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Unlocking the power of peer influence: Strategies for bridging the adoption chasm in new product diffusion

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  • Zhihui Li
  • Yuanyuan Jiao
  • Yangyang Cheng
  • Zhifeng Shen
  • Mi Zhou

Abstract

New product diffusion relies heavily on interpersonal relationships, yet the adoption chasm between the early adopters and the early majority poses a significant challenge. The pervasiveness of peer influence in social networks presents unique opportunities for businesses to effectively bridge this chasm between user groups. This study, grounded in social–psychological theory, examines the micro‐processes of early adopters' peer influence on the early majority within social networks. Utilizing the stimulus–organism–response model, we propose that early adopter peer influence can enhance early majority adoption intention, thereby crossing the chasm of new product diffusion. Empirical findings reveal that early adopters' peer influence, directly and indirectly, affects the early majority's adoption decisions. When indirect effects are at play, the early majority emphasizes the emotional and social values embedded within the new product, driven by the early adopters' peer influence. These insights contribute to understanding how interactions between user types can help bridge the adoption chasm. The study augments, refines, and expands upon existing research on new product diffusion chasms and offers valuable practical guidance for businesses seeking to harness peer influence more effectively to overcome this barrier.

Suggested Citation

  • Zhihui Li & Yuanyuan Jiao & Yangyang Cheng & Zhifeng Shen & Mi Zhou, 2025. "Unlocking the power of peer influence: Strategies for bridging the adoption chasm in new product diffusion," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 46(1), pages 361-377, January.
    • Handle: RePEc:wly:mgtdec:v:46:y:2025:i:1:p:361-377
    DOI: 10.1002/mde.4379
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    1. Lee, Youseok & Kim, Sang-Hoon & Cha, Kyoung Cheon, 2021. "Impact of online information on the diffusion of movies: Focusing on cultural differences," Journal of Business Research, Elsevier, vol. 130(C), pages 603-609.
    2. Bearden, William O & Netemeyer, Richard G & Teel, Jesse E, 1989. "Measurement of Consumer Susceptibility to Interpersonal Influence," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(4), pages 473-481, March.
    3. Oh, Ga-Eun (Grace) & Aliyev, Murod & Kafouros, Mario & Au, Alan Kai Ming, 2022. "The role of consumer characteristics in explaining product innovation performance: Evidence from emerging economies," Journal of Business Research, Elsevier, vol. 149(C), pages 713-727.
    4. Ravi Bapna & Akhmed Umyarov, 2015. "Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks," Management Science, INFORMS, vol. 61(8), pages 1902-1920, August.
    5. David Bell & Sangyoung Song, 2007. "Neighborhood effects and trial on the internet: Evidence from online grocery retailing," Quantitative Marketing and Economics (QME), Springer, vol. 5(4), pages 361-400, December.
    6. Sumedha Gupta & Laura Montenovo & Thuy Nguyen & Felipe Lozano‐Rojas & Ian Schmutte & Kosali Simon & Bruce A. Weinberg & Coady Wing, 2023. "Effects of social distancing policy on labor market outcomes," Contemporary Economic Policy, Western Economic Association International, vol. 41(1), pages 166-193, January.
    7. Yang, Kiseol, 2012. "Consumer technology traits in determining mobile shopping adoption: An application of the extended theory of planned behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 19(5), pages 484-491.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Yogesh V. Joshi & Andres Musalem, 2021. "When Consumers Learn, Money Burns: Signaling Quality via Advertising with Observational Learning and Word of Mouth," Marketing Science, INFORMS, vol. 40(1), pages 168-188, January.
    10. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    11. Sinan Aral, 2011. "Commentary--Identifying Social Influence: A Comment on Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 217-223, 03-04.
    12. Rogers, Everett M, 1976. "New Product Adoption and Diffusion," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 2(4), pages 290-301, March.
    13. Ki, Chung-Wha (Chloe) & Chenn, Ashley & Man Chong, Sze & Cho, Erin, 2024. "Is livestream shopping conceptually New? a comparative literature review of livestream shopping and TV home shopping research," Journal of Business Research, Elsevier, vol. 174(C).
    14. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    15. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    16. Upadhyay, Nitin & Kamble, Aakash, 2023. "Examining Indian consumer pro-environment purchase intention of electric vehicles: Perspective of stimulus-organism-response," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    17. Quoc Tran-Nam & Tuyen Tiet, 2022. "The role of peer influence and norms in organic farming adoption: Accounting for farmers’ heterogeneity," Post-Print hal-04028068, HAL.
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