Structural analysis of value creation in software service platforms

Abstract

As software service platforms grow in number of users and variety of service offerings, it raises the question of how this phenomenon impacts the value obtained by users. This paper identifies system usability, service variety, and personal connectivity to be the major determinants that contribute to the value offered to users on mobile software service platforms. A structural equation model, which is based on utility theory, technology acceptance theory, and the theory of network externalities, has been constructed from seven observed constructs, reflecting the three determinants and the user value. The lower bound of user value is estimated through the user’s willingness-to-pay for services and the user’s willingness to spend time on using services. For the validation, a co-variance-based structural equation analysis has been conducted on online survey data of 210 users of mobile service platforms (e.g., Android, iOS). The results show that the number of services used and the number of active user connections were found to be the strongest constructs explaining user value. Perceived usefulness did not explain user value as much. In total, they can explain 49 % of the value that the user receives from the platform. The implication of this result is that users’ value from a software service platform cannot be explained by the technology acceptance model itself. Instead, an approach that, as used in this research, of integrating network externality theory, utility theory, and technology acceptance theory is necessary.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly, 16, 227–247.

    Article  Google Scholar 

  2. Altmann, J., Ion, M., & Bany Mohammed, A. (2007). Taxonomy of grid business models. GECON 2007, Workshop on Grid Economics and Business Models, Springer LNCS.

  3. Amit, R., & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22, 493–520.

    Article  Google Scholar 

  4. Appbrain (2015a). Google Play stats: number of available apps. [Retrieved September 2015 from http://www.appbrain.com/stats/].

  5. Appbrain (2015b). Google Play stats: free vs. paid Android apps. [Retrieved September 2015 from http://www.appbrain.com/stats/free-and-paid-android-applications].

  6. Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. The economic journal, 99(394), 116–131.

    Article  Google Scholar 

  7. Baek, S., Kim, K., & Altmann, J. (2014). Role of platform providers in service networks: the case of Salesforce.com AppExchange. CBI 2014, 16th IEEE Conference on Business Informatics.

  8. Bany Mohammed, A., Altmann, J., & Hwang, J. (2009). Cloud computing value chains: understanding business and value creation in the cloud. Economic Models and Algorithms for Distributed Systems, Springer Birkhäuser Autonomic Systems.

  9. Bevan, N., & Macleod, M. (1994). Usability measurement in context. Behaviour & Information Technology, 13(1–2), 132–145.

    Article  Google Scholar 

  10. Brooke, J. (1996). SUS: a “quick and dirty” usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & A. L. McClelland (Eds.), Usability evaluation in industry. London: Taylor and Francis.

    Google Scholar 

  11. Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16, 74–94.

    Article  Google Scholar 

  12. Calisir, F., Bayraktaroglu, A. E., Gumussoy, C. A., Topcu, Y. I., & Mutlu, T. (2010). The relative importance of usability and functionality factors for online auction and shopping web sites. Online Information Review, 34(3), 420–439.

    Article  Google Scholar 

  13. Coursey, D. L., Hovis, J. L., & Schulze, W. D. (1987). The disparity between willingness to accept and willingness to pay measures of value. The Quarterly Journal of Economics, 102(3), 679–690.

    Article  Google Scholar 

  14. Cusumano, M. A., & Gawer, A. (2002). The elements of platform leadership. MIT Sloan Management Review, 43(3), 51–58.

    Google Scholar 

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

    Article  Google Scholar 

  16. 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.

    Article  Google Scholar 

  17. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.

    Article  Google Scholar 

  18. Economides, N. (1996). The economics of networks. International journal of industrial organization, 14(6), 673–699.

    Article  Google Scholar 

  19. Faletski, I. (2012). Apple’s App Store: an economy for 1 percent of developers. [Retrieved September 2015 from http://www.cnet.com/news/apples-app-store-an-economy-for-1-percent-of-developers].

  20. Farrell, J., & Saloner, G. (1985). Standardization, compatibility, and innovation. The RAND Journal of Economics, 16, 70–83.

    Article  Google Scholar 

  21. Farrell, J., & Saloner, G. (1986). Installed base and compatibility: innovation, product preannouncements, and predation. The American economic review, 76, 940–955.

    Google Scholar 

  22. Forbes (2015). Apple iOS and Google Android smartphone market share flattening: IDC. [Retrieved September 2015 from http://www.forbes.com/sites/dougolenick/2015/05/27/apple-ios-and-google-android-smartphone-market-share-flattening-idc/].

  23. Gawer, A., & Cusumano, M. A. (2008). How companies become platform leaders. MIT Sloan Management Review, 49(2), 28–35.

    Google Scholar 

  24. Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. In: Communications of the Association for Information Systems.

  25. Gruber, T. (2008). Collective knowledge systems: where the social web meets the semantic web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(1), 4–13.

    Article  Google Scholar 

  26. Heckathorn, D. D. (2002). Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations. Social Problems, 49, 11–34.

    Article  Google Scholar 

  27. Haile, N., & Altmann, J. (2012). Value creation in IT service platforms through two-sided network effects. In: Proceedings of GECON2012, Intl. Conference on Economics of Grids, Clouds, Systems, and Services, 139–153. Springer.

  28. Haile, N., & Altmann, J. (2013). Estimating the value obtained from using a software service platform. In: Proceedings of GECON 2013, Intl. Conference on Economics of Grids, Clouds, Systems, and Services, 244–255. Springer.

  29. Haile, N., & Altmann, J. (2015). Value creation in software Service platforms. Future Generation Computer Systems, Elsevier. doi:10.1016/j.future.2015.09.029, 2015.

  30. Hong, W., Thong, J. Y., Wong, W. M., & Tam, K. Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. Management Information Systems, 18(3), 97–124.

    Google Scholar 

  31. Holbrook, M. B. (2006). Consumption experience, customer value, and subjective personal introspection: an illustrative photographic essay. Journal of Business Research, 59(6), 714–725.

    Article  Google Scholar 

  32. Hökby, S., & Söderqvist, T. (2003). Elasticities of demand and willingness to pay for environmental services in Sweden. Environmental and resource economics, 26(3), 361–383.

    Article  Google Scholar 

  33. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modelling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  34. Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47(4), 699–712.

    Article  Google Scholar 

  35. Jansen, S., Brinkkemper, S., & Finkelstein, A. (2009). Business network management as a survival strategy: a tale of two software ecosystems. In: Proceedings of the first International Workshop on Software Ecosystems

  36. Jansen, S., & Cusumano, M. (2013). Defining software ecosystems: a survey of software platforms and business network governance. Edward Elgar Pub, 13–28.

  37. Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American economic review, 75(3), 424–440.

    Google Scholar 

  38. Katz, M. L., & Shapiro, C. (1986). Technology adoption in the presence of network externalities. Journal of Political Economy, 94, 822–841.

    Article  Google Scholar 

  39. Katz, M. L., & Shapiro, C. (1994). Systems competition and network effects. Journal of Economic Perspectives, 8(2), 93–115.

    Article  Google Scholar 

  40. Kim, K., & Altmann, J. (2013). Evolution of the software-as-a-service innovation system through collective intelligence. International Journal of Cooperative Information Systems, 22(3).

  41. Kim, K., Altmann, J., & Hwang, J. (2010). Measuring and analyzing the openness of the Web2.0 service network for improving the innovation capacity of the Web2.0 system through collective intelligence. COLLIN 2010, Symposium on Collective Intelligence, Springer Advances in Intelligent and Soft Computing.

  42. Kim, K., Lee, W.-R., & Altmann, J. (2014). SNA-based innovation trend analysis in software service networks. Electronic Markets. doi:10.1007/s12525-014-0164-8.

    Google Scholar 

  43. Lee, S., Kim, T., Noh, Y., & Lee, B. (2010). Success factors of platform leadership in Web 2.0 service business. Service Business, 4(2), 89–103.

    Article  Google Scholar 

  44. Messerschmitt, D. G., & Szyperski, C. (2003). Software ecosystem: understanding an indispensable technology and industry. Cambridge: MIT Press.

    Google Scholar 

  45. Mitchell, R. C., & Carson, R. T. (1989). Using surveys to value public goods: the contingent valuation method. Hopkins University Press

  46. Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function: An interpretation of cross-section data. Franco Modigliani, 1.

  47. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222.

    Article  Google Scholar 

  48. von Neumann, J., & Morgenstern, O. (1953). Theory of games and economic behaviour. Princeton: Princeton University Press.

    Google Scholar 

  49. Oh, S., Ahn, J., & Kim, B. (2003). Adoption of broadband Internet in Korea: the role of experience in building attitudes. Journal of Information Technology, 18(4), 267–280.

    Article  Google Scholar 

  50. Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362–379.

    Article  Google Scholar 

  51. Post, G. V., & Kagan, A. (2007). Evaluating information security tradeoffs: Restricting access can interfere with user tasks. Computers & Security, 26(3), 229–237.

    Article  Google Scholar 

  52. Priem, R. L. (2007). A consumer perspective on value creation. Academy of Management Review, 32(1), 219–235.

    Article  Google Scholar 

  53. Rohlfs, J. (1974). A theory of interdependent demand for a communications service. The Bell Journal of Economics and Management Science, 16–37.

  54. Shogren, J. F., Shin, S. Y., Hayes, D. J., & Kliebenstein, J. B. (1994). Resolving differences in willingness to pay and willingness to accept. The American Economic Review, 255–270.

  55. Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Quarterly, 517–525.

  56. Smedlund, A. (2012). Value co-creation in service platform business models. Service Science, 4(1), 79–88.

    Article  Google Scholar 

  57. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125.

    Article  Google Scholar 

  58. Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: a reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.

    Article  Google Scholar 

  59. Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems, 12(1), 41–48.

    Article  Google Scholar 

  60. Venkatesh, V., & Davis, F. D. (1994). Modeling the determinants of perceived ease of use. In: Proceedings of ICIS, 213–227.

  61. Venkatesh, V. (2000). Determinants of perceived ease of use: integrating perceived behavioral control, computer anxiety and enjoyment into the technology acceptance model. Information Systems Research, 11(4), 342–365.

    Article  Google Scholar 

  62. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186–204.

    Article  Google Scholar 

  63. 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.

  64. Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

    Google Scholar 

  65. Wang, J., & Senecal, S. (2007). Measuring perceived website usability. Journal of Internet Commerce, 6(4), 97–112.

    Article  Google Scholar 

  66. Wejnert, C., & Heckathorn, D. D. (2008). Web-based network sampling: efficiency and efficacy of respondent-driven sampling for online research. Sociological Methods & Research, 37(1), 105–134.

    Article  Google Scholar 

  67. Wheaton, D. E. (1977). Assessing reliability and stability in panel models. Sociological Methodology.

  68. Zdnet (2015). iOS vs. Android. [Retrieved September 2015 from http://www.zdnet.com/article/ios-versus-android-apple-app-store-versus-google-play-here-comes-the-next-battle-in-the-app-wars/].

  69. Zeithaml, V. A., Parasuraman, A., & Berry, L. L. (1990). Delivering quality service: balancing customer perceptions and expectations. Collier Macmillan: Free Press.

    Google Scholar 

  70. Zhu, F., & Iansiti, M. (2012). Entry into platform-based markets. Strategic Management Journal, 33(1), 88–106.

    Article  Google Scholar 

Download references

Acknowledgments

This research was partly supported by the Korea Institute for Advancement of Technology (KIAT) within the ITEA 2 project 10014 EASI-CLOUDS and by the Research Grant for Foreign Professors through Seoul National University (SNU) in 2014.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jörn Altmann.

Additional information

Responsible Editor: Andreja Pucihar

Appendix 1: survey for mobile software service (apps) users

Appendix 1: survey for mobile software service (apps) users

Respondent Profile

  • Please specify your gender.

  • Please specify your age.

  • Please specify your occupation.

  • Please specify your income level during the past year (in US dollars).

  • When did you start using smartphones for the first time?

  • Which mobile service platform are you using?

System Usability

(PEOU) It is easy to find and use the apps you need among what is offered by your platform.

(PU) I find the apps offered on my platform useful.

Service Variety

(SI) How many apps do you have on your smartphone?

(SU) On average, how many apps do you use per day?

User Connectivity

(SC) How many connections (number of friends) in total do you have in your social media apps (e.g., Facebook, Google+, Twitter, LinkedIn, Skype)?

(AC) Among the above connections (friends), how many people did you communicate with during the last month?

User Value

(WTP1) On average, how much time per day do you spend using apps on your smartphone?

(WTP2) How much did you spend on average per month on usage for apps (e.g., for gaming, listening to music, watching movies)?

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Haile, N., Altmann, J. Structural analysis of value creation in software service platforms. Electron Markets 26, 129–142 (2016). https://doi.org/10.1007/s12525-015-0208-8

Download citation

Keywords

  • Software ecosystem
  • Network effects
  • TAM
  • Utility theory
  • Value creation
  • Mobile software service platforms

JEL Classification

  • C13
  • C42
  • C51
  • C88
  • D46
  • L86
  • M15
  • M21
  • O32