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.
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 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.
Engaging patients in health behaviors is critical for better outcomes, yet many patient partnership behaviors are not widely adopted. Behavioral economics-based interventions offer potential ...solutions, but it is challenging to assess the time and cost needed for different options. Crowdsourcing platforms can efficiently and rapidly assess the efficacy of such interventions, but it is unclear if web-based participants respond to simulated incentives in the same way as they would to actual incentives.
The goals of this study were (1) to assess the feasibility of using crowdsourced surveys to evaluate behavioral economics interventions for patient partnerships by examining whether web-based participants responded to simulated incentives in the same way they would have responded to actual incentives, and (2) to assess the impact of 2 behavioral economics-based intervention designs, psychological rewards and loss of framing, on simulated medication reconciliation behaviors in a simulated primary care setting.
We conducted a randomized controlled trial using a between-subject design on a crowdsourcing platform (Amazon Mechanical Turk) to evaluate the effectiveness of behavioral interventions designed to improve medication adherence in primary care visits. The study included a control group that represented the participants' baseline behavior and 3 simulated interventions, namely monetary compensation, a status effect as a psychological reward, and a loss frame as a modification of the status effect. Participants' willingness to bring medicines to a primary care visit was measured on a 5-point Likert scale. A reverse-coding question was included to ensure response intentionality.
A total of 569 study participants were recruited. There were 132 in the baseline group, 187 in the monetary compensation group, 149 in the psychological reward group, and 101 in the loss frame group. All 3 nudge interventions increased participants' willingness to bring medicines significantly when compared to the baseline scenario. The monetary compensation intervention caused an increase of 17.51% (P<.001), psychological rewards on status increased willingness by 11.85% (P<.001), and a loss frame on psychological rewards increased willingness by 24.35% (P<.001). Responses to the reverse-coding question were consistent with the willingness questions.
In primary care, bringing medications to office visits is a frequently advocated patient partnership behavior that is nonetheless not widely adopted. Crowdsourcing platforms such as Amazon Mechanical Turk support efforts to efficiently and rapidly reach large groups of individuals to assess the efficacy of behavioral interventions. We found that crowdsourced survey-based experiments with simulated incentives can produce valid simulated behavioral responses. The use of psychological status design, particularly with a loss framing approach, can effectively enhance patient engagement in primary care. These results support the use of crowdsourcing platforms to augment and complement traditional approaches to learning about behavioral economics for patient engagement.
Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a ...decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects (Formula: see text) or trials (Formula: see text). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.
We measure the value of promotional activities and referrals by content creators to an online platform of user-generated content. To do so, we develop a modeling approach that explains ...individual-level choices of visiting the platform, creating, and purchasing content as a function of consumer characteristics and marketing activities, allowing for the possibility of interdependence of decisions within and across users. Empirically, we apply our model to Hewlett-Packard's (HP) print-on-demand service of user-created magazines, named MagCloud. We use two distinct data sets to show the applicability of our approach: an aggregate-level data set from Google Analytics, which is a widely available source of data to managers, and an individual-level data set from HP. Our results compare content creator activities, which include referrals and word-of-mouth efforts, with firm-based actions, such as price promotions and public relations. We show that price promotions have strong effects but are limited to the purchase decisions, whereas content creator referrals and public relations efforts have broader effects that impact all consumer decisions at the platform. We provide recommendations as to the level of a firm's investments when "free" promotional activities by content creators exist. These free marketing campaigns are likely to have a substantial presence in most online services of user-generated content.
•Prediction is linked to simulation for predicted decision-making in primary care.•The predictive decision analytics approach enables more effective decision-making.•Trade-offs of primary care ...measures provide more insights for decision evaluations.•Prediction-based double-booking strategy helps achieve better no-show management.
Primary care plays a vital role for individuals and families in accessing care, staying well, and improving quality of life. However, the complexities and uncertainties in the primary care delivery system (e.g., patient no-shows/walk-ins, staffing shortage) have brought significant challenges in its operations management, which can potentially lead to poor patient outcomes and negative primary care operations (e.g., loss of productivity, inefficiency). This paper presents a decision analytics approach developed based on predictive analytics and simulation modeling to better facilitate management of the underlying complexities and uncertainties in primary care operations. A case study was conducted in a local family medicine clinic to demonstrate the use of this approach to manage patient no-shows. In this case study, a patient no-show prediction model was used in conjunction with an integrated agent-based and discrete-event simulation model to design and evaluate double-booking strategies. Using the predicted patient no-show information, a prediction-based double-booking strategy was created and compared against two other strategies, namely random and designated time. Scenario-based experiments were then conducted to examine the impacts of different double-booking strategies on clinic’s operational outcomes, focusing on the trade-offs between the clinic productivity (measured by daily patient throughput) and efficiency (measured by visit cycle and patient wait time for doctor). The results showed that the best productivity-efficiency balance was derived under the prediction-based double-booking strategy. The proposed hybrid decision analytics approach has the potential to better support decision-making in primary care operations management and improve the system’s performance. Further, it can be generalized in the context of various healthcare settings for broader applications.
