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Hate crimes, which stem from prejudiced attitudes, have a distributionally detrimental impact on societal stability. Although inter-group contacts are potentially an effective means for reducing prejudice and subsequently decreasing the number of hate crimes, scholars have recently recognized the possibility of negative contacts that might actually amplify prejudice. As a result, the question of whether intergroup contacts truly possess the ability to effectively decrease hate crime numbers remains inconclusive. In addition, prior contact research primarily relies on laboratory experiments because the establishment of intergroup contacts in a field setting is challenging. Examination of the effectiveness of intergroup contacts hence merits further investigation in a real-world setting. In this article, we propose that ride-hailing services, which naturally connect individuals from different backgrounds, offer an avenue to facilitate intergroup contacts in practice, which could potentially reduce prejudice and the volume of hate crimes. Leveraging the staggered introduction of this technology into counties in the United States, we conducted a series of analyses to empirically evaluate the contact effects in the open field. Our analysis reveals a notable decrease in the number of hate crimes (particularly a 5.75% reduction in racial hate crimes) after the introduction of ride-hailing services. These findings remained consistent across various robustness tests. Additional moderation analysis suggests that the increased interaction between different groups, facilitated by ride-hailing services, is the most likely explanation for the observed decrease in hate crimes. We further conducted an extensive survey involving real ride-hailing drivers and passengers. The results from our survey provide direct evidence that ride-hailing services create natural and constructive environments where positive interactions and mutual understanding can develop among diverse groups of people. This, in turn, helps mitigate prejudice and hate crimes within society, as observed in our analysis. This study not only extends the existing body of literature on contact theory but also sheds light on how modern technologies can play a pivotal role in curtailing hate crime, yielding both theoretical and practical implications. [ABSTRACT FROM AUTHOR]

This paper studies whether social media personal branding (PB) improves a job candidate's labor market performance in the context of executive employment and compensation. We focus on executives employed by Standard & Poor's 500 constituent companies from 2010 to 2013 and evaluate their PB on social media by analyzing their Twitter accounts. To disentangle the effect of PB from that of personality traits, we exploit a (positive) shock to the effectiveness of PB caused by a series of technology upgrades by Twitter. Estimations from a two-sided matching model suggest that social media PB benefits executive candidates in job markets. This paper contributes to the literature by initiating the study of the emerging phenomenon of social media PB and testing its effect on job market performance. [ABSTRACT FROM AUTHOR]

Numerous online information systems (e.g., question and answer [Q&A] forums, citizen science communities, and review websites) rely heavily on volunteer contributions. Managers have used monetary incentives to induce individuals to increase their contribution level. However, monetary incentives could also generate adverse effects, which could dampen individuals' intrinsic motivation and lead to lower contribution quality when incentives are small. To address this issue, we propose two intervention strategies, goal-setting and challenge-seeking, and conduct a series of randomized experiments. We find that small monetary incentives, when combined with the appropriate intervention strategies, can motivate users to increase contribution quantity while simultaneously sustaining high quality. Thus, integrating such intervention strategies with small incentives can be a cost-effective way to encourage voluntary contributions. Our research contributes to the literature on incentive provision and provides theoretical and practical implications for platforms relying on voluntary contributions. [ABSTRACT FROM AUTHOR]

This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research. [ABSTRACT FROM AUTHOR]