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An empirical examination of user adoption of location-based services

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Abstract

Location-based services (LBS) can present the optimal information and services to users based on their locations. This will improve their experience. However, this may also arouse users’ privacy concern and increase their perceived privacy risk. From both perspectives of flow experience and perceived risk, this research examined user adoption of LBS. We conducted data analysis with structural equation modeling. The results indicated that contextual offering affects trust and flow, whereas privacy concern affects trust and perceived risk. Trust, flow and perceived risk affect the usage intention. Among them, flow has a relatively larger effect.

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References

  1. Agarwal, R., & Karahanna, E. (2000). Time flies when you are having fun: cognitive absorption and beliefs about information technology usage. Management Information Systems Quarterly, 24(4), 665–694.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. Animesh, A., Pinsonneault, A., Yang, S. B., & Oh, W. (2011). An odyssey into virtual worlds: exploring the impacts of technological and spatial environments on intention to purchase virtual products. Management Information Systems Quarterly, 35(3), 789–810.

    Google Scholar 

  4. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. Belanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. Journal of Strategic Information Systems, 17(2), 165–176.

    Article  Google Scholar 

  7. Beldad, A., de Jong, M., & Steehouder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Computers in Human Behavior, 26(5), 857–869.

    Article  Google Scholar 

  8. Benbasat, I., Gefen, D., & Pavlou, P. A. (2008). Special issue: trust in online environments. Journal of Management Information Systems, 24(4), 5–11.

    Article  Google Scholar 

  9. CNNIC (2012). 29th statistical survey report on the Internet development in China. China Internet Network Information Center.

  10. CNNIC (2012). 30th statistical survey report on the Internet development in China. China Internet Network Information Center.

  11. Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (1988). Optimal experience: psychological studies of flow in consciousness. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  12. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  13. Eurich, M., Oertel, N., & Boutellier, R. (2010). The impact of perceived privacy risks on organizations’ willingness to share item-level event data across the supply chain. Electronic Commerce Research, 10(3–4), 423–440.

    Article  Google Scholar 

  14. Finneran, C. M., & Zhang, P. (2005). Flow in computer-mediated environments: promises and challenges. Communications of the Association for Information Systems, 15, 82–101.

    Google Scholar 

  15. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: an introduction to theory and research. Reading: Addison-Wesley.

    Google Scholar 

  16. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  17. 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.

    Google Scholar 

  18. Gefen, D., Benbasat, I., & Pavlou, P. A. (2008). A research agenda for trust in online environments. Journal of Management Information Systems, 24(4), 275–286.

    Article  Google Scholar 

  19. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. Management Information Systems Quarterly, 19(2), 213–236.

    Article  Google Scholar 

  20. Guo, Y. M., & Klein, B. D. (2009). Beyond the test of the four channel model of flow in the context of online shopping. Communications of the Association for Information Systems, 24(1), 837–856.

    Google Scholar 

  21. Guo, Y. M., & Poole, M. S. (2009). Antecedents of flow in online shopping: a test of alternative models. Information Systems Journal, 19(4), 369–390.

    Article  Google Scholar 

  22. Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under mobile broadband wireless access environment. Information & Management, 44(3), 276–286.

    Article  Google Scholar 

  23. Ho, L.-A., & Kuo, T.-H. (2010). How can one amplify the effect of e-learning? An examination of high-tech employees’ computer attitude and flow experience. Computers in Human Behavior, 26(1), 23–31.

    Article  Google Scholar 

  24. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: conceptual foundations. Journal of Marketing, 60(3), 50–68.

    Article  Google Scholar 

  25. Hoffman, D. L., & Novak, T. P. (2009). Flow online: lessons learned and future prospects. Journal of Interactive Marketing, 23(1), 23–34.

    Article  Google Scholar 

  26. Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: examining psychological flow and media content. Computers in Human Behavior, 25(1), 123–129.

    Article  Google Scholar 

  27. Junglas, I. A., & Watson, R. T. (2008). Location-based services. Communications of the ACM, 51(3), 65–69.

    Article  Google Scholar 

  28. Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057.

    Article  Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. Kim, J. B. (2012). An empirical study on consumer first purchase intention in online shopping: integrating initial trust and TAM. Electronic Commerce Research, 12(2), 125–150.

    Article  Google Scholar 

  31. Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544–564.

    Article  Google Scholar 

  32. 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.

    Article  Google Scholar 

  33. Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205–223.

    Article  Google Scholar 

  34. 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.

    Google Scholar 

  35. Lee, S. M., & Chen, L. Q. (2010). The impact of flow on online consumer behavior. The Journal of Computer Information Systems, 50(4), 1–10.

    Google Scholar 

  36. Lee, K. C., Kang, I. W., & McKnight, D. H. (2007). Transfer from offline trust to key online perceptions: an empirical study. IEEE Transactions on Engineering Management, 54(4), 729–741.

    Article  Google Scholar 

  37. Lin, H.-F. (2011). An empirical investigation of mobile banking adoption: the effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252–260.

    Article  Google Scholar 

  38. Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25(1), 29–39.

    Article  Google Scholar 

  39. 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.

    Article  Google Scholar 

  40. Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users’ information privacy concerns(IUIPC): the construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355.

    Article  Google Scholar 

  41. 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.

    Article  Google Scholar 

  42. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. The Academy of Management Review, 20(3), 709–734.

    Google Scholar 

  43. O’Cass, A., & Carlson, J. (2010). Examining the effects of website induced flow in professional sporting team websites. Internet Research, 20(2), 115–134.

    Article  Google Scholar 

  44. Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.

    Article  Google Scholar 

  45. Petrova, K., & Wang, B. (2011). Location-based services deployment and demand: a roadmap model. Electronic Commerce Research, 11(1), 5–29.

    Article  Google Scholar 

  46. Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509–538.

    Article  Google Scholar 

  47. Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: measuring individuals’ concerns about organizational practices. Management Information Systems Quarterly, 20(2), 167–196.

    Article  Google Scholar 

  48. Son, J.-Y., & Kim, S. S. (2008). Internet users’ information privacy-protective responses: a taxonomy and a nomological model. Management Information Systems Quarterly, 32(3), 503–529.

    Google Scholar 

  49. Straub, D., Boudreau, M.-C., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13, 380–427.

    Google Scholar 

  50. Taylor, D. G., Davis, D. F., & Jillapalli, R. (2009). Privacy concern and online personalization: the moderating effects of information control and compensation. Electronic Commerce Research, 9(3), 203–223.

    Article  Google Scholar 

  51. Valvi, A. C., & Fragkos, K. C. (2012). Critical review of the e-loyalty literature: a purchase-centred framework. Electronic Commerce Research, 12(3), 331–378.

    Article  Google Scholar 

  52. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. Management Information Systems Quarterly, 27(3), 425–478.

    Google Scholar 

  53. Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services. EM, 19(2–3), 137–149.

    Google Scholar 

  54. 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.

    Article  Google Scholar 

  55. 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.

    Article  Google Scholar 

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Acknowledgement

This work was partially supported by a grant from the National Natural Science Foundation of China (71001030), and a grant from Zhejiang Provincial Zhijiang Social Science Young Scholar Plan (G94).

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Authors and Affiliations

  1. School of Management, Hangzhou Dianzi University, Hangzhou, 310018, P.R. China

    Tao Zhou

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  1. Tao Zhou
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Correspondence to Tao Zhou.

Appendix: Measurement scales and items

Appendix: Measurement scales and items

Privacy concern (PC) :

(adapted from Son and Kim [48])

  1. PC1:

    I am concerned that the information I disclosed to this service provider may be misused.

  2. PC2:

    I am concerned that a person can find private information about me on the Internet.

  3. PC3:

    I am concerned about providing personal information to this service provider, because of what others might do with it.

  4. PC4:

    I am concerned about providing personal information to this service provider, because it could be used in a way I did not foresee.

Contextual offering (CO) :

(adapted from Lee [34])

  1. CO1:

    This service provider presents real-time information to me.

  2. CO2:

    This service provider presents specific location information to me.

  3. CO3:

    This service provider can present the optimal information and services to me based on my interests and location.

Perceived risk (RISK) :

(adapted from Xu et al. [55])

  1. RISK1:

    Providing this service provider with my personal information would involve many unexpected problems.

  2. RISK2:

    It would be risky to disclose my personal information to this service provider.

  3. RISK3:

    There would be high potential for loss in disclosing my personal information to this service provider.

Trust (TRU) :

(adapted from Pavlou and Gefen [44])

  1. TRU1:

    This service provider is trustworthy.

  2. TRU2:

    This service provider keeps its promise.

  3. TRU3:

    This service provider keeps customer interests in mind.

Flow (FLOW) :

(adapted from Lee et al. [36])

  1. FLOW1:

    When using this service, my attention is focused on the activity.

  2. FLOW2:

    When using this service, I feel in control.

  3. FLOW3:

    When using this service, I find a lot of pleasure.

Usage intention (USE) :

(adapted from Lee [34])

  1. USE1:

    Given the chance, I intend to use this service.

  2. USE2:

    I expect my use of this service to continue in the future.

  3. USE3:

    I have intention to use this service.

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Zhou, T. An empirical examination of user adoption of location-based services. Electron Commer Res 13, 25–39 (2013). https://doi.org/10.1007/s10660-013-9106-3

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  • DOI: https://doi.org/10.1007/s10660-013-9106-3

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