Bin Shen

Mandatory ESG disclosure regulations vary across countries and regions. Some, such as Singapore, require all listed firms to disclose ESG reports (i.e., full ESG disclosure), while others, including the UK and the EU, mandate disclosure only for large firms and allow small firms to disclose voluntarily (i.e., partial ESG disclosure). To examine the implications of these regulatory designs, we develop a game-theoretic model with one large firm and one small firm in a competitive market, and assess how disclosure regulations affect firms' strategic disclosure choices and performance outcomes. Our results reveal that the small firm voluntarily discloses ESG reports only when the penalty for violations falls below a certain threshold. An all-win outcome for the large firm, the small firm, and consumers arises only under a moderate level of penalty intensity, under which both firms disclose ESG reports. Moreover, while partial disclosure can mitigate the competitive disparity between large and small firms (i.e., the Matthew Effect), this effect is realized only when the penalty is sufficiently high; otherwise, partial disclosure may even exacerbate this disparity. Overall, our analysis underscores that the interaction between the scope of mandatory disclosure and penalty intensity fundamentally shapes disclosure incentives and market fairness. Therefore, well-aligned regulation can improve transparency while preventing excessive market polarization, which provides valuable insights for policymakers seeking to balance regulatory objectives with market equity.

Deploying product visualization tools (PVTs) can reduce consumers' product fit uncertainty in e-marketplaces. This study examines the impact of PVTs on selling products. Consumers are fairness-minded. We develop a game-theoretical framework and find that PVTs can be deployed when consumer fairness concerns are relatively weak. Highly precise PVTs can facilitate an all-win outcome for the supplier, retailer, and consumers if the product has low quality. Such an all-win outcome may also be achieved if product quality is higher than a certain level and the degree of product visualization precision is relatively low. We find that the region of an all-win outcome of deploying PVTs will expand with decreased degree of consumer fairness concerns. Thus, consumer fairness concerns are advised to be measured when deploying PVTs in e-marketplaces. Moreover, demand will decrease if product quality and the degree of product visualization precision are high. Consequently, an all-lose outcome for the supplier, retailer, and consumers will occur. Thus, deploying PVTs for high-quality products is not wise when the degree of product visualization precision is relatively high. Our research provides managerial insights into the optimal strategy for deploying product visualization tools in e-marketplaces when selling to fairness-minded consumers.

New technology innovations and operational research (OR) models can enhance the efficiency and competitiveness of e-commerce platforms. The rapid expansion of e-commerce has led to increased complexity in operations, including dynamic pricing, inventory management, last-mile delivery, and fraud detection. This special issue features nine articles exploring how advanced OR models, such as game-theory-based approaches, machine learning, and big data analytics, offer solutions to these challenges by enabling platforms to make data-driven decisions. As e-commerce evolves, these technological advancements, combined with OR models, will continue to drive platform innovation, improve efficiency, and strengthen resilience in the face of disruptions and fluctuating market demands.

In the e-marketplace, consumers can know how products fit their preferences using artificial intelligence consumer experience tools (AI-CE), which have been adopted by the retailer and/or supplier. Motivated by e-commerce practices, the question of who provides AI-CE is optimal for the supply chain remains unclear. We evaluate the impacts of implementing AI-CE on the supply chain. The supplier sells two horizontally differentiated products. One is sold through the retailer, and one is sold directly on the supplier's own channel. Our analytical results show that the optimal AI-CE implementation strategy of the retailer and supplier depends on consumer sensitivity toward the privacy of using AI-CE. To realize a win-win outcome for the supplier and retailer, the retailer (supplier) should implement AI-CE, but the supplier (retailer) should not when consumer sensitivity toward privacy is low (high). Surprisingly, when consumer sensitivity toward privacy is moderate, neither the supplier nor the retailer should implement AI-CE. The reason is that joint implementation will eliminate the positive effect of comparative disadvantage and channel competition. Moreover, we find that the enhancement in the precision of AI-CE may either benefit or harm the supplier and retailer. Our study guides AI-CE implementation in e-commerce supply chains.

E-commerce platforms are digital marketplaces for selling goods and services online. Operational research (OR) models support business model innovation in e-commerce platforms. This issue covers eight papers that utilize OR models to examine business model innovation in e-commerce platforms. They all introduce new research problems and concepts, develop novel and rigorous OR methodologies to tackle them, and present innovative OR applications according to the real problems in e-commerce platforms. Based on the eight papers’ discussions, we propose future research directions.