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Buchgeschichte
(2019)
This dissertation investigates different applications of data analytics in supply chain planning. In the last years, data analytics became more important, because of the increase of computational power and the larger availability of data. Data analytics is used in various domains to improve operations performance, increase customer satisfaction and revenues. However, both the research and the application of data analytics in supply chain management is still lacking behind other industries. We1 analyze the potential of data analytics in the field of supply chain planning in three exemplary fields: demand forecasting, partial defection prediction and price discrimination. In addition, we demonstrate how to deal with three common challenges in the field of data analytics: the manual effort for method selection and hyperparameter tuning, the difficult interpretability of machine learning methods and the risks associated with data collection through randomized experiments. In the first paper, we develop a method selection approach in the field of intermittent demand prediction. Our model combines high predictive performance with automation and calculation efficiency. Unlike common practice, the prediction method gets automatically chosen for each data set without any manual selection. Our results are stable across three different data sets that come from different sources but all contain intermittent demand time series. We showcase the impact of the proposed forecasting approach with a warehouse operation simulation. We thereby prove the financial benefit with empirical data. In the second paper, we deal with partial defection prediction in a business-tobusiness environment in the logistics industry. The predictions must combine predictive performance with interpretability and profit maximization. Our model uses a large variety of customer-based and time-series-based features to predict the probability of partial defection for each customer. We use a data permutation approach to make the best performing, black-box models interpretable. Furthermore, we use a profit assessment to identify the method that leads to the highest revenue through successful retention actions. In the third paper, we study price sensitivity prediction. We do not use any randomized experiments, because the risk of loosing customers through such experiments is too high. Thereby, we address the challenge of data availability
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Faber and Spinler (2019a,b,c).
Exploring business ecosystem properties with a focus on innovation and entrepreneurship activities
(2019)
With the rising importance of the external economic environment of organisations and open and collaborative innovation, the last two decades have seen an emergence of diverse literature streams and practitioner interpretations of business ecosystems. However, most of the scholars’ attention has been on ecosystems that function in the information and telecommunication technology industries and interactions between actors driven by economic interests. Moreover, business ecosystems analysis was embedded in the strategic management and organizational design literature, which primarily investigated the ecosystem leader using examples of powerful multinational corporations such as IBM, Microsoft, Google and Apple. These new powerful corporations significantly influenced the nature of the global competitive landscape (e.g. app economy), enabled the emergence of new business models (e.g. platform business) and even new forms of entrepreneurship (e.g. blogging and self-publishing). However, little remained known about the actors at the periphery of business ecosystems – that enable the functioning of such ecosystems - and the entrepreneurship processes that take place in the ecosystem environment.
With the goal of contributing to the extant literature and creating a better understanding of the central factors that foster evolution, and the functioning and growth of today’s business ecosystems, the first project of this doctoral dissertation provides an overview of the ecosystem concept evolution in literature and in practice. Based on the findings of this project, this dissertation takes two qualitative empirical directions. To expand on the scope of the ecosystem concept, in the second project it looks at open and collaborative innovation processes in the fashion industry, analyses the role of individuals in business ecosystems and highlights the emergence of new forms of entrepreneurship. Following a worldwide growing startup trend and increased corporate engagement in diverse corporate venturing activities, the third project empirically investigates inter-organisational knowledge flows and value creation and capture processes in the innovation ecosystem environment. This dissertation contributes to the open and user innovation and entrepreneurship literature and provides practical implications for managers and entrepreneurs interested in participating in an existing business ecosystem or considering starting one on their own.
Future oriented libraries can make use of the current start-up trend. An orientation towards new and unorthodox target groups can lead to an enhanced extension of demand and can emphasize the status of libraries. The library of the WHU – Otto Beisheim School of Management is considering to involve a new target group, start-up founders amongst their alumni. To that end, a survey was carried out and evaluated in cooperation with the Institute of Information Science at the TH Köln – University of Applied Sciences in form of a bachelor thesis, which this article is based upon. Here, a structured pre-analysis tries to determine the demand of this specific target group (founders) and develops a concept to serve the demand of this target group specifically. The example of the case study illustrates a method for target groups specific information demand and also checks the consequences for libraries and their services who venture out of their regular clientele.
With the start of digitalization and the initiation of a whole new industry of digital services, some of the well-established marketing wisdoms are now under scrutiny. This also holds for the importance of customer win-back for any firm – a belief that has actually never really been questioned. While related research focuses on mature industries and highlights the profitability of win-back, to date no study known to the authors has addressed the role of reacquired customers compared to first-lifetime customers in the interplay of acquisition and retention for digital firms. This is especially important when assessing firms’ current winback strategy and forecasting the development of acquisition and retention success of reacquired compared to first-lifetime customers. This study proposes a win-back assessment framework that enables firms to assess their win-back strategy and to detect problems in acquisition or retention of first-lifetime and reacquired customers. The authors demonstrate the use of framework on transaction data from a digital subscription service and data from a field experiment with that same service provider. They analyze the data from a new analytical perspective compared to extant research, which they call “cohort perspective” and jointly investigates acquisition and retention of first-lifetime and reacquired customers, who started their subscription around the same time (i.e. cohort). The results show the need for the cohort perspective by unveiling the contradiction that reacquired customers can “live” longer than in their previous lifetime (on average 328 days), but that they “live” shorter than first-lifetime customers of the same cohort ( ̶ 19%). The authors explain the rationale of this observation and discuss its implications.