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Learning Resource Centre Monthly Bulletin |
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Learning Resource Centre Monthly Bulletin |
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| Articles |
| Contracting in live streaming e‐commerce retailing under dual information asymmetry: the role of competition. By Zhang, Haiyue;Feng, Shuting;Li, Mengli International Transactions in Operational Research. Nov2026, Vol. 33 Issue 6, p4315-4351. 37p. Abstract :In the live streaming e‐commerce market, heterogeneous live streamers compete over a cooperation contract to promote a manufacturer`s product interactively. Considering the small live streamer`s selling ability information and the manufacturer`s product quality information are private, we develop a principal–agent model to screen the private information. Since the big live streamer is stronger while the small live streamer is weaker than the manufacturer, the contract decision authority of the manufacturer (i.e., the principal) would shift to the big live streamer when cooperating with the big live streamer (i.e., the agent). After analyzing the impacts of competition and information asymmetry on the manufacturer, we find that information advantage could hurt the high‐quality manufacturer in some conditions, which is contrary to the common sense that information advantage always generates non‐negative information rent for its owners. Moreover, with the increase of quality differentiation, the manufacturer first suffers greater losses and then suffers smaller losses and then benefits more revenues and then benefits less revenues from its information advantage under a low‐commission separating strategy. In addition, we find that the manufacturer can benefit from the competition among heterogeneous live streamers. The higher the quality of the manufacturer, the more capable they are of benefiting. This result underscores the urgency for manufacturers to cultivate their own live streamers to compete with the big ones in practice. | |||
| Competitive strategies of online retailers: Should they introduce livestream channels? By Liu, Keqi;He, Li;He, Yi;Xu, Qingyun International Transactions in Operational Research. Nov2026, Vol. 33 Issue 6, p4275-4314. 40p. Abstract :Through livestream channels, consumers gain more product information, which allows online retailers to engage with more consumers. However, online retailers may encounter challenges such as increased costs and the risk of free‐riding, leading to hesitation in adopting this emerging marketing strategy. This paper investigates the factors motivating online retailers` decisions to introduce livestream channels in a competitive environment. The results reveal that when the differentiation between online retailers is relatively low, both tend to introduce livestream channels. With moderate differentiation, only one online retailer introduces livestream channel as the equilibrium strategy. When differentiation is high enough, neither online retailer introduces livestream channels. Notably, when both online retailers introduce livestream channels, they fall into a prisoner`s dilemma. The study also extends the main model to consider the additional utility that consumers gain from viewing livestream, the impact of free‐riding behavior, and impulse purchases in livestream rooms. The main conclusions remain consistent across these extensions. | |||
| When Will Customers Buy? A Deep Learning Approach Incorporating Adaptive Irregularity for Next Purchase Prediction. By Sheng, Jessica Qiuhua;Xu, Da;Eslami, Pouyan;Choi, Daeeun Daniel Journal of Electronic Commerce Research. 2026, Vol. 27 Issue 4, p335-350. 16p. Abstract :The ability to accurately predict the timing of the next purchase is critical for business decision-making yet challenging. Shopping regularity is often disrupted by negligible transaction costs of e-commerce and the ease of responding to promotions, fostering increasingly irregular behavior and thereby complicating prediction efforts. In addition, existing predictive models often overlook how buying in one product category affects future purchases in others. To address these issues, this study proposes a deep learning framework that integrates the purchase irregularity, captures category-specific purchase patterns, and learns cross-category interactions for effective next purchase time prediction at product category level. Specifically, we model purchase irregularity as a latent state that adaptively captures whether a purchase in each product category tends to follow a routine pattern or not. Then we utilize the LSTM networks to capture recurring purchase patterns based on past inter-purchase intervals. Finally, a self-attention mechanism is applied to capture interactions of shopping behaviors among distinct product categories, learning how the timing of purchases in one category can affect purchasing behavior in others. Experimental evaluations on a largescale retail dataset demonstrate the effectiveness of our approach. The proposed model improves purchasing time prediction and enables businesses to better anticipate demand fluctuations and optimize resource allocation in online marketplaces. | |||
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