scholarly journals Is Diversity (Un-)Biased? Project Selection Decisions in Executive Committees

2020 ◽  
Vol 22 (5) ◽  
pp. 906-924 ◽  
Author(s):  
Nektarios Oraiopoulos ◽  
Stylianos Kavadias
Keyword(s):  
Operations Management ◽  
Project Selection ◽  
Problem Definition ◽  
Decision Quality ◽  
Theoretic Model ◽  
Type I ◽  
Wrong Decision ◽  
Business Units ◽  
Selection Decisions ◽  
Managerial Implications

Problem definition: Is a committee composed of more or less cognitively diverse members better at approving the “good” projects and rejecting the “bad” ones? Academic/practical relevance: We contribute to the operations management literature by accounting for the fact that critical selection decisions are often made by a committee rather than a single decision maker. Understanding how the magnitude of diversity affects the decision quality of such a committee is an important consideration for practitioners. Methodology: We utilize a game-theoretic model to show that diverse perspectives are rarely “averaged out.” Results: Diversity leads to systematic biases in project selection. To mitigate the effect of diverse perspectives, managers need to uncover the sources of diversity: do they originate from different individual valuations and preferences, or do they express different assimilations of the information that arises during the project execution? We show that this distinction is crucial. Higher preference diversity always leads to higher likelihood of making the wrong decision. Higher interpretive diversity may be beneficial for the organization. Managerial implications: A clear managerial action is the need to identify and reduce such preference diversity. Senior management can achieve this by highlighting the need for more transparency in the pipeline of the business units. Moreover, our analysis shows that interpretive diversity can be a powerful managerial lever to influence the propensity for Type I and II errors. The latter might be easier to manage than the organizational structure.

The Effects of Patent Extension and Take-Back Regulation on Green Pharmacy

2021 ◽  
Author(s):  
Tianqin Shi ◽  
Nicholas C. Petruzzi ◽  
Dilip Chhajed
Keyword(s):  
Pharmaceutical Industry ◽  
Social Welfare ◽  
Technical Information ◽  
Problem Definition ◽  
Theoretic Model ◽  
Innovative Company ◽  
Take Back ◽  
Starting Point ◽  
European Environmental Agency ◽  
Managerial Implications

Problem definition: The eco-toxicity arising from unused pharmaceuticals has regulators advocating the benign design concept of “green pharmacy,” but high research and development expenses can be prohibitive. We therefore examine the impacts of two regulatory mechanisms, patent extension and take-back regulation, on inducing drug manufacturers to go green. Academic/practical relevance: One incentive suggested by the European Environmental Agency is a patent extension for a company that redesigns its already patented pharmaceutical to be more environmentally friendly. This incentive can encourage both the development of degradable drugs and the disclosure of technical information. Yet, it is unclear how effective the extension would be in inducing green pharmacy and in maximizing social welfare. Methodology: We develop a game-theoretic model in which an innovative company collects monopoly profits for a patented pharmaceutical but faces competition from a generic rival after the patent expires. A social-welfare-maximizing regulator is the Stackelberg leader. The regulator leads by offering a patent extension to the innovative company while also imposing take-back regulation on the pharmaceutical industry. Then the two-profit maximizing companies respond by setting drug prices and choosing whether to invest in green pharmacy. Results: The regulator’s optimal patent extension offer can induce green pharmacy but only if the offer exceeds a threshold length that depends on the degree of product differentiation present in the pharmaceutical industry. The regulator’s correspondingly optimal take-back regulation generally prescribes a required collection rate that decreases as its optimal patent extension offer increases, and vice versa. Managerial implications: By isolating green pharmacy as a potential target to address pharmaceutical eco-toxicity at its source, the regulatory policy that we consider, which combines the incentive inherent in earning a patent extension on the one hand with the penalty inherent in complying with take-back regulation on the other hand, serves as a useful starting point for policymakers to optimally balance economic welfare considerations with environmental stewardship considerations.

Truthful Mechanisms for Medical Surplus Product Allocation

2020 ◽  
Vol 22 (4) ◽  
pp. 735-753 ◽  
Author(s):  
Can Zhang ◽  
Atalay Atasu ◽  
Turgay Ayer ◽  
L. Beril Toktay
Keyword(s):  
Private Information ◽  
Value Added ◽  
Information Provision ◽  
Problem Definition ◽  
Preference Ranking ◽  
Additional Increase ◽  
Healthcare Facilities ◽  
Selection Decisions ◽  
Managerial Implications ◽  
Recipient Selection

Problem definition: We analyze a resource allocation problem faced by medical surplus recovery organizations (MSROs) that recover medical surplus products to fulfill the needs of underserved healthcare facilities in developing countries. The objective of this study is to identify implementable strategies to support recipient selection decisions to improve MSROs’ value provision capability. Academic/practical relevance: MSRO supply chains face several challenges that differ from those in traditional for-profit settings, and there is a lack of both academic and practical understanding of how to better match supply with demand in this setting where recipient needs are typically private information. Methodology: We propose a mechanism design approach to determine which recipient to serve at each shipping opportunity based on recipients’ reported preference rankings of different products. Results: We find that when MSRO inventory information is shared with recipients, the only truthful mechanism is random selection among recipients, which defeats the purpose of eliciting information. Subsequently, we show that (1) eliminating inventory information provision enlarges the set of truthful mechanisms, thereby increasing the total value provision; and (2) further withholding information regarding other recipients leads to an additional increase in total value provision. Finally, we show that under a class of implementable mechanisms, eliciting recipient valuations has no value added beyond eliciting preference rankings. Managerial implications: (1) MSROs with large recipient bases and low inventory levels can significantly improve their value provision by appropriately determining the recipients to serve through a simple scoring mechanism; (2) to truthfully elicit recipient needs information to support the recipient selection decisions, MSROs should withhold inventory and recipient-base information; and (3) under a set of easy-to-implement scoring mechanisms, it is sufficient for MSROs to elicit recipients’ preference ranking information. Our findings have already led to a change in the practice of an award-winning MSRO.

Managing Congestion in Matching Markets

2021 ◽  
Author(s):  
Nick Arnosti ◽  
Ramesh Johari ◽  
Yash Kanoria
Keyword(s):  
Problem Definition ◽  
Theoretic Model ◽  
Matching Markets ◽  
Contact Potential ◽  
Managerial Implications ◽  
The Commons ◽  
Game Theoretic ◽  
Face Search ◽  
Uniqueness Of Equilibrium ◽  
Never Screened

Problem definition: Participants in matching markets face search and screening costs when seeking a match. We study how platform design can reduce the effort required to find a suitable partner. Practical/academic relevance: The success of matching platforms requires designs that minimize search effort and facilitate efficient market clearing. Methodology: We study a game-theoretic model in which “applicants” and “employers” pay costs to search and screen. An important feature of our model is that both sides may waste effort: Some applications are never screened, and employers screen applicants who may have already matched. We prove existence and uniqueness of equilibrium and characterize welfare for participants on both sides of the market. Results: We identify that the market operates in one of two regimes: It is either screening-limited or application-limited. In screening-limited markets, employer welfare is low, and some employers choose not to participate. This occurs when application costs are low and there are enough employers that most applicants match, implying that many screened applicants are unavailable. In application-limited markets, applicants face a “tragedy of the commons” and send many applications that are never read. The resulting inefficiency is worst when there is a shortage of employers. We show that simple interventions—such as limiting the number of applications that an individual can send, making it more costly to apply, or setting an appropriate market-wide wage—can significantly improve the welfare of agents on one or both sides of the market. Managerial implications: Our results suggest that platforms cannot focus exclusively on attracting participants and making it easy to contact potential match partners. A good user experience requires that participants not waste effort considering possibilities that are unlikely to be available. The operational interventions we study alleviate congestion by ensuring that potential match partners are likely to be available.

Pricing and Matching with Forward-Looking Buyers and Sellers

2020 ◽  
Vol 22 (4) ◽  
pp. 717-734 ◽  
Author(s):  
Yiwei Chen ◽  
Ming Hu
Keyword(s):  
Operations Management ◽  
Sharing Economy ◽  
Problem Definition ◽  
Fixed Base ◽  
Managerial Implications ◽  
Dynamic Stochastic ◽  
Bid Prices ◽  
Supply Costs ◽  
Heuristic Policy ◽  
Forward Looking

Problem definition: We study a dynamic market over a finite horizon for a single product or service in which buyers with private valuations and sellers with private supply costs arrive following Poisson processes. A single market-making intermediary decides dynamically on the ask and bid prices that will be posted to buyers and sellers, respectively, and on the matching decisions after buyers and sellers agree to buy and sell. Buyers and sellers can wait strategically for better prices after they arrive. Academic/practical relevance: This problem is motivated by the emerging sharing economy and directly speaks to the core of operations management that is about matching supply with demand. Methodology: The dynamic, stochastic, and game-theoretic nature makes the problem intractable. We employ the mechanism-design methodology to establish a tractable upper bound on the optimal profit, which motivates a simple heuristic policy. Results: Our heuristic policy is: fixed ask and bid prices plus price adjustments as compensation for waiting costs, in conjunction with the greedy matching policy on a first-come-first-served basis. These fixed base prices balance demand and supply in expectation and can be computed efficiently. The waiting-compensated price processes are time-dependent and tend to have opposite trends at the beginning and end of the horizon. Under this heuristic policy, forward-looking buyers and sellers behave myopically. This policy is shown to be asymptotically optimal. Managerial implications: Our results suggest that the intermediary might not lose much optimality by maintaining stable prices unless the underlying market conditions have significantly changed, not to mention that frequent surge pricing may antagonize riders and induce riders and drivers to behave strategically in ways that are hard to account for with traditional pricing models.

Incentivized Actions in Freemium Games

2020 ◽  
Author(s):  
Lifei Sheng ◽  
Christopher Thomas Ryan ◽  
Mahesh Nagarajan ◽  
Yuan Cheng ◽  
Chunyang Tong
Keyword(s):  
Operations Management ◽  
Sufficient Conditions ◽  
Problem Definition ◽  
Data Set ◽  
New Class ◽  
Recent Innovation ◽  
Markov Decision ◽  
Managerial Implications ◽  
Operational Issues ◽  
Data Driven Decisions

Problem definition: Games are the fastest-growing sector of the entertainment industry. Freemium games are the fastest-growing segment within games. The concept behind freemium is to attract large pools of players, many of whom will never spend money on the game. When game publishers cannot earn directly from the pockets of consumers, they employ other revenue-generating content, such as advertising. Players can become irritated by revenue-generating content. A recent innovation is to offer incentives for players to interact with such content, such as clicking an ad or watching a video. These are termed incentivized (incented) actions. We study the optimal deployment of incented actions. Academic/practical relevance: Removing or adding incented actions can essentially be done in real-time. Accordingly, the deployment of incented actions is a tactical, operational question for game designers. Methodology: We model the deployment problem as a Markov decision process (MDP). We study the performance of simple policies, as well as the structure of optimal policies. We use a proprietary data set to calibrate our MDP and derive insights. Results: Cannibalization—the degree to which incented actions distract players from making in-app purchases—is the key parameter for determining how to deploy incented actions. If cannibalization is sufficiently high, it is never optimal to offer incented actions. If cannibalization is sufficiently low, it is always optimal to offer. We find sufficient conditions for the optimality of threshold strategies that offer incented actions to low-engagement users and later remove them once a player is sufficiently engaged. Managerial implications: This research introduces operations management academics to a new class of operational issues in the games industry. Managers in the games industry can gain insights into when incentivized actions can be more or less effective. Game designers can use our MDP model to make data-driven decisions for deploying incented actions.

Artificial Shortage in Agricultural Supply Chains

2021 ◽  
Author(s):  
Retsef Levi ◽  
Somya Singhvi ◽  
Yanchong Zheng
Keyword(s):  
Supply Chain ◽  
Dynamic Game ◽  
Consumer Welfare ◽  
Problem Definition ◽  
Theoretic Model ◽  
Government Interventions ◽  
Behavioral Game ◽  
Managerial Implications ◽  
The Government ◽  
Preemptive Effect

Problem definition: Price surge of essential commodities despite inventory availability, due to artificial shortage, presents a serious threat to food security in many countries. To protect consumers’ welfare, governments intervene reactively with either (i) cash subsidy, to increase consumers’ purchasing power by directly transferring cash; or (ii) supply allocation, to increase product availability by importing the commodity from foreign markets and selling it at subsidized rates. Academic/practical relevance: This paper develops a new behavioral game-theoretic model to examine the supply chain and market dynamics that engender artificial shortage as well as to analyze the effectiveness of various government interventions in improving consumer welfare. Methodology: We analyze a three-stage dynamic game between the government and the trader. We fully characterize the market equilibrium and the resulting consumer welfare under the base scenario of no government intervention as well as under each of the interventions being studied. Results: The analysis demonstrates the disparate effects of different interventions on artificial shortage; whereas supply allocation schemes often mitigate shortage, cash subsidy can inadvertently aggravate shortage in the market. Furthermore, empirical analysis with actual data on onion prices in India shows that the proposed model explains the data well and provides specific estimates on the implied artificial shortage. A counterfactual analysis quantifies the potential impacts of government interventions on market outcomes. Managerial implications: The analysis shows that reactive government interventions with supply allocation schemes can have a preemptive effect to reduce the trader’s incentive to create artificial shortage. Although cash subsidy schemes have recently gained wide popularity in many countries, we caution governments to carefully consider the strategic responses of different stakeholders in the supply chain when implementing cash subsidy schemes.

The Effects of Multisiting on Productivity and Quality

2020 ◽  
Author(s):  
Diwas KC ◽  
Sokol Tushe
Keyword(s):  
Operations Management ◽  
Low Complexity ◽  
Cognitive Effort ◽  
Problem Definition ◽  
Negative Effects ◽  
Data Set ◽  
Managerial Implications ◽  
Potential Benefits ◽  
Frequent Site

Problem definition: In the modern workplace, it is increasingly common for workers to concurrently attend to tasks across multiple physical locations. However, frequent site switching can lead to increased setup and overhead costs. Specifically, workers expend significant time and cognitive effort getting reoriented with personnel, operating processes, tools, and resources whenever they switch sites. In this paper, we look at the productivity and quality implications of multisite work. Academic/practical relevance: Although multisite workplace deployment is increasingly common, its impact on people operations has not been examined in the operations management literature. We contribute to the literature by studying the effect of multisiting on individual worker productivity and quality of output. Methodology: To estimate the effect of multisite operations on performance, we turn to a setting where multisite worker assignment is common—that of physicians who have admitting privileges at multiple hospitals. We collected detailed data on individual physicians practicing in 83 hospitals between 1999 and 2010. Our extensive data set includes detailed operational and clinical factors associated with more than 950,000 patient encounters. Our empirical analysis takes the form of a panel, where we follow a given physician over time and link short-term multisiting to patient-level outcomes. Results: We find that multisiting negatively impacts productivity. Specifically, for each additional site at which a physician works, we observe a 2% increase in patient length of stay. For each site served, the likelihood of a patient developing a complication increases by 3%. Greater travel distance between sites and lack of focus at a given site explain the performance declines due to multisiting. In addition, we find that the performance declines resulting from multisite operation are reduced among low-complexity patients and among highly experienced physicians. Managerial implications: Multisite performance losses need to be traded off against the potential benefits. The negative effects of multisiting can be mitigated by limiting multisite deployment to simpler tasks and among highly experienced physicians. Managers can decrease switching costs of multisite work by standardizing workflows, processes, and tools across sites. In addition, the practice of multisite work can be limited to sites that are physically proximate to avoid the overhead costs associated with excessive travel.

scholarly journals The Role of Problem Specification in Crowdsourcing Contests for Design Problems: A Theoretical and Empirical Analysis

2020 ◽  
Author(s):  
Zhaohui (Zoey) Jiang ◽  
Yan Huang ◽  
Damian R. Beil
Keyword(s):  
Problem Definition ◽  
Empirical Literature ◽  
Theoretic Model ◽  
Real World Data ◽  
Solution Quality ◽  
Design Problems ◽  
Managerial Implications ◽  
Game Theoretic ◽  
Types Of Information

Problem definition: This paper studies the role of seekers’ problem specification in crowdsourcing contests for design problems. Academic/practical relevance: Platforms hosting design contests offer detailed guidance for seekers to specify their problems when launching a contest. Yet problem specification in such crowdsourcing contests is something the theoretical and empirical literature has largely overlooked. We aim to fill this gap by offering an empirically validated model to generate insights for the provision of information at contest launch. Methodology: We develop a game-theoretic model featuring different types of information (categorized as “conceptual objectives” or “execution guidelines”) in problem specifications and assess their impact on design processes and submission qualities. Real-world data are used to empirically test hypotheses and policy recommendations generated from the model, and a quasi-natural experiment provides further empirical validation. Results: We show theoretically and verify empirically that with more conceptual objectives disclosed in the problem specification, the number of participants in a contest eventually decreases; with more execution guidelines in the problem specification, the trial effort provision by each participant increases; and the best solution quality always increases with more execution guidelines but eventually decreases with more conceptual objectives. Managerial implications: To maximize the best solution quality in crowdsourced design problems, seekers should always provide more execution guidelines and only a moderate number of conceptual objectives.

scholarly journals Forecasting Airport Transfer Passenger Flow Using Real-Time Data and Machine Learning

2021 ◽  
Author(s):  
Xiaojia Guo ◽  
Yael Grushka-Cockayne ◽  
Bert De Reyck
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Operations Management ◽  
Regression Tree ◽  
Problem Definition ◽  
Second Phase ◽  
Time Data ◽  
Managerial Implications ◽  
Passenger Flows ◽  
Data Driven Decisions

Problem definition: Airports and airlines have been challenged to improve decision making by producing accurate forecasts in real time. We develop a two-phased predictive system that produces forecasts of transfer passenger flows at an airport. In the first phase, the system predicts the distribution of individual transfer passengers’ connection times. In the second phase, the system samples from the distribution of individual connection times and produces distributional forecasts for the number of passengers arriving at the immigration and security areas. Academic/practical relevance: To our knowledge, this work is the first to apply machine learning for predicting real-time distributional forecasts of journeys in an airport using passenger level data. Better forecasts of these journeys can help optimize passenger experience and improve airport resource deployment. Methodology: The predictive system developed is based on a regression tree combined with copula-based simulations. We generalize the tree method to predict distributions, moving beyond point forecasts. We also formulate a newsvendor-based resourcing problem to evaluate decisions made by applying the new predictive system. Results: We show that, when compared with benchmarks, our two-phased approach is more accurate in predicting both connection times and passenger flows. Our approach also has the potential to reduce resourcing costs at the immigration and transfer security areas. Managerial implications: Our predictive system can produce accurate forecasts frequently and in real time. With these forecasts, an airport’s operating team can make data-driven decisions, identify late passengers, and assist them to make their connections. The airport can also update its resourcing plans based on the prediction of passenger flows. Our predictive system can be generalized to other operations management domains, such as hospitals or theme parks, in which customer flows need to be accurately predicted.

scholarly journals Optimal Duration of Innovation Contests

2020 ◽  
Author(s):  
C. Gizem Korpeoglu ◽  
Ersin Körpeoğlu ◽  
Sıdıka Tunç
Keyword(s):  
Problem Definition ◽  
Theory And Practice ◽  
Theoretic Model ◽  
Output Uncertainty ◽  
Support Tools ◽  
Managerial Implications ◽  
Game Theoretic ◽  
Award Scheme ◽  
Over Time

Problem definition: We study the contest duration and the award scheme of an innovation contest where an organizer elicits solutions to an innovation-related problem from a group of agents. Academic/practical relevance: Our interviews with practitioners at crowdsourcing platforms have revealed that the duration of a contest is an important operational decision. Yet, the theoretical literature has long overlooked this decision. Also, the literature fails to adequately explain why giving multiple unequal awards is so common in crowdsourcing platforms. We aim to fill these gaps between the theory and practice. We generate insights that seem consistent with both practice and empirical evidence. Methodology: We use a game-theoretic model where the organizer decides on the contest duration and the award scheme while each agent decides on her participation and determines her effort over the contest duration by considering potential changes in her productivity over time. The quality of an agent’s solution improves with her effort, but it is also subject to an output uncertainty. Results: We show that the optimal contest duration increases as the relative impact of the agent uncertainty on her output increases, and it decreases if the agent productivity increases over time. We characterize an optimal award scheme and show that giving multiple (almost always) unequal awards is optimal when the organizer’s urgency in obtaining solutions is below a certain threshold. We also show that this threshold is larger when the agent productivity increases over time. Finally, consistent with empirical findings, we show that there is a positive correlation between the optimal contest duration and the optimal total award. Managerial implications: Our results suggest that the optimal contest duration increases with the novelty or sophistication of solutions that the organizer seeks, and it decreases when the organizer can offer support tools that can increase the agent productivity over time. These insights and their drivers seem consistent with practice. Our findings also suggest that giving multiple unequal awards is advisable for an organizer who has low urgency in obtaining solutions. Finally, giving multiple awards goes hand in hand with offering support tools that increase the agent productivity over time. These results help explain why many contests on crowdsourcing platforms give multiple unequal awards.

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