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- Fakultät Wirtschaftswissenschaften (12) (entfernen)
Corporate irresponsibility is often the result of intentionally irresponsible strategies, decisions, or actions, which negatively affect an identifiable stakeholder or environment. For instance, these range from the violation of the human rights and labor standards to environmental damages. Organizations enacting irresponsible practices rely on different factors upon multiple levels (field, organizational, individual) and its interrelations as well as processes evolving within the organization leading to such behavior. However, reasons for the occurrence of and explanations for corporate irresponsibility so far have been limited, leaving a fragmented understanding of this phenomenon. This dissertation helps to improve the understanding and explanation of corporate irresponsibility by identifying driving patterns of corporate irresponsibility and showing how the interactions across multiple levels add to this phenomenon. Chapter 1 provides an overview of the topic of corporate irresponsibility, the theoretical approaches of this dissertation and an introduction to the chapters. The second chapter offers a review and analysis of the corporate irresponsibility literature. The chapter presents a variance model outlining the concept, antecedents, moderators and outcomes of recent corporate irresponsibility literature as well as the different factors across levels (field, organizational, individual). Chapter 2 offers a critical analysis of what we know by referring to current literature and offers insights on what we don't know by deriving main implications for future research on corporate irresponsibility. Chapter 3 enlarges the understanding of corporate irresponsibility introducing a process approach to explain how corporate irresponsibility evolves over time and under which conditions. Based on a qualitative meta-analysis findings converge around two distinct process paths of corporate irresponsibility, the opportunistic-proactive, and, the emerging-reactive, subdivided into three phases. Chapter 3 sheds different lights upon the phases of corporate irresponsibility and its underlying mechanisms. The final chapter 4 focuses on different underlying mechanisms driving the final downfall or demise of organizations, organizational failure. Chapter 4 offers an alternative explanation to the competing extremism and inertia mechanisms driving organizational failure in recent studies by suggesting that these explanations are rather complementary. In addition, chapter 4 enlarges the explanation of organizational failure identifying the role of conflict mechanisms and its interplay with rigidity mechanisms. In sum, this dissertation contributes to a better understanding of what causes and increases corporate irresponsibility, and a better explanation of how and why corporate irresponsibility and organizational failure emerges, develops, grows or terminates over time.
Online marketing, especially Paid Search Advertising, has become one of the most important paid media channels for companies to sell their products and services online. Despite being under intensive examination by a number of researchers for several years, this topic still offers interesting opportunities to contribute to the community, particularly because of its large economic impact and practical relevance as well as the detailed and widely unfiltered view of consumer behavior that such marketing offers. To provide answers to some of the important questions from advertisers in this context, the author present four papers in his thesis, in which he extends previous works on optimization topics such as click and conversion prediction. He applies and extends methods from other fields of research to specific problems in Paid Search. After a short introduction, the dissertation starts with a paper in which the authors illustrates a new method that helps advertisers to predict conversion probabilities in Paid Search using sparse keyword-level data. They address one of the central problems in Paid search advertising, which is optimizing own investments in this channel by placing bids in keyword auctions. In many cases, evaluations and decisions are made with extremely sparse data, although anecdotal evidence suggests that online marketing is a typical "Big Data" topic. In the developed algorithm presented in this paper, the authors use information such as the average time that users spend on the advertiser's website and bounce rates for every given keyword. This previously unused data set is shared between all keywords and used as prior knowledge in the proposed model. A modified version of this algorithm is now the core prediction engine in a productive Paid Search Bid Optimization System that calculates and places millions of bids every day for some of the most recognized retailers and service providers in the German market. Next, the author illustrates the development of a non-reactive experimental method for A/B testing of Paid Search Advertising activities. In that paper, the authors provide an answer to the question of whether and under what circumstances it makes economic sense for brand owners to pay for Paid Search ads for their own brand keywords in Google AdWords auctions. Finally, the author presents two consecutive papers with the same theoretical foundation in which he applies Bayesian methods to evaluate the impact of specific text features in Paid Search Advertisements.