关键词: 亲组织不道德行为, 平台绩效, 平台规模, 平台质量, 流量分配

Abstract: In recent years, the nonstandard and immoral behaviors of participants in the development of the platform have become increasingly prominent. To increase exposure, e-commerce participants choose scalping, which can improve the current performance of the platform. But why does Amazon still forbid such a behavior? By referring to employee unethical pro-organizational behavior (UPB), the e-commerce behavior that positively affects the current performance of the company and the platform (“organization”) but violates social morality and social responsibility is defined as the pro-organizational unethical behavior of platform participants. The theoretical significance of this paper is to expand the relationship of unethical pro-organizational behavior from the relationship between the enterprise and its internal actors at different levels (individuals, teams, departments) to the relationship between the platform and its participants, expand the main body of platform research, reveal the symbiotic relationship between the platform and its participants, and enrich the research level of UPB.
On the whole, this paper constructs the logic thread of “unethical pro-organizational behavior-platform scale/quality-platform performance”, and reveals the moderating role of traffic distribution in the process of unethical pro-organizational behavior affecting platform long-term and short-term performance. In addition, the sample data collected in this paper mainly adopts the questionnaire survey method. 370 questionnaires are issued and 301 valid questionnaires are recovered, with an effective recovery rate of 81.4%. The sample data is reasonably distributed, with a certain degree of objectivity and representativeness. This paper will also conduct regression analysis through SPSS26 and AMOS24. It is found that the positive impact of UPB on the short-term performance of the platform can be regarded as a spillover effect, while the negative impact of UPB on the long-term performance of the platform is a stigma effect. UPB improves short-term performance by expanding the scale of the platform, and UPB reduces long-term performance by weakening the quality of the platform.The more accurate the traffic distribution of platform, the weaker the negative relationship between UPB and platform quality, which improves the long-term performance of the platform.
This paper reveals the mechanism of the impact of UPB on the short-term and long-term performance of the platform under its moderation of traffic distribution, and provides suggestions and supports for the platform to improve the accuracy of traffic distribution and curb the negative impact of participant UPB. Future research can focus on the following three aspects: First, on the issue of how platform enterprises govern UPB participants, we can balance long-term and short-term benefits based on the temptation and self-control framework in the future, and explain why different platforms choose different governance mechanisms. It can also further explore how the synergy of technological governance and institutional governance in the digital intelligence era can inhibit participants' UPB. Second, the path of UPB's impact on the platform's long-term performance has been moderated by traffic distribution, but the impact of traffic distribution as a governance means on the platform's performance has not been clarified. In the future, we can further study how to optimize traffic distribution to promote the healthy development of the platform and reveal the mechanism of traffic distribution affecting the platform's performance. Third, the development of platform performance is affected by many factors. This paper includes the contingent factors of participant behavior and platform traffic allocation. In the future, we can study the process of multiple antecedent variables jointly affecting platform performance from the perspective of configuration.

Key words: unethical pro-organizational behavior, platform performance, platform scale, platform quality, traffic distribution