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.

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.

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.

Star and Ruhleder’s (1996) influential “relational view” of infrastructure is usually understood as a relation between technologies and organizational practices. However, a significant part of Star and Ruhleder’s original proposal has been overlooked—that infrastructure becomes a home for somebody. In this paper, we give an alternative interpretation of this relational view by focusing on the relation between a person and their infrastructure, rather than on the relation between technologies and practices. We use Heidegger’s (1927/1962) phenomenology in Being and Time to theorize what such a home might entail and a novel data collection method to study infrastructuring empirically from the perspective of a person. On this basis, we offer new theoretically grounded interpretations of infrastructure and infrastructuring. Empirically, we identify two modes of infrastructuring not previously distinguished. The perspective sheds new light on a number of key themes and debates in the literature and on infrastructuring in practice.

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.

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection.