Research Spotlights

A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions (p. 1099)

Abhishek Ray, Mario Ventresca, Karthik Kannan

Iterative combinatorial auctions are known to resolve bidder preference elicitation problems. However, winner determination is a known key bottleneck that has prevented widespread adoption of such auctions, and adding a time-bound to winner determination further complicates the mechanism. As a result, heuristic-based methods have enjoyed an increase in applicability. The authors add to the growing body of work in heuristic-based winner determination by proposing an ant colony metaheuristic–based anytime algorithm that produces optimal or near-optimal winner determination results within specified time. Their proposed algorithm resolves the speed versus accuracy problem and displays superior performance compared with 20 past state-of-the-art heuristics and two exact algorithms, for 94 open test auction instances that display a wide variety in bid-bundle composition. Furthermore, the authors contribute to the literature in two predominant ways: first, they represent the winner determination problem as one of finding the maximum weighted path on a directed cyclic graph; second, they improve upon existing ant colony heuristic–based exploration methods by implementing randomized pheromone updating and randomized graph pruning. Finally, to aid auction designers, they implement the anytime property of the algorithm, which allows auctioneers to stop the algorithm and return a valid solution to the winner determination problem even if it is interrupted before computation ends.

Show Me the Money: The Economic Impact of Membership-Based Free Shipping Programs on E-Tailers (p. 1115)

Zhen Fang, Yi-Chun (Chad) Ho, Xue (Jane) Tan, Yong Tan

Membership-based free shipping (MFS) has emerged as an augmentation to contingent free shipping (CFS) on e-commerce platforms. Under CFS, consumers qualify for a shipping fee waiver if the order exceeds a certain threshold, whereas, under MFS, shoppers pay an upfront fee and enjoy free shipping throughout the membership period. Leveraging a game-theoretical model, the authors study the optimal design of e-tailers' shipping policies when consumers are heterogeneous in two dimensions: (1) disutility from acquiring an auxiliary product that they would only purchase to qualify for CFS; and (2) frequency of orders. Consistent with anecdotes, the authors find that the introduction of MFS possibly leads to a higher product price. When the disutility from purchasing the auxiliary product is moderate, the e-tailer should charge a high membership fee to target only frequent shoppers who forgo CFS. However, the fee should be lowered to lure also: frequent shoppers who would otherwise take CFS, when such disutility is high; or infrequent shoppers who would otherwise forgo CFS, when such disutility is low. In all cases, the collected fee revenue can never compensate for the free shipping costs, yet the e-tailer can still make a higher profit from a more segmented market.

Measuring Brand Favorability Using Large-Scale Social Media Data (p. 1128)

Kunpeng Zhang, Wendy Moe

For decades, brand managers have monitored brand health with the use of consumer surveys, which have been refined to address issues related to sampling bias, response bias, leading questions, etc. However, with the advance of Web 2.0 and the internet, consumers have turned to social media to express their opinions on a variety of topics and, subsequently, have generated an extremely large amount of interaction data with brands. Analyzing these publicly available data to measure brand health has attracted great research attention. In this study, the authors focus on developing a method to measure brand favorability while accounting for the measure biases exhibited by social media posters. Specifically, they propose a probabilistic graphical model–based collective inference framework and implement a block-based Markov chain Monte Carlo sampling technique to obtain an adjusted brand favorability measure that is correlated with traditional survey-based measures used by brands. To demonstrate the effectiveness of their model, the authors evaluate it using more than 3,300 brands and about 205 million unique users that interact with those brands collected through Facebook. Their model performs very well, providing brand managers with a new method to more accurately measure consumer opinions toward the brand using social media data.

Competition and Distortion: A Theory of Information Bias on the Peer-to-Peer Lending Market (p. 1140)

Zhenhua Wu, Lin Hu, Zhijie Lin, Yong Tan

Despite the popular emergence of peer-to-peer (P2P) lending platforms, relevant research investigating the role of these platforms on P2P markets still lags. In this paper, the authors present a model to study the market incentives of P2P lending platforms’ optimal information-reporting strategies when the following exist: (i) uncertainty on the return of loans and (ii) competition from entrants. They focus on the information bias of platforms driven by demand-side actors—investors’ optimism/pessimism about risk—while keeping the platforms being rational. The authors characterize platforms’ equilibrium reporting strategies under different market conditions. Surprisingly, they find that when uncertainty is significant, and the threat of entry is strong but not detrimental, the platform has incentives to bias information toward investors’ biased beliefs. This result demonstrates a case where competition and uncertainty may jointly lead to information bias. However, a properly designed uncertainty-resolution mechanism could reduce the incentive. Their findings contribute to the literature on the P2P lending market by analyzing platform decisions and offer policy implications for regulating P2P lending market.

And the Winner Is …? The Desirable and Undesirable Effects of Platform Awards (p. 1155)

Jens Foerderer, Nele Lueker, Armin Heinzl

The authors study platform firms’ decision to recognize innovative products by complementors ex post through awards. They find that awards—despite being purely symbolic—might set incentives for complementors’ product strategies that can eventually lead to both desirable and undesirable outcomes for the platform firm. First, awards encourage recipients to focus on releasing complement improvements rather than new complements. Second, awards increase recipients’ likelihood of multihoming. Finally, awards increase new complement releases in the recipients’ market niche by attracting other complementors. Firms can directly benefit from the analyses presented in this study. For platform firms, this study’s results suggest that awards have significant effects on complementors’ behavior despite representing only symbolic and nonfinancial mechanisms. Platform managers should consider the flexibility of awards as a particular strength: an award can, for instance, be given to “the best complement” with no need to define the criteria exactly. Nevertheless, platform managers should be aware that employing awards requires a careful evaluation of whether their effects are desirable. Given that awards can encourage multihoming, platform managers are advised to employ additional mechanisms to increase recipients’ loyalty to the platform, for example, through contractual exclusivity arrangements.

Manufacturer’s “1-Up” from Used Games: Insights from the Secondhand Market for Video Games (p. 1173)

Antino Kim, Rajib L. Saha, Warut Khern-am-nuai

The video game industry has a robust secondhand market for games, even though some of the major gaming-console companies possess the means to shut it down. What is the special ingredient in this industry that would incentivize a manufacturer to give tacit approval to buying and selling used games? In this study, leveraging a game-theoretic model, the authors investigate the effect of gaming console on a manufacturer’s strategy in the presence of a secondhand market for games. They find that when the manufacturer offers a console that provides additional value outside of playing games (e.g., media hub with apps), the secondhand market improves the manufacturer’s profit, consumer surplus, and social welfare, all at the same time. Moreover, the manufacturer enjoys greater benefit from the secondhand market as the intrinsic value of the console increases. This is in stark contrast with cases where there are no consoles involved or the consoles do not offer any intrinsic value; in such settings, the manufacturer would opt to shut down the secondhand market. Overall, the results have implications that apply not only to the past and present of the gaming industry but also to its future and to other types of platform-based markets for contents.

The Unknowability of Autonomous Tools and the Liminal Experience of Their Use (p. 1192)

Zhewei Zhang, Youngjin Yoo, Kalle Lyytinen, Aron Lindberg

Recently companies are increasingly adopting intelligent technologies, such as autonomous tools and artificial intelligence, to assist complex knowledge works that are traditionally carried out by human experts. These tools can independently learn and execute novel actions. The input–output relationships of these tools, however, are unknowable to human experts. This calls for analysis of how humans may work differently while interacting with such tools. To this end, the authors conduct a comparative case study at one of the world’s largest semiconductor manufacturers. They investigate how chip designers interact with two families of design technologies: one following a traditional designer-centric approach in which the designer knows what outputs particular inputs to the tools will generate, and another relying on autonomous tools that continually surprise the user. The authors' inquiry reveals that, when using autonomous tools, designers can hardly understand the design generated by the tools and become more like laboratory experimentalists in that their primary job is experimenting with different inputs and assessing the quality of the finished design. Their interactions with the tools are marked by ambiguity, and the design is moved forward along multiple design trajectories in accordance with a multifarious temporality.

Learning to Be Creative: A Mutually Exciting Spatiotemporal Point Process Model for Idea Generation in Open Innovation (p. 1214)

Vipul Aggarwal, Elina H. Hwang, Yong Tan

This study investigates the creative idea generation process in an open innovation platform. Idea generation is simultaneously influenced by multiple activities: knowledge acquisition from participants’ interactions, deliberate practice, and learning through failures. Due to the dynamic interplay across these activities, it is challenging to identify each activity’s influence on creative ideation outcomes. To overcome these challenges, the authors employ the mutually exciting spatiotemporal point process model. They find that knowledge acquired through interaction with others plays a vital role in the creative ideation process, but their effect is more nuanced than what we have known so far. In contrast to the prior belief, the authors find that distant analogies lead to failures. Yet, they further find that such failures are indispensable to the creative ideation process because failures motivate idea generators (1) to acquire more knowledge by increasing their future interactions with other participants’ ideas (learning from others), and (2) to persist in generating ideas that lead to improvements in their ability to apply the acquired knowledge and to identify innovation tasks that are relevant to their stock of acquired knowledge (learning by doing). The authors' results indicate that failures are a stronger driver of the learning activities than successes.

The Power of Renegotiation and Monitoring in Software Outsourcing: Substitutes or Complements? (p. 1236)

He Huang, Minhui Hu, Robert J. Kauffman, Hongyan Xu

Monitoring and renegotiation are two common solutions for addressing information asymmetry and uncertainty between a client and a vendor of software outsourcing. Monitoring is applied in time-and-materials contracts for inspecting and reimbursing the vendor’s efforts. Renegotiation is deployed in fixed-price and time-and-materials contracts to mitigate the surplus loss from uncertainty. This article investigates the interaction between monitoring and renegotiation and examines the corresponding contract choice problem. It finds the client will benefit from renegotiation via an uncertainty-resolution effect and a postdevelopment incentive that incentivizes the vendor’s effort in system development. Monitoring incentivizes the vendor’s development effort, which is a predevelopment incentive. The analysis shows the choice of renegotiation or monitoring by a client depends on interactions among the above effects, which are moderated by renegotiation cost, monitoring cost, and bargaining power in renegotiation. When renegotiation cost is low, if a client has high bargaining power and low monitoring cost, monitoring and renegotiation will be complements and both will be selected; otherwise, the two instruments will be substitutes and renegotiation will be preferred. When renegotiation cost is high, monitoring will substitute for renegotiation, and the client will only choose monitoring if the cost is low, or neither will be used.

The Race for Online Reputation: Implications for Platforms, Firms, and Consumers (p. 1262)

Mingwen Yang, Zhiqiang (Eric) Zheng, Vijay Mookerjee

Online reputation has become a key marketing-mix variable in the digital economy. This study helps managers decide on the effort they should use to manage online reputation. The authors consider an online reputation race in which it is important not just to manage the absolute reputation, but also the relative rating. That is, to stay ahead, a firm should try to have ratings that are better than those of its competitors. The authors' findings are particularly significant for platform owners (such as Expedia or Yelp) to strategically grow their base of participating firms: growing the middle of the market (firms with average ratings) is the best option considering the goals of the platform and the other stakeholders, namely incumbents and consumers. For firms, the authors find that they should increase their effort when the mean market rating increases. Another key insight for firms is that, sometimes, adversity can come disguised as an opportunity. When an adverse event strikes the industry (such as a reduction in sales margin or an increase in the cost of effort), a firm’s profit can increase if it can manage this event better than its competitors.

How to Sell a Data Set? Pricing Policies for Data Monetization (p. 1281)

Sameer Mehta, Milind Dawande, Ganesh Janakiraman, Vijay Mookerjee

In this paper, the authors develop a utility framework that is appropriate for data buyers and the corresponding pricing of the data by the data seller. A buyer interested in purchasing a data set has private valuations in two aspects—the ideal record that the buyer values the most, and the rate at which the buyer’s valuation for the records in the data set decays as they differ from the buyer’s ideal record. The multidimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the data set a challenging one. The authors formulate a tractable model and exploit its special structure to obtain optimal and near-optimal data-selling mechanisms. Specifically, we provide insights into the conditions under which a commonly used mechanism—namely, a price-quantity schedule—is optimal for the data seller. When the conditions leading to the optimality of a price-quantity schedule do not hold, the authors show that the optimal price-quantity schedule offers an attractive worst-case guarantee relative to an optimal mechanism. Further, they numerically solve for the optimal mechanism and show that the actual performance of two well-known price-quantity schedules—namely, two-part tariff and two-block tariff—is near optimal.

Personal Achievement Goals, Learning Strategies, and Perceived IT Affordances (p. 1298)

Saggi Nevo, Dorit Nevo, Alain Pinsonneault

What people perceive when they interact with technologies are not the features and functionalities of the technology but rather the behaviors it affords them. Affordance perception determines how organizational information technology (IT) is used by employees and the benefits they provide to organizations and their members. In this article, the authors explain how employees who pursue different personal goals and use various learning strategies come to perceive different IT affordances. The authors identify three distinct pathways: (1) performance-avoidance goals are positively associated with surface processing, which leads to perceptions of common in-role IT affordances; (2) performance-approach goals are positively associated with surface processing and effort regulation and these learning strategies lead to perceptions of common and specialized in-role IT affordances; and (3) mastery goals are associated with deep processing, effort regulation, and peer learning, which are positively associated with perceptions of specialized in-role and extra-role IT affordances. By identifying the different pathways to perceived affordances, the article identifies potential interventions that can help managers steer employees toward certain affordances and away from other, less desirable affordances.

FairPlay: Detecting and Deterring Online Customer Misbehavior (p. 1323)

Ji Wu, Zhiqiang (Eric) Zheng, J. Leon Zhao

This study examines how firms can detect and manage customer misbehavior in online brand communities. The authors first develop a data science approach to detect customer misbehavior on social media and devise intervention strategies to deter it. Their design science approach achieves superior performance, improving detection by 7%–9% compared with traditional methods. The authors then implement two types of intervention policies based on injunctive (i.e., a punishment policy) and descriptive norms (i.e., a common identity policy) to restrain customer misbehavior. The results of field experiments indicate that punishment considerably reduces customer misbehavior in the short term, but this effect decays over time, whereas common identity has a smaller but more persistent effect on misbehavior reduction. In addition, punishing dysfunctional customers decreases their purchase frequency, whereas imposing a common identity increases it. The results also show that combining the two policies effectively alleviates the detrimental effect of punishment, especially in the long run.

Does Congestion Always Hurt? Managing Discount Under Congestion in a Game-Theoretic Setting (p. 1347)

Rajib L. Saha, Sumanta Singha, Subodha Kumar

Many firms buy cloud services from cloud vendors, such as Amazon Web Services to serve end users. One of the key factors that affect the quality of cloud services is congestion. Congestion leads to a potential loss of end users, resulting in lower demand for cloud services. Although discount can stimulate demand, its effect under congestion is ambiguous; a higher discount leads to higher demand, but it can further lead to higher congestion, thereby lowering demand. The authors explore how congestion moderates both cloud vendor pricing and the buyer’s fulfillment decisions. They seek to answer how the congestion sensitivity of the end users and the cost of technology impact buyer profitability and the cloud vendor’s choice of discount. The authors also examine how the cost of technology determines the buyer’s willingness to pass on savings to end users. Their results show that the buyer is not necessarily worse off even when the end users are more intolerant to congestion. In fact, when end users are more congestion sensitive, the demand for cloud services can sometimes increase, and the discount offered by the vendor can decrease. The authors also observe that a lower cost of technology can sometimes hurt the buyer, and the buyer can pass on lower benefits to end users.

Reviewing Before Reading? An Empirical Investigation of Book-Consumption Patterns and Their Effects on Reviews and Sales (p. 1368)

Heeseung Andrew Lee, Angela Aerry Choi, Tianshu Sun, Wonseok Oh

Prior research on online reviews has taken for granted that consumers submit reviews only after they have fully consumed purchased products or services. Contrastingly, this study uncovered the hidden truth that many book consumers post assessments before, during, and after consumption. Interestingly, many provide numerical review ratings, even with no consumption at all. We also found that review comments formed after incomplete product engagement adversely affect subsequent sales. Consequently, online evaluations crafted on the basis of fragmentary encounters can become a new source of challenges to managers and policymakers who are responsible for preserving the accuracy and informativeness of product critiques. Digital platforms may leverage our findings to improve their design of review systems and policies in ways that enhance the trustworthiness of peer evaluations and correct potential inaccuracies from inadequately informed assessments. For example, managers can revise their review “sorting” structure so that consumers can flexibly reposition text-based reviews in accordance with a consumption index. Managers can likewise take advantage of this study’s insights to effectively re-establish review-posting policies and schemes that encourage consumers to submit reviews after a sufficient amount of products have been consumed.

Where You Live Matters: Local Bank Competition, Online Marketplace Lending, and Disparity in Borrower Benefits (p. 1390)

Mohammed Alyakoob, Mohammad S. Rahman, Zaiyan Wei

In the past decade, the proliferation of online marketplace lending has been disrupting the consumer credit market, especially for personal loans for debt consolidation. These lenders, for example, Lending Club, transcend the geographic boundaries within which local banks operate and offer homogeneous access and terms to borrowers. However, the ultimate benefits borrowers derive from marketplace lending can differ significantly because local alternatives may replace marketplace loans when available and favorable. Correspondingly, if local bank competition drives the substitution of an existing marketplace loan with a traditional bank loan, the promise of equal benefits to all borrowers from marketplace lending is unlikely to fully materialize. This competitive dynamic has implications for policy making, particularly in judging the ramifications of bank mergers and acquisitions (M&As). Our results indicate that a borrower who resides in a more competitive market is more likely to pay off a P2P loan early by making a large, one-time payment compared with a borrower from a less competitive market, indicating a substitution with a local bank loan. Thus, borrowers from different markets do not benefit equally from online marketplace lending, disrupting the consumer credit market. In particular, consumers in smaller markets continue to be disadvantaged because of the absence of competitive intensity. This is a consequence of traditional banks competing within their local markets and incentivized to attract marketplace borrowers to traditional loans primarily by their local market conditions. Therefore, unless geographic frictions in traditional lending markets are removed, digital disruptions cannot equalize the benefits to consumers.

The Effects of Price Rank on Clicks and Conversions in Product List Advertising on Online Retail Platforms (p. 1412)

Mengzhou Zhuang, Eric (Er) Fang, Jongkuk Lee, Xiaoling Li

In light of the critical role of price information in consumers’ decision making, this study investigates the effect of price rank on consumers’ responses to product list advertising (PLA). The research documents that the price rank is more influential than actual price for PLA. In addition, the research highlights a tradeoff in price-rank decisions: A price rank that drives more clicks does not necessarily lead to more conversions; to drive traffic, managers should strive for an extreme (i.e., either high or low) to elicit more clicks, then follow up with online engagement tools (e.g., cross-selling and product recommendations). To maximize direct revenue, managers instead should strive for moderate ranks to satisfy consumers’ desire for a compromise between price and quality. However, consumers without uncertainty tend to rely less on price rank, so the effects diminish among specific keywords and increase among popular keywords. In order to achieve the desired price ranks, firms participating in PLA might monitor and adjust their advertising offers. There are commonly two specific avenues: Change the product price if the required change is within a certain range or change the advertised product if the required price change is beyond a certain range.

Mobile Consumer Scanning Technology: A Replacement for Interorganizational Information Systems for Demand Information Learning in Supply Chains? (p. 1431)

Ye Shi, Layth Alwan, Srinivasan Raghunathan, Yugang Yu, Xiaohang Yue

Recently, firms in supply chains have begun to deploy popular mobile apps (e.g., WeChat) into their supply chain practices to improve demand visibility. These efforts rely on consumers to scan the products they purchase using these apps, which we refer to as consumer scanning technology (CST). CST can be an alternative to the conventional interorganizational information technology (IOIT) that relies on collaboration between supply chain firms. This paper develops a theoretical model to examine the value of CST to learn supply chain (demand) information and the impact of CST on IOIT. Using an extensive simulation analysis based on real-world data from a manufacturer that has implemented a CST program, the authors show that the value of CST to a manufacturer can be substantial and provide insights into how market conditions affect the value.

Business Models in the Sharing Economy: Manufacturing Durable Goods in the Presence of Peer-to-Peer Rental Markets (p. 1450)

Vibhanshu Abhishek, Jose A. Guajardo, Zhe Zhang

With peer-to-peer sharing of durable goods like cars, boats, and condominiums, it is unclear how manufacturers should react. They could seek to encourage these markets or compete against them by offering their own rentals. This work shows why the best business model depends on whether consumer usage rates vary or not. Contrary to what might be expected, this paper shows that manufacturers have an incentive to facilitate transactions of P2P rental markets in a large variety of cases. The authors find that when consumer variation in usage rates is intermediate, the manufacturer is surprisingly best off avoiding offering its own direct rentals option and instead, facilitating a peer-to-peer rental market where consumers can share among themselves. The reason for this is an effect unique to the sharing economy, the equalizing effect. The equalizing effect shows that peer-to-peer rentals uniquely make previously heterogeneous willingness-to-pay among consumers more similar, making it easier for the firm to discriminate between the higher- and lower-value consumers, thus allowing it to extract a higher portion of consumers’ surplus. Surprisingly, there are some cases where peer-to-peer rentals benefit the manufacturer, but consumers are hurt overall (though the lower-usage consumers do always benefit from the availability of peer-to-peer rentals).

Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application (p. 1470)

Peiyu Chen, Lorin M. Hitt, Yili Hong, Shinyi Wu

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In this paper, the authors propose a set of theory-grounded data-driven measures that allow them to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. They used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Their results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.