Knowledge that Transforms

To increase diversity and inclusion in IT enrollment and employment, we must first answer the question: What makes one intrinsically interested in technology in the first place? To the extent that one’s choice of an IT education and career is driven by such intrinsic interest, the answer to this question will inform the various educational and organizational efforts to enhance social inclusion through increasing neurodiversity and gender diversity. Building on prior literature on the empathizing-systemizing (E-S) theory of autism, we employ two studies to explore the influences of autistic tendency and gender on intrinsic interest in IT. In Study 1, survey data from a U.S. sample provide support for autistic tendency as an antecedent of IT interest. The data also show that after controlling for individual variations in autistic tendency, the seemingly higher IT interest exhibited by U.S. men versus women becomes nonsignificant, demonstrating autistic tendency as an underlying mechanism by which differences in IT interest manifest between men and women. In Study 2, we replicate the model with respondents from India. Survey results again provide support for autistic tendency as an antecedent of IT interest and further show that there exists no significant gender difference in IT interest in India, regardless of whether autistic tendency is controlled for. This research offers a belated academic acknowledgment of the autism-IT linkage for the IS field and a comprehensive introduction of the E-S theory as a theoretical lens for multiple areas of IS research, including social inclusion, adoption, neuroIS, and evolutionary theory building. The finding of a nonsignificant difference in IT interest between men and women in the U.S. and India dispels a gender stereotype and demonstrates that collective-level gender labels may yield misleading results when individual-level factors, such as autistic tendency, masquerade as gender differences. Implications for IS practice are also discussed.

The rise of technology-enabled ride-hailing services has affected individuals’ transportation-related decisions. The impact of these ride-hailing services likely varies across traveler segments that differ in their usage of various modes of transportation. In this paper, we develop and leverage a framework that allows us to examine the impact of ride-hailing services on the transportation mode choice for three traveler segments: drivers (who primarily use a personal automobile to travel), riders (who primarily use public transit to travel), and walkers (who primarily use non-motorized modes of transport). We first develop a framework outlining how the behavior of different traveler segments would be impacted by the introduction of ride-hailing services and show how this affects traffic congestion and public transportation ridership. To test the framework, we compiled a rich dataset, combining data on public transportation ridership, traffic congestion, and individual transportation mode choice. Employing a difference-in-differences methodology, we show that the Uber entry in a market enabled those who were walkers and riders prior to the entry of Uber to travel more conveniently, leading to an increase in traffic congestion, and induced those who were drivers to substitute their use of private automobiles with a combination of Uber and public transit. We introduced urban compactness to assess the heterogeneous impact of ride-hailing services for cities that differ in their distribution of traveler segments. We found that Uber entry increases traffic congestion and reduces public transit demand more in cities with higher levels of urban compactness, i.e., where the proportion of riders and walkers is higher than that of drivers. This work provides a holistic framework to understand the mechanism underlying the impact of ride-hailing services on public transit and traffic congestion. Urban planners and policy makers can leverage our framework, methodology, and empirical results to guide city planning decisions that have implications for sustainability.

Under increasing pressure to demonstrate environmental responsibility, organizations realize that they cannot claim to be environmentally sustainable if their supply chains are not. This research seeks to understand how an interorganizational green IS influences environmental sustainability (ES) initiatives within organizations in a supply chain. We examine a green IS taking the form of an interorganizational ES platform. Our analysis sheds insights into how role conflicts arising from the various roles played by the platform users compromise the nature of actions associated with platform beliefs. In particular, cooptition conflict arising from participants’ roles as supplier to the platform owner and competitor to other platform participants explains the symbolic organizational content contribution, whereas the professional conflict resulting from participants’ roles as employee of an organization and knowledge peer to participants from other organizations explains the substantive personal content contribution. The lack of organizational substantive content creates content paucity, which platform users respond to by developing off-platform relationships with content contributors. The personal ES knowledge acquired through platform content consumption and the relationships with content contributors help individuals advocate for ES initiatives within their organizations. Our research is among the first to consider green IS at an interorganizational level and the corresponding multilevel perspective of the green IS users as they are at once organizational and individual actors.

How do green information technology (IT) standards and organizational strategies jointly influence firms’ environmental sustainability and financial performance? This is an important question, as many firms adopt green IT standards without considering the fit with their organizational strategies and therefore face uncertain or mixed outcomes. We address this question by developing a theory-driven conceptual framework and collecting archival data on green IT standards and green IT organizational strategies from more than 230 firms in India. Our analysis yields two main findings. First, an environment-focused green IT organizational strategy has a stronger positive moderating effect than a cost-focused green IT organizational strategy on the association between green IT standards and sustainability-monitoring capability. Similarly, an environment-focused green IT organizational strategy has a stronger positive moderating effect than a cost-focused green IT organizational strategy on the association between green IT standards and financial profit. Second, a dual-focused green IT organizational strategy positively moderates the association between green IT standards and profit. This study provides a theoretical explanation and empirical evidence to support the salience of green IT standards and complementary organizational strategies in advancing environmental sustainability and financial performance objectives. It also informs managerial decision-making about how firms can choose the appropriate green IT organizational strategy to enhance sustainability-monitoring capability and the financial benefits of green IT standards.

Online retailers use product ratings to signal quality and help consumers identify products for purchase. These ratings commonly take the form of either non-personalized, aggregate product ratings (i.e., the average rating a product received from a number of consumers such as “the average rating is 4.5/5 based on 100 reviews”), or personalized predicted preference ratings for a product (i.e., recommender-system-generated predictions for a consumer’s rating of a product such as “we think you’d rate this product 4.5/5”). Ratings in either format can provide decision aid to the consumer, but the two formats convey different types of product quality information and operate with different psychological mechanisms. Prior research has indicated that each recommendation type can significantly affect consumer’s post-experience preference ratings, constituting a judgmental bias, but has not compared the effects of these two common product-rating formats. Using a laboratory experiment, we show that aggregate ratings and personalized recommendations create similar biases on post-experience preference ratings when shown separately. Shown together, there is no cumulative increase in the effect. Instead, personalized recommendations tend to dominate. Our findings can help retailers determine how to use these different types of product ratings to most effectively serve their customers. Additionally, these results help to educate the consumer on how product-rating displays influence their stated preferences.

The emergence of the sharing economy has provided the market with an untapped wealth of supplies, posing a threat to incumbents. In response to competition from the sharing economy, incumbents must adjust their competitive strategies. In this paper, we focus our investigation on a nascent competitive strategy—consumer opinion manipulation—in the lodging sector of the hospitality industry. We examine two types of opinion manipulations through online reviews: promoting oneself and demoting one’s competitors. Combining data from Airbnb, Expedia, TripAdvisor, AirDNA, the Texas Comptroller’s Office, and Smith Travel Research, we estimate the impact of a new sharing economy entrant, Airbnb, on conventional hotels’ manipulation strategies by exploring the supply variation of the competing Airbnb listings around each hotel. We find that, intriguingly, hotels tend to reduce mutual demotion when facing the common “enemy” of Airbnb competition. However, there is considerable heterogeneity among hotels in response to Airbnb competition. Low-end hotels tend to not increase their review manipulation activities for purposes of either self-promotion or demotion, while high-end hotels tend to demote competing hotels less and promote themselves more in the presence of higher levels of Airbnb competition.

Data breaches are a major threat to organizations from both financial and customer relations perspectives. We developed a nomological network linking post-breach compensation strategies to key outcomes, namely continued shopping intentions, positive word-of-mouth, and online complaining, with the effects being mediated by customers’ justice perceptions. We conducted a longitudinal field study investigating Target’s data breach in 2013 that affected more than 110 million customers. We examined customers’ expectations toward compensation immediately after the breach was confirmed (survey 1) and their experiences after reparations were made (survey 2). Evidence from polynomial regression and response surface analyses of data collected from 388 affected customers showed that customers’ justice perceptions were influenced by the actual compensation provided as well as the type and extent of compensation an organization could and should have provided (i.e., customers’ compensation expectations). Interestingly, both positive and negative expectation disconfirmation led to less favorable justice perceptions compared to when expectations were met. Justice perceptions were, in turn, associated with key outcomes. We discuss implications for research on data security, information systems, and justice theory.

A herding cue is a lean information signal that an individual receives about the aggregate number of others who have engaged in a behavior that may result in herd behavior. Given the ease with which they can be leveraged as implementation interventions or design features on online sites, herding cues hold the promise to provide a means to influence adoption behaviors. Yet, little attention has been devoted in the IS adoption literature to understanding the effects of herding cues. Given that herding cues are just one of several forms of social influence on adoption behaviors and are relatively lean in nature, understanding their viability as an implementation intervention necessitates understanding their effects in the presence of (1) other forms of social influence, which also serve to reduce uncertainty and signal the appropriateness of technology adoption, and (2) an individual’s own beliefs about adopting. In this vein, we conducted a randomized field experiment to examine the use of a herding cue as an implementation intervention to hasten adoption behaviors. The research model was evaluated using survival analysis by combining the data from the field experiment with two waves of surveys, and archival logs of adoption. Our results show that a herding cue (1) directly impacts the time it takes an individual to adopt a technology, (2) amplifies the effects of peer behaviors (another type of informative social influence), but has no impact on the effect of subjective norm (a form of normative social influence), and (3) dampens the effects of an individual’s private beliefs about the usefulness of a technology. Our paper disentangles herding information signals to define a herding cue as distinct from other herd behavior triggers, explores how it may interact with other forms of social influences and private beliefs to influence adoption behaviors, and, on a practical level, provides evidence of how a herding cue can be a tangible intervention to accelerate technology adoption.

In recent years, there has been a heated discussion on opinion polarization on social media platforms. Extant research attributes the emergence of echo chambers to higher exposure to information from users’ existing social networks, which consists of like-minded others and argues that the provision of information from outside users’ networks could alleviate opinion polarization. In this paper, we formulate a hierarchical Bayesian learning model to investigate the impact of replies, one of the main channels for information outside of users’ networks, on opinion polarization. We leverage a unique natural experiment contained in the data from a leading microblogging website in China in which the reply function was shut down for three days. This setting allows us to identify the impact of replies from that of peer microblogs. We found that shutting down reply function reduced sentiment polarization on the microblogging site. In addition, this effect was more significant for individuals with higher social media participation. The results of this study shed light on marketing campaign strategies as well as the ways in which platform design can reduce polarization.