© UCI Paul Merage School of Business, University of California - Irvine, CA 92697-3125
The Paul Merage School of Business
University of California
Irvine, CA 92697-3125
University of California
Irvine, CA 92697-3125
© UC Irvine
Professor: Professor Behnaz G. Bojd
Co-author(s): Hema Yoganarasimhan
Accepted at: Marketing Science (Journal on Financial Times Top 50 list)
We examine the effect of user's popularity information on their demand in a mobile dating platform. Knowing that a potential partner is popular can increase their appeal. However, popular people may be less likely to reciprocate. Hence, users may strategically shade down or lower their revealed preferences for popular people to avoid rejection. In our setting, users play a game where they rank-order members of the opposite sex and are then matched based on a Stable Matching Algorithm. Users can message and chat with their match after the game. We quantify the causal effect of a user's popularity (star-rating) on the rankings received during the game and the likelihood of receiving messages after the game. To overcome the endogeneity between a user's star-rating and her unobserved attractiveness, we employ non-linear fixed-effects models. We find that popular users receive worse rankings during the game, but receive more messages after the game. We link the heterogeneity across outcomes to the perceived severity of rejection concerns and provide support for the strategic shading hypothesis. We find that popularity information can lead to strategic behavior even in centralized matching markets if users have post-match rejection concerns.
Professor: Doctoral Candidate Haonan Yin
Co-author(s): Ni Huang and Zhijun Yan
Accepted at: Production and Operations Management (Journal on Financial Times Top 50 list)
E-healthcare platforms start to integrate medical services across online and offline channels, where providers can perform online consultations, schedule patients’ offline visits, synchronize relevant medical records, and finally, answer online inquiries regarding follow-up and recovery anytime and anywhere within one operations management function (i.e., online-offline service integration). In this study, we seek to quantify the effects of online-offline service integration on the e-healthcare providers’ demand and reputational outcomes, noting that it is not altogether clear how the service integration function will affect the providers who adopt such a function in e-healthcare platforms. Leveraging a quasi-natural experiment on an e-healthcare platform, weconducted difference-in-differences analyses in tandem with a variety of matching strategies, including propensity score matching and look-ahead propensity score matching. Further, we explored the moderating roles of provider-specific characteristics. Our results reveal a set of robust and interesting findings: i) e-healthcare providers, on average, experience increases in online demand and decreases in offline demand post online-offline service integration; ii) the service integration function also improves the professional reputation of participating providers; iii) the impact of channel integration on the outcomes are weaker for providers with lower (vs. higher) professional titles; and iv) the providers who specialize in treating chronic (vs. acute) diseases experience greater increases in online demand and reputational outcomes, yet insignificant changes in offline demand. This work contributes to related prior literatures on healthcare operations management, e-healthcare, and online-offline channel integration, offering design implications for the service operations of e-healthcare platforms.
Professors: Professor Tingting Nian
Co-author: Arun Sundararajan
Accepted at: Information Systems Research (Journal on Financial Times Top 50 list)
Embraced by a rapidly increasing number of companies, social media marketing has become an integral part of companies’ business strategies. However, not all the firms plan on a big spend on social media marketing. Our stylized model investigates the strategic effects of social media marketing spending (SMM spending) with the presence of exogenous quality revelation through sources over which firms have no direct control. Unlike traditional advertising, social media marketing has two roles - awareness enhancement and information revelation. Consumers are heterogeneous in their awareness of the product (e.g., whether they know the existence of the product). Our analysis yields three main findings. First, our results show the existence of a separating equilibrium and a partial-pooling equilibrium where the high-/low-quality firms pool together and spend less on social media marketing than the mid-quality firm. Intuitively, the highquality firm gets enough quality transparency from background user-generated discussions, and the cost of maintaining a social presence outweighs the benefits. The low-quality firm avoids social media marketing because quality transparency is broadly detrimental, whereas the mid-tier firm is “just right” to benefit from social media discussions they encourage. Second, we also find that in the partial-pooling equilibrium, as the fraction of aware consumers increases, the mid-quality firm spends less on social media marketing, whereas its optimal spending on social media marketing first decreases, and then increases with the precision of the external information signal. Third, by comparing these two equilibria, we show that if the partial-pooling equilibrium survives the Intuitive Criterion, it Pareto-dominates the separating equilibrium. A number of extensions are discussed, including a two-period model, a noisy signaling model, a continuum-of-type model and a generic-form model.
Professors: Professor Vibhanshu Abhishek
Co-authors: Mi Zhou, Geng Dan, and Beibei Li
Accepted at: Information Systems Research (Journal on Financial Times Top 50 list)
Banks today have been increasingly reducing their physical presence and redirecting customers to digital channels, and yet, the consequences of this strategy are not well studied. This paper investigates the effects of banks’ branch network changes (i.e., branch openings and branch closures) on customer om-ni-channel banking behavior. Using approximately 0.85 million (33 months’) anonymized individual-level banking transactions from a large commercial bank in the U.S., this paper shows the asymmetric effects of branch openings and branch closures on customer omni-channel banking behavior. In particular, we find that branch openings increase customers’ branch transactions, however the first branch opening leads to a migration of complex transactions to the branches, which might result in a net decrease in online banking in the short term. As consumers interact more with the physical channel, there is a gradual synergistic increase in customers’ transactions via online banking as well as alternative channels due to a learning spillover effect. The learning spillover effect goes from easy online inquiries to more complex online transactions as additional branches open. On the contrary, branch closures result in a favorable migration pattern from the branch channel to online banking. This pattern, however, could be reversed once the last branch closes within the customer’s residential neighborhood. Our study teases out the underlying mechanisms that drive customer omni-channel banking behavior in the context of branch openings and branch closures, and discusses the managerial implications for branch network restructuring and banking channel management.
Professors: Professor Vibhanshu Abhishek
Co-authors: Jose Guajardo and Zhe Zhang
Accepted at: Information Systems Research (Journal on Financial Times Top 50 list)
Business models that focus on providing access to assets rather than on transferring ownership of goods have become an important industry trend, representing a challenge for incumbent firms. This paper analyzes the interaction of a peer-to-peer (P2P) rental market and an original equipment manufacturer (OEM). The analysis highlights the important role of consumer heterogeneity in usage rates in determining which business model would be preferred by the OEM. The introduction of a P2P rental market creates an equalizing effect, which leads to purchases from low-usage consumers. P2P rentals act as a discrimination device, allowing the OEM to implicitly segment consumers and extract a larger fraction of surplus, which might hurt consumers. Furthermore, the OEM is better off with P2P rentals when the heterogeneity in usage rates is intermediate, while the consumers are better off with P2P rentals when the heterogeneity is sufficiently high. This paper examines different business models such as the sales-only OEM, the OEM offerings rentals in addition to sales (the “dual” firm), introducing a P2P platform to the market (the “P2P-sponsoring” firm), and a mixed structure in which the OEM competes against a P2P by introducing its own direct rentals (the “dual-plus-P2P” firm). Consumer heterogeneity in usage rates continues to play a fundamental role in market outcomes. When usage rates and heterogeneity in usage rates are sufficiently large, the OEM is better off offering sales and facilitating a P2P rental market. In contrast, if heterogeneity in usage rates is too low, the OEM prefers to operate as a sales-only monopoly. If heterogeneity is too high and usage rates are below a threshold, the OEM prefers to operate as a dual firm that offers both sales and rentals directly to consumers. If P2P rentals are unavoidable, the OEM would not necessarily be better off by introducing its own rentals to compete against P2P. Overall, contrary to what could be expected, the OEM has an incentive to facilitate P2P rentals in a large variety of cases.
Professors: Professor Vibhanshu Abhishek
Co-authors: Mustafa Dogan and Alexandre Jacquillat
Accepted at: Management Science (Journal on Financial Times Top 50 list)
This paper optimizes dynamic pricing and realtime resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogeneous price- and timesensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers’ time-sensitivities. The optimal policy may feature strategic idleness (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers) and deliberate late service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand — high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared to dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is historyindependent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes.
Professors: Professor Tingting Nian
Co-authors: Yuheng Hu and Cheng Chen
Accepted at: Information Systems Research (Journal on Financial Times Top 50 list)
The proliferation of social media platforms allows marketers to gauge consumers' opinions toward brands directly from online word of mouth (WOM). In this paper, we exploit a large-scale television program (the Super Bowl 2016) and exogenous game events that may cause a positive emotional context for fans of the winning team, while creating a negative emotional context for fans of the losing team, to investigate the impact of television-program-induced emotions on viewers' online WOM behavior toward ads which were aired during program breaks. The results obtained from a difference-in-differences analysis (DID) with Coarsened Exact Matching generally support our hypotheses on the direct and congruence effects of television-program-induced emotions. Findings on the direct effect suggest that television-program-induced emotional shocks have a significant effect on the arousal and valence of viewers' online WOM toward ads. We additionally find that a match between television-program-induced emotional shocks and the emotional content of ads leads to a more significant increase in the arousal and more favorable valence of online WOM responses to ads. We discuss the implications of our findings for advertisement design and media planning strategies
Professors: Professor Sanjeev Dewan
Co-authors: Ian Ho (Merage PhD ’16) and Chad Ho
Accepted at: Information Systems Research
This research studies the performance of geofencing, a practice where mobile users are targeted within a pre-defined virtual geographic boundary around an advertiser’s establishment. Drawing on the notion of the purchase funnel, we develop a two-stage hierarchical Bayesian model to examine consumer click and conversion responses. The analysis emphasizes the impact of distance (between the focal establishment of the geofence and a mobile user) and local competition (defined in terms of the number of alternative establishments in the consumer vicinity zone) in geofencing. A unique data set of geofencing ad impressions is collected from one of the largest location-based marketing agencies in the United States. The results suggest that local competition matters in the click stage, while distance influences the propensity of conversion. Quantitatively, one additional competitor in the consumer vicinity zone lowers the clickthrough rate by 1.03%, whereas a one-mile increase in distance results in a 17.64% decrease in the conversion rate. We also find a significant interactive effect, whereby a higher degree of local competition amplifies the negative impact of distance on the likelihood of conversions. Additionally, product differentiation ameliorates the effects of distance and local competition, while these effects are found to be more prominent during office working hours. This study discovers the stage-varying roles of distance and local competition along the customer journey and offers new directions for leveraging locations for more effective mobile targeting.
Professors: Vijay Gurbaxani, Tingting Nian
Co-author: Yuyuan Zhu
Accepted at: MIS Quarterly
Powered by digital technologies, many peer-to-peer platforms, or what is called the sharing economy, have emerged in the past decade. While the impact of the sharing economy has received a great deal of attention in the past few years, extant research hasn’t fully documented the impact of the sharing economy on consumers, workers, industry, and society as a whole. In this study, we exploit the geographic and temporal variation in Uber’s entry to examine its impact on the personal bankruptcy rate as well as on other consumer credit default rates. We empirically document the changes in personal bankruptcy filings after Uber’s entry, and show that personal bankruptcy filings under Chapter 7 experience a drop of 0.047 per thousand people after Uber enters a county, which translates to a 3.26% reduction in quarterly bankruptcy filings. Uber’s entry also led to a reduction in Chapter 13 personal bankruptcy filings, but to a smaller degree (0.018 cases per thousand people per quarter). We check the validity of our estimates using business bankruptcy filings, which are found to be uncorrelated with Uber’s entry.
Professor(s): Vidyanand Choudhary, Mingdi Xin
Co-author(s): Zhe Zhang (Merage PhD ’14)
Accepted at: Information Systems Research
An extensive literature has studied the benefits for a firm to be the first to invest in innovative technologies such as Information Technologies (ITs). However, investment in innovative technologies faces high levels of uncertainty. How would such uncertainty affect a pioneering firm’s incentive to invest? Do late adopters benefit from information about the pioneer’s investment? In this paper, we investigate these questions in a context where firms engage in sequential investment in an innovative IT. This paper differs from prior literature in two aspects: First, IT adoption is nonexclusive and available to all client firms. Second, IT implementation can fail. In this case, a late adopter may have an information advantage since he can make investment decisions contingent on knowledge about the early adopter’s IT investment and implementation outcome. We use a standard Hotelling model of duopoly competition to examine firms’ incentive to sequentially invest in IT given the risk of IT implementation failure. Our results show that the probability of IT implementation failure impacts firms’ investment incentives and profit through three distinct effects: the first-mover advantage mitigation effect, competition mitigation effect, and uncertainty-driven cost-based differentiation effect, although these three effects may drive the firms’ investment and profit in opposite directions. The follower’s knowledge about the leader’s IT investment level before making his own IT investment decision gives the leader a first-mover advantage and the follower a disadvantage. In contrast, the follower’s knowledge about the leader’s IT implementation outcome can benefit both the leader and the follower. Finally, we find that a spaced-out IT investment schedule in which the follower makes his investment decision after the Leader’s IT investment level and implementation outcome are known leads to the highest industry-wide IT investment and social surplus.
Professor(s): Professor Vijay Gurbaxani
Co-author(s): Debora Dunkle
Accepted at: MIS Quarterly Executive (forthcoming)
Digital transformation – the reinvention of a company's vision and strategy, organizational structure, processes, capabilities, and culture to match the evolving digital business context – is not only changing companies but also redefining markets and industries. Executives require frameworks to guide their transformations and assess their digital journeys over time. Given the scope of digital transformation, a useful framework must encompass strategic, technological, human capital, and organizational cultural considerations.
Our research identifies six dimensions of digital transformation at the enterprise level. They are: a company’s strategic vision, alignment of the vision and its investments in digital transformation, the suitability of the culture for innovation, possession of sufficient intellectual property assets and knowhow, the strength of its digital capabilities (talent), and its use of digital technologies. The six-dimension framework facilitates benchmarking one's company with others - either within a sector or against companies that are in the same state of progress towards digital transformation. In addition, executives can measure their company's progress over time. Perhaps most importantly, the framework helps diagnose gaps in a company's capabilities by identifying how it performs across each of the six dimensions relative to any comparison group.
Professor(s): Professor Vijay Gurbaxani
Co-author(s): Shivendu Shivendu and David Zeng (PhD Alumnus)
Accepted at: MIS Quarterly
Many IT outsourcing arrangements include the purchase of the client’s IT assets by the vendor. Asset transfer benefits the client who can recapture some of their value through the sale and may even negotiate a lower price because the vendor may be more efficient in using these assets. On the other hand, asset transfer creates lock-in for the client and limits her future contractual options. To study these tradeoffs, we develop a game-theoretic framework wherein asset transfer creates a one-sided switching cost to the client, and vendors have private information both on their intrinsic capabilities, either high or low, and on the level of quality-improving effort they exert. The quality of IT services depends on the vendor’s capability and quality-improving effort. In a two-period model, we show that when quality is verifiable, the client uses asset transfer as a device to design efficient screening contracts, so that a high capability vendor is selected. On the other hand, when quality is non-verifiable, the client mitigates contractual inefficiency by voluntarily locking herself into a long-term relationship with the vendor and may transfer assets at a lower than efficient level, even to a high-capability vendor. Our results show that asset transfer can play a strategic role in outsourcing relationships, not just an operational one.
Professor(s): Professor Tingting Nian
Co-author(s): Lei Xu and Luis Cabral
Accepted at: Management Science
Many online platforms rely on users to voluntarily provide content. What motivates users to contribute content for free, however, is not well understood. In this paper, we use a revealed-preference approach to show that career concerns play an important role in user contributions to Stack Overflow, the largest online Q&A community. We investigate how activities that can enhance a user’s reputation vary before and after the user finds a new job. We contrast this reputation-generating activity with activities that do not improve a user’s reputation. After finding a new job, users contribute 23.7% less in reputation-generating activity; by contrast, they reduce their non-reputation-generating activity by only 7.4%. These findings suggest that users contribute to Stack Overflow in part because they perceive such contributions as a way to improve future employment prospects. We provide direct evidence against alternative explanations such as integer constraints, skills mismatch, and dynamic selection effects.
Professor(s): Mingdi Xin
Co-author(s): Evangelos Katsamakas
Accepted at: Electronic Commerce Research and Applications
Open innovation in the form of open-source software (OSS) has been a transformative force in the software industry and beyond. The growth of open source has created new ways to develop, distribute and adopt software in organizations. Despite the associated impressive growth of open source research, a rigorous analytical examination of open-source adoption in organizations constitutes a gap in the literature. This article fills this gap by providing insights toward a comprehensive open-source strategy. It develops a game-theoretic analytical model to explain when organizations adopt open source software applications and platforms, and what the implications are. The analysis characterizes conditions under which organizations adopt open source software, and examines whether these adoption patterns are socially beneficial. The article shows that open-source adoption depends crucially on organizational IT capabilities, network effects, and the fit of OSS with the organizations' application needs. The model predicts that firms may sometimes adopt a heterogeneous IT architecture that consists of open source and proprietary software. Moreover, the results suggest that open-source adoption is sometimes socially inefficient. Overall, this analysis contributes a nuanced understanding of the adoption of open innovation in the form of OSS that should be useful to managers and policy-makers involved in related decisions. The article concludes with practical managerial recommendations on formulating a comprehensive open-source strategy.
Professor(s): Professor Mingdi Xin
Accepted at: MIS Quarterly
This paper studies how an important feature of software adoption impacts a software vendor’s preference between perpetual and subscription-based licensing: customers are uncertain about their valuation for the software prior to adoption. We show that consumer valuation uncertainty causes the equilibrium outcome to depart from that of standard durable-goods theories in two aspects: 1) Contrary to the conventional wisdom of the durable-goods theory that subscription-based licensing is optimal, perpetual licensing can be more profitable than subscription-based licensing. This result offers a possible explanation for the historical prevalence of perpetual licensing in some software markets. 2) When subscription-based licensing is optimal, our theory suggests a low-then-high variable pricing path. In contrast, standard durable-goods theories suggest to charge the monopoly leasing price in each period. Such a variable pricing path and the resulting adoption pattern are consistent with observed industry practice (e.g., pricing strategy by Adobe Systems). We also examine a variation of subscription-based licensing in which the vendor offers a menu of subscription options varying in license duration and show that valuation uncertainty is critical for this strategy to outperform perpetual licensing under some conditions. Furthermore, we find that valuation uncertainty can cause the monopolist to prefer a licensing strategy that is not socially optimal. Finally, we compare customers’ valuation uncertainty with usage volume uncertainty and show that different types of uncertainties have very different revenue implications for a software vendor.
Professor(s): Professor Mingdi Xin
Co-authors: Arun Sundararajan
Accepted at: Information Systems Research
Nonlinear usage-based pricing is applied extensively to price software products. Different from other products, software customers often cannot vary their required usage volume, a property we label local demand inelasticity. For instance, a client firm that needs a salesforce automation software either buys one user license for every salesperson in its organization or does not buy at all. It is unlikely to buy licenses for some salespersons, but not the others. This demand feature violates a critical assumption of the standard nonlinear pricing literature that consumers are flexible with their usage volume, and the utility that consumers derive from using the product changes smoothly with their usage volume. Consequently, standard pricing solutions are inapplicable to many software products. This paper studies the optimal nonlinear usage-based pricing of software when customers demand is locally inelastic. We demonstrate that this unique demand feature necessitates a fundamental reformulation of the traditional nonlinear pricing problem. We provide this reformulation and characterize the solution to a complicated nonlinear pricing problem with discontinuous and inelastic individual demand functions, virtually no restriction on demand distribution, and no single-crossing restriction on utility functions. We show that under a weak ordering condition of customer types, this complex pricing problem can be decomposed into a set of much simpler pricing problems with known solutions. Our pricing solution is applicable to a broad range of demand systems including the very popular normal and exponential distributions in prior literature. Managerially, our solution is based on parameters that are easily measurable in practice: the required consumption volume and value from this consumption for each customer. In contrast, typical nonlinear pricing problems require one to specify a complete utility function for every customer drawn from a continuum of customer types. Therefore, our solution likely makes empirical demand estimation and the real-world use of the analytical solution more viable. We discuss the characteristics of the optimal nonlinear pricing schedule and its welfare implications at the end.
Professor(s): Professor Tingting Nian
Co-Author(s): Jingchuan Pu, Liangfei Qiu, and Hsing K. Cheng
Accepted at: Journal of Management Information Systems (Journal on Financial Times Top 50 list)
On e-commerce platforms, consumers rely heavily on online product reviews, sales volume, number of product page visits, and social media discussions to infer product quality. As a result, the past decade has witnessed an explosive growth of seller-initiated misrepresentation of quality through fake reviews, fake sales volume, and fake clicks, all used to manipulate consumers’ quality perception of products. In this study, we develop an analytical model to investigate sellers’ quality misrepresentation decisions under the agency pricing regime. The platform can use two strategies to discourage sellers’ quality misrepresentations: increasing the cost of misrepresentation and implementing a more lenient product return policy. We find that while a stricter anti-misrepresentation strategy can deter the misrepresentation level of the high-quality seller, such strategy may unintendedly incentivize the low-quality seller to conduct more quality misrepresentation. Further, increasing return leniency can deter low-quality seller’s quality misrepresentation level in a wider range of market conditions than increasing the misrepresentation cost. Finally, the platform can decide the anti-misrepresentation strategies and the strength of these strategies based on its specific objective. The findings demonstrate the necessity of evaluating anti-misrepresentation strategies in a competitive setting