External financing is critical to ventures that do not have a revenue source but need to recruit employees, develop products, pay suppliers, and market their products/services. There is an increasing belief among entrepreneurs that electronic word-of-mouth (eWOM), specifically blog coverage, can aid in achieving venture capital financing. Conflicting findings reported by past studies examining eWOM make it unclear what to make of such beliefs of entrepreneurs. Even if there were generally agreed-upon results, a stream of literature indicates that because of the differences in traits between the prior investigated contexts and venture capital financing, the findings from the prior studies cannot be generalized to venture capital financing. Extant studies also fall short in examining the role of time and the status of entities generating eWOM in determining the influence of eWOM on decision making. To address this dearth of literature in a context that attracts billions of dollars every year, we investigate the effect of eWOM on venture capital financing. This study entails the challenging task of gathering data from hundreds of ventures along with other sources including VentureXpert, surveys, Google Blogsearch, Lexis-Nexis, and Archive.org. The key findings of our econometric analysis are that the impact of negative eWOM is greater than is the impact of positive eWOM and that the effect of eWOM on financing decreases with the progress through the financing stages. We also find that the eWOM of popular bloggers helps ventures in getting higher funding amounts and valuations. The empirical model used in this work accounts for inherent selection biases of entrepreneurs and venture capitalists, and we conduct numerous robustness checks for potential issues of endogeneity, selection bias, nonlinearities, and popularity cutoff for blogs. The findings have important implications for entrepreneurs and suggest ways by which entrepreneurs can take advantage of eWOM.

Gamification has been shown to encourage contributions of user-generated reviews (word-of-mouth: WOM) in various domains, including travel and leisure related platforms (Foursquare, TripAdvisor), e-commerce (Amazon), and auctions (eBay). WOM contributors write reviews about products/services provided by business venues and WOM consumers read reviews and use them to form attitudes and make purchase decisions. Gamification elements such as points and badges, awarded to WOM contributors for various reasons, and displayed to WOM consumers, have a dual role in WOM context. First, points awarded for user contributions help motivate WOM contributors to increase their participation. Second, badges awarded to users for visiting business venues signal prior experience or competence, and they help determine how WOM consumers perceive WOM contributors and form their judgments based on the reviews. While the first role of gamification (i.e., motivating users) has been widely studied, the impact of WOM presented along with gamification elements on the perceptions and behavior of the target audience, WOM consumers, has not been examined. This is important to businesses that are looking to attract customers. Drawing on social psychology literature, we show that gamification symbols signaling experience that accompany WOM leads to perceptions of positive WOM contributors as more competent. This leads to important changes in behavioral outcomes such as willingness to visit/buy and willingness to recommend the reviewed outlets.

A practical model and an associated method are developed for providing consistent, deterministically correct responses to ad-hoc queries to a database containing a field of binary confidential data. COUNT queries, i.e., the number of selected subjects whose confidential datum is positive, are to be answered. Exact answers may allow users to determine an individual's confidential information. Instead, the proposed technique gives responses in the form of a number plus a guarantee so that the user can determine an interval that is sure to contain the exact answer. At the same time, the method is also able to provide both deterministic and stochastic protection of the confidential data to the subjects of the database. Insider threat is defined precisely and a simple option for defense against it is given. Computational results on a simulated database are very encouraging in that most queries are answered with tight intervals, and that the quality of the responses improves with the number of subjects identified by the query. Thus the results are very appropriate for the very large databases prevalent in business and governmental organizations. The technique is very efficient in terms of both time and storage requirements, and is readily scalable and implementable.

Faced with the sustained problem of piracy that costs nearly $40 billion in annual revenue losses, the software industry has adopted a number of technical, legal, and economic strategies to curb piracy and stem the resulting losses. Our work complements and contributes to the existing literature by exploring the possible effect of another economic lever—product bundling—on the relationship governing piracy and seller profits. The traditional economic rationale of demand pooling from bundling that enables sellers to extract higher surplus and its particular attractiveness for information goods with negligible marginal and bundling costs carry over to our analysis. However, the presence of piracy injects several new facets to our analysis. Bundling creates a shared level of piracy of disparate products, and under certain conditions to the detriment of one of the products. We argue that by construction of the copyright laws, the act of bundling itself can have a deterrence effect. This deterrence effect, along with shared piracy of products and demand pooling are ingredients that together dictate the overall piracy, pricing, profit, and welfare outcomes. Our analysis reveals several interesting insights. Bundling can be profitable even when the very act of bundling increases the piracy level of one of the products in the bundle. Termed phantom piracy, this represents a situation where sellers trade off higher piracy for one product in favor of lower piracy for the other product while deriving overall higher profits. Extensive simulation analysis shows that the region of phantom piracy is vastly expanded when additional products are introduced to the bundle. Conversely, under certain conditions, a profit maximizing seller opts not to bundle even when bundling can serve to lower the overall level of piracy. Price discounts that are typically offered by bundling are sharply deepened when piracy enters the equation. When piracy is a phenomenon to contend with, product bundling always increases consumer surplus even in scenarios where the seller may not realize higher profits. Unlike other forms of price discrimination that are often viewed by consumers with a jaundiced eye as they attempt to extract additional surplus from the consumers, product bundling in the software context can be a win-win scenario for both the buyers and the sellers.

This paper proposes a novel algorithmic market mechanism to address key challenges in individual data markets. Current data collection practices lack transparency and proper compensation, leading privacy-conscious users to opt out and creating biased data sets. Our proposed mechanism enables an intermediary platform to obtain unbiased samples of individual-level data while appropriately compensating users for privacy loss. Through theoretical analysis and simulations using both synthetic and real-world data sets, the authors demonstrate that their mechanism provides unbiased data samples at near-optimal cost compared with benchmark approaches. The mechanism outperforms both fixed-compensation methods and centralized-optimization approaches, even when platforms have partial information about user privacy preferences. Surprisingly, platforms achieve better outcomes by using this market mechanism rather than relying on estimated privacy preferences from user behavior. The approach is practical to implement, using straightforward sampling and conventional compensation mechanisms rather than complex techniques, like differential privacy. The mechanism enables creation of effective data markets that benefit both data subjects and buyers while ensuring compliance with regulations requiring transparency and consent. The findings are particularly relevant as new privacy regulations emerge globally and third-party tracking faces increased constraints, providing a viable solution for improving data quality and fairness in digital markets.

Research summary : Innovation requires inventors to have both new knowledge and the ability to combine and configure knowledge (i.e., combinatory knowledge), and such knowledge may flow through networks. We argue that both combinatory knowledge and new knowledge are accessed through collaboration networks, but that inventors' abilities to access such knowledge depends on its location in the network. Combinatory knowledge transfers from direct contacts, but not easily from indirect contacts. In contrast, new knowledge transfers from both direct and indirect contacts, but is far more likely to be new and useful when it comes from indirect contacts. Exploring knowledge flows in 69,476 patents and 89,930 unique inventors reveals evidence that combinatory knowledge from direct contacts and new knowledge from indirect contacts significantly affects innovative performance . Managerial summary : Inventors often combine ideas to create innovations. To do this, they need ideas to combine and they need the ability to combine those ideas. Inventors can get ideas to combine as well as the ability to combine ideas through prior co‐workers. Prior co‐workers can share ideas that may be relevant for the inventor's project and can tell the inventor about other things that other people are working on, especially people the inventor may not know. This can help inventors easily learn about ideas from friends‐of‐friends. The ability to combine ideas, however, is much harder to pass on. Prior co‐workers must carefully work with the inventor to teach him or her the complex processes of combining ideas. This means that it is very hard to learn how to combine knowledge from a friend‐of‐a‐friend, but it may be possible to learn from prior co‐workers. We explore this phenomenon in the social relationships of software inventors . Copyright © 2015 John Wiley & Sons, Ltd.

Funding: A. Susarla was funded by an R01 grant from the National Library of Medicine, through [Grant R01LM013443].

Efforts to promote Health Information Exchanges (HIEs) on a nationwide scale are beset with major challenges, and one of them is its meaningful use for both physicians and patients. Referrals potentially provide a context for the meaningful use of HIE, and we are yet to understand how HIEs affect referrals. This research studies the impact of HIE on referral patterns. We establish that participation in an HIE network increases the referrals sent to and received from other HIE participants. We investigate this relationship using both econometric and network-analytic methods. Whereas the econometric analysis focuses on the underlying associations between HIE adoption and referral patterns, the network analysis addresses the transformation process by which HIE adoption and referrals coevolve over time. This study has significant implications for healthcare policy making, development of innovative HIE business models, and management of healthcare organizations. This paper was accepted by Anindya Ghose, information systems. Supplemental Material: Data and the online supplement is available at https://doi.org/10.1287/mnsc.2022.4435 .