Knowledge that Transforms

Design science research (DSR) aims to generate knowledge about innovative solutions to real-world problems. Consequently, DSR needs to deal with the complexity related to problem and solution spaces involving sociotechnical phenomena that people perceive differently and are subject to constant change. This complexity poses challenges to sequential, process-based approaches—specifically, the existing DSR methodology. We designed a DSR methodology that extends existing approaches by adding a complementary organizing logic to address complexity. Based on the theory of hierarchical, multilevel systems, we suggest organizing DSR based on the concept of “echelons”—meaning decomposing DSR projects into smaller logically coherent self-contained parts—and suggest a set of five design echelons that imply a hierarchical organizing logic for DSR projects. The echeloned DSR (eDSR) methodology was developed in five iterations, involving seven design and evaluation episodes.

Social media influencer marketing has grown substantially in the last decade and is a major advertising channel for many brands. Social media influencers weave sponsored posts with organic content into their feeds, which raises concerns among regulators and consumer advocates that users may not be able to clearly distinguish between sponsored and organic influencer content. Thus, regulators often mandate the explicit disclosure of sponsored content. However, there is little empirical evidence based on field data about the effects of explicit sponsorship disclosure. Therefore, we empirically investigate the effects of explicitly disclosing sponsorship in influencers’ content on users’ engagement using a large-scale field dataset collected from Facebook and Instagram. Our empirical results suggest that explicit sponsorship disclosure increases user awareness of the advertising nature and earns users’ favorability by enhancing transparency about the sponsored content. We further designed two online experiments to corroborate our empirical results and directly test the underlying mechanisms. Our findings have novel and important implications for marketers, influencers, social media platforms, and regulators in the influencer marketing industry.

Enterprise social media (ESM) is changing how knowledge workers interact and share information; however, a debate persists as to whether ESM is an adequate knowledge management system. ESM provides a rich set of affordances for organizational knowledge work, such as improved organizational memory, but also constrains knowledge work performance because of digital interruptions. Extending and complementing existing scholarship, this study asks the following research question: How can organizations design ESM to realize its knowledge work benefits? Using a computational agent-based model that incorporates the design features of ESM, workers’ attitudes, and resulting ESM-use affordances and constraints, this study shows how ESM-use outcomes are contingent both on the design of and users’ attitudes toward ESM. Specifically, the negative effects of ESM interactivity are mitigated when employees have a low transparency preference and access ESM without posting as much. The study further unpacks asymmetric engagement as the mechanism that leads low transparency configurations to be more resilient to the negative effects of interruptions driven by ESM interactivity. Asymmetric engagement—learning from posted content without interacting often—enables the gradual creation of organizational memory while maintaining a broad user base by minimizing interruptions. These results ultimately contribute a multilevel model of ESM use and knowledge work outcomes, enhancing the theoretical understanding of previously studied mechanisms such as communication visibility and providing implications for organizations designing ESM.

Racism in the financial sector is a complex global phenomenon involving intertwined social, economic, and digital systems. Credit denial rates for Black applicants suggest that financial systems have assimilated racial discrimination into their algorithms and credit scoring tools. Considering that digital tools incorporate their developers’ life experiences and perspectives, and in view of the low representation of the Black community in the digital startup universe, it is evident that even well-meaning fintechs are reproducing racial bias in their technology-intensive business models. In this paper, we investigate how three Black-owned Brazilian fintechs design and use inclusive algorithms to decrease persistent social injustice involving racial financial inclusion. We combine the concept of sociotechnical reconfiguration, taken from the South American tradition of “tecnologia social” (social technology), with the three core concepts of Nancy Fraser’s theory of social justice—representation, recognition, and redistribution—to analyze the process by which Black-owned fintechs develop their particular solutions for combating racial bias. We found that pressured by the need to be profitable, the lack of capital to develop their own credit scoring tools, and the low profile maintained by the Black community ecosystem, Black-owned fintechs focus on a sort of affirmative experimentation to gradually create original digital solutions aimed at achieving social justice in the financial system.

We investigate how bots influence human-to-human interaction in online communities. In doing so, we distinguish between reflexive and supervisory bots delegated by community participants and moderators, respectively. We hypothesize that reflexive bot activity will reduce direct reciprocity and increase generalized reciprocity and that supervisory bot activity will reduce preferential attachment among human participants. Through an analysis of almost 70 million posts on the discussion communities on Reddit, a popular platform for online discussions, we found support for the hypotheses.

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.