........................................................................... 155
4.4.3 Examine the measurement model (CLC) .......................................... 162
4.4.4 Examine the structure model (CLC) ................................................. 168
4.4.5 Nonlinearity effects (CLC) ............................................................... 172
4.4.6 Endogeneity (CLC) ........................................................................... 173
4.4.7 Unobserved Heterogeneity (CLC) .................................................... 174
4.4.8 Examine the measurement model (ILC) ........................................... 176
4.4.9 Examine the structure model (ILC) .................................................. 181

4.4.10 Nonlinearity effects (ILC) ............................................................... 185
4.4.11 Endogeneity (ILC) .......................................................................... 186
4.4.12 Unobserved Heterogeneity (ILC) ................................................... 187
4.5 Results .......................................................................................................... 189
5. Implications and Conclusion.............................................................................. 193
5.1 Theoretical implications ............................................................................... 193
5.2 Practical implication .................................................................................... 194
5.3 Limitations and future research ................................................................... 196
5.4 Conclusion ................................................................................................... 197
References ................................................................................................................. 197
Appendix A. Item variables for each study construct (2017)............................... 212
Appendix B. Item variables for each study construct (2023) ............................... 213

Aarts, H., Verplanken, B., & Van Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of applied social psychology, 28(15), 1355-1374.
Abbas, H. A. (2013). Quality as Determinant Factor of Customer Satisfaction: Case Study of Zain-Kuwait, iBusiness, 5(03), 182-189.
Ahrholdt DC, Gudergan SP and Ringle CM (2019). Enhancing loyalty: when improving consumer satisfaction and delight matters. Journal of Business Research, 94, 18–27.
Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned action perspectives. Personality and Social Psychology Review, 6(2), 107–122.
Amoroso, D., & Lim, R. (2017). The mediating effects of habit on continuance intention. International Journal of Information Management, 37(6), 693-702.
Anderson, C.L. and Agarwal, R. (2011), The digitization of healthcare: boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469-490.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
APPLAUSE. (2017). Smartphone Owners Use At Least Nine Different Apps Every Day, APPLAUSE, https://www.applause.com/blog/daily-app-usage-app-annie.
Augusto de Matos, C., Luiz Henrique, J., & De Rosa, F. (2009). The different roles of switching costs on the satisfaction‐loyalty relationship. International Journal of Bank Marketing, 27(7), 506-523.
Aydin, G. (2018), Effect of demographics on use intention of gamified systems. International Journal of Technology and Human Interaction, 14(1), 1-21.
Aydin, S., Özer, G., & Arasil, Ö. (2005). Customer loyalty and the effect of switching costs as a moderator variable: A case in the Turkish mobile phone market. Marketing intelligence & planning, 23(1), 89-103.
Bagozzi, R. P., & Yi, Y. (1991). Multitrait-multimethod matrices in consumer research, Journal of consumer research, 17(4), 426-439.
Bansal, H. S., Irving, P. G., & Taylor, S. F. (2004). A three-component model of customer to service providers. Journal of the Academy of Marketing Science, 32(3), 234-250.
Bansal, H. S., Taylor, S. F., & St. James, Y. J. J. o. t. A. o. M. S. (2005). “Migrating” to new service providers: Toward a unifying framework of consumers’ switching behaviors. 33(1), 96-115.
Bargh, J. A., & Ferguson, M. J. (2000). Beyond behaviorism: On the automaticity of Higher mental process. Psychological Bulletin, 126(6), 925–945.
Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic organization, 6(3), 285-327.
Becker J-M, Rai A, Ringle CM, et al. (2013). Discovering unobserved heterogeneity in structural equation models to avert validity threats. MIS Quarterly, 37, 665–694.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606.
Bhattacherjee, A., Limayem, M., & Cheung, C. M. K. (2012). User switching of information technology: A theoretical synthesis and empirical test, Information & Management, 49(7-8), 327-333.
Blut, M., Evanschitzky, H., Backhaus, C., Rudd, J., & Marck, M. (2016). Securing business-to-business relationships: The impact of switching costs. Industrial Marketing Management, 52, 82-90.
Bölen, M. C. (2020). From traditional wristwatch to smartwatch: Understanding the relationship between innovation attributes, switching costs and consumers' switching intention. Technology in Society, 63, 101439.
Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers' assessments of service quality and value. Journal of consumer research, 17(4), 375-384.
Bonfield, E. H. (1974). Attitude, social influence, personal norm, and intention interactions as related to brand purchase behavior. Journal of marketing research, 11(4), 379-389.
Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: a typology, antecedents, and consequences, Journal of the Academy of marketing Science, 31(2), 109-126.
Calisir, F., & Calisir, F. (2004). The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems. Computers in Human Behavior, 20(4), 505-515.
Chaouali, W., Yahia, I. B., & Souiden, N. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers' intention to adopt Internet banking services: The case of an emerging country. Journal of Retailing and Consumer Services, 28, 209-218.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study, Information systems research, 14(2), 189-217.
Chin, W. W., Thatcher, J. B., Wright, R. T., & Steel, D. (2013). Controlling for common method variance in PLS analysis: The measured latent marker variable approach. New Springer Proceedings in Mathematics & Statistics, 56, 231-239.
Chou, S.-Y., Shen, G. C., Chiu, H.-C., & Chou, Y.-T. (2016). Multichannel service providers' strategy: Understanding customers' switching and free-riding behavior, Journal of Business Research, 69(6), 2226-2232.
Chuan, C. L., & Penyelidikan, J. (2006). Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. Journal Penyelidikan IPBL, 7(1), 78-86.
Churchill Jr, G. A., Ford, N. M., & Walker Jr, O. C. (1974). Measuring the job satisfaction of industrial salesmen. Journal of marketing research, 11(3), 254-260.
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Erlbaum, Hillsdale, NJ.
Cokins, G., Oncioiu, I., Türkeș, M. C., Topor, D. I., Căpuşneanu, S., Paștiu, C. A., Deliu, D., Solovăstru, A. N. (2020). Intention to use accounting platforms in Romania: A quantitative study on sustainability and social influence. Sustainability, 12(15), 6127.
Cronin, J., Brady, M., & Hult, G. (2000). Assessing the Effects of Quality, Value and Customer Satisfaction on Customer Behavioural Intents in Service Environment, Journal of Retailing, 76(2), 193-218.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.
Deterding, S., Björk, S. L., Nacke, L. E., Dixon, D., & Lawley, E. (2013). Designing gamification: creating gameful and playful experiences, In CHI'13 Extended Abstracts on Human Factors in Computing Systems, 3263-3266.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness: defining” gamification”. In Proceedings of the 15th international academic MindTrek conference on envisioning future media environments - MindTrek ’11, 9-15.
Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P. and Kaiser, S. (2012), “Guidelines for choosing between multi-item and single-item scales for construct measurement: a predictive validity perspective”, Journal of the Academy of Marketing Science, 40(3), 434-449.
Dienlin, T. and Trepte, S. (2015), Is the privacy paradox a relic of the past? An in ‐ depth analysis of privacy attitudes and privacy behaviors. European Journal of Social Psychology, 45(3), 285-297.
Diney, T. and Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80.
Doty, D. H., & Glick, W. H. (1998). Common methods bias: does common methods variance really bias results? Organizational research methods, 1(4), 374-406.
Drolet, A.L. and Morrison, D.G. (2001), “Do we really need multiple-item measures in service research?” Journal of Service Research, 3(3), 196-204.
Ebbes, P., Papies, D., & van Heerde, H. J. (2021). Dealing with endogeneity: A nontechnical guide for marketing researchers. In Handbook of market research (pp. 181-217): Springer.
Fan, L., & Suh, Y.-H. (2014). Why do users switch to a disruptive technology? An empirical study based on expectation-disconfirmation theory. Information & Management, 51(2), 240-248.
Fang, Y., Qureshi, I., Sun, H., McCole, P., Ramsey, E., & Lim, K. H. (2014). Trust, satisfaction, and online repurchase intention. MIS quarterly, 38(2), 407-409.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research, Reading, MA: Addison-Wesley.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fornell, C., & Larcker, D. F. J. J. o. m. r. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American customer satisfaction index: nature, purpose, and findings. Journal of marketing, 60(4), 7-18.
Fornell, C.G. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, 18(1), 39-50.
Francisco, A., Luis, F., González, J. L., & Isla, J. L. (2012). International Conference Proceeding Series (ICPS) , 17(1), 1-2.
Friedkin, N. E., & Johnsen, E. C. (1999). Social Influence Networks and Opinion Change, Advances in Group Processes, 16, 1-29.
Ganster, D. C., Hennessey, H. W., & Luthans, F. (1983). Social desirability response effects: Three alternative models. Academy of Management Journal, 26(2), 321–331.
Gardner, B. (2015a). A review and analysis of the use of “habit” in understanding, predicting and influencing health-related behaviour. Health Psychology Review, 9(3), 277 – 295.
Gefen, D., Straub, D., & Boudreau, M.-C. J. C. o. t. a. f. i. s. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of AIS, 4(7), 1-79.
Geisser, S. (1974), “A predictive approach to the random effects model”, Biometrika, 61(1), 101-107.
Giese, J. L., & Cote, J. A. (2000). Defining consumer satisfaction. Academy of marketing science review, 1(1), 1-22.
Gong, J., Said, F., Ting, H., Firdaus, A., Aksar, I. A., & Xu, J. (2022). Do privacy stress and brand trust still matter? implications on continuous online purchasing intention in China. Current Psychology, 1-13.
Gu, J., Xu, Y., Xu, H., Zhang, C. and Ling, H. (2017). Privacy concerns for mobile app download: anelaboration likelihood model perspective. Decision Support Systems, 94, 19-28
H.J. Smith, T. Dinev, H. Xu, Information privacy research: an interdisciplinary review, MIS Quarterly, 35 (4) (2011) 989–1015.
Hair JF, Hult GTM, Ringle CM, et al. (2017c). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45, 616–632.
Hair JF, Sarstedt M, Ringle CM, et al. (2018). Advanced Issues in Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage, Thousand Oaks, CA.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet, Journal of Marketing theory and Practice, 19(2), 139-152.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.
Hair, J., Joe F, Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I–method. European business review, 28(1), 63-76.
Hair, J.F., Hult, G.T.M., Ringle, C. and Sarstedt, M. (2017), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), International Journal of Research & Method in Education, 38(2), 220-221.
Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. and Thiele, K.O. (2017). Mirror, Mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45(5), 616-632.
Hallowell, Roger. (1996). The Relationship of Customer Satisfaction, Customer Loyalty, and Profitability: An Empirical Study. The International Journal of Service Industry Management, 7 (4), 27–42.
Hamari, J., & Koivisto, J. (2015). Why do people use gamification services? International Journal of Information Management, 35(4), 419-431.
Hassan, L. and Hamari, J. (2019). Gamification of e-participation: a literature review. Proceedings of the 52nd HI International Conference on System Sciences, University of HI, Manoa, 3077-3086.
Henseler, J. and Sarstedt, M. (2013), “Goodness-of- fit indices for partial least squares path modeling”, Computational Statistics, 28(2), 565-580.
Henseler, J., Hubona, G.S. and Ray, P.A. (2016), “Using PLS path modeling in new technology research: Updated guidelines”, Industrial Management and Data Systems, 116(1), 2-20.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, 43(1), 115-135.
Holmes, J. G. (1991). Trust and the appraisal process in close relationships. In Advances in Personal Relationships, W. Jones and D. Perlman, Eds., Jessica Kingsley, London, 57–104.
Hsiao, C.C., & Chiou, J.S. (2012). The effects of a player’s network centrality on resource accessibility, game enjoyment, and continuance intention: A study on online gaming communities. Electronic Commerce Research and Applications, 11(1), 75-84.
Hsieh, J.-K., Hsieh, Y.-C., Chiu, H.-C., & Feng, Y.-C. (2012). Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework. Computers in Human Behavior, 28(5), 1912-1920.
Hsu, C.-L., & Lu, H.-P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
Hu, L.-t., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological methods, 3(4), 424-453.
Huang, C.K., Chen, S.H., Tang, C.P., & Huang, H.Y. (2019a). A trade-off dual-factor model to investigate discontinuous intention of health app users: From the perspective of information disclosure, Journal of Biomedical Informatics, 100, 103302.
Huang, C.K., Chen, C.D., & Liu, Y.T. (2019b). To stay or not to stay? Discontinuance intention of gamification apps. Information Technology & People, 32(6), 1423-1445.
Huang, C.K., Lee, N. C.A., & Chen, W.C. (2022). Dilemmatic dual-factor determinants of discontinuous intention in cryptocurrency usage. Information Technology & People, 36(2), 564–594.
Huang, W., Wang, J., Jiang, J., & Tang, J. (2021). The role of procedural, financial and relational switching costs in the Chinese online hotel booking market: antecedents and consequences. Information Technology & Tourism, 23(3), 439-470.
Hult GTM, Hair JF, Proksch D, et al. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26, 1–21.
Huotari Kai & Hamari Juho (2011). “Gamification” from the perspective of service marketing, Proceedings of the CHI 2011 Gamification Workshop, Vancouver, British Columbia, Canada.
Huotari, K., & Hamari, J. (2012). Defining gamification: a service marketing perspective. Proceedings of the 16th International Academic MindTrek Conference (Tampere, Finland, 3-5 October), 17-22.
Huotari, K., and Hamari, J. (2017). A definition for gamification: anchoring gamification in the service marketing literature. Electronic Markets, 27, 21–31.
Ilgen, D. R. (1971). Satisfaction with performance as a function of the initial level of expected performance and the deviation from expectations. Organizational Behavior and Human Performance, 6(3), 345-361.
J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson, R.L. Tatham, Multivariate Data Analysis, sixth ed., Pearson Prentice Hall, Uppersaddle River, 2006.
James, W. 1890. The Principles of Psychology, Henry Holt & Co., New York.
Jia, L., Hall, D., & Sun, S. (2014). The effect of technology usage habits on consumers’ intention to continue use mobile payments, Proceedings of the Twentieth Americas Conference on Information Systems (AMCIS). Savannah, GA, USA.
Johnston, M. W., Parasuraman, A., Futrell, C. M., & Sager, J. (1988). Performance and job satisfaction effects on salesperson turnover: A replication and extension. Journal of Business Research, 16(1), 67-83.
Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2000). Switching barriers and repurchase intentions in services. Journal of retailing, 76(2), 259-274.
Jones, M. A., Reynolds, K. E., Mothersbaugh, D. L., & Beatty, S. E. (2007). The positive and negative effects of switching costs on relational outcomes. Journal of Service Research, 9(4), 335-355.
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
Kaplowitz, M.D., Hadlock, T.D. and Levine, R. (2004). A comparison of web and mail survey response rates. Public Opinion Quarterly, 68(1), 94-101.
Kara, A., & DeShields, O. (2004). Business student satisfaction, intentions and retention in higher education: An empirical investigation. Marketing Educator Quarterly, 3(1), 1-25.
Kayhan, V.O. and Davis, C.J. (2016), Situational privacy concerns and antecedent factors. Journal of Computer Information Systems, 56(3), 228-237,
Kim, G., Shin, B., Lee, H. G. J. I., & Management. (2006). A study of factors that affect user intentions toward email service switching, Information and Management, 43(7), 884-893.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration , 11(4), 1-10.
Koo, D.-M. (2009). The moderating role of locus of control on the links between experiential motives and intention to play online games. Computers in Human Behavior, 25(2), 466-474.
Korzaan, M. L., & Boswell, K. T. (2008). The influence of personality traits and information privacy concerns on behavioral intentions. Journal of Computer Information Systems, 48(4), 15-24.
Kuo, Y. F., Hu, T. L., & Yang, S. C. J. M. S. Q. A. I. J. (2013). Effects of inertia and satisfaction in female online shoppers on repeat‐purchase intention. Managing Service Quarterly, 23(3), 168-187.
LaBarbera, Priscilla A. and David Mazursky (1983). A Longitudinal Assessment of Consumer Satisfaction/ Dissatisfaction: The Dynamic Aspect of the Cognitive Process. Journal of Marketing Research, 20 (4), 393-404.
Lai, T. L. (2004). Service quality and perceived value's impact on satisfaction, intention and usage of short message service (SMS). Information Systems Frontiers, 6(4), 353-368.
Lally, P., van Jaarsveld, C. H. M., Potts, H. W. W., & Wardle, J. (2010). How are habits formed: Modelling habit formation in the real world. European Journal of Social Psychology, 40, 998 – 1009.
Lam, L. W., Chan, K. W., Fong, D., & Lo, F. (2011). Does the look matter? The impact of casino servicescape on gaming customer satisfaction, intention to revisit, and desire to stay. International Journal of Hospitality Management, 30(3), 558-567.
Lee, C.L., & Huang, M.K. (2014). A nonlinear relationship between the consumer satisfaction and the continued intention to use SNS services: The moderating role of switching cost. International Journal of Electronic Business Management, 12(2), 90-101.
Lee, G., & Kim, Y. J. S. O. (2022). Effects of Resistance Barriers to Service Robots on Alternative Attractiveness and Intention to Use. SAGE Open, 12(2), 21582440221099293.
Lee, J., Lee, J., & Feick, L. (2001). The impact of switching costs on the customer satisfaction‐loyalty link: mobile phone service in France. Journal of services marketing, 15(1), 35-48.
Lee, M. C. (2009). Understanding the behavioural intention to play online games: An extension of the theory of planned behaviour. Online Information Review, 33(5), 849-872.
Liane Nagengast, Heiner Evanschitzky, Markus Blut, & Thomas Rudolph (2014). New Insights in the Moderating Effect of Switching Costs on the Satisfaction–Repurchase Behavior Link. Journal of retailing, 90(3), 408-427.
Lai, L.H., Liu, C.T., Lin, J.T (2011). The moderating effects of switching costs and inertia on the customer satisfaction-retention link: auto liability insurance service in Taiwan. Insurance Markets and Companies: analyses and Actuarial Computations, 2(1), 69-78.
Limayem, M., & Hirt, S. G. (2003). Force of habit and information systems usage: Theory and initial validation. Journal of the Association for Information Systems, 4(1), 65-97.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS quarterly, 31(4), 705-737.
Limayem, M., Hirt, S. G., & Chin, W. W. (2001). Intention does not always matter: the contingent role of habit on IT usage behavior. ECIS 2001 proceedings, 56.
Lindell, M. K., & Whitney, D. J. J. J. o. a. p. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114-121.
Lu X and White H (2014). Robustness checks and robustness tests in applied economics. Journal of Econometrics, 178, 194–206.
Magal, S. R. (1991). A model for evaluating information center success. Journal of Management Information Systems, 8(1), 91-106.
Mannan, M., Mohiuddin, M. F., Chowdhury, N., & Sarker, P. (2017). Customer satisfaction, switching intentions, perceived switching costs, and perceived alternative attractiveness in Bangladesh mobile telecommunications market, South Asian Journal of Business Studies, 6(2), 142-160.
Markus Blut, Carly M. Frennea, Vikas Mittal, & David L. Mothersbaugh (2015). How procedural, financial and relational switching costs affect customer satisfaction, repurchase intentions, and repurchase behavior: A meta-analysis, International Journal of Research in Marketing, 32(9), 226-229.
Marsden, P. V., & Friedkin, N. E. (1993). Network studies of social influence. Sociological Methods & Research, 22(1), 127-151.
Mehran, J., & Olya, H. G. (2020). Canal boat tourism: Application of complexity theory. Journal of Retailing and Consumer Services, 53, 101954.
Mitchell, R., Schuster, L., and Jin, H. S. (2020). Gamification and the impact of extrinsic motivation on needs satisfaction: making work fun? Journal of Business Research, 106, 323–330.
Miura, A., & Yamashita, K. (2007). Psychological and Social Influences on Blog Writing: An Online Survey of Blog Authors in Japan. Journal of Computer-Mediated Communication, 12(4), 1452-1471.
Muylle, S., Moenaert, R., & Despontin, M. (2004). The conceptualization and empirical validation of web site user satisfaction. Information & management, 41(5), 543-560.
N.K. Malhotra, S.S. Kim, J. Agarwal (2004). Internet users’ information privacy concerns (IUIPC): The construct, the scale, and a causal model, Information Systems Research. 15 (4), 336–355.
Nagengast, L., Evanschitzky, H., Blut, M., & Rudolph, T. (2014). New insights in the moderating effect of switching costs on the satisfaction–repurchase behavior link. Journal of Retailing, 90(3), 408-427.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, 17(4), 460-469.
Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57(3), 25-48.
Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of consumer research, 14(4), 495-507.
Oliver, R., 1981. Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57 (3), 25–48
Oliver, R., 1999. Whence consumer loyalty? Journal of Marketing, 63, 33–44.
Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological bulletin, 124(1), 54-74.
Paul, R. K. J. I., New Delhi. (2006). Multicollinearity: Causes, effects and remedies, LASRI, New Delhi, 1(1), 58-65.
Paulhus, D. L. (1991). Measurement and control of response bias. Academic Press, 17-59.
Pavlou, P. A., Liang, H., & Xue, Y. J. M. q. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105-136.
Pham, T.-T. T., & Ho, J. C. J. T. i. s. (2015). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Socirty, 43, 159-172.
Ping Jr, R. A. J. J. o. r. (1993). The effects of satisfaction and structural constraints on retailer exiting, voice, loyalty, opportunism, and neglect. Journal of Retailing, 69(3), 320-352.
Podsakoff, Philip M & Organ, Dennis W (1986). Self-reports in organizational research: Problems and prospects. Journal of management, 12(4), 531-544.
Podsakoff, Philip M, MacKenzie, Scott b., & Lee, Jeong-Yeon (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
Profitability: An Empirical Study. The International Journal of Service Industry Management, 7 (4), 27–42
Raman, P. (2021). Examining the importance of gamification, social interaction and perceived enjoyment among young female online buyers in India. Young Consumers, 22(3), 387-412.
Rashotte, L. (2007). Social influence. In G. Ritzer (Ed.), The Blackwell encyclopedia of sociology, 6(1), 4426-4429.
Richardson, H. A., Simmering, M. J., & Sturman, M. C. J. O. R. M. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods , 12(4), 762-800.
Rigdon EE, Becker J-M and Sarstedt M (2019). Factor indeterminacy as metrological uncertainty: Implications for advancing psychological measurement. Multivariate Behavioral Research, 54(3), 429-443.
Rodrigues, L. F., Costa, C. J., & Oliveira, A. (2013). The adoption of gamification in e-banking. Proceedings of the 2013 international conference on information systems and design of communication, 47-55.
Rodrigues, L. F., Oliveira, A., & Costa, C. J. (2016). Playing seriously–How gamification and social cues influence bank customers to use gamified e-business applications. Computers in Human Behavior, 63, 392-407.
Ronis, D. L., Yates, J. F., & Kirscht, J. P. (1989). Attitudes, decisions, habits as determinants of repeated behavior. In: Pratkanis AR, Breckler SJ, Greenwald AG, eds. Attitude Structure and Function. Hillsdale, NJ: Erlbaum, 213–239.
Sarstedt M, Hair JF, Ringle CM, et al. (2016a). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69, 3998–4010.
Sarstedt, M. and Mooi, E.A. (2019), A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics (3rd ed.), Springer-Verlag Berlin Heidelberg.
Sarstedt, M., Ringle, C.M. and Hair, J.F. (2017a), “Partial least squares structural equation modeling”, in Homburg, C., Klarmann, M. and Vomberg, A. (Eds), Handbook of Market Research, Springer International Publishing, Cham, 1-47.
Sawang, S., Newton, C., & Jamieson, K. (2013). Increasing learners’ satisfaction/intention to adopt more e‐learning. Education And Training, 55(1), 83-105.
Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of Human-Computer Studies, 74, 14-31.
Sharma, S., Crossler, R. E. J. E. C. R., & Applications. (2014). Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electronic Commerce Research and Applications, 13(5), 305-319.
Shemwell, D. J., Yavas, U., & Bilgin, Z. J. I. j. o. s. i. m. (1998). Customer‐service provider relationships: an empirical test of a model of service quality, satisfaction and relationship‐oriented outcomes. International Journal of Service Industry Management, 9(2), 155-168.
Shen, A. X., Cheung, C. M., Lee, M. K., & Chen, H. (2011). How social influence affects we-intention to use instant messaging: The moderating effect of usage experience. Information Systems Frontiers, 13, 157-169.
Sigala, M. (2015). The application and impact of gamification funware on trip planning and experiences: the case of TripAdvisor’s funware. Electronic Markets, 25(3), 189–209.
Smith, H. J.; Milberg, S. J.; Burke, S. 1 (1996). Information Privacy: Measuring Individuals' Concerns About Organizational Practices. MIS Quarterly, 20(2), 167-196.
Spector, P. E. (1987). Method variance as an artifact in self-reported affect and perceptions at work: Myth or significant problem? Journal of applied psychology, 72(3), 438-443.
Spector, P. E. J. O. r. m. (2006). Method variance in organizational research: Truth or urban legend? Organizational Research Methods, 9(2), 221-232.
Stone, M. (1974), “Cross-validatory choice and assessment of statistical predictions”, Journal of the Royal Statistical Society, 36(2), 111-147.
Tang, J., Akram, U., & Shi, W. (2021). Why people need privacy? The role of privacy fatigue in app users' intention to disclose privacy: based on personality traits. Journal of Enterprise Information Management, 34(4), 1097-1120.
TechCrunch. (2017). Report: Smartphone owners are using 9 apps per day, 30 per month. https://techcrunch.com/2017/05/04/report-smartphone-owners-are-using-9-apps-per-day-30-per-month/
Ting, S.-C. (2014). The relationship between customers’ switching cost and repurchase intention: The moderating role of satisfaction. Journal of Service Science and Management, 7(4), 313-322.
Torkzadeh, G., Koufteros, X., & Pflughoeft, K. (2003). Confirmatory analysis of computer self-efficacy. Structural Equation Modeling, 10(2), 263-275.
Triandis, H. C. (1977). Interpersonal behavior. Monterey, CA: Brooks/Cole Pub. Co.
Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.
Vale, L., Silcock, J., & Rawles, J. J. B. (1997). An economic evaluation of thrombolysis in a remote rural community, BMJ, 314, 570-572.
Veenhoven, R. J. S. i. r. (1996). Developments in satisfaction-research. Social Indicators Research, 37(1), 1-46.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Verplanken, B. (2006). Beyond frequency: Habit as mental construct. British Journal of Social Psychology, 45, 639–656.
Verplanken, B., & Orbell, S. (2003). Reflection on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33(6), 1313–1330.
Verplanken, B., Aarts, H., & Van Knippenberg, A. (1997). Habit, information acquisition, and the process of making travel mode choices. European journal of social psychology, 27(5), 539-560.
Verplanken, B., Aarts, H., van Knippenberg, A., & Moonen, A. (1998). Habit versus planned behaviour: a field experiment. Br J Soc Psychol, 37(1), 111-128.
Vitória, P. D., Salgueiro, M. F., Silva, S. A., & De Vries, H. (2009). The impact of social influence on adolescent intention to smoke: Combining types and referents of influence. British Journal of Health Psychology, 14(4), 681-699.
Vitória, P. D., Salgueiro, M. F., Silva, S. A., & de Vries, H. (2011). Social influence, intention to smoke, and adolescent smoking behaviour longitudinal relations. British Journal of Health Psychology, 16(4), 779-798.
Waltz, C. F., Strickland, O. L., & Lenz, E. R. (1984). Measurement in nursing research. (2nd ed.), Philadelphia, PA: F.A. Davis co.
Wang, S. M., & Lin, J. C. C. (2011). The effect of social influence on bloggers' usage intention. Online Information Review, 35(1), 50-65.
Wang, Y.-S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1), 75-86.
Williams, L. J., & Hazer, J. T. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of applied psychology, 71(2), 219-231.
Williams, L. J., & Hazer, J. T. J. J. o. a. p. (1986). Antecedents and consequences of satisfaction and commitment in turnover models: A reanalysis using latent variable structural equation methods. Journal of Applied Psychology, 71(2), 219-223.
Williams, L. J., Brown, B. K. J. O. b., & processes, h. d. (1994). Method variance in organizational behavior and human resources research: Effects on correlations, path coefficients, and hypothesis testing. Organizational Behavior and Human Decision Processes, 57(2), 185-209.
Williams, L. J., Cote, J. A., & Buckley, M. R. (1989). Lack of method variance in self-reported affect and perceptions at work: reality or artifact? Journal of applied psychology, 74(3), 462-468.
Willys, N. (2018). Customer Satisfaction, Switching Costs and Customer Loyalty: An Empirical Study on the Mobile Telecommunication Service. American Journal of Industrial and Business Management, 8(4), 1022-1037.
Wold, H.O.A. (1982), “Soft modeling: the basic design and some extensions”, in Jöreskog, K.G. and Wold, H.O.A. (Eds), Systems under Indirect Observations: Part II, North-Holland, Amsterdam, 1-54.
Xu, H., Dinev, T., Smith, J., & Hart, P. J. J. o. t. A. f. I. S. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 798-824.
Xu, J., Benbasat, I. and Cenfetelli, R.T. (2014), The nature and consequences of trade-off transparency in the context of recommendation agents, MIS Quarterly, 38(2), 379-406.
Xu, Y., Chen, Z., Peng, M. Y.-P., & Anser, M. K. (2020). Enhancing consumer online purchase intention through gamification in China: Perspective of cognitive evaluation theory. Frontiers in Psychology, 11, 581200.
Yang, M., Jiang, J., Kiang, M., & Yuan, F. (2021). Re-examining the impact of multidimensional trust on patients’ online medical consultation service continuance decision. Information Systems Frontiers, 1-25.
Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & marketing, 21(10), 799-822.
Yen, Y.-X., Horng, D.-J. J. I. J. o. M., & Development, E. (2010). Effects of satisfaction, trust and alternative attractiveness on switching intentions in industrial customers. International Journal of Management and Enterprise Development, 8(1), 82-101.
Zhang, J., Luximon, Y., & Li, Q. (2022). Seeking medical advice in mobile applications: How social cue design and privacy concerns influence trust and behavioral intention in impersonal patient–physician interactions. Computers in Human Behavior, 130, 107178.
Zhang, K. Z., Cheung, C. M., & Lee, M. K. (2012). Online service switching behavior: the case of blog service providers. Journal of Electronic Commerce Research, 13(3), 184-197.
Zhang, K. Z., Cheung, C. M., & Lee, M. K. J. J. o. E. C. R. (2012). Online service switching behavior: The case of blog service providers. Journal of Electronic Commerce Research, 13(3), 184-197.