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Problem Definition: We propose a novel framework to understand the role of non-instrumental information disclosure in service operations management, i.e., information that is shared by the service provider (firm) not to affect consumers' actions, but to better manage their experience in the firm's process. In contrast to prior literature that exclusively considers the role of information sharing in influencing consumers' actions, our setting is one where consumers take no action -- our goal is to study how the consumer-waiting experience is influenced by non-instrumental information disclosure. Academic/Practical Relevance: The process view of a firm and operational metrics for process analysis are basic ideas in OM. Our work in this paper is an attempt to understand the economics behind post-sales process transparency, i.e., how transparent should a firm design its process, as customers await the completion of their service. Methodology: Our work draws upon the literature on belief-based utility in Economics. We model the interactions between a firm and a consumer, and how consumers react to good and bad news (the shape of the belief-based utility). The firm’s service process consists of a sequence of tasks, each of random duration. We analyze and compare commonly observed information-disclosure strategies in real-life processes. Results and Managerial Implications: Interestingly, we find that complete transparency may be sub-optimal for the customer, even if available at no cost. We examine conditions under which less informative strategies such as opaque (where the service process becomes a black-box), current-task-identity (where the firm discloses the current task being performed) and next-task-information (where the firm discloses task duration at the beginning of each task) are preferred to complete transparency. As the number of tasks in the firm's process increases, the opaque strategy becomes more favorable. Consumer anxiety exacerbates the cost of waiting, and makes transparency more attractive.

Organizations routinely introduce hitherto‐unexplored interventions to improve their supply chains. Consider a principal (e.g., a firm) that implements a “seemingly helpful” intervention: For any fixed actions of the principal and the agents (e.g., consumers), the principal's payoff is higher in the presence of the intervention than in its absence. While one would expect such well‐intentioned interventions to benefit the principal, several papers within the operations management (OM) literature show that the principal's equilibrium payoff can be hurt, even ignoring the intervention's implementation cost. While this conclusion is often based on analyzing a single‐shot, simultaneous‐move game, repeated interactions can also serve as an appropriate environment in many cases. A fundamental question arises: Does this conclusion hold even under repeated interactions? We study this question using the framework of infinitely repeated games and the notion of a precommitment equilibrium from the literature on reputation in repeated games. We identify two key characteristics that determine whether a seemingly beneficial intervention helps, or can possibly hurt the firm: (i) nature of the intervention (ceteris paribus, does it induce agents to react in a manner favorable to the principal?), and (ii) extent of interaction (single shot at one extreme and infinitely repeated at the other). Interestingly, we demonstrate the following two possibilities using settings analyzed in the recent OM literature: seemingly beneficial interventions can (a) hurt the firm in a single‐shot analysis but benefit under repeated interactions, and (b) continue to hurt the firm under repeated interactions. We also obtain easy‐to‐interpret conditions under which the benefit of such interventions is guaranteed under repeated interactions.

The goals of the guaranteed support price (GSP) scheme, adopted by several developing countries to support their farmers and the underprivileged population, are threefold: (a) as a supply‐side incentive, to ensure high output from the farmers, (b) as a demand‐side provisioning tool, to subsidize the consumption needs of the poor, and (c) to maintain an adequate amount of foodgrains as reserve stock, to mitigate the adverse effects of yield uncertainty (food security). We offer analytically supported insights on the fundamental aspects of this scheme by analyzing a Stackelberg game between a homogenous population of small farmers and a social planner. We model the strategic behavior of the farmers and the consuming population (Above‐ and Below‐Poverty‐Line consumers), and compare the equilibrium outcome with that under the direct benefit transfer (DBT) scheme, where the social planner simply distributes the budget among the BPL consumers. The comparison of the social planner's surplus depends on the marginal value from maintaining a reserve stock (i.e., the significance of food security). If this value is high, then the surplus under the GSP scheme strictly dominates that under DBT; otherwise, the surplus is identical. The comparison of the production by the farmers depends on two economic forces—the poorness of the BPL consumers and yield uncertainty. If the poorness is extreme, then the two schemes lead to identical production. If yield uncertainty is dominant, then DBT is ineffective in improving production while the GSP scheme can induce a strictly higher production by strategically choosing the reserve stock.

The unprecedented rate at which data are being generated has led to the growth of data markets where valuable data sets are bought and sold. A salient feature of this market is that a data-buyer (agent) is endowed with multidimensional private information, namely, her “ideal” record that she values the most and how her valuation for a given record changes as its distance from her ideal record changes. Consequently, the revenue-maximization problem faced by a data-seller (principal), who serves multiple buyers, is a multidimensional mechanism-design problem, which is well recognized as being difficult to solve. Our main result in this paper is an approximation scheme that guarantees a revenue within as close a positive amount from the optimal revenue as desired. The scheme generates a posted-price menu consisting of a set of item–price pairs—each entry in the menu consists of an item, that is, a set of records from the data set, and the price corresponding to that item. As a trade-off, the length of the menu resulting from the scheme increases as the desired guarantee gets closer to zero. For convenience in practice, data-sellers may want the ability to limit the length of the menu used by the scheme. To facilitate this, we extend our analysis to obtain a general approximation guarantee corresponding to a menu of any given length. We also demonstrate how the seller can exploit buyers' preferences to generate intuitive and useful rules of thumb for an effective practical implementation of the scheme.