Knowledge that Transforms

Information generated in online markets can affect both buyers’ and sellers’ expectations and therefore their choices. In this research, we investigate the effect of market information, generated in online auction markets, on buyers’ expectations and choices. To clear large inventories, sellers often conduct many auctions selling identical items over time, which creates an online auction market where competition dynamics spill over from one to auction to another. In these markets, bidders can participate in many auctions over a period of time, observe market information (supply, demand, and competition), and gain experience to increase their payoffs. We observe that despite having an opportunity to compete and win in future auctions, many bidders stop participating in these auction markets. We argue that observed market information affects their choices. We explore how bidders form expectations about market supply, demand, and competition based on information from two sources—market design parameters and behavior of market participants. By employing a hierarchical Bayesian latent attrition model, we empirically detect and investigate the effect of bidders’ expected market supply, demand, and competition on their attrition in these markets. Our study shows that the effect of market information on attrition is nuanced by bidders’ value heterogeneity. Through the lenses of behavioral economics theories, we show that the attrition behavior of high-value bidders is completely opposite that of low-value bidders. We discuss the practical implications of our findings.

A common problem in many mature markets is how to deal with congestion—a situation in which transaction requests from market participants cannot be accommodated in an expedited manner. This paper examines the congestion problem in sequential Dutch auction markets. Transactions in these markets typically involve perishable goods, making market clearing speed crucial. Traditionally, sequential Dutch auctions have been implemented with fast-paced auction clocks that process equally attractive bids in the order they arrive and only award the first bidder as the winner of each round, which can lead to serious congestion in case of a demand surge. We propose a hybrid mechanism that capitalizes on the discrete nature of the auction clock and batches together the highest bids, allowing multiple transactions at the same price in each round. To evaluate the performance of the hybrid mechanism, we first develop a game-theoretic model comparing the hybrid mechanism to the traditional sequential Dutch auction mechanism. Our model predicts that the hybrid mechanism will achieve higher operational efficiency without compromising allocative efficiency. We then complement the theoretical analysis by evaluating the hybrid mechanism through a quasi-natural field experiment. The empirical analysis of the field data shows that the hybrid mechanism can significantly speed up the market clearing process and increase price stability without affecting the expected revenue. Our findings shed new light on the design and operation of multi-unit auctions.

This study examines the impact of coordination capabilities provided by enterprise systems (ES), manifested in ES standardization and extensiveness, on merger and acquisition (M&A) outcomes in the short and long term. Specifically, we examine the extent to which the ES standardization and ES extensiveness of the acquiring and target firms contribute to value creation in M&A initiatives. We also study the relationship between the ES standardization and ES extensiveness of the acquiring and target firms and M&A offer premiums. The empirical analysis suggests that the ES standardization of acquirers is related to lower offer premiums and a higher market response to the acquisition for the acquirer. However, it is the ES extensiveness of the acquirer that improves long-term performance i.e., decreases goodwill impairment and increases operating performance. The analysis also indicates that the target’s ES standardization increases the premium for the target firm and generates a positive market response to the acquisition for the target firm. Overall, the analysis indicates that the ES standardization likely affects the integration cost that influences market response to the M&A and to the M&A premium in the short term, but it is ES extensiveness that affects the realized synergy from the M&A that affects long-term performance.

Work-related social media networks (SMNs) like LinkedIn introduce novel networking opportunities and features that promise to help individuals establish, extend, and maintain social capital (SC). Typically, work-related SMNs offer access to advanced networking features exclusively to premium users in order to encourage basic users to become paying members. Yet little is known about whether access to these advanced networking features has a causal impact on the accumulation of SC. To close this research gap, we conducted a randomized field experiment and recruited 215 freelancers in a freemium, work-related SMN. Of these recruited participants, more than 70 received a randomly assigned voucher for a free 12-month premium membership. We observe that individuals do not necessarily accumulate more SC from their ability to access advanced networking features, as the treated freelancers did not automatically change their online networking engagement. Those features only reveal their full utility if individuals are motivated to proactively engage in networking. We found that freelancers who had access to advanced networking features increased their SC by 4.609% for each unit increase on the strategic networking behavior scale. We confirmed this finding in another study utilizing a second, individual-level panel dataset covering 52,392 freelancers. We also investigated the dynamics that active vs. passive features play in SC accumulation. Based on these findings, we introduce the “theory of purposeful feature utilization”: essentially, individuals must not only possess an efficacious “networking weapon”—they also need the intent to “shoot” it.

The sales effect of product reviews has been a contentious issue with competing perspectives about when product reviews serve as a benefit, a burden, or a trifle. Unlike previous research that separately investigates the impact of each eWOM system, our study empirically examines the interaction effects of dual eWOM systems, i.e., product reviews and seller reputation. Drawing on reference point theory, we find that seller reputation systems play a reference-point role and determine the efficacy of product reviews. Specifically, negative reviews cause a significant loss in sales for high-reputation sellers but are less detrimental for low-reputation sellers. In contrast, positive reviews can boost sales for low-reputation sellers but are less helpful for high-reputation sellers. These results highlight that seller reputation is a double-edged sword. While a high seller reputation can reduce seller uncertainty and attract more consumers, it may also raise consumers’ expectations and lead to potential negative expectancy violations. Moreover, we explore what strategies may help mitigate the potentially detrimental effect of reference points for high-reputation sellers. Through the lens of restructuring reference points, the reputation reference effect can be adjusted in a more dynamic reputation system (e.g., a reputation badge). Compared to sellers that have never lost their top-rated badge, sellers that have lost their top-rated badge may face an attenuated detrimental impact on sales from the negative expectancy violation due to negative reviews and enjoy a positive impact from positive reviews. We discuss the implications of our findings for both theory and practice.

Social network data collected from digital sources is increasingly being used to gain insights into human behavior. However, while these observable networks constitute an empirical ground truth, the individuals within the network can perceive the network’s structure differently—and they often act on these perceptions. As such, we argue that there is a distinct gap between the data used to model behaviors in a network, and the data internalized by people when they actually engage in behaviors. We find that statistical analyses of observable network structure do not consistently take these discrepancies into account, and this omission may lead to inaccurate inferences about hypothesized network mechanisms. To remedy this issue, we apply techniques of robust optimization to statistical models for social network analysis. Using robust maximum likelihood, we derive an estimation technique that immunizes inference to errors such as false positives and false negatives, without knowing a priori the source or realized magnitude of the error. We demonstrate the efficacy of our methodology on real social network datasets and simulated data. Our contributions extend beyond the social network context, as perception gaps may exist in many other economic contexts.

The conversion funnel is a model describing the stages consumers go through in their journey toward a purchase. This journey often lasts several days to weeks and can include multiple visits to a seller’s website. A large body of literature has focused on using observable search patterns to identify consumers’ hidden purchasing stages and to estimate their likelihood of conversion. We propose a novel set of measures to better reveal the consumer’s hidden stage in the funnel. These measures are based on the diversity of the searches that a customer engages in while browsing an e-commerce website, and they include not only the number of different products that are searched for, but also measures that rely on unobserved similarities among products, captured in a product network (in which products are assumed to be “similar” if they are frequently co-searched). We operationalize and evaluate our proposed measures using a large-scale dataset from a medium-sized tourism website used for comparing and booking flights. We estimate a hidden Markov model to show that our proposed diversity measures are associated with progress in the funnel and consumers’ conversion likelihood. Specifically, we show that consumers go through different distinguishable stages (states) in their journey, characterized by different values of our proposed diversity measures. To demonstrate the managerial and business implications of our theory, we show that incorporating search-diversity measures into a baseline prediction model significantly improves the model’s performance in predicting purchase likelihood and churn.

While research on the consequences of organizational commitment to IT has focused on outcomes of interest to shareholders, such as profitability and firm value, recent research has also considered other stakeholders that might benefit from an increased organizational commitment to IT, especially customers. We extend this line of the literature by investigating the benefits of a firm’s organizational commitment to IT for firms’ employees, a stakeholder group that uses and depends heavily on IT in its daily work. This exploratory study links a firm’s organizational commitment to IT with the nonmonetary employee metrics of job satisfaction and work-life balance and embeds these associations in the industry’s dynamism and concentration. We test our research model with a multi-industry dataset of 523 firms from the S&P 500 (2008-2017 period). Our findings indicate that an organizational commitment to IT may facilitate job satisfaction and work-life balance but only when industry dynamism and industry concentration are low. Additional analyses show that IT commitment’s influence on these outcomes depends on the firm’s commitment to particular IT technologies; for instance, organizational commitments to cloud technology and remote technology are particularly positively associated with work-life balance.