IRPG

Menu
Management

Steam Review Characteristics and Chinese Users’ Purchase Intention The Roles of Perceived Value and Trust in the Game

IJEBSM | Published: October 28, 2026 | Vol. 2 Issue 4 | ISSN: 3139-0919
Hu Zhiguang1,
Nutteera Phakdeephirot1✉
1 Rattanakosin International College of Creative Entrepreneurship, Rajamangala University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand
Download PDF View DOI

Abstract

This study examines how perceived Steam review valence, volume, and credibility are associated with Chinese users’ purchase intention through perceived value and trust in the focal game. Guided by the Stimulus–Organism–Response framework, a cross-sectional questionnaire survey produced 417 submissions, of which 412 were retained after eligibility and response-quality screening. Confirmatory factor analysis supported the six-construct measurement model. The structural model showed good fit and explained 42.5% of the variance in purchase intention. All eight hypothesized direct associations were positive and statistically significant. Game trust had a larger standardized association with purchase intention than perceived value, although the difference between coefficients was not formally tested. A reproducible bootstrap analysis supported five of six specific indirect associations; the credibility–trust–purchase intention pathway was unsupported. Sensitivity analysis of 89 responses containing game titles did not support three weaker stimulus paths. The findings suggest that review characteristics relate to purchase intention through distinguishable value and trust evaluations. Platforms and publishers may support informed decisions by contextualizing review summaries and communicating product performance and expected benefits clearly. Cross-sectional self-report data, non-probability sampling, and substantial unavailable game-title information limit causal inference and generalizability.
Keywords: Steam; Online reviews; Perceived value; Game trust; Purchase intention.

References

  1. Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184. https://doi.org/10.1016/S0148-2963(99)00087-9
  2. Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261–1270. https://doi.org/10.1016/j.jbusres.2014.11.006
  3. Filieri, R. (2016). What makes an online consumer review trustworthy? Annals of Tourism Research, 58, 46–64. https://doi.org/10.1016/j.annals.2015.12.019
  4. Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956–970. https://doi.org/10.1016/j.im.2018.04.010
  5. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  6. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519
  7. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
  8. Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  9. Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. https://doi.org/10.1037/1082-989X.12.1.23
  10. Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. https://doi.org/10.1037/a0028085
  11. Park, D.-H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. https://doi.org/10.2753/JEC1086-4415110405
  12. 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. https://doi.org/10.1037/0021-9010.88.5.879
  13. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
  14. Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 77(2), 203–220. https://doi.org/10.1016/S0022-4359(01)00041-0
  15. Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22. https://doi.org/10.1177/002224298805200302
  16. Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148. https://doi.org/10.1509/jmkg.74.2.133
🔓 Open Access | Published by IR Publishing Group. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Log in

Sign up
Sign up
Scroll to Top