Motivated by the emergence of the Internet‐enabled inventory sharing across firms, we investigate different commitment scenarios of decentralized inventory sharing platforms, through a behavioral ...lens. In particular, we consider two transfer price commitment settings (ex ante or ex post depending on whether or not the inventory transfer price is committed before demand realization) and two sharing commitment rules (automatic or voluntary depending on whether or not inventory sharing are pre‐committed). Our experimental results suggest that individuals set transfer prices much lower, and order much less, than what Nash equilibrium predicts. We also find substantial treatment effects that the rational model cannot explain. The magnitude of disparities relative to the Nash equilibrium prediction appears to be most substantial in the situation where transfer price is set ex ante (i.e., transfer price commitment) and inventory sharing is voluntary (i.e., no sharing commitment). Motivated by these observations, we develop a behavioral model that incorporates quantal response equilibrium and fairness concerns. Empirical analysis indicates that our model provides a compelling explanation of the behavior observed in the data. This study provides implication on the design of the commitment rules in decentralized inventory‐sharing platforms. Specifically, in order to gain the most benefit from inventory sharing, parties should either postpone the decision of transfer price until the need for sharing arises, or pre‐commit to sharing.
In this article, we model various forms of non‐optimizing behavior in a newsvendor setting, including biases such as recency, reinforcement, demand chasing, and anchoring, as well as unsystematic ...decision errors. We assume that a newsvendor may evaluate decisions by examining both past outcomes and future expected payoffs. Our model is motivated by laboratory observations under several types of supply chain contracts. Ordering decisions are found to follow multi‐modal distributions that are dependent on contract structures and incentives. We differ from previous research by using statistics to determine which behavioral factors are applicable to each decision maker. A great deal of heterogeneity was discovered, indicating the importance of calibrating a contract to the individual. Our analysis also shows that the profit performance and the effectiveness of co‐ordinating contracts can be affected by non‐optimizing behaviors significantly. We conclude that, in addition to the aggregate order quantities, the decision distributions should be considered in designing contracts.
Compensation systems have rapidly been shifting away from a fixed wage contractual payment basis. Many companies today are creating incentive compensation contracts to reward hard-working employees ...for jobs done well. Profit sharing (“sharing compensation contract”) and target with bonus (“target compensation contract”) are two common performance-based compensation contracts prevalent in business. We theoretically and behaviorally study the sharing and target compensation contracts in an operational context where a firm sets the parameters of the compensation contracts and a store manager, after observing the compensation contract offered to him, chooses his effort level (unobservable by the firm) and makes ordering decisions for the store. Our experimental data suggest systematic deviations from the theoretical benchmark and reveal behavioral promise and pitfalls under the two compensation contracts. In particular, the store manager is more willing to exert high effort under the target contract all else being equal. However, the store manager is also more likely to punish the firm for perceived “unfair” offers by submitting an extremely low order quantity. We find that bounded rationality plays an important role in driving a higher effort rate under the target contract than the sharing contract. We introduce a new formulation of the fairness concerns, which is referred to as
by-state
fairness, where individuals, rather than considering whether the expected profits received are fair, consider the fairness in the potential realized outcomes. This new formulation explains why managers are more likely to order very little to punish the firm under the target contract. In addition, we conduct validation experiments to verify our behavioral explanation.
This paper was accepted by Jayashankar Swaminathan, operations management
.
Digital extortion has emerged as a significant threat to organizations that rely on information technologies for their operations. Using human subject experimentation, we study the effectiveness of ...message appeals in encouraging defenders to adopt two mitigation strategies, investment in security and refusal to pay ransoms, to digital extortion threats. We explore two types of appeals, benefit and normative, for this purpose. We find that the decisions of the defenders (representing any organization that can be a potential victim) deviate from the predictions of game theory. However, given the strategic interactions between the defenders and the attacker as well as noisy decision-making behaviors, it is challenging to untangle the influence of the appeals on the defenders. We develop a structural model based on the quantal response equilibrium framework to measure how message appeals change the defenders’ utilities of investment and payment refusal. Although the interventions may be successful in increasing the utilities of investment and/or payment refusal, their impacts on investment rate and payment rate are mitigated by the attacker reducing ransoms. Thus, it is challenging for an intervention to significantly boost a community’s investment rate or to suppress the ransom payment rate. We characterize how security outcomes of a community (including expected ransom, attack rate, investment rate, and payment rate) vary with the defenders’ utilities of investment and pay refusal.
This paper was accepted by Chris Forman, information systems.
Trust in Forecast Information Sharing Özer, Özalp; Zheng, Yanchong; Chen, Kay-Yut
Management science,
06/2011, Letnik:
57, Številka:
6
Journal Article
Recenzirano
This paper investigates the capacity investment decision of a supplier who solicits private forecast information from a manufacturer. To ensure abundant supply, the manufacturer has an incentive to ...inflate her forecast in a costless, nonbinding, and nonverifiable type of communication known as "cheap talk." According to standard game theory, parties do not cooperate and the only equilibrium is uninformative-the manufacturer's report is independent of her forecast and the supplier does not use the report to determine capacity. However, we observe in controlled laboratory experiments that parties cooperate even in the absence of reputation-building mechanisms and complex contracts. We argue that the underlying reason for cooperation is trust and trustworthiness. The extant literature on forecast sharing and supply chain coordination implicitly assumes that supply chain members either absolutely trust each other and cooperate when sharing forecast information, or do not trust each other at all. Contrary to this all-or-nothing view, we determine that a continuum exists between these two extremes. In addition, we determine (i) when trust is important in forecast information sharing, (ii) how trust is affected by changes in the supply chain environment, and (iii) how trust affects related operational decisions. To explain and better understand the observed behavioral regularities, we also develop an analytical model of trust to incorporate both pecuniary and nonpecuniary incentives in the game-theoretic analysis of cheap-talk forecast communication. The model identifies and quantifies how trust and trustworthiness induce effective cheap-talk forecast sharing under the wholesale price contract. We also determine the impact of repeated interactions and information feedback on trust and cooperation in forecast sharing. We conclude with a discussion on the implications of our results for developing effective forecast management policies.
This paper was accepted by Ananth Iyer, operations and supply chain management.
Exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. The goal of this paper is to understand why simpler contracts may ...commonly be preferred in practice despite being theoretically suboptimal. We study a two-tier supply chain with a single supplier and a single buyer to characterize the impact of contract complexity and asymmetric information on performance and to compare theoretical predictions to actual behavior in human subject experiments. In the experiments, the computerized buyer faces a newsvendor setting and has better information on end-consumer demand than the human supplier. The supplier offers either a quantity discount contract (with two or three price blocks) or a price-only contract, contracts that are commonplace in practice, yet different in complexity. Results show that, contrary to theoretical predictions, quantity discounts do not necessarily increase the supplier's profits. We also observe a more equitable distribution of profits between the supplier and the buyer than what theory predicts. These observations can be described with three decision biases (the probabilistic choice bias, the reinforcement bias, and the memory bias) and can be modeled using the experience-weighted attraction learning model. Our results demonstrate that simpler contracts, such as a price-only contract or a quantity discount contract with a low number of price blocks, are sufficient for a supplier designing contracts under asymmetric demand information.
This paper was accepted by Christian Terwiesch, operations and supply chain management.
The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial ...intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively.
This paper was accepted by Yan Chen, behavioral economics and decision analysis.
Funding:
Y. (D.) Wu extends her gratitude for the financial support provided through the RSCA Seed Grant 22-RSG-01-004 from the San Jose State University.
Supplemental Material:
Data are available at
https://doi.org/10.1287/mnsc.2023.4782
.
The goal of our research is to shed light on the existence of an effect of seeing the images of human faces (i.e. "a face effect") on economic decision-making behavior. We conduct a series of ...controlled experiments using photographs of human faces in a newsvendor setting. Our experimental data provides evidence that the human face plays the role of an environmental moderator which triggers and intensifies the social considerations. To gain a deeper understanding of behavioral responses, we examined the impact of faces with varying characteristics, with a particular focus on the effects of facial attractiveness and perceived gender. We find that the decision-makers systematically deviate from their choices of wholesale prices when they imagine seeing the counterpart's face. To explain how facial attractiveness and gender affect the decision choices, we develop a behavioral model that incorporates altruistic and fairness concerns.
We study a manufacturer's problem of managing his direct online sales channel together with an independently owned bricks-and-mortar retail channel, when the channels compete in service. We ...incorporate a detailed consumer channel choice model in which the demand faced in each channel depends on the service levels of both channels as well as the consumers' valuation of the product and shopping experience. The direct channel's service is measured by the delivery lead time for the product; the retail channel's service is measured by product availability. We identify optimal dual channel strategies that depend on the channel environment described by factors such as the cost of managing a direct channel, retailer inconvenience, and some product characteristics. We also determine when the manufacturer should establish a direct channel or a retail channel if he is already selling through one of these channels. Finally, we conduct a sequence of controlled experiments with human subjects to investigate whether our model makes reasonable predictions of human behavior. We determine that the model accurately predicts the direction of changes in the subjects' decisions, as well as their channel strategies in response to the changes in the channel environment. These observations suggest that the model can be used in designing channel strategies for an actual dual channel environment. 1
Despite being theoretically suboptimal, simpler contracts (such as price‐only contracts and quantity discount contracts with limited number of price blocks) are commonly preferred in practice. Thus, ...exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. Using human subject experiments, Kalkancı et al. (2011) showed that such simpler contracts perform effectively for a supplier interacting with a computerized buyer under asymmetric demand information. We use a similar set of experiments with the modification that a human supplier interacts with a human buyer. We show that human interactions strengthen the supplier's preference for simpler contracts. We find that suppliers have fairness concerns even when they interact with computerized buyers. These fairness concerns tend to be even stronger when suppliers interact with human buyers, particularly when the complexity of the contract is low. We also find that suppliers are more prone to random decision errors (i.e., bounded rationality) when interacting with human buyers. In the absence of social preferences, Kalkancı et al. identified reinforcement and bounded rationality as key biases that impact suppliers' decisions. In human‐to‐human experiments, we find evidence for social preference effects. However, these effects may be secondary to bounded rationality.
