This study investigates the interactions between a manufacturer's information acquisition and quality disclosure strategies in a supply chain setting in which the manufacturer privately knows his product quality but is uncertain about consumer preferences. We argue that the manufacturer should treat his information acquisition and quality disclosure decisions as an integrated process because these decisions can significantly influence a retailer's rational inferences about product quality and can have conflicting effects on his own profitability. Although information acquisition helps a manufacturer subsequently craft better pricing and quality disclosure strategies, it also leaks certain product information to the retailer, thus helping the retailer better estimate product quality. Therefore, in equilibrium, a manufacturer may choose not to acquire any consumer information, even when such acquisition is costless. Moreover, we find that this adverse effect of acquisition is highly dependent on the cost of disclosure and consumers’ preference differentiation. Increased consumer preference differentiation may have a non‐monotonic relationship with the manufacturer's profit, and information acquisition can become detrimental to the manufacturer once the disclosure cost is sufficiently high. 相似文献
This paper attempts to modify financial portfolio theory for application in product portfolio decisions. The proposed multiperiod portfolio framework should help marketers in allocating scarce corporate resources to various competing products as well as contribute to developing a body of theory to solve an important problem in marketing management. Managerial and theoretical implications are discussed. 相似文献
Journal of Combinatorial Optimization - Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed... 相似文献
The minimum dominating set of graph has been widely used in many fields, but its solution is NP-hard. The complexity and approximation accuracy of existing algorithms need to be improved. In this paper, we introduce rough set theory to solve the dominating set of undirected graph. First, the adjacency matrix of undirected graph is used to establish an induced decision table, and the minimum dominating set of undirected graph is equivalent to the minimum attribute reduction of its induced decision table. Second, based on rough set theory, the significance of attributes (i.e., vertices) based on the approximate quality is defined in induced decision table, and a heuristic approximation algorithm of minimum dominating set is designed by using the significance of attributes (i.e., vertices) as heuristic information. This algorithm uses forward and backward search mechanism, which not only ensures to find a minimal dominating set, but also improves the approximation accuracy of minimum dominating set. In addition, a cumulative strategy is used to calculate the positive region of induced decision table, which effectively reduces the computational complexity. Finally, the experimental results on public datasets show that our algorithm has obvious advantages in running time and approximation accuracy of the minimum dominating set.