Explore the vast range of titles available.

Secondhand marketplace has received a dramatic growth as secondhand trading becomes a sustainable way of life worldwide. We propose a game-theoretical model to explore online retail platform’s strategy of secondhand marketplace introduction when facing consumer’s uncertain preferences. The platform sells new product for the manufacturer and decides whether to open a secondhand product for consumers to purchase secondhand product. Consumers are strategic in their purchase of new product and secondhand product in two sequential periods. We find that the presence of secondhand marketplace induces the manufacturer to lower the retail price for new product when retail price of secondhand product is low but increase the price otherwise. The secondhand marketplace may benefit the platform when the retail price of secondhand product is moderate, but may harm the platform when the price is low or high, depending on the unit production cost of new product. Interestingly, it is never optimal for the manufacturer when the retail price of secondhand product is substantially low, but may be beneficial otherwise, depending on the unit production cost of new product. Examining the value of secondhand marketplace, it creates a win–win situation for the two parties when the retail price of secondhand product is not low, and improves the consumer surplus under certain circumstances. The extended case of consumer depreciation of secondhand product verifies the robustness of the basic model.

Information asymmetry between manufacturers and online retailers regarding consumer preferences for store brands profoundly influences operational strategy. By leveraging information technology, online retailers can collect valuable consumer data, creating a strategic dilemma: whether to share this information with manufacturers and, if so, with which manufacturer (national or third-party). This study aims to explore an online retailer’s strategic decisions regarding sharing information with manufacturers, filling a gap in the literature on store brands and consumer preferences. Using game theory, we analyze the interactions among an online retailer, a national manufacturer, and a third-party manufacturer, incorporating the Hotelling model to capture consumer preference and product differentiation. Our findings reveal that information sharing does not consistently benefit the online retailer or manufacturers. Notably, without side payment, the online retailer is unwilling to share information with either manufacturer, and manufacturers do not always gain more from receiving such information—a result that challenges conventional wisdom. However, when side payment is introduced, the online retailer’s willingness to share information depends on key factors: the probability of low brand loyalty (low-type) consumers, the proportion of comparison shoppers, the side payment, and the degree of information uncertainty. These findings provide innovative insights for operations managers, highlighting the critical role of information management in shaping strategic decisions and enhancing the efficacy and financial outcomes of information sharing in the context of store brands.

Consumer uncertainty about product valuation is a significant economic issue in online retailing. In many markets, even though the product quality information is completely disclosed, consumers may still remain unsure about their valuation-for-quality. This paper investigates the effects of consumers’ uncertain valuation-for-quality on a platform’s and a firm’s profits under commonly used wholesale price contract and consignment contract in a distribution channel. Our analysis reveals that regardless of the contract types, when the product quality is relatively low, both the platform and the firm benefit from a higher degree of informativeness of information; when the product quality is relatively high, however, the profits of both the platform and the firm show nonmonotonicity with respect to the informativeness degree (namely, first decrease then increase), which is driven by the joint effects of the value-enhancement effect and the segmentation effect incurred from the consumers’ uncertain valuation-for-quality. Additionally, we find that when the quality is of medium level, the firm and the platform may achieve a consensus on contract preferences on the wholesale price contract or the consignment contract, critically dependent on the informative degree.

Uncertain promotional strategies (UPSs) have gained popularity due to their cost-reducing benefits and positive impact on consumer satisfaction within the realm of marketing. Investigating the antecedents of consumers’ preference for UPSs holds significant theoretical and practical value. Drawing on socioemotional selectivity theory and regulatory focus theory, the current study proposes and examines the temporal landmarks (TLs)’ effect on consumers’ preference for UPSs, as well as the underlying mechanism of perceived luck and the moderating effect of locus of control. Three studies within different marketing scenarios reveal that, compared with terminal temporal landmarks (TTLs) and control group, start temporal landmarks (STLs) could increase consumers’ preference for UPSs, and the effect is mediated by perceived luck. Additionally, locus of control moderates the effects of TLs’ on consumers’ UPSs. Specifically, the effect of STLs on consumers’ preference for UPSs would be reinforced for those with high (vs. low) level of internal locus of control (or low level of external locus of control). The study contributes to research of UPSs and TLs, and provides practical suggestions for policymakers, marketing managers and consumers.

The extensive electronic word-of-mouth (eWOM) data generated by consumers encapsulates authentic product experience information. By leveraging advanced data analysis technologies, enterprises can extract sustainable consumer behavior preference knowledge, thereby supporting the optimization of their marketing and management strategies. However, existing data-driven product ranking processes predominantly focus on single-source eWOM data and rarely mine product insights from a multi-source perspective. Moreover, the quality of eWOM data cannot be overlooked. Consequently, this study uses automobile products as a case example and integrates rating eWOM data, complaint eWOM data, and safety test data to construct a multi-source data-driven personalized product ranking recommendation algorithm. Specifically, an evaluation index system is established for each of the three data types. To model information quality, these data are transformed into basic uncertain information (BUI), which incorporates scoring information and credibility metrics. The XLNet model is employed to convert complaint text data into scoring data, and three targeted credibility evaluation models are developed to assess the reliability of the three data types. Subsequently, BUI is aggregated using the BUI ordered weighted average (BUIOWA) aggregation operator. Based on this, a personalized product ranking method aligned with user preferences is proposed, offering consumers recommendation results that match their preferences. Finally, using automobile products as an illustrative example, this study elucidates the multi-source data-driven personalized product recommendation process and provides managerial implications for enterprises.

PurposeThe development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the uncertain risk of emission reduction investment. With the government's carbon emission constraints, green manufacturers implement the advance selling strategy to increase both profit and reduction level. However, few studies consider the consumer's green preference and emission constraints in advance selling market and spot market independently. The authors' paper investigates the optimal strategies of advance selling pricing and reduction effort for green manufacturers to maximize profits.Design/methodology/approachThe authors' paper designs a stochastic model and investigates the manufacturer's optimal strategies of advance selling price and emission reduction efforts by categorizing different purchasing periods of low-carbon consumers. With the challenges of uncertain demand and government's emission constraints, the authors' develop the non-linear optimization model to investigate the manufacturer's profit-oriented decisions.FindingsThe results show the government's carbon constraints cannot influence the manufacturer's profit, but the consumer's low-carbon preference in the advance selling period can. Interestingly, the manufacturer will make fewer reduction efforts even when the consumers have stronger environmental awareness. In addition, the increasing consumer price sensitivity will exacerbate the profit loss from mandatory emissions reduction. Overall, for achieving a win–win situation between emission reduction and profit growth, green manufacturers should not only consider the sales strategies, market demand, and government constraints in a low-carbon market, but also pay attention to the uncertainty of green technology innovation.Originality/valueWith the consideration of the government's carbon emission constraints, uncertain demand, and low-carbon consumer's preferences, the authors' study innovatively incorporates the joint impacts of advance selling strategy and emission reduction effort strategy and then differentiates between two cases that pertain to the diverse carbon emission regulations.

We model a decision maker who anticipates being tempted but is also uncertain about what is normatively best. Our model is an extended version of Gul and Pesendorfer's (2001) with three time periods: in the ex ante period, the agent chooses a set of menus; in the interim period, she chooses a menu from this set; in the final period, she chooses from the menu. We posit axioms from the ex ante perspective. Our main axioms on preference state that the agent prefers flexibility in the ex ante period and the option to commit in the interim period. Our representation is a generalization of Dekel et al.. (2009) and identifies the agent's multiple normative preferences and multiple temptations. We also characterize the uncertain normative preference analogue to the representation of Stovall (2010). Finally, we characterize the special case where normative preference is not uncertain. This special case allows us to uniquely identify all components of the representations of Dekel et al.. (2009) and Stovall (2010).

We investigate in this paper a version of pure exchange competitive equilibrium under uncertain circumstances. Those uncertain factors are embedded in each agent’s preference, which is characterized by the uncertain utility function. By maximizing the expected utility of each agent, we formulate this kind of pure exchange competitive equilibrium problem into a quasi-variational inequality problem. This idea is applied in a pure exchange economy which consists of two agents and two goods. And we find the competitive equilibrium of this economy with each agent’s preference being an uncertain variable.