The Role of Customer Retention in Business Outcomes of Online Service Providers

  • Behrang AssemiEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 345)


Despite great business outcomes that some service providers achieve on crowdsourcing marketplaces, many are unable to find customers, transact on a regular basis and survive. Previous research on the customer–provider relationship development in these marketplaces has shown the impact of provider profiles on customers’ decision-making and highlighted the role of prior relationship between the two parties as a major determinant of provider selection decisions of customers. However, little is known about the role of prior relationship and customer retention on the business outcomes of providers. This study theorizes a mediating impact versus a moderating impact of customer retention on the association between profile information and business outcomes of providers, building on the integrated information response model (IIRM). This study investigates two competing theoretical models using archival data from a leading crowdsourcing marketplace. The results show that a provider’s profile information is significantly associated with the provider’s business outcomes, while customer retention partially mediates this relationship for the self-provided information on the profile. Moreover, customer retention negatively moderates the positive impact of the customer-provided information on the provider’s business outcomes, implying the important role of new customers in achieving better business outcomes on crowdsourcing marketplaces.


Crowdsourcing marketplace Online service provider Business outcomes Customer retention Integrated information response model (IIRM) Partial least squares (PLS) 


  1. 1.
    Banker, R., Wattal, S., Hwang, I.: Determinants of firm survival in e-Markets: an analysis with software service providers. In: Hawaii International Conference on System Sciences (HICSS), Hawaii, USA (2011)Google Scholar
  2. 2.
    Alexa. Alexa Site Information (2016). Accessed 01 Feb 2016
  3. 3. Full Year Results 2014 (2015). Accessed 13 Jan 2016
  4. 4. About the Company (2016). Accessed 13 Jan 2016
  5. 5. About the Company (2015). Accessed 13 Jan 2016
  6. 6. Online Work Report: Global, 2014 Full Year Data (2015). Accessed 13 Jan 2016
  7. 7. About the Company (2015). Accessed 13 Jan 2016
  8. 8. About the Company (2018). Accessed 26 Feb 2018
  9. 9.
    Barrie, M.: Freelancer 1H17 Half Year Results (2017).
  10. 10. About the Company (2018). Accessed 26 Feb 2018
  11. 11. Elance Contractors (2013). Accessed 30 Jan 2013
  12. 12.
    Gandia, E.: Freelance Industry Report: Data and Analysis of Freelancer Demographics, Earnings, Habits and Attitudes. International Freelancers Academy (2011)Google Scholar
  13. 13. Elance Freelance Talent Report: A Look at the Demographics, Satisfaction Leveles and Expectations of Online Freelancers (2010). Accessed 16 Apr 2012
  14. 14.
    Banker, R., Hwang, I.: Importance of measures of past performance: empirical evidence an quality of e-service providers. Contemp. Account. Res. 25(2), 307–337 (2008)Google Scholar
  15. 15.
    Gefen, D., Carmel, E.: Is the world really flat? A look at offshoring in an online programming marketplace. MIS Q. 32(2), 367–384 (2008)Google Scholar
  16. 16.
    Gefen, D., Carmel, E.: Why the first provider takes it all: the consequences of a low trust culture on pricing and ratings in online sourcing markets. Eur. J. Inf. Syst. 22, 604–618 (2013)Google Scholar
  17. 17.
    Assemi, B., Schlagwein, D.: Profile information and business outcomes of providers in electronic service marketplaces: an empirical investigation. In: 23rd Australasian Conference on Information Systems. Deakin University, Geelong, Australia (2012)Google Scholar
  18. 18.
    Holthaus, C., Stock, R.M.: Facts vs. stories-assessment and conventional signals as predictors of freelancers’ performance in online labor markets. In: Proceedings of the 51st Hawaii International Conference on System Sciences, Hawaii, USA (2018)Google Scholar
  19. 19.
    Assemi, B., Schlagwein, D.: Provider feedback information and customer choice decisions on crowdsourcing marketplaces: evidence from two discrete choice experiments. Decis. Support Syst. 82, 1–11 (2016)Google Scholar
  20. 20.
    Hong, Y., Pavlou, P.A.: On buyer selection of service providers in online outsourcing platforms for IT services. Inf. Syst. Res. 28(3), 547–562 (2017)Google Scholar
  21. 21.
    Snir, E.M., Hitt, L.M.: Costly bidding in online markets for it services. Manage. Sci. 49(11), 1504–1520 (2003)Google Scholar
  22. 22.
    Zheng, A., Hong, Y., Pavlou, P.: Value uncertainty and buyer contracting: evidence from online labor markets. In: International Conference on Information Systems (ICIS), Fort Worth, Texas, USA (2015)Google Scholar
  23. 23.
    Hong, Y., Pavlou, P.: An empirical investigation on provider pricing in online crowdsourcing markets for IT services. In: Thirty Third International Conference on Information Systems (ICIS), Orlando, USA (2012)Google Scholar
  24. 24.
    Diller, H.: Customer loyalty: fata morgana or realistic goal? Managing relationships with customers. In: Hennig-Thurau, T., Hansen, U. (eds.) Relationship Marketing: Gaining Competitive Advantage Through Customer Satisfaction and Customer Retention, pp. 29–48. Springer, Heidelberg, Germany (2000). Scholar
  25. 25.
    Gefen, D.: Customer loyalty in e-commerce. J. Assoc. Inf. Syst. 3(1), 27–51 (2002)Google Scholar
  26. 26.
    Luarn, P., Lin, H.H.: A customer loyalty model for e-service context. J. Electron. Commer. Res. 4(4), 156–167 (2003)Google Scholar
  27. 27.
    Zahedi, F.M., Bansal, G., Ische, J.: Success factors in cooperative online marketplaces: trust as the social capital and value generator in vendors—exchange relationships. J. Organ. Comput. Electron. Commer. 20(4), 295–327 (2010)Google Scholar
  28. 28.
    Malone, T.W., Yates, J., Benjamin, R.I.: Electronic markets and electronic hierarchies. Commun. ACM 30(6), 484–497 (1987)Google Scholar
  29. 29.
    Kim, J.Y., Wulf, E.: Move to depth: buyer–provider interactions in online service marketplaces. e-Service J. 7(1), 2–14 (2010)Google Scholar
  30. 30.
    Kim, J.Y.: Online reverse auctions for outsourcing small software projects: determinants of vendor selection. e-Service J. 6(3), 40–55 (2009)Google Scholar
  31. 31.
    Smith, R.E., Swinyard, W.R.: Information response models: an integrated approach. J. Mark. 46(1), 81–93 (1982)Google Scholar
  32. 32.
    Jap, S.D.: Online reverse auctions: issues, themes, and prospects for the future. J. Acad. Mark. Sci. 30(4), 506–525 (2002)Google Scholar
  33. 33.
    Pavlou, P.A., Dimoka, A.: The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation. Inf. Syst. Res. 17(4), 392–414 (2006)Google Scholar
  34. 34.
    Gefen, D., Carmel, E.: Does reputation really signal potential success in online marketplaces, or is it only a trigger? In: Mediterranean Conference on Information Systems (MCIS), Tel Aviv, Israel (2010)Google Scholar
  35. 35.
    Radkevitch, U., van Heck, E., Koppius, O.: Portfolios of buyer-supplier exchange relationships in an online marketplace for IT services. Decis. Support Syst. 47(4), 297–306 (2009)Google Scholar
  36. 36.
    Kim, J.Y., Wulf, E.: Service encounters and relationships: buyer–supplier interactions in online service marketplaces. In: Americas Conference on Information Systems (AMCIS), San Francisco, California, USA (2009)Google Scholar
  37. 37.
    Holthaus, C., Stock, R.M.: Good signals, bad signals: performance and trait implications of signaling in online labor markets. In: International Conference on Information Systems (ICIS), Seoul, South Korea (2017)Google Scholar
  38. 38.
    Hu, X., Wu, G., Wu, Y., Zhang, H.: The effects of web assurance seals on consumers’ initial trust in an online vendor: a functional perspective. Decis. Support Syst. 48(2), 407–418 (2010)Google Scholar
  39. 39.
    Clemons, E.K., Wilson, J., Matt, C., Hess, T., Ren, F., Jin, F., Koh, N.S.: Global differences in online shopping behavior: understanding factors leading to trust. J. Manage. Inf. Syst. 33(4), 1117–1148 (2016)Google Scholar
  40. 40.
    Kim, D.J., Ferrin, D.L., Rao, H.R.: Trust and satisfaction, two stepping stones for successful e-commerce relationships: a longitudinal exploration. Inf. Syst. Res. 20(2), 237–257 (2009)Google Scholar
  41. 41.
    Gefen, D., Karahanna, E., Straub, D.W.: Trust and TAM in online shopping: an integrated model. MIS Q. 27(1), 51–90 (2003)Google Scholar
  42. 42.
    Guo, W., Straub, D., Han, X., Zhang, P.: Understanding vendor preference in the crowdsourcing marketplace: the influence of vendor-task fit and swift trust. J. Electron. Commer. Res. 18(1), 1–17 (2017)Google Scholar
  43. 43.
    Wang, J.-C., Chiang, M.-J.: Social interaction and continuance intention in online auctions: a social capital perspective. Decis. Support Syst. 47(4), 466–476 (2009)Google Scholar
  44. 44.
    Palvia, P.: The role of trust in e-commerce relational exchange: a unified model. Inf. Manag. 46(4), 213–220 (2009)Google Scholar
  45. 45.
    Gao, G., Greenwood, B.N., Agarwal, R., McCullough, J.S.: Vocal minority and silent majority: how do online ratings reflect population perceptions of quality? MIS Q. 39(3), 565–589 (2015)Google Scholar
  46. 46.
    Dellarocas, C.: Reputation mechanism design in online trading environments with pure moral hazard. Inf. Syst. Res. 16(2), 209–230 (2005)Google Scholar
  47. 47.
    Dellarocas, C.: The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manage. Sci. 49(10), 1407–1424 (2003)Google Scholar
  48. 48.
    Yen, C.H., Lu, H.P.: Factors influencing online auction repurchase intention. Internet Res. 18(1), 7–25 (2008)Google Scholar
  49. 49.
    Kim, M.S., Ahn, J.H.: Comparison of trust sources of an online market-maker in the e-marketplace: buyer’s and seller’s perspectives. J. Comput. Inf. Syst. 47(1), 84–94 (2006)Google Scholar
  50. 50.
    Chiou, J.S., Wu, L.Y., Sung, Y.P.: Buyer satisfaction and loyalty intention in online auctions: online auction web site versus online auction seller. J. Serv. Manage. 20(5), 521–543 (2009)Google Scholar
  51. 51.
    Srinivasan, S.S., Anderson, R., Ponnavolu, K.: Customer loyalty in e-commerce: an exploration of its antecedents and consequences. J. Retail. 78(1), 41–50 (2002)Google Scholar
  52. 52.
    Qi, H., Mao, J.: Facilitating transactions on a crowdsourcing platform: a cognitive frame perspective. In: International Conference on Information Systems (ICIS), Dublin, Ireland (2016)Google Scholar
  53. 53.
    Lu, B., Hirschheim, R.: Online sourcing: investigations from service clients’ perspective. In: Americas Conference on Information Systems (AMCIS), Detroit, Michigan, USA (2011)Google Scholar
  54. 54.
    Tomlinson-Keasey, C.: Opportunities and challenges posed by archival data sets. In: Funder, D.C., et al. (eds.) Studying Lives through Time: Personality and Development, pp. 65–92. American Psychological Association, Washington, DC, USA (1993)Google Scholar
  55. 55. Elance Online Employment Report, Quarter 3 2011 (2011). Accessed 19 July 2012
  56. 56.
    Misra, R.B.: Global IT outsourcing: metrics for success of all parties. J. Inf. Technol. Cases Appl. 6(3), 21–34 (2004)Google Scholar
  57. 57.
    Dibbern, J., Goles, T., Hirschheim, R., Jayatilaka, B.: Information systems outsourcing: a survey and analysis of the literature. Data Base Adv. Inf. Syst. 35(4), 6–98 (2004)Google Scholar
  58. 58.
    Shen, W.: Essays on Online Reviews: The Relationships between Reviewers, Reviews, and Product Sales, and the Temporal Patterns of Online Reviews (2008)Google Scholar
  59. 59.
    Hennig-Thurau, T., Hansen, U.: Relationship Marketing: Gaining Competitive Advantage through Customer Satisfaction and Customer Retention. Springer, Heidelberg, Germany (2000). Scholar
  60. 60.
    Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 295–336. Lawrence Erlbaum Associates, Mahwah, USA (1998)Google Scholar
  61. 61.
    Hair, J.F., Tomas, G., Hult, M., Ringle, C., Sarstedt, M.: A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd edn. Sage, Thousand Oaks, USA (2016)Google Scholar
  62. 62.
    Urbach, N., Ahlemann, F.: Structural equation modeling in information systems research using partial least squares. J. Inf. Technol. Theory Appl. 11(2), 5–40 (2010)Google Scholar
  63. 63.
    Haenlein, M., Kaplan, A.M.: A beginner’s guide to partial least squares analysis. Underst. Stat. 3(4), 283–297 (2004)Google Scholar
  64. 64.
    Hair, J.F., Black, W.C., Babin, B.J.: Multivariate Data Analysis: A Global Perspective. Pearson Education, Upper Saddle River, USA (2010)Google Scholar
  65. 65.
    Ringle, C.M., Wende, S., Becker, J.-M.: SmartPLS 3.0. SmartPLS GmbH (2015)Google Scholar
  66. 66.
    Chin, W.W., Marcolin, B.L., Newsted, P.R.: 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. Inf. Syst. Res. 14(2), 189–217 (2003)Google Scholar
  67. 67.
    Henseler, J., Fassott, G.: Testing moderating effects in PLS path models: an illustration of available procedures. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares, pp. 713–735. Springer, Heidelberg, Germany (2010). Scholar
  68. 68.
    Henseler, J., Chin, W.W.: A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct. Eqn. Model. Multi. J. 17(1), 82–109 (2010)MathSciNetGoogle Scholar
  69. 69.
    Neuman, W.L.: Social Research Methods: Qualitative and Quantitative Approach, 6th edn. Pearson Education, Boston, USA (2006)Google Scholar
  70. 70.
    Limayem, M., Hirt, S.G., Cheung, C.M.K.: How habit limits the predictive power of intention: the case of information systems continuance. MIS Q. 31(4), 705–737 (2007)Google Scholar
  71. 71.
    Conway, J., Lance, C.: What reviewers should expect from authors regarding common method bias in organizational research. J. Bus. Psychol. 25(3), 325–334 (2010)Google Scholar
  72. 72.
    Malhotra, N.K., Kim, S.S., Patil, A.: Common method variance in is research: a comparison of alternative approaches and a reanalysis of past research. Manage. Sci. 52(12), 1865–1883 (2006)Google Scholar
  73. 73.
    Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Lawrence Erlbaum Associates, Hillsdale, USA (1988)Google Scholar
  74. 74.
    Lasrado, L.A., Lugmayr, A.: Crowdfunding in Finland: a new alternative disruptive funding instrument for businesses. In: Proceedings of International Conference on Making Sense of Converging Media, pp. 194–201. ACM, Tampere, Finland (2013)Google Scholar
  75. 75.
    Zanatta, A.L., Machado, L.S., Pereira, G.B., Prikladnicki, R., Carmel, E.: Software crowdsourcing platforms. IEEE Softw. 33(6), 112–116 (2016)Google Scholar
  76. 76.
    Kanat, I.E., Hong, Y., Raghu, T.S.: Surviving in global online labor markets for IT services: a geo-economic analysis. Inf. Syst. Res. 29, 893 (2018)Google Scholar
  77. 77.
    Ghezzi, A., Gabelloni, D., Martini, A., Natalicchio, A.: Crowdsourcing: a review and suggestions for future research. Int. J. Manage. Rev. 20(2), 343–363 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.UNSW Business SchoolUniversity of New South Wales (UNSW) SydneySydneyAustralia
  2. 2.Science and Engineering FacultyQueensland University of Technology (QUT)BrisbaneAustralia

Personalised recommendations