We study decision behavior in the selection, forecasting, and production for a new product. In a stylized behavioral model and five experiments, we generate new insight into when and why this combination of tasks can lead to overconfidence (specifically, overestimating the demand). We theorize that cognitive limitations lead to noisy interpretations of signal information, which itself is noisy. Because people are statistically naive, they directly use their noisy interpretation of the signal information as their forecast, thereby underaccounting for the uncertainty that underlies it. This process leads to unbiased forecast errors when considering products in isolation, but leads to positively biased forecasts for the products people choose to launch due to a selection effect. We show that this selection-driven overconfidence can be sufficiently problematic that, under certain conditions, choosing the product randomly can actually yield higher profits than when individuals themselves choose the product to launch. We provide mechanism evidence by manipulating the interpretation noise through information complexity—showing that even when the information is equivalent from a Bayesian perspective, more complicated information leads to more noise, which, in turn, leads to more overconfidence in the chosen products. Finally, we leverage this insight to show that getting a second independent forecast for a chosen product can significantly mitigate the overconfidence problem, even when both individuals have the same information. This paper was accepted by Charles Corbett, operations management.
{"title":"From Noise to Bias: Overconfidence in New Product Forecasting","authors":"D. Feiler, Jordan D. Tong","doi":"10.2139/ssrn.3660069","DOIUrl":"https://doi.org/10.2139/ssrn.3660069","url":null,"abstract":"We study decision behavior in the selection, forecasting, and production for a new product. In a stylized behavioral model and five experiments, we generate new insight into when and why this combination of tasks can lead to overconfidence (specifically, overestimating the demand). We theorize that cognitive limitations lead to noisy interpretations of signal information, which itself is noisy. Because people are statistically naive, they directly use their noisy interpretation of the signal information as their forecast, thereby underaccounting for the uncertainty that underlies it. This process leads to unbiased forecast errors when considering products in isolation, but leads to positively biased forecasts for the products people choose to launch due to a selection effect. We show that this selection-driven overconfidence can be sufficiently problematic that, under certain conditions, choosing the product randomly can actually yield higher profits than when individuals themselves choose the product to launch. We provide mechanism evidence by manipulating the interpretation noise through information complexity—showing that even when the information is equivalent from a Bayesian perspective, more complicated information leads to more noise, which, in turn, leads to more overconfidence in the chosen products. Finally, we leverage this insight to show that getting a second independent forecast for a chosen product can significantly mitigate the overconfidence problem, even when both individuals have the same information. This paper was accepted by Charles Corbett, operations management.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79283399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
How to avoid project failures driven by overoptimistic schedules? Managers often attempt to mitigate the duration underestimation and improve the accuracy of project schedules by providing their planners with excessively detailed project specifications. While this traditional approach may be intuitive, solely providing more detailed information has proven to have a limited effect on eliminating behavioral biases. We experimentally test the effectiveness of providing detailed specification and compare it to an alternative intervention of providing historical information about the average duration of similar projects in the past. We find that both interventions mitigate the underestimation bias. However, since providing detailed project specification results in high variance of estimation errors due to sizable over- and underestimates, only the provision of historical information leads to more accurate project duration estimates. We also test whether it is more effective to anchor planners by providing historical information simultaneously with the project specification or to provide the historical information only after beliefs regarding the project duration are formed, in which case planners can regress their initial estimates towards the historical average. We find that the timing of disclosing information does not play a role as the estimation bias is mitigated and the accuracy is improved in both conditions. Finally, we observe that the subjective confidence in the accuracy of duration estimates does not vary across the interventions, suggesting that the confidence is neither a function of the amount nor the detail of available information.
{"title":"Improving the accuracy of project schedules","authors":"M. Lorko, Maroš Servátka, Le Zhang","doi":"10.2139/ssrn.3437760","DOIUrl":"https://doi.org/10.2139/ssrn.3437760","url":null,"abstract":"How to avoid project failures driven by overoptimistic schedules? Managers often attempt to mitigate the duration underestimation and improve the accuracy of project schedules by providing their planners with excessively detailed project specifications. While this traditional approach may be intuitive, solely providing more detailed information has proven to have a limited effect on eliminating behavioral biases. We experimentally test the effectiveness of providing detailed specification and compare it to an alternative intervention of providing historical information about the average duration of similar projects in the past. We find that both interventions mitigate the underestimation bias. However, since providing detailed project specification results in high variance of estimation errors due to sizable over- and underestimates, only the provision of historical information leads to more accurate project duration estimates. We also test whether it is more effective to anchor planners by providing historical information simultaneously with the project specification or to provide the historical information only after beliefs regarding the project duration are formed, in which case planners can regress their initial estimates towards the historical average. We find that the timing of disclosing information does not play a role as the estimation bias is mitigated and the accuracy is improved in both conditions. Finally, we observe that the subjective confidence in the accuracy of duration estimates does not vary across the interventions, suggesting that the confidence is neither a function of the amount nor the detail of available information.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87143598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper focuses on the freemium paradigm that imposes a usage limit on free users and allows for paying users unlimited usage. We formulate a two-period model that incorporates the responses of users who are heterogeneous in both their valuations of marginal usage and total usage. Two freemium strategies with distinct market-coverage approaches emerge. The first strategy is an aggressive strategy that attempts to acquire the whole market, while the second strategy selectively targets heavy users. Optimal strategic decisions are derived for both freemium strategies and benchmarked against a conventional subscription strategy. We identify the strategic dominance criteria and examine the benefits of freemium strategies. The degree of intolerance towards insufficient usage and the proportion of high-valuation users determine which strategy is dominant. The need to alleviate users’ inconvenience drives the free usage limit higher and thus weighs against the performance of freemium strategies. In such circumstances, a more aggressive freemium strategy that offers generous free usage at a high price underperforms a selective variant that offers modest free usage and a lower price. The proportion of high valuation users determines the optimal strategy between a selective freemium strategy and a conventional subscription strategy. The latter outperforms if the proportion of high-valuation users is either very small or very large. Additionally, this study discusses the appropriate level for the conversion rate by showing that a firm should expect a lower conversion rate when implementing an aggressive freemium strategy.
{"title":"Designing Freemium with Usage Limitation: When Is It a Viable Strategy?","authors":"Tanet Kato, A. Dumrongsiri","doi":"10.2139/ssrn.3715568","DOIUrl":"https://doi.org/10.2139/ssrn.3715568","url":null,"abstract":"This paper focuses on the freemium paradigm that imposes a usage limit on free users and allows for paying users unlimited usage. We formulate a two-period model that incorporates the responses of users who are heterogeneous in both their valuations of marginal usage and total usage. Two freemium strategies with distinct market-coverage approaches emerge. The first strategy is an aggressive strategy that attempts to acquire the whole market, while the second strategy selectively targets heavy users. Optimal strategic decisions are derived for both freemium strategies and benchmarked against a conventional subscription strategy. We identify the strategic dominance criteria and examine the benefits of freemium strategies. The degree of intolerance towards insufficient usage and the proportion of high-valuation users determine which strategy is dominant. The need to alleviate users’ inconvenience drives the free usage limit higher and thus weighs against the performance of freemium strategies. In such circumstances, a more aggressive freemium strategy that offers generous free usage at a high price underperforms a selective variant that offers modest free usage and a lower price. The proportion of high valuation users determines the optimal strategy between a selective freemium strategy and a conventional subscription strategy. The latter outperforms if the proportion of high-valuation users is either very small or very large. Additionally, this study discusses the appropriate level for the conversion rate by showing that a firm should expect a lower conversion rate when implementing an aggressive freemium strategy.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86006766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the implications of selling through a voice-based virtual assistant (VA). The seller has a set of products available and the VA decides which product to offer and at what price, seeking to maximize its revenue, consumer- or total-surplus. The consumer is impatient and rational, seeking to maximize her expected utility given the information available to her. The VA selects products based on the consumer's request and other information available to it and then presents them sequentially. Once a product is presented and priced, the consumer evaluates it and decides whether to make a purchase. The consumer's valuation of each product comprises a pre-evaluation value, which is common knowledge, and a post-evaluation component which is private to the consumer. We solve for the equilibria and develop efficient algorithms for implementing the solution. We examine the effects of information asymmetry on the outcomes and study how incentive misalignment depends on the distribution of private valuations. We find that monotone rankings are optimal in the cases of a highly patient or impatient consumer and provide a good approximation for other levels of patience. The relationship between products' expected valuations and prices depends on the consumer's patience level and is monotone increasing (decreasing) when the consumer is highly impatient (patient). Also, the seller's share of total surplus decreases in the amount of private information. We compare the VA to a traditional web-based interface, where multiple products are presented simultaneously on each page. We find that within a page, the higher-value products are priced lower than the lower-value products when the private valuations are exponentially distributed. Finally, the web-based interface generally achieves higher profits for the seller than a VA due to the greater commitment power inherent in its presentation.
{"title":"Sales Policies for a Virtual Assistant","authors":"Wenjia Ba, H. Mendelson, Mingxi Zhu","doi":"10.2139/ssrn.3718080","DOIUrl":"https://doi.org/10.2139/ssrn.3718080","url":null,"abstract":"We study the implications of selling through a voice-based virtual assistant (VA). The seller has a set of products available and the VA decides which product to offer and at what price, seeking to maximize its revenue, consumer- or total-surplus. The consumer is impatient and rational, seeking to maximize her expected utility given the information available to her. The VA selects products based on the consumer's request and other information available to it and then presents them sequentially. Once a product is presented and priced, the consumer evaluates it and decides whether to make a purchase. The consumer's valuation of each product comprises a pre-evaluation value, which is common knowledge, and a post-evaluation component which is private to the consumer. We solve for the equilibria and develop efficient algorithms for implementing the solution. We examine the effects of information asymmetry on the outcomes and study how incentive misalignment depends on the distribution of private valuations. We find that monotone rankings are optimal in the cases of a highly patient or impatient consumer and provide a good approximation for other levels of patience. The relationship between products' expected valuations and prices depends on the consumer's patience level and is monotone increasing (decreasing) when the consumer is highly impatient (patient). Also, the seller's share of total surplus decreases in the amount of private information. We compare the VA to a traditional web-based interface, where multiple products are presented simultaneously on each page. We find that within a page, the higher-value products are priced lower than the lower-value products when the private valuations are exponentially distributed. Finally, the web-based interface generally achieves higher profits for the seller than a VA due to the greater commitment power inherent in its presentation.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80363482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This review covers some fundamental operations of online business and marketplaces. Topics include introduction to online retailing, online marketplaces, analytics and AI for online retailing, supply chain management for online retailing, logistics equipment and technologies for online retailing, order fulfillment for online retailing, last-mile delivery for online retailing, and omni-channel retailing.
{"title":"Online Business and Marketplaces","authors":"Yun Fong Lim","doi":"10.2139/ssrn.3689037","DOIUrl":"https://doi.org/10.2139/ssrn.3689037","url":null,"abstract":"This review covers some fundamental operations of online business and marketplaces. Topics include introduction to online retailing, online marketplaces, analytics and AI for online retailing, supply chain management for online retailing, logistics equipment and technologies for online retailing, order fulfillment for online retailing, last-mile delivery for online retailing, and omni-channel retailing.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73473818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public–private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium—that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public–private partnership.
{"title":"Price-Directed Cost Sharing and Demand Allocation Among Service Providers with Multiple Demand Sources and Multiple Facilities","authors":"Hanlin Liu, Yimin Yu, S. Benjaafar, Huihui Wang","doi":"10.1287/MSOM.2020.0931","DOIUrl":"https://doi.org/10.1287/MSOM.2020.0931","url":null,"abstract":"Problem definition: We consider capacity sharing through demand allocation among firms with multiple demand sources and multiple service facilities. Firms decide on the allocation of demand from different sources to different facilities to minimize delay costs and service-fulfillment costs possibly subject to service-level requirements. If firms decide to operate collectively as a coalition, they must also decide on a scheme for sharing the total cost. Academic/practical relevance: We study capacity sharing through demand allocation in service systems in the presence of service-fulfillment costs. Our problem is motivated by service collaboration in healthcare involving public–private partnerships. Methodology: We formulate the problem as a cooperative game and identify a cost allocation that is in the core. Results: The cost-allocation scheme we identify is price-directed, and the cost of each firm consists of three components: (1) the delay cost incurred within the firm; (2) a cost paid for the capacity used by the firm at facilities owned by other firms; and (3) a payment received for fulfilling demand of other firms at facilities owned by the firm. Interestingly, we show that the cost-allocation scheme is equivalent to a market equilibrium—that is, it can be implemented in a decentralized fashion. We extend our analysis to settings where the capacity of each facility is endogenously determined and to settings where a service-priority policy is deployed. Our results are robust to a variety of generalizations: partial sharing, partial transfer, facilities modeled as general queueing systems, and convex delay costs. Managerial implications: Our findings provide guidelines for and insights into how to carry out demand allocation and cost sharing among different firms in the presence of service-fulfillment costs to foster service collaboration. In particular, the equilibrium market prices can be viewed as the prices/subsidies for service collaboration in a public–private partnership.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83748594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In supply chain transactions, members care how profit is distributed as well as their own payoff. A retailer prefers fairness when he earns less than his supplier. While existing research focuses on fairness in the vertical competition between an upstream supplier and a downstream retailer, this paper studies the impacts of fairness when a retailer is engaged simultaneously in vertical competition with the supplier and in horizontal competition with other retailers. In particular, we consider a setting where a supplier sells differentiable products through two retailers who compete either on quantity or on price. We analyze the influence of fairness on the behavior and performance of the supply chain members. We characterize and compare equilibrium solutions in different competition situations. Our analysis identifies circumstances where fairness may influence the economic outcomes to the fair-minded, the rational retailers, and the supply chain as a whole for either better or worse. More importantly, we find that the presence of horizontal competition can reverse the impact of fairness.
{"title":"Fairness and Competition in Supply Chains","authors":"Jin Qi, Yaozhong Wu, C. Xie","doi":"10.2139/ssrn.3625681","DOIUrl":"https://doi.org/10.2139/ssrn.3625681","url":null,"abstract":"In supply chain transactions, members care how profit is distributed as well as their own payoff. A retailer prefers fairness when he earns less than his supplier. While existing research focuses on fairness in the vertical competition between an upstream supplier and a downstream retailer, this paper studies the impacts of fairness when a retailer is engaged simultaneously in vertical competition with the supplier and in horizontal competition with other retailers. In particular, we consider a setting where a supplier sells differentiable products through two retailers who compete either on quantity or on price. We analyze the influence of fairness on the behavior and performance of the supply chain members. We characterize and compare equilibrium solutions in different competition situations. Our analysis identifies circumstances where fairness may influence the economic outcomes to the fair-minded, the rational retailers, and the supply chain as a whole for either better or worse. More importantly, we find that the presence of horizontal competition can reverse the impact of fairness.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87990235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study and quantify how much, in a markdown pricing situation, customers anticipate regret of paying a high price during the regular season or facing the possibility of a stockout during markdown season. In addition, we provide a framework to quantify the impact of customer regret on retailer’s optimal markdown pricing strategy and resulting revenues across various product categories. We model a regret-prone customer’s purchase decision using a utility based economic model, and estimate the parameters of this model with data obtained from the online channel of a luxury fashion retailer. We consider two types of anticipated regret, which are both incurred when a customer engages in counterfactual thinking at the beginning of a product’s selling season. If customers buy the product at a regular price, they anticipate high-price regret and hence incur the mental cost of knowing that the product may be available later at a discounted price. However, if customers wait for the discounted price, they anticipate stockout regret and hence incur the mental cost of possibly facing a stockout during the markdown season. We show that retailers only need to know the ratio between the two types of anticipated regret to account for their impact on customer purchase decisions and optimize pricing decisions accordingly. We develop an empirical strategy to jointly estimate this regret ratio and customers’ reservation price distribution parameters for each product. We find significant heterogeneity in regret characteristics across product categories. We show that stockout regret generally dominates high-price regret in magnitude. This dominance is even stronger for products that attract more high-spending customers and/or more frequent website visitors. Our counterfactual analysis shows that the luxury fashion retailer could have increased its revenues by up to 11.64% if it were to account for the anticipated customer regret in its pricing strategy. We also propose a method to disentangle the high-price and stockout regrets from the estimated regret ratio and show, for example, that stockout regret in a womenswear category is significantly larger than that of a similar menswear category.
{"title":"Damned if You Buy, Damned if You Wait: An Empirical Investigation of Customer Regret Under Markdown Pricing and Its Implications to Retailing","authors":"Ö. Özer, In-hyo Sul, A. S. Şimşek","doi":"10.2139/ssrn.3729497","DOIUrl":"https://doi.org/10.2139/ssrn.3729497","url":null,"abstract":"We study and quantify how much, in a markdown pricing situation, customers anticipate regret of paying a high price during the regular season or facing the possibility of a stockout during markdown season. In addition, we provide a framework to quantify the impact of customer regret on retailer’s optimal markdown pricing strategy and resulting revenues across various product categories. We model a regret-prone customer’s purchase decision using a utility based economic model, and estimate the parameters of this model with data obtained from the online channel of a luxury fashion retailer. We consider two types of anticipated regret, which are both incurred when a customer engages in counterfactual thinking at the beginning of a product’s selling season. If customers buy the product at a regular price, they anticipate high-price regret and hence incur the mental cost of knowing that the product may be available later at a discounted price. However, if customers wait for the discounted price, they anticipate stockout regret and hence incur the mental cost of possibly facing a stockout during the markdown season. We show that retailers only need to know the ratio between the two types of anticipated regret to account for their impact on customer purchase decisions and optimize pricing decisions accordingly. We develop an empirical strategy to jointly estimate this regret ratio and customers’ reservation price distribution parameters for each product. We find significant heterogeneity in regret characteristics across product categories. We show that stockout regret generally dominates high-price regret in magnitude. This dominance is even stronger for products that attract more high-spending customers and/or more frequent website visitors. Our counterfactual analysis shows that the luxury fashion retailer could have increased its revenues by up to 11.64% if it were to account for the anticipated customer regret in its pricing strategy. We also propose a method to disentangle the high-price and stockout regrets from the estimated regret ratio and show, for example, that stockout regret in a womenswear category is significantly larger than that of a similar menswear category.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84847882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sıdıka Tunç Candoğan, C. G. Korpeoglu, Christopher S. Tang
In an innovation contest, an organizer elicits solutions to an innovation-related problem from a group of internal solvers (e.g., in-house employees) or from a group of external solvers (e.g., crowdsourcing-platform members). These solvers may develop individual solutions and make individual submissions, or if the organizer encourages it, they may collaborate as teams and make team submissions. The quality of an individual (resp., team) submission depends on the solver's (resp., team's) effort as well as the solver's (resp., team's) output shock. Motivated from different policies adopted by various crowdsourcing platforms in practice, we identify conditions under which the organizer and solvers can benefit from team submissions. By examining equilibrium outcomes of a game-theoretic model, we show that both the organizer and solvers may benefit from team submissions when the organizer seeks solutions to a shock-driven problem from external solvers (e.g., via a crowdsourcing platform). However, the organizer may not benefit from team submissions when he seeks solutions to an effort-driven problem from external solvers. These results provide a plausible explanation for why some platforms encourage team submissions, while others discourage them. Interestingly, we also show that when the organizer elicits solutions to an effort-driven problem from internal solvers, the organizer may benefit from team submissions but internal solvers may not. In this case, it may be advisable for the organizer to obligate solvers to make team submissions, if possible.
{"title":"Team Collaboration in Innovation Contests","authors":"Sıdıka Tunç Candoğan, C. G. Korpeoglu, Christopher S. Tang","doi":"10.2139/ssrn.3607769","DOIUrl":"https://doi.org/10.2139/ssrn.3607769","url":null,"abstract":"In an innovation contest, an organizer elicits solutions to an innovation-related problem from a group of internal solvers (e.g., in-house employees) or from a group of external solvers (e.g., crowdsourcing-platform members). These solvers may develop individual solutions and make individual submissions, or if the organizer encourages it, they may collaborate as teams and make team submissions. The quality of an individual (resp., team) submission depends on the solver's (resp., team's) effort as well as the solver's (resp., team's) output shock. Motivated from different policies adopted by various crowdsourcing platforms in practice, we identify conditions under which the organizer and solvers can benefit from team submissions. By examining equilibrium outcomes of a game-theoretic model, we show that both the organizer and solvers may benefit from team submissions when the organizer seeks solutions to a shock-driven problem from external solvers (e.g., via a crowdsourcing platform). However, the organizer may not benefit from team submissions when he seeks solutions to an effort-driven problem from external solvers. These results provide a plausible explanation for why some platforms encourage team submissions, while others discourage them. Interestingly, we also show that when the organizer elicits solutions to an effort-driven problem from internal solvers, the organizer may benefit from team submissions but internal solvers may not. In this case, it may be advisable for the organizer to obligate solvers to make team submissions, if possible.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"C-19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85053178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In many supply chains, the brand-owning retailer designs product quality and decides the retail price, but often outsources its production to suppliers. For products with a short selling season, low quick-response capability in the supply chain requires the supplier to carry out the production before the selling season, but the uncertain market demand creates risks of stock-out or excess inventory. This paper studies the impacts of the supplier’s quick-response capability and demand uncertainty on product quality and firm profitability under pull contracts in the supply chain. We find that an increase in the supplier’s quick-response capability can lead to higher or lower equilibrium product quality, benefiting the retailer but potentially reducing the supplier’s profit. Our analysis suggests that the supplier can have incentives to keep secret its improvement in process efficiency or quick-response capability to mitigate the retailer’s strategic behavior. Interestingly, both the retailer and the supplier can be worse off with a higher probability for high market state (with more high-valuation consumers). Further, a higher probability of the high market state can lead to lower product quality.
{"title":"Effects of Quick Response and Market Uncertainty on Product Quality and Firm Profitability","authors":"Baojun Jiang, Lin Tian","doi":"10.2139/ssrn.3308215","DOIUrl":"https://doi.org/10.2139/ssrn.3308215","url":null,"abstract":"In many supply chains, the brand-owning retailer designs product quality and decides the retail price, but often outsources its production to suppliers. For products with a short selling season, low quick-response capability in the supply chain requires the supplier to carry out the production before the selling season, but the uncertain market demand creates risks of stock-out or excess inventory. This paper studies the impacts of the supplier’s quick-response capability and demand uncertainty on product quality and firm profitability under pull contracts in the supply chain. We find that an increase in the supplier’s quick-response capability can lead to higher or lower equilibrium product quality, benefiting the retailer but potentially reducing the supplier’s profit. Our analysis suggests that the supplier can have incentives to keep secret its improvement in process efficiency or quick-response capability to mitigate the retailer’s strategic behavior. Interestingly, both the retailer and the supplier can be worse off with a higher probability for high market state (with more high-valuation consumers). Further, a higher probability of the high market state can lead to lower product quality.","PeriodicalId":82888,"journal":{"name":"Technology (Elmsford, N.Y.)","volume":"122 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80781713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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