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.

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 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.

Dynamic Type Matching

2021 ◽  
Author(s):  
Ming Hu ◽  
Yun Zhou
Keyword(s):  
Sufficient Conditions ◽  
Sharing Economy ◽  
Problem Definition ◽  
Periodic Review ◽  
Optimal Matching ◽  
Demand And Supply ◽  
Holding Costs ◽  
Current Period ◽  
Dynamic Type ◽  
Managerial Implications

Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.

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.

scholarly journals Understanding the Value of Fulfillment Flexibility in an Online Retailing Environment

2021 ◽  
Author(s):  
Levi DeValve ◽  
Yehua Wei ◽  
Di Wu ◽  
Rong Yuan
Keyword(s):  
Theoretical Analysis ◽  
Numerical Simulations ◽  
Problem Definition ◽  
Order Fulfillment ◽  
Distribution Centers ◽  
Online Retailing ◽  
New Class ◽  
Managerial Implications ◽  
The Cost ◽  
Improve Service Quality

Problem definition: Fulfillment flexibility, the ability of distribution centers (DCs) to fulfill demand originating from other DCs, can help e-retailers reduce lost sales and improve service quality. Because the cost of full flexibility is prohibitive, we seek to understand the value of partially flexible fulfillment networks under simple and effective fulfillment policies. Academic/practical relevance: We propose a general method for understanding the practical value of (partial) fulfillment flexibility using a data-driven model, theoretical analysis, and numerical simulations. Our method applies to settings with local fulfillment (i.e., order fulfillment from the originating DC) prioritization and possible customer abandonment, two features that are new to the fulfillment literature. We then apply this method for a large e-retailer. We also introduce a new class of spillover limit fulfillment policies with attractive theoretical and practical features. Methodology: Our analysis uses dynamic and stochastic optimization, applied probability, and numerical simulations. Results: We derive optimal fulfillment policies in stylized settings, as well as bounds on the performance under an optimal policy using theoretical analysis, to provide guidelines on which policies to test in numerical simulations. We then use simulations to estimate for our industrial partner that a proposed fulfillment network with additional flexibility equates to a profit improvement on the order of tens of millions of U.S. dollars. Managerial implications: We provide an approach for e-retailers to understand when fulfillment flexibility is most valuable. We find that fulfillment flexibility provides the most benefit for our collaborator when gross profits are high relative to fulfillment costs or centrally held inventory is low. Also, we identify the risks of myopic fulfillment with additional flexibility and demonstrate that an effective spillover limit policy mitigates these risks.

Beyond Related Experience: Upstream vs. Downstream Experience in Innovation Contest Platforms with Interdependent Problem Domains

2020 ◽  
Vol 22 (5) ◽  
pp. 1045-1065 ◽  
Author(s):  
Nirup Menon ◽  
Anant Mishra ◽  
Shun Ye
Keyword(s):  
Problem Solving ◽  
Software Development ◽  
Problem Definition ◽  
Problem Domain ◽  
Data Set ◽  
Job Rotation ◽  
Managerial Implications ◽  
Significant Performance ◽  
Innovation Contest ◽  
Output Information

Problem definition: Innovation contest platforms are often organized around specific fields and host contests that span a variety of interdependent problem domains. Whereas contestants may benefit from related experience in contests whose problem domains share an interdependency with the focal problem domain, it is unclear whether the benefits of related experience arise symmetrically from upstream experience (i.e., experience in problem domains that provide input information to the focal problem domain) and downstream experience (i.e., experience in problem domains that use output information from the focal problem domain) or differ among them. Academic/practical relevance: Given that innovation contest platforms serve to effectively match contest problem requirements with contestants’ skills, it is important to understand how a contestant’s prior experience on a platform contributes to her problem-solving performance. Our research provides a more granular examination of the benefits of related experience than what has been examined in prior studies on individual learning or innovation contests. Methodology: We collected detailed archival data from TopCoder, a leading innovation contest platform that hosts contests across multiple interdependent software development problem domains, from its launch in 2001 to September 2013. Our data set comprises detailed participation histories of 821 contestants in 3,274 contests across eight interdependent problem domains involving 8,985 observations. Results: Whereas a contestant’s related experience on the innovation contest platform is more positively associated with her focal contest performance compared with unrelated experience, the benefits of related experience arise only from downstream experience. That is, there are no significant performance benefits of upstream experience. Furthermore, the performance benefits of downstream experience are greater when the contest duration is shorter, highlighting its role in enabling more efficient search and problem solving in innovation contest platforms with interdependent problem domains. Managerial implications: Contrary to the notion of “hyperspecialization,” our findings suggest that contestants can reap benefits from diversifying their experience into downstream problem domains on innovation contest platforms. Furthermore, innovation contest platforms could facilitate such targeted diversification of contestant experience by developing more granular metrics of contestant experience across problem domains. Our findings also have implications for resource allocation and job rotation decisions in software development projects within firms.

Does Online Training Work in Retail?

2020 ◽  
Author(s):  
Marshall Fisher ◽  
Santiago Gallino ◽  
Serguei Netessine
Keyword(s):  
Causal Effect ◽  
Indirect Costs ◽  
Online Training ◽  
Problem Definition ◽  
Product Knowledge ◽  
Data Set ◽  
Sales Training ◽  
Training Methodology ◽  
Managerial Implications ◽  
The One

Problem definition: How much, if at all, does training in product features increase a sales associate’s sales productivity? Academic/practical relevance: A knowledgeable retail sales associate (SA) can explain the features of available product variants and give a customer sufficient confidence in the customer’s choice or suggest alternatives so that the customer becomes willing to purchase. Although it is plausible that increasing an SA’s product knowledge will increase sales, training is not without cost and turnover is high in retail, so most retailers provide little product-knowledge training. Methodology: We partner with two firms and collect data on more than 50,000 SAs who had access to training. We assemble a detailed data set of the training history and individual sales productivity over a two-year period. We conduct econometric analysis to quantify the causal effect of training on sales. Results: For SAs who engaged in training, the sales rate increases by 1.8% for every online module taken, which is a much higher benefit than the direct or indirect costs associated with this training. Brand-specific training has a larger effect on the focal brand; however, there is a positive effect on other brands the SA sells. We also assess how the training benefit varies depending on the SA’s tenure, sales rate prior to training, and number of modules taken. Managerial implications: We present evidence of a novel training mechanism that can be extremely attractive to retailers. Online training tools, such as the one we study, have two characteristics that should not be overlooked. First, it is the brands, not the retailers, that create, develop, and pay for the training content. Second, the incentives are such that SAs invest their own time, rather than time on the job, to train, and this makes the retailer’s investment in the training a profitable proposition.

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.

Reveal the Supplier List? A Trade-off in Capacity vs. Responsibility

2020 ◽  
Vol 22 (6) ◽  
pp. 1251-1267 ◽  
Author(s):  
Basak Kalkanci ◽  
Erica L. Plambeck
Keyword(s):  
Operations Management ◽  
Cost Reduction ◽  
Problem Definition ◽  
Expected Profit ◽  
Environmental Responsibility ◽  
Management Literature ◽  
New Business ◽  
Managerial Implications ◽  
Electrical Wiring ◽  
Do So

Problem definition: Under what conditions and how can a buying firm, by committing to publish a list of its suppliers and/or the identities and violations of terminated suppliers, increase its expected profit and supplier responsibility? Academic/practical relevance: This paper contributes to a recent thrust in the operations-management literature on how various sorts of transparency influence social and environmental responsibility in a supply chain. In practice, companies are under pressure to publish their supplier lists and suppliers’ violations, and some are beginning to do so. This paper could help guide their decisions. Methodology: The methodology is game theory. Results: This paper shows how a buying firm can use transparency to reward a supplier for responsibility effort to eliminate social or environmental violations. By publishing its supplier list, the buying firm can signal that a supplier is responsible and generate profitable new business for the supplier. However, the resulting competition for the supplier’s scarce capacity could cause the buying firm to obtain fewer units or pay a higher price. We identify the conditions under which a buying firm should commit to publish its supplier list and conditions under which the buying firm should also help a supplier with cost reduction or capacity expansion. In addition, the paper shows how a buying firm can use transparency to punish a supplier for a responsibility violation—by warning other buying firms not to source from that supplier. Commitment to do so increases the supplier’s responsibility effort and can screen out a supplier with a known responsibility violation, thereby increasing a buying firm’s expected profit. If the supplier is uncertain whether it has a violation (e.g., faulty electrical wiring likely to cause a fire), then the two forms of transparency can be complementary. Managerial implications: Buying firms should consider transparency as a potentially profitable approach to mitigating social and environmental violations in their supply chains.

scholarly journals Budgeting in International Humanitarian Organizations

2021 ◽  
Author(s):  
Milad Keshvari Fard ◽  
Ivana Ljubić ◽  
Felix Papier
Keyword(s):  
Utility Function ◽  
Operations Management ◽  
Benders Decomposition ◽  
International Committee ◽  
Problem Definition ◽  
Red Cross ◽  
Solution Approach ◽  
Humanitarian Organizations ◽  
Managerial Implications ◽  
Using Data

Problem definition: International humanitarian organizations (IHOs) prepare a detailed annual allocation plan for operations that are conducted in the countries they serve. The annual plan is strongly affected by the available financial budget. The budget of IHOs is derived from donations, which are typically limited, uncertain, and to a large extent earmarked for specific countries or programs. These factors, together with the specific utility function of IHOs, render budgeting for IHOs a challenging managerial problem. In this paper, we develop an approach to optimize budget allocation plans for each country of operations. Academic/practical relevance: The current research provides a better understanding of the budgeting problem in IHOs given the increasing interest of the operations management community for nonprofit operations. Methodology: We model the problem as a two-stage stochastic optimization model with a concave utility function and identify a number of analytical properties for the problem. We develop an efficient generalized Benders decomposition algorithm as well as a fast heuristic. Results: Using data from the International Committee of the Red Cross, our results indicate 21.3% improvement in the IHO’s utility by adopting stochastic programming instead of the expected value solution. Moreover, our solution approach is computationally more efficient than other approaches. Managerial implications: Our analysis highlights the importance of nonearmarked donations for the overall performance of IHOs. We also find that putting pressure on IHOs to fulfill the targeted missions (e.g., by donors or media) results in lower beneficiaries’ welfare. Moreover, the IHOs benefit from negative correlation among donations. Finally, our findings indicate that, if donors allow the IHO to allocate unused earmarked donations to other delegations, the performance of the IHO improves significantly.

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