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Addressing the Review-Based Learning and Private Information Approaches to Foster Platform Continuance

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Abstract

Multi-sided platforms (MSPs) play the role as tech-enabled intermediaries that provide social networking sites to serve heterogeneous customer needs via complementary offerings, fostering direct and indirect connections between customers and third parties. However, the phenomenon of switching behavior in the post-adoption would likely destruct the success of platform business that depends on repeated customers and their continuance. Such a “use-to-goal-attainment gap” reveals the information about distinct driving forces of platform continuance. From the rational perspective, we take uses and gratifications (U&G) as the theoretical vehicle to examine the private information of personal experiences. We consider review-based learning as the adaptive approach to informational cascades. From the empirical surveys of 309 TripAdvisor (Taiwan) users, we found that the review-based learning approach plays a dual-role as the competing and supplemental driver of private information to foster platform continuance. Theoretical and managerial implications of platform continuance and business are discussed accordingly.

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Acknowledgments

The authors would like to thank the two anonymous referees for giving helpful comments on earlier versions of this paper.

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Appendices

Appendix A. Research constructs and measurement items

Process Gratification (PG) [source: self-developed].

After using the TripAdvisor platform, I feel ...

  • PG1 The platform provides useful searching functions as expected.

  • PG2 The platform provides easy-to-use searching functions as expected.

  • PG3 The platform provides satisfactory searching functions as expected.

  • PG4 The platform provides interactive searching functions as expected.

Content Gratification (CG) [source: self-developed].

After accessing the information content on the TripAdvisor platform, I feel ...

  • CG1 The platform provides real-time price/rating information of hotel accommodations as expected (dropped).

  • CG2 The platform provides route-map information around hotel accommodations as expected.

  • CG3 The platform provides urban geography information around hotel accommodations as expected.

Social Gratification (SG) [source: self-developed].

After communicating with other users/trippers on the TripAdvisor platform, I feel ...

  • SG1 The platform provides communication channels for sharing reviews that meet my expectations.

  • SG2 The platform provides social networks for disclosing personal preferences that meet my expectations.

  • SG3 The platform provides communication channels for sharing experiences with others that meet my expectations.

  • SG4 The platform provides social networks for seeking advice that meet my expectations.

Argument Quality (AQ) [source: Cheung et al. (2009)].

After observing the reviews on the TripAdvisor platform, I learn that ...

  • AQ1 Most review arguments in the platform are convincing.

  • AQ2 Most review arguments in the platform are strong.

  • AQ3 Most review arguments in the platform are persuasive.

  • AQ4 Most review arguments in the platform have credible quality.

Review Sidedness (RS) [source: Cheung et al. (2009)].

After observing the reviews on the TripAdvisor platform, I learn that ...

  • RS1 Reviews in the platform include both pros and cons in the comments (dropped).

  • RS2 Reviews in the platform include only one-sided comments (either positive or negative).

  • RS3 Reviews in the platform often include personal bias in the comments.

Continuance Intention (CI) [source: self-developed].

  • CI1 I will spend more time on the continued use of the TripAdvisor platform.

  • CI2 I will visit more frequently on the continued use of the TripAdvisor platform.

Appendix B. Tests of common method variance

We followed the steps as recommended by Podsakoff et al. (2003) to avoid the effects of common method variance (CMV) in the questionnaire survey. Moreover, our measurements were measured in terms of either validated or previous scales for achieving the scale validity (Sharma et al. 2009). We also examined CMV using a post-hoc method to test the self-reported data. First, the test of inter-construct correlations (Table 2) indicates that the highest correlation between the research constructs (0.609 for CG-PG) is far below the threshold of 0.90 (Bagozzi et al. 1991), suggesting that the effect of CMV can be ignored. Second, we applied the partial correlation approach to test CMV (Lindell and Whitney 2001). The correlations between non-partial out variables did not cause a significant inflation, suggesting that CMV is not a major concern in this study. In sum, the effect of CMV is not crucial to the survey.

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Shih, HP., Sung, PC. Addressing the Review-Based Learning and Private Information Approaches to Foster Platform Continuance. Inf Syst Front 23, 649–661 (2021). https://doi.org/10.1007/s10796-020-09985-4

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