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Members of Western organizations differ in various diversity attributes. In response, research aims to provide evidence-based recommendations on how to effectively manage diversity in teams. Within diversity research, the diversity faultlines approach has been particularly fruitful. It considers the impact of the alignment of multiple diversity attributes in teams. Strong diversity faultlines are associated with the emergence of relatively homogeneous subgroups in teams and have an overall negative impact on team processes and outcomes. This dissertation investigates factors that mitigate the detrimental consequences of strong diversity faultlines in teams, namely pro-diversity beliefs. It extends faultline literature beyond the conventional focus on processes and outcomes related to team members by emphasizing the leaders' perspective. The three empirical papers included in this dissertation systematically examine how strong pro-diversity beliefs can help unleashing the positive effects of team diversity despite strong faultlines. The first paper highlights the role of leaders' pro-diversity beliefs in mitigating the negative impact of diversity faultlines on two team processes: perceived cohesion and social loafing. Moreover, it compares the impact of socio-demographic faultlines (based on gender and age) and experience-based faultlines (based on team tenure and education level). Data was collected in a multisource field sample with 217 team members nested in 44 teams and the corresponding leaders. The second paper takes the impact of members' pro-diversity beliefs into account. It examines whether the impact of sociodemographic faultlines on performance is contingent on leaders' and members' pro-diversity beliefs. Moreover, the research group assumed that aggregate LMX would mediate this relationship. In a multisource data set obtained from 41 teams with 219 members and the corresponding leaders working for the German Ministry of Foreign Affairs, the investigators found partial support for their hypotheses. As expected, the impact of strong socio-demographic faultlines on diplomats' performance was least negative when both leaders and members held strong pro-diversity beliefs. The third paper zooms into processes and outcomes related to team leaders. It investigates how leaders' pro-diversity beliefs and their perceptions of members' prodiversity beliefs in teams with strong socio-demographic faultlines impact leaders´ task role assignment, performance expectation, and motivation. The research group conducted two experimental studies with students, one in Germany (N=55) and one in the US (N=134). Findings showed that strong pro-diversity beliefs held and perceived by leaders made them assign task roles that cross-cut rather than aligned with the subgroup structure created by faultlines. Moreover, leaders' perceptions of members' pro-diversity beliefs, but not their own beliefs, had a positive impact on their motivation, mediated by their performance expectation.
Online advertising has become one of the most important dimension of corporate communications. In recent years, a new form of advertising on the Internet has emerged: real-time advertising. Among others, it allows companies to identify potential customers and target them with respect to their interests. In this way, real-time advertising can increase advertising effectiveness and it could, at the same time, improve user experience. With the emerge of this new form of advertising, statistical models have become even more important because they are now being increasingly used to predict online user behavior. The articles included in this dissertation analyze user-level clickstream data generated during multi-channel advertising campaigns (including TV advertising) and during real-time auctions. The goal of the analyses conducted here is to better understand advertising effects and to support decision-making in this context. Most of the analyses are based on Bayesian models. These models allow for a very flexible structure, which enables researchers to model, for instance, heterogeneity across different types of users or non-linear parameters such as users´ reaction times and exponential decay of advertising effects. In addition, these models allow for the inclusion of prior knowledge of parameter distributions, and, therefore, they are well suited for iterative analyses based on clickstream data. Bayesian models can be evaluated in different ways. Instead of only relying on statistical metrics, the articles included in this dissertation aim to estimate the economic value of these models based on their predictive performance. Although this measure can only approximate their true economic value, this approach can be used to compare and evaluate different models and to illustrate the impact of predictive analyses for companies in the context of big data. This dissertation contributes to both information systems research and marketing research and has many managerial implications. First, a process is developed to determine optimal sample sizes representing the best balance between computational costs and predictive accuracy in e-commerce in particular and big data contexts in general. In practice, this process can be used to reduce infrastructure and computational costs. Second, the articles included here describe models that can be used to measure the impact of television ads on users' online shopping behavior. The models can provide insights concerning the effectiveness of individual television ads, the interactions between different advertising channels and the difference in user behavior of TV-induced customers and their non-TV-induced counterparts. Thereby, the models could support decision-making with respect to future advertising campaigns and targeting. Third, the articles describe several possibilities to extend and improve decision support systems currently used in e-commerce and marketing. These improvements enable practitioners to predict users´ interests for arbitrary products and services by using corresponding websites as dependent variables. This approach can be used to improve the effectiveness of real-time advertising campaigns, especially those intended to raise brand awareness among customers.