Information Systems Frontiers

, Volume 17, Issue 2, pp 413–422 | Cite as

Understanding user adoption of location-based services from a dual perspective of enablers and inhibitors

  • Tao ZhouEmail author


Location-based services (LBS) can present the personalized information and services to users based on their positions and contexts. This may improve users’ experience and bring a positive utility to them. However, their privacy concern may be aroused and perceived risk be increased because LBS need to utilize their location information. From a dual perspective of enablers and inhibitors, this research examined the factors affecting user adoption of LBS. Enablers include perceived usefulness and trust, whereas the inhibitor is privacy risk. The results indicate that contextual offering is the main factor affecting trust, whereas ubiquitous connection is the main factor affecting perceived usefulness. Privacy concern affects privacy risk. Trust has significant effects on perceived usefulness and privacy risk. And these three factors predict user adoption and usage behavior.


LBS Trust Privacy concern Perceived usefulness 



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).


  1. 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.CrossRefGoogle Scholar
  2. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.CrossRefGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. 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.CrossRefGoogle Scholar
  5. 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.CrossRefGoogle Scholar
  6. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25(3), 351–370.CrossRefGoogle Scholar
  7. Byrne, M. B. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLS: basic concepts, applications, and programming. NJ: Lawrence Erlbaum Associates, Publishers.Google Scholar
  8. 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.Google Scholar
  9. CNNIC (2012). 30th statistical survey report on the internet development in China, China Internet Network Information Center.Google Scholar
  10. Davis, F. D. (1989). Perceived usefulness, Perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.CrossRefGoogle Scholar
  11. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003.CrossRefGoogle Scholar
  12. Eastlick, M. A., Lotz, S. L., & Warrington, P. (2006). Understanding online B-to-C relationships: an integrated model of privacy concerns, trust, and commitment. Journal of Business Research, 59(8), 877–886.CrossRefGoogle Scholar
  13. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading: Addison-Wesley.Google Scholar
  14. Fogel, J., & Nehmad, E. (2009). Internet social network communities: risk taking, trust, and privacy concerns. Computers in Human Behavior, 25(1), 153–160.CrossRefGoogle Scholar
  15. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  16. Gefen, D. (2002). Customer loyalty in e-commerce. Journal of the Association for Information Systems, 3, 27–51.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., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90.Google Scholar
  19. 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.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Junglas, I. A., & Watson, R. T. (2008). Location-based services. Communications of the ACM, 51(3), 65–69.CrossRefGoogle Scholar
  22. Junglas, I., Abraham, C., & Watson, R. T. (2008). Task-technology fit for mobile locatable information systems. Decision Support Systems, 45(4), 1046–1057.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Kim, S. S., & Son, J.-Y. (2009). Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services. MIS Quarterly, 33(1), 49–70.Google Scholar
  25. Kim, H. W., Xu, Y., & Koh, J. (2004). A comparison of online trust building factors between potential customers and repeat customers. Journal of the Association for Information Systems, 5(10), 392–420.Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. Kim, G., Shin, B., & Lee, H. G. (2009). Understanding dynamics between initial trust and usage intentions of mobile banking. Information Systems Journal, 19(3), 283–311.CrossRefGoogle Scholar
  28. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310–322.CrossRefGoogle Scholar
  29. Kumar, N., Mohan, K., & Holowczak, R. (2008). Locking the door but leaving the computer vulnerable: factors inhibiting home users’ adoption of software firewalls. Decision Support Systems, 46(1), 254–264.CrossRefGoogle Scholar
  30. Kuo, Y.-F., & Yen, S.-N. (2009). Towards an understanding of the behavioral intention to use 3 G mobile value-added services. Computers in Human Behavior, 25(1), 103–110.CrossRefGoogle Scholar
  31. 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
  32. Lee, I., Choi, B., Kim, J., & Hong, S.-J. (2007). Culture-technology fit: effects of cultural characteristics on the post-adoption beliefs of mobile internet users. International Journal of Electronic Commerce, 11(4), 11–51.CrossRefGoogle Scholar
  33. Li, Y.-M., & Yeh, Y.-S. (2010). Increasing trust in mobile commerce through design aesthetics. Computers in Human Behavior, 26(4), 673–684.CrossRefGoogle Scholar
  34. Lu, H.-P., & Su, P. Y.-J. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442–458.CrossRefGoogle Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. Mallat, N. (2007). Exploring consumer adoption of mobile payments - A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432.CrossRefGoogle Scholar
  40. 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.CrossRefGoogle Scholar
  41. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.Google Scholar
  42. McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships: an interdisciplinary conceptual typology. International Journal of Electronic Commerce, 6(2), 35–59.Google Scholar
  43. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: an integrative typology. Information Systems Research, 13(3), 334–359.CrossRefGoogle Scholar
  44. Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.Google Scholar
  45. Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.CrossRefGoogle Scholar
  46. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.CrossRefGoogle Scholar
  47. Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509–538.CrossRefGoogle Scholar
  48. Schierz, P. G., Schilke, O., & Wirtz, B. W. (2010). Understanding consumer acceptance of mobile payment services: an empirical analysis. Electronic Commerce Research and Applications, 9(3), 209–216.CrossRefGoogle Scholar
  49. 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 Association for Information Systems, 9(6), 344–376.Google Scholar
  50. Shin, D. H. (2009). Understanding user acceptance of DMB in South Korea using the modified technology acceptance model. International Journal of Human Computer Interaction, 25(3), 173–198.CrossRefGoogle Scholar
  51. 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 Frontiers, 12(5), 595–606.CrossRefGoogle Scholar
  52. Slyke, C. V., Shim, J. T., Johnson, R., & Jiang, J. (2006). Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems, 7(6), 415–444.Google Scholar
  53. 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.Google Scholar
  54. 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
  55. 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.CrossRefGoogle Scholar
  56. Thong, J. Y. L., Hong, S.-J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human Computer Studies, 64(9), 799–810.CrossRefGoogle Scholar
  57. 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, 22(2), 254–268.CrossRefGoogle Scholar
  58. Vance, A., Christophe, E.-D.-C., & Straub, D. W. (2008). Examining trust in information technology artifacts: the effects of system quality and culture. Journal of Management Information Systems, 24(4), 73–100.CrossRefGoogle Scholar
  59. Varnali, K., & Toker, A. (2010). Mobile marketing research: the-state-of-the-art. International Journal of Information Management, 30(2), 144–151.CrossRefGoogle Scholar
  60. 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.CrossRefGoogle Scholar
  61. 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.CrossRefGoogle Scholar
  62. Xu, H., Luo, X., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: an exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42–52.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of ManagementHangzhou Dianzi UniversityHangzhouPeople’s Republic of China

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