Sharing of truthful information involving business intelligence between supply chain partners is a challenge on account of the asymmetric nature of the information, where one party possesses information such as market intelligence that is neither available in the public domain nor verifiable through third parties. While busesinss-to-business (B2B) technology solutions, such as CPFR (collaborative planning, forecasting, and replenishment), facilitate the sharing of historical information (e.g., transaction records), business intelligence (e.g., potential customer demand) is considered private. Central to CPFR is collaborative demand forecasting (CDF) that allows supply chain partners to share private demand information and incorporate the jointly derived demand forecast into production planning and product replenishment decisions. Implementing CDF, however, is a challenge because of the high costs of the laborious collaboration effort (e.g., to resolve forecast differences). Hence, companies are unable to realize the benefits of CDF and, in turn, the full potential of CPFR. Typically, the issues of information truthfulness and collaboration cost are addressed through an exception management mechanism that defines a range of forecast updates within which collaboration is automated without any human intervention in B2B trading partners. In this paper, we develop incentive-based contracts that explicitly consider the truth-telling behavior and exception resolution in decisions related to the threshold values of demand information. Our first contribution to B2B information management is in establishing the strategic value of exception management and resolution mechanisms in B2B relationships, leading to truthful revelation of demand information. Our second contribution is in developing exception-based incentive contracts, especially in light of the advances in today’s business practices and technology, to address issues associated with unobservable and asymmetric demand information. Specifically, we propose a resolution contract to coordinate the supply chain that directly incorporates both exceptions and resolution in an incentive mechanism. We show that these alternative contracts are all viable solutions in assuring truthful exchange of demand information but excel individually in specific situations and, thus, provide practitioners with alternative demand collaboration tools when price negotiation is not an option.
Personalization technologies today enable retailers to tailor online purchase interactions to the individual preferences and needs of customers. With personalization being increasingly perceived as a source of competitive advantage, there is a growing trend toward pursuing technology-enabled personalization strategies in online retailing. However, the choice of a retailer whether or not to select into technology-enabled personalization and its implications for customer loyalty are at best ambiguous. This paper is an attempt to resolve this apparent ambiguity. Specifically, the paper conceptualizes retailer selection into technology-enabled personalization strategies relevant to two steps of an online purchase, namely, transaction personalization strategy and decision personalization strategy, based on the operating characteristics of a retailer. The implications of the retailers' self-selection into technology-enabled personalization strategies for customer loyalty are then empirically investigated with data collected from 422 retailers. Further, based on a counterfactual analysis, the paper reveals the implications of making a normatively incorrect decision with respect to personalization strategy. Contrary to popular belief, the results of this study indicate that personalization may not be uniformly beneficial in terms of customer loyalty to all retailers. Although a majority of retailers pursue transaction personalization and realize benefits by way of improved customer loyalty, we find that the choice of a retailer to pursue decision personalization is self-selected and dependent on idiosyncratic characteristics related to its operating context. Retailers that have relatively large-scale operations, provide greater variety and realize higher customer satisfaction with product selection, and that do not necessarily compete on price (i.e., realize lower customer satisfaction with prices relative to competing retailers) are more likely to pursue the decision personalization strategy. Although some retailers pursue decision personalization because they clearly stand to benefit from doing so, other retailers are better off not following suit. Theoretical contributions of the study, managerial implications of the study findings, limitations, and directions for future research are identified.