Abstract
Due to the privacy risk associated with using location-based services (LBS), users are often reluctant to adopt and use them. Drawing on the justice theory, this research identified the factors affecting continuous usage of LBS. Perceived justice reflects a set of fairness perceptions and involves three dimensions: distributive justice, procedural justice and interactional justice, which reflect outcome fairness, process fairness and treatment fairness, respectively. We conducted data analysis with structural equation modeling (SEM). The results show that procedural justice is the main factor affecting privacy risk, whereas distributive justice is the main factor affecting perceived usefulness. Privacy risk and perceived usefulness influence continuous usage. Thus mobile service providers need to improve users’ perceived justice to facilitate their usage of LBS.
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References
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.
Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS Quarterly, 33(2), 339–370.
Bansal, G., Zahedi, F. M., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.
Bauer, H. H., Barnes, S. J., Reichardt, T., & Neumann, M. M. (2005). Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research, 6(3), 181–192.
Chandra, S., Srivastava, S. C., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems, 27, 561–588.
CNNIC (2010). 26th Statistical Survey Report on the Internet Development in China, China Internet Network Information Center. http://www.cnnic.cn
Cocosila, M., Archer, N., & Yuan, Y. (2009). Early investigation of new information technology acceptance: A perceived risk-motivation model. Communications of the AIS, 25, 340–358.
Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001). Justice at the millennium: A meta-analytic review of 25 years of organizational justice research. Journal of Applied Psychology, 86(3), 425–445.
Dai, H., & Palvia, P. C. (2009). Mobile commerce adoption in China and the United States: A cross-cultural study. The DATA BASE for Advances in Information Systems, 40(4), 43–61.
Dai, H., Singh, R., Iyer, L. S. (2007). An investigation of consumer’s security and privacy perceptions in mobile commerce. American Conference on Information Systems
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
DeConinck, J. B. (2010). The effect of organizational justice, perceived organizational support, and perceived supervisor support on marketing employees’ level of trust. Journal of Business Research, 63(12), 1349–1355.
del Rio-Lanza, A. B., Vazquez-Casielles, R., & Diaz-Martin, A. M. (2009). Satisfaction with service recovery: Perceived justice and emotional responses. Journal of Business Research, 62(8), 775–781.
Dinev, T., Hart, P., & Mullen, M. R. (2008). Internet privacy concerns and beliefs about government surveillance—an empirical investigation. The Journal of Strategic Information Systems, 17(3), 214–233.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. MA: Addison-Wesley Reading.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–70.
Ha, J., & Jang, S. (2009). Perceived justice in service recovery and behavioral intentions: The role of relationship quality. International Journal of Hospitality Management, 28(3), 319–327.
Hu, X., Wu, G., Wu, Y., & Zhang, H. (2010). The effects of Web assurance seals on consumers’ initial trust in an online vendor: A functional perspective. Decision Support Systems, 48(2), 407–418.
iResearch (2010). A research report on China mobile commerce market
Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057.
Junglas, I. A., Johnson, N. A., & Spitzmuller, C. (2008). Personality traits and concern for privacy: An empirical study in the context of location-based services. European Journal of Information Systems, 17(4), 387–402.
Junglas, I. A., & Watson, R. T. (2008). Location-based services. Communications of the ACM, 51(3), 65–69.
Kim, T., Kim, W. G., & Kim, H.-B. (2009). The effects of perceived justice on recovery satisfaction, trust, word-of-mouth, and revisit intention in upscale hotels. Tourism Management, 30(1), 51–62.
Kofod-Petersen, A., Gransaether, P. A., & Krogstie, J. (2010). An empirical investigation of attitude towards location-aware social network service. International Journal of Mobile Communications, 8(1), 53–70.
Lee, T. (2005). The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. Journal of Electronic Commerce Research, 6(3), 165–180.
Liu, C., Marchewka, J. T., Lu, J., & Yu, C.-S. (2005). Beyond concern—a privacy-trust-behavioral intention model of electronic commerce. Information Management, 42(2), 289–304.
Liu, C. T., Guo, Y. M., & Lee, C. H. (2011). The effects of relationship quality and switching barriers on customer loyalty. International Journal of Information Management, 31(1), 71–79.
Lu, H.-P., & Su, P. Y.-J. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442–458.
Lu, Y., Deng, Z., & Wang, B. (2010). Exploring factors affecting Chinese consumers’ usage of short message service for personal communication. Information Systems Journal, 20(2), 183–208.
Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234.
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common method variance in IS research: A comparison of alternative approaches and a reanalysis of past research. Management Science, 52(12), 1865–1883.
Mallat, N., Rossi, M., Tuunainen, V. K., & Oorni, A. (2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information Management, 46(3), 190–195.
Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
Pan, Y., & Zinkhan, G. M. (2006). Exploring the impact of online privacy disclosures on consumer trust. Journal of Retailing, 82(4), 331–338.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12(4), 531–544.
Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509–538.
Rao, B., & Minakakis, L. (2003). Evolution of mobile location-based services. Communications of the ACM, 46(12), 61–65.
Sheng, H., Nah, F. F.-H., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the AIS, 9(6), 344–376.
Shin, D.-H. (2010). Ubiquitous computing acceptance model: End user concern about security, privacy and risk. International Journal of Mobile Communications, 8(2), 169–186.
Shin, Y. M., Lee, S. C., Shin, B., & Lee, H. G. (2010). Examining influencing factors of post-adoption usage of mobile internet: Focus on the user perception of supplier-side attributes. Information Systems Frontier, 12(5), 595–606.
Slyke, C. V., Shim, J. T., Johnson, R., & Jiang, J. (2006). Concern for information privacy and online consumer purchasing. Journal of the AIS, 7(6), 415–444.
Son, J.-Y., & Kim, S. S. (2008). Internet users’ information privacy-protective responses: A taxonomy and a nomological model. MIS Quarterly, 32(3), 503–529.
Straub, D., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the AIS, 13, 380–427.
Thatcher, J. B., McKnight, D. H., Baker, E. W., Arsal, R. E., & Roberts, N. H. (2011). The role of trust in post-adoption IT exploration: An empirical examination of knowledge management systems. IEEE Transactions on Engineering Management, 58(1), 56–70.
Tsai, J. Y., Egelman, S., Cranor, L., Acquisti, A. (2011). The effect of online privacy information on purchasing behavior: An experimental study. Information Systems Research. In press
Turel, O., Yuan, Y. F., & Connelly, C. E. (2008). In justice we trust: Predicting user acceptance of e-customer services. Journal of Management Information Systems, 24(4), 123–151.
Vail, M. W., Earp, J. B., & Anton, A. I. (2008). An empirical study of consumer perceptions and comprehension of web site privacy policies. IEEE Transactions on Engineering Management, 55(3), 442–454.
Wang, Y.-S., Wu, S.-C., Lin, H.-H., Wang, Y.-Y. (2011). The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing. International Journal of Information Management, In Press, Corrected Proof
Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. Electronic Markets, 19(2–3), 137–149.
Xu, H., Oh, L.-B., & Teo, H.-H. (2009). Perceived effectiveness of text vs. multimedia location-based advertising messaging. International Journal of Mobile Communications, 7(2), 133–153.
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. (2009). The role of push–pull technology in privacy calculus: The case of location-based services. Journal of Management Information Systems, 26(3), 135–173.
Zhang, D., Adipat, B., & Mowafi, Y. (2009). User-centered context-aware mobile applications-the next generation of personal mobile computing. Communications of the AIS, 24, 27–46.
Acknowledgement
This work was partially supported by a grant from the National Natural Science Foundation of China (71001030), and a grant from the Zhejiang Provincial Natural Science Foundation (Y7100057).
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Appendix A: Measurement items and their sources
Appendix A: Measurement items and their sources
Distributive justice (DJ) (adapted from Son and Kim (2008))
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DJ1:
The service level of this service provider is superior to that of the service providers that do not use my personal information.
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DJ2:
This mobile service provider presents better values than the service providers that do not have my personal information.
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DJ3:
What I give up in terms of disclosing my personal information to the service provider is commensurate with what I acquire from it.
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DJ4:
Given the potential problems derived from disclosing my personal information to the service provider, the benefits I acquire are fair.
Procedural justice (PJ) (adapted from Son and Kim (2008))
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PJ1:
This service provider clearly states how users’ personal information was collected and used.
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PJ2:
This service provider gets users’ permission before collecting their sensitive personal information.
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PJ3:
This service provider allows users to update their personal information.
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PJ4:
This service provider takes measures to prevent the unauthorized access to users’ personal information.
Interactional justice (IJ) (adapted from Son and Kim (2008))
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IJ1:
This service provider tells the truth about collecting and using the personal information of their customers.
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IJ2:
This service provider is honest when collecting and using the personal information of their customers.
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IJ3:
This service provider keeps its promises about collecting and using the personal information of their customers.
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IJ4:
This service provider is trustworthy on collecting and using the personal information of their customers.
Privacy risk (PR) (adapted from Xu et al. (2009b))
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PR1:
Disclosing my personal information to this service provider may bring many unpredicted problems.
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PR2:
Disclosing my personal information to this service provider is risky.
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PR3:
Disclosing my personal information to this service provider may bring potential losses.
Perceived usefulness (PU) (adapted from Davis (1989))
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PU1:
LBS improve my living and working efficiency.
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PU2:
LBS improve my living and working effectiveness.
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PU3:
I feel that LBS are useful.
Continuous usage intention (USE) (adapted from Liu et al. (2005))
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USE1:
I will continue using LBS.
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USE2:
I will recommend LBS to other people.
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USE3:
I have positive comments on LBS.
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Zhou, T. Examining continuous usage of location-based services from the perspective of perceived justice. Inf Syst Front 15, 141–150 (2013). https://doi.org/10.1007/s10796-011-9311-3
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DOI: https://doi.org/10.1007/s10796-011-9311-3