Knowledge that Transforms

Charity fundraising is becoming increasingly reliant on online platforms such as crowdfunding platforms. However, overwhelmingly, crowdfunding campaigns are not meeting their goals. Therefore, it is imperative to examine how the success of charity fundraising campaigns can be improved. In this paper, we focus on the design of project images on a crowdfunding website, which portray the themes and content of the projects. Employing the stimulus-organism-response (S-O-R) model, we investigate the relationships between image attributes (S) and image emotions (O), and between image emotions (O) and campaign outcomes (R). We developed and trained a deep neural network model to identify the emotions conveyed in the images, and then implemented it to analyze project images from a popular crowdfunding platform. We applied the obtained image emotions together with the objective image attributes and the project outcome metrics to explore from a design perspective, what image attributes evoke image emotions, and how image emotions are related to the success of charity fundraising projects. Our results confirm these relationships and further suggest that the roles of image emotions on the success of crowdfunding campaigns vary with project characteristics such as the project budget and category. In addition, the image emotions of competing projects on the crowdfunding platform were found to reduce the project’s performance. In an extended study, we conducted an online randomized controlled experiment by manipulating image attributes to reexamine the causal relationships and verify the mediating roles of positive and negative empathies between image emotions and campaign outcomes. This research contributes to the charity fundraising literature from a novel perspective of emotions in project images. It presents new and unique findings regarding the mediation roles of positive and negative empathies and the limitations of the emotion of sadness in certain types of charity fundraising. In addition, our findings provide useful insights for practitioners seeking to design successful online charity campaigns.

Despite a large body of literature on online reviews, none have considered the nuanced impacts of how multidimensional reviews affect product sales differently for mainstream vs. niche products. This study seeks to fill this knowledge gap by conducting complementary studies in two product categories (i.e., automobiles and laptops) with different methods (a field study and three lab experiments). Our paper reveals three key insights into the emerging literature and phenomenon on multidimensional review systems: (1) the interdimensional rating variance is more negatively related to product sales for mainstream products than for niche products in the same category, (2) the intradimensional rating valence on the dominant dimension of a product is more positively related to product sales for niche products than for mainstream products, and (3) the intradimensional rating variance on the dominant dimension of a product is more negatively related to product sales for niche products than for mainstream products. Our research provides important managerial implications for both product providers and review platforms.

Online health communities (OHCs) play an important role in enabling patients to exchange information and obtain social support from each other. However, do OHC interactions always benefit patients? In this research, we investigate different mechanisms by which OHC content may affect patients’ emotions. Specifically, we notice users can read not only emotional support intended to help them but also emotional support targeting other persons or posts that are not intended to generate any emotional support (auxiliary content). Drawing from emotional contagion theories, we argue that even though emotional support may benefit targeted support seekers, it could have a negative impact on the emotions of other support seekers. Our empirical study on an OHC for depression patients supports these arguments. Our findings are new to the literature and have critical practical implications since they suggest that we should carefully manage OHC-based interventions for depression patients to avoid unintended consequences. We design a novel deep learning model to differentiate emotional support from auxiliary content. Such differentiation is critical for identifying the negative effect of emotional support on unintended recipients. We also discuss options to alter the intervention volume, length, and frequency to tackle the challenge of the negative effect.

With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue—multiperiod default prediction (MPDP)—to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decision-support in granting loans and selecting loan portfolios.

User engagement, a key factor in the success of social media platforms, has long been based on permanent content. A recent paradigm shift in platform design has led large social media providers to implement ephemerality features that by default make shared content disappear after a certain amount of time. However, very little is known about how ephemerality features affect user engagement and behavior in social media. Drawing upon the technology affordance perspective, we conducted a qualitative multimethod study involving individual interviews and focus groups. Our findings show that the affordances arising from features with varying degrees of ephemerality (i.e., snaps and stories) differ from those of permanent content features in terms of self-presentation, browsing others’ content, and communication. Adopting a multidimensional conceptualization of user engagement, we show the positive (e.g., more content sharing) and negative (e.g., cognitive burden from context loss) effects for snaps and stories that should be cautiously considered by social media platforms aiming to introduce such features. Finally, we reveal new user behaviors that relate to sharing snapshots of fleeting value as snaps or experiences of transient value as stories.

We contribute to the emerging literature on quality certification by digital platforms by studying the launch of the Airbnb Plus service, wherein the platform inspects properties and provides a badge that presumably signals the quality of the property and the reliability of the host. Our identification strategy relies on the fact that the Airbnb Plus service was launched in different cities at different times, and listings within the cities received the certification at different times. Using a staggered difference-in-differences estimation strategy in conjunction with suitable matching methods, we found that the Airbnb Plus certification increased the weekly booking rate of Plus listings by about 6.8% on average (direct effect). We also found some evidence that non-Plus listings saw a temporary decline in booking rate when one or more nearby properties received a Plus certification (externality effect). The net impact of the Airbnb Plus service on the platform itself was an annual increase in revenue of about $37,500 for the average 2-kilometer zone in a U.S. city that included one or more Plus listings, as compared to matched zones without any Plus listings (local platform effect). We performed additional analyses, including a randomized experiment, to demonstrate the robustness of our findings. Overall, our results suggest that platform-endorsed quality certification has significant economic impacts—not just on the listings that receive the certification but on other listings on the platform as well as on the platform itself.

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.