Knowledge that Transforms

Emotional expressions are ubiquitous in electronic word-of-mouth (eWOM) communication, but their effect on eWOM persuasiveness and the underlying mechanisms in the context of social networking services (SNS) have been underexplored. This research focuses on an extensively used nonverbal emotional cue in computer-mediated communication—the emoticon. Drawing on the emotion as social information model (EASI), we propose a conceptual framework to understand whether, how, and when emoticons influence the persuasiveness of eWOM on SNS. Results from a field experiment and a series of online experiments show that emoticons can increase eWOM persuasiveness through the mediating effects of enhanced recipient empathy and trust toward the sender and that these effects vary across situations. Specifically, the persuasive effect of emoticons occurs for both positive and negative eWOM when recipients and senders are close to each other. However, this effect occurs only for negative eWOM when recipients and senders have distant relationships. We discuss the theoretical and practical implications of these findings and identify several opportunities for future research.

Algorithm aversion is an important and persistent issue that prevents harvesting the benefits of advancements in artificial intelligence. The literature thus far has provided explanations that primarily focus on conscious reflective processes. Here, we supplement this view by taking an unconscious perspective that can be highly informative. Building on theories of implicit prejudice, in a preregistered study, we suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species,” the behavior of which is unknown. Like in other contexts of prejudice, we expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation), which is reduced as a function of people’s implicit prejudice against the machine. Next, building on the somatic marker hypothesis and the accessibility-diagnosticity perspective, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI’s advice. Two studies (n1 = 675, n2 = 317) that use the implicit association test consistently support this view. Two additional studies (n3 = 255, n4 = 332) rule out alternative explanations and provide stronger support for our assertions. The findings ultimately suggest that moving the needle between aversion and appreciation depends initially on one’s general unconscious bias against AI because there is insufficient information to override it. They further suggest that in later use stages, this shift depends on accessibility to diagnostic information about the AI’s performance, which reduces the weight given to unconscious prejudice.

We draw on the attention-based view of the firm to examine whether and when the presence of a CIO in the TMT has a positive effect on both firms’ ideated digital innovation (IDI) (i.e., the intensity of firms’ digital patenting activity) and commercialized digital innovation (CDI) (i.e., the digital sophistication of firms’ new products). Building on the idea that attention processes are context dependent, we also explore the moderating roles of CEO characteristics (IT background and role tenure) as well as environmental characteristics (the industry’s IT attention). We analyze data from a cross-industry panel of U.S. S&P 500 firms over eight years that includes up to 2,852 firm-year observations. The results indicate that CIO presence in the TMT is positively related to a firm’s IDI and CDI. Furthermore, they show that the organizational context related to CEO characteristics moderates the CIO-CDI relationship and that the environmental context related to the industry’s IT attention moderates the CIO-IDI relationship. Our research contributes to the information systems literature by providing robust evidence that CIO presence in the TMT positively influences a firm’s digital innovation outcomes, showing how internal and external boundary conditions affect the work of CIOs, and elaborating the role of managerial attention as an underlying mechanism explaining digital innovation.

Organizations replace their legacy systems for technical, economic, and operational reasons. Replacement is a risky proposition, as high levels of technical and social inertia make these systems hard to withdraw. Failure to fully replace systems results in complex system architectures involving manifold hidden dependencies that carry technical debt. To understand how a process for replacing a complex legacy system unfolds and accumulates technical debt, we conducted an explanatory case study at a local manufacturing site that had struggled to replace its mission-critical legacy systems as part of the larger global company’s commercial-off-the-shelf (COTS) system implementation. We approach the replacement as a sociotechnical change and leverage the punctuated sociotechnical information system change model in combination with the design-moves framework to analyze how the site balanced creating digital options, countering social inertia, and managing (architectural) technical debt. The findings generalize to a two-level (local/global) system-dynamics model delineating how replacing a deeply entrenched mission-critical system generates positive and negative feedback loops within and between social and technical changes at local and global levels. The loops, unless addressed, accrue technical debt that hinders legacy system discontinuance and gradually locks the organization into a debt-constrained state. The model helps managers anticipate challenges that accompany replacing highly entrenched systems and formulate effective strategies to address them.

Biases on online platforms pose a threat to social inclusion. We examine the influence of a novel source of bias in online philanthropic lending, namely that associated with religious differences. We first propose religion distance as a probabilistic measure of differences between pairs of individuals residing in different countries. We then incorporate this measure into a gravity model of trade to explain variation in country-to-country lending volumes. We further propose a set of contextual moderators that characterize individuals’ offline (local) and online social contexts, which we argue combine to determine the influence of religion distance on lending activity. We empirically estimate our gravity model using data from Kiva.org, reflecting all lending actions that took place between 2006 and 2017. We demonstrate the negative and significant effect of religion distance on lending activity, over and above other established factors in the literature. Further, we demonstrate the moderating role of lenders’ offline social context (diversity, social hostilities, and governmental favoritism of religion) on the aforementioned relationship to online lending behavior. Finally, we offer empirical evidence of the parallel role of online contextual factors, namely those related to community features offered by the Kiva platform (lending teams), which appear to amplify the role of religious bias. In particular, we show that religious team membership is a double-edged sword that has both favorable and unfavorable consequences, increasing lending in general but skewing said lending toward religiously similar borrowers. Our findings speak to the important frictions associated with religious differences in individual philanthropy; they point to the role of governmental policy vis-à-vis religious tolerance as a determinant of citizens’ global philanthropic behavior, and they highlight design implications for online platforms with an eye toward managing religious bias.

We collaborated with a leading fast-moving consumer goods (FMCG) manufacturer to investigate how intelligent image processing (IIP)-based shelf monitoring aids manufacturers’ shelf management by using data from a quasi-experiment and a field experiment. We discovered that such artificial intelligence (AI) assistance significantly and consistently improves product sales. Several underlying mechanisms were revealed by our quantitative and qualitative analysis. First, retailers are more likely to comply due to the greater monitoring effectiveness enabled by AI assistance. Second, the positive effect of IIP-based shelf monitoring partially persists after it is terminated, implying that human learning takes place. Third, the value of IIP-based shelf monitoring can be attributed to independent retailers rather than chain retailers. Since the degree of contract heterogeneity is the major difference between these retailers in terms of monitoring, this finding further suggests that AI is relatively more scalable when coping with more heterogeneous instances. Apart from these great benefits, we demonstrate the low marginal costs of implementing IIP-powered shelf monitoring, which indicates its long-term applicability and potential to generate incremental value. Our research contributes to several literature streams and provides managerial insights for practitioners who consider AI-assisted operational models.

Contributions by individual open source software (OSS) community members are the lifeblood of the OSS projects that power today’s digital economy and are important for the very survival of such communities. Individual contributions by OSS community members to others’ projects and their own determine whether OSS communities are resilient in the face of major shocks. Arguably, if crises such as the COVID-19 pandemic prompt users to reduce their contributions to others’ projects relative to the contributions to their own projects, such behavior can have implications for the overall resilience of the OSS community. Therefore, whether and how individuals change their contributions in the face of a crisis is an important question. We examine whether members in an OSS community increased or decreased their contributions to others’ projects relative to their own in the face of the COVID-19 pandemic, a sudden and unexpected global health-related shock that has affected almost everyone. We also compare and contrast this behavior when the OSS community faced increasing unemployment, an economic cyclic shock that is arguably and relatively more personal. Drawing on the concept of prosocial behavior and conservation of resources (COR) theory, we hypothesize that the pandemic increased OSS community members’ contributions to others’ projects relative to their own; on the other hand, the threat of rising unemployment decreased OSS community members’ contributions to others’ projects relative to their own. Our empirical analyses of a longitudinal dataset of over 18,000 OSS community members on GitHub, with more than 1.4 million member-day observations, support our hypotheses. This study contributes by uncovering the differential effects of exogenous health-related and economic shocks on the resilience of the OSS community. We conclude with a discussion of our findings’ implications for OSS community resilience.

The COVID-19 pandemic has underscored the urgent need for healthcare entities to develop resilient strategies to cope with disruptions caused by the pandemic. This study focuses on the digital resilience of certified physicians who adopted an online healthcare community (OHC) to acquire patients and conduct telemedicine services during the pandemic. We synthesize the resilience literature and identify two effects of digital resilience—the resistance effect and the recovery effect. We use a proprietary dataset that matches online and offline data sources to study the digital resilience of physicians. A difference-in-differences (DID) analysis shows that physicians who adopted an OHC had strong resistance and recovery effects during the pandemic. Remarkably, after the COVID-19 outbreak, these physicians had 35.0% less reduction in medical consultations in the immediate period and 31.0% more bounce-back in the subsequent period as compared to physicians who did not adopt the OHC. We further analyze the sources of physicians’ digital resilience by distinguishing between new and existing patients from both online and offline channels. Our subgroup analysis shows that, in general, digital resilience is more pronounced when physicians have a higher online reputation rating or have more positive interactions with patients on the OHC platform, providing further support for the mechanisms underlying digital resilience. Our research has significant theoretical and managerial implications beyond the context of the pandemic.

Online equity markets have significantly changed the dynamics of connecting angels and individual equity investors to new ventures that seek early-stage capital. However, for those early-stage investors, information pointing to the success of business-to-business (B2B) new ventures (B2BNVs) is scattered and disconnected. This paper focuses on social media narratives (SMNs) as a source of insight for such investors and proposes that predicting a B2BNV’s likelihood of success requires a comparative view, i.e., a comparison of its SMNs with those of its competitors and customers. We expect that higher (lower) lingual similarity between the SMNs of an early-stage B2BNV and those of its prospective customers (competitors) predict its success. Using a longitudinal panel of 574 B2BNVs resulting in more than 2,700 venture-round observations, we find that a comparative view of a venture’s SMNs can give early-stage investors reliable predictions about the B2BNV’s ability to manage its market presence and its success in later stages. Our models show that a comparative view of SMNs increases the accuracy of predicting a B2BNV’s later-stage fundraising success by an average of 15%. Furthermore, predictive models can reliably point to a successful market presence in later stages, including the landing of customers, the winning of awards and competitions, the receiving of endorsements, the generating of revenue, and the successful patenting of products. Our study contributes to existing literature that focuses on the business impacts of social media by demonstrating the usefulness of comparative linguistics in social media analytics, i.e., comparing the firm’s social media communications to those of its competitors and business customers in the prediction of the entrepreneurial firm’s success.

This paper addresses a thus-far neglected dimension in human-artificial intelligence (AI) augmentation: machine-induced reflections. By establishing a grounded theoretical-informed model of machine-induced reflection, we contribute to the ongoing discussion in information systems (IS) regarding AI and research on reflection theories. In our multistage study, physicians used a machine learning-based (ML) clinical decision support system (CDSS) to see if and how this interaction can stimulate reflective practice in the context of an X-ray diagnosis task. By analyzing verbal protocols, performance metrics, and survey data, we developed an integrative theoretical foundation to explain how ML-based systems can help stimulate reflective practice. Individuals engage in more critical or shallower modes depending on whether they perceive a conflict or agreement with these CDSS systems, which in turn leads to different levels of reflection depth. By uncovering the process of machine-induced reflections, we offer IS research a different perspective on how such AI-based systems can help individuals become more reflective, and consequently more effective, professionals. This perspective stands in stark contrast to the traditional, efficiency-focused view of ML-based decision support systems and also enriches theories on human-AI augmentation.