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This cumulative dissertation examines possible applications of digital assistants and their optimal design. The first article analyzes whether the emotional support from a digital assistant has a positive effect on the customer’s satisfaction with the service or his persistence. The second article examines digital assistants in their possible role as companions. The effects of the presence of a digital companion on customer satisfaction are examined. The third article deals with digital assistants in their possible role as instrumental supporters. While digital assistants appear through written/recorded language or a visualized embodiment in the context of emotional support or as a companion, they act more inconspicuously while providing instrumental support (e.g. as an autopilot of an autonomous vehicle). Therefore, the article focuses on a possible anthropomorphization of the autonomous digital assistant and examines whether the perceived risk of the user is reduced and his satisfaction with the service can be increased.
This study comprises three individual contributions to the research focus on pricing of auditing services. Using German and European data sets, the empirical investigation examines, among other things, how different dimensions of size, possible interactions from a so-called Big 4 premium or fee cutting behavior, as well as different financial risk factors affect the level of auditing services.
The study 'Size effects and audit pricing: Evidence from Germany' shows, among other things, that of those auditors who also provide consulting services for their clients, only Big 4 auditors achieve higher fees for auditing services. Furthermore, the results of the study suggest that the Big 4 premium shown in previous studies for the German market is strongly influenced by a market leader premium. Taking into account the high relevance of company size as a factor in the pricing of audit services, the study also concludes that company size should rather be represented by non-financial variables such as the natural logarithm of the number of employees in order to exclude any interdependencies with other financial variables.
The study entitled 'The Big 4 premium: Does it survive an auditor change? Evidence from Europe' presents new findings on the impact of auditor changes on audit fees, with a special focus on a possible Big 4 premium. This combines the previous research on the Big 4 premium and fee cutting. Matching analyses are used to compare the audit fees of companies that switch to a Big 4 auditor with those companies that switch to a non-Big 4 auditor. As a result, a Big 4 premium can only be shown for those companies that do not change auditors. In the case of a change of auditor, the results show that Big 4 auditors are willing to give up this bonus or even accept a discount on non-Big 4 auditors. This discount usually persists in the first few years. Accordingly, the assumption is that Big 4 auditors are pursuing a "foot-in-the-door" strategy to win new clients. Based on these results, the existence of a Big 4 premium is strongly dependent on the decision of the respective company to change auditors.
The study 'Variation of financial risk over time and the impact on audit pricing' focuses on the consideration of financial risk factors and their development over time. The key assumption of the study is that continuity of relevant financial ratios reduces the risk position of the auditor and should be positively reflected in the audit fees. For companies that do not change auditors, the results show an influence of increased volatility of financial ratios on the level of audit fees. This leads to the assumption that in case of increased volatility in the financial ratios, companies could deliberately not change the auditor in order to send a sign of stability to third parties and, if necessary, accept a premium on the existing auditor's fees. In the case of a change of auditor, however, new auditors could waive a corresponding premium in order to win new clients.
“While each of our individual companies serves its own corporate purpose, we share a fundamental commitment to all of our stakeholders” (Business Roundtable, 2019). With this statement, 181 managers of the Business Roundtable renewed their fundamental view about the purpose of business. This symbolic statement generated a vivid discussion and proved the importance and actuality of addressing organisational purpose as a research topic.
From an academic perspective the discussion about organizational purpose is not new but diverse (Bartlett & Ghoshal, 1994; Basu, 1999; Canals, 2010; George, 1999; Henderson & Steen, 2015; Hollensbe, Wookey, Hickey, George, & Nichols, 2014; Koslowski, 2001; Loza Adaui & Mion, 2016; Porter & Kramer, 2011; Quinn & Thakor, 2018; Sisodia, Wolfe, & Sheth, 2006). There are many overlapping points between the discussion on the organisational purpose and the study of corporate sustainability. Because from a sustainability management perspective, companies are administrated and valued, taking into consideration not only their economic performance but also the ecological and social impact that they generate (Elkington, 1994).
This cumulative dissertation addresses in a broad perspective the overlapping points of the discussion on organizational purpose and sustainability and entails four modules:
Module I: Matthias S. Fifka, Anna-Lena Kühn, Cristian R. Loza Adaui & Markus Stiglbauer (2016) Promoting Development in Weak Institutional Environments: The Understanding and Transmission of Sustainability by NGOs in Latin America. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 27(3), 1091-1122.
Module II: Giorgio Mion & Cristian R. Loza Adaui (2020) Understanding the Purpose of Benefit Corporations: An Empirical Study on the Italian Case. International Journal of Corporate Social Responsibility, 5(4), 1-15.
Module III: Giorgio Mion & Cristian R. Loza Adaui (2019) Mandatory Nonfinancial Disclosure and Its Consequences on the Sustainability Reporting Quality of Italian and German Companies. Sustainability, 11(17), 4612.
Module IV: Cristian R. Loza Adaui (2020) Sustainability Reporting Quality of Peruvian Listed Companies and the Impact of Regulatory Requirements of Sustainability Disclosures. Sustainability, 2020, 12(3), 1135.
Reinforcement learning constitutes a valuable framework for reward-based decision making in humans, as it breaks down learning into a few computational steps. These computations are embedded in a task representation that links together stimuli, actions, and outcomes, and an internal model that derives contingencies from explicit knowledge. Although research on reinforcement learning has already greatly advanced our insights into the brain, there remain many open questions regarding the interaction between reinforcement learning, task representations, and internal models. Through the combination of computational modelling, experimental manipulation, and electrophysiological recording, the three studies of this thesis aim to elucidate how task representations and internal models are shaped and how they affect reinforcement learning. In Study 1, the manipulation of action-outcome contingencies in a simple one-stage decision task allowed to investigate the impact of explicit knowledge about task learnability on reinforcement learning. The results highlight the flexible adjustment of internal models and the suppression of central computations of reinforcement learning when a task is represented as not learnable. Using a similar manipulation, Study 2 investigates how this influence of explicit knowledge on reinforcement learning holds under the increasing complexity of a two-stage environment. Again, pronounced neural differences between task conditions indicate separable computations of reinforcement learning and, more importantly, the selective influence of explicit knowledge and internal models on reinforcement learning. Study 3 uses a novel task design which necessitates inference about plausible action-outcome mappings, and thus, credit assignment. The findings suggest that multiple task representations are neurally conceptualized and compete for action selection, thereby solving the structural credit assignment problem. In sum, the studies of this thesis highlight the importance of reinforcement learning as a central biological principle and draw attention to the necessity of flexible interactions between reinforcement learning, task representations, and internal models to cope with the varying demands from the environment.
Order picking and delivery are integral parts of many supply chains, especially those of retail and online trade. They are mostly non-value-adding, downstream processes, but they are responsible for the majority of logistics costs. Accordingly, the academic literature deals with both order picking and vehicle routing in a diverse and detailed manner. However, a holistic view of both processes is mostly not sufficiently taken into account. The interdependent effects on the operational planning level are only considered to a limited extent since the processes are usually considered isolated or sequential planning is assumed. This thesis examines different order picking and vehicle routing problems. Furthermore, it shows the advantages of integrative planning and develops exact and heuristic solution methods for the investigated problems.
The first paper develops a structural understanding of the subproblems of picking and delivery. In addition, the article illustrates the development of the research branch of integrative order picking and vehicle routing.
Further contributions examine practice-oriented problems in the field of micro-store deliveries (contribution 2), same-day deliveries (contribution 3), and the supply of supermarkets (contribution 4). In each case, different real-world constraints are included and different objectives are addressed. Furthermore, problem-specific integrative solution methods are developed and compared to classical approaches.
Automation technologies and human capital are fundamental drivers of economic growth. The motivation of this thesis is to advance the literature by providing novel insights into the determinants of automation innovation and human capital. For this purpose, this thesis exploits immigration episodes, uses empirical methods for causal inference and introduces new sources of data on immigrants and automation innovation. Chapter 2 and Chapter 3 each examine the relationship between labor endowments and automation innovation. Chapter 4 examines the effects of ethnic concentration on immigrant childrens' acquisition of human capital.
In many countries today, a rising life expectancy and the associated demographic shift, coupled with the advancements of modern medicine, has fueled an ever-increasing cost pressure on healthcare systems. A driving factor for these rising costs can be seen in inpatient stays in hospitals that in many cases are connected to cost-intensive treatments. A central concern of any hospital management in such an environment is therefore to understand how to make the best possible use of available resources. A decisive factor in this regard is the management of bed capacities.
The present cumulative dissertation comprises four contributions, which address
open research questions in the field of strategic, tactical and operative bed planning:
1 Walther, M., 2020. Strategical, tactical, and operational aspects of bed
planning problems in hospital environments. Submission planned to
Social Science Research Network (SSRN)
2 Hübner, A., Kuhn, H., Walther, M., 2018. Combining clinical departments
and wards in maximum-care hospitals. OR Spectrum 40, 679-709
3 Schäfer, F., Walther, M., Hübner, A., Kuhn, H., 2019. Operational
patient-bed assignment problem in large hospital settings including overflow
and uncertainty management. Flexible Services and Manufacturing
Journal 31, 1012–1041
4 Schäfer, F., Walther, M., Hübner, A., Grimm, D., 2020. Machine learning
and pilot method: tackling uncertainty in the operational patient-bed
assignment problem. Submitted to OR Spectrum on 13 February 2020
The first contribution sets out to provide an overview over the different hierarchical planning levels on which bed planning problems may be addressed. It should be noted in this context that several different aspects may be combined under the collective term “bed planning”. These may be delimited in terms of their scope and their planning horizon. A frequently used taxonomy in this context is the hierarchical subdivision of typical problems in health care into strategical, tactical and operational levels as provided by Hulshof et al. (2012). In the context of bed planning, a typical strategical problem is how to combine departments and wards to obtain benefits from pooled ward capacity. On a tactical level, an exemplary problem setting related to bed planning can be seen in devising master surgery schedules that optimize downstream bed occupancy levels as patients returning from surgery will require a bed for post-surgical recovery and monitoring. Finally,
on an operational level, patient-bed allocations need to be optimized while taking the objectives and constraints of patients and medical staff alike into account.
To start, the second contribution deals with the strategical problem of combining departments into groups and assigning pooled ward capacity to these groups with the goal of balancing bed occupancy levels within a hospital. Specifically, one of the underlying goals is to minimize the amount of beds required to meet a predetermined service level. However, merging ward capacities with the aim of simultaneously accommodating patients from different medical departments increases the complexity of organizing and ensuring proper care for these patients. This leads to so-called pooling costs. To tackle this problem, a modeling and solution approach is developed which is based on a generalized partitioning problem and is solved by integer
linear programming (ILP). This enables hospital management to determine the cost-optimal combination of all departments and wards in a hospital, while ensuring that predetermined thresholds with regard to maximum
walking distances for doctors and patients are adhered to.
Once pooled ward capacities are established, the solution space for allocating incoming patients to beds is greatly increased and the underlying allocation problem quickly becomes too complex to be handled without computational support. In this regard the third contribution ties in with the second contribution in that it deals with optimizing the operational patient-bed allocation problem. In order to enable optimal allocation of patients to beds, it is important to identify and take into account the individual needs and
limitations of the three main stakeholders involved, namely patients, doctors, and nursing staff. All of these stakeholders exhibit different and sometimes contradicting objectives and constraints, such that a trade-off has to be made that maximizes the overall utility for the hospital. In addition, the complexity of the problem is increased by the high volatility and uncertainty regarding patient arrivals, types of illnesses, and the resulting remaining lengths of stay of newly arriving patients. In order to address this situation,
a mathematical model and solution approach for the patient-bed allocation problem is developed that is designed to generate solutions for large, real-life operative planning situations. In addition to being able to deal with overflow situations, this solution approach further takes different patient types into account, for example by anticipating emergency patient arrivals.
Finally, the fourth contribution builds on the third contribution in that the modeling and solution approach to allocate patients to beds is extended by several aspects. As mentioned above, hospitals have to deal with uncertainty regarding the actual demand for beds. Here, the fourth contribution improves the anticipation of emergency patients by using machine learning. Specifically, weather data, seasons, important local and regional events, and current and historical occupancy rates are combined to better anticipate emergency inpatient arrivals. In addition, a hyper-heuristic approach is developed based on the pilot method defined by Voß et al. (2005). By combining the improved anticipation of emergency patients with this hyperheuristic approach significant improvements can be achieved compared to the solution approach presented in the third contribution.
Recommending products that are helpful to customers and tailored to their needs is of pivotal importance for successful online retailing. Online purchase data is typically used to generate such recommendations. This dissertation studies two topic models that use purchase data to make product recommendations. The Author Topic Model (ATM) and Sticky Author Topic Model (Sticky ATM) are applied to the purchase data of an online retailer of animal health products, and their predictive performances are contrasted with those of the benchmark methods Unigram, Bigram, and Collaborative Filtering (CF). This work focuses on the generation of new product recommendations. To increase novelty in recommendations, a new pre-processing approach is presented. The data is prepared prior to model application such that more novel products are included in the recommendations. A total of six data preparation variants are tested. The key finding is that topic models are very competitive with the benchmark methods and outperform them with the data preparation variant, where repetitively purchased items (repeat items) and customers with one item transaction (single-item customers) are eliminated from the data. Marketing practitioners should consider this pre-processing when implementing topic models as recommender models in their online shops.
The cumulative dissertation "Optimization models for shelf space allocation in retail stores" consists of the four individual scientific contributions listed below:
1. Düsterhöft, Tobias, Hübner, Alexander and Schaal, Kai (2020): A practical approach to the shelf-space allocation and replenishment problem with heterogeneously sized shelves.
2. Hübner, Alexander, Düsterhöft, Tobias and Ostermeier, Manuel (2020): An optimization approach for product allocation with integrated shelf space dimensioning in retail stores.
3. Ostermeier, Manuel, Düsterhöft, Tobias and Hübner, Alexander (2020): A model for the store wide shelf space allocation.
4 Düsterhöft, Tobias (2020): Retail shelf space planning - Differences, problems and opportunities of applied optimization models.
The planning and best possible utilization of the available shelf space is of central importance for retailers. Shelf space is a scarce resource in stores today. Shelf planners need to determine optimal shelf spaces for each item within product allocation. Researchers have already developed several decision support models. Usually, these models have in common that they can make a decision on the number of facings per product. A facing is a visible sales unit of a product on the shelf. Behind a facing, depending on the depth of the shelf further sales units are located. With an increase in the number of facings the visibility of the product for customers is also increasing, which is associated with a certain demand effect, the so-called space elasticity.
The content of this dissertation are optimization models that extend existing approaches to product allocation significantly and thus also enable a practical application of these approaches. Within the framework of a practical project substantial new contents for the product allocation can be determined. The resulting optimization models build on each other. Initially, in the 1st article the product allocation is extended by an exact consideration of the shelf space dimensions. The resulting question of the optimal shelf layout is the central part of the 2nd article. If the layouts of shelves are determined on the shelf, the total shelf space per category must be known beforehand. This question is dealt with in the 3rd article. Finally, new research fields are identified in the 4th article based on current real processes and requirements.
In this cumulative dissertation, I investigate the relation between the cognitive complexity of work tasks and economic outcomes such as earnings, migration, and aggregate income growth. Existing theoretical and empirical research firmly established that human capital plays a major role in determining these outcomes. However, the literature to date has focused on a limited set of human capital measures, such as education, years of work experience, and basic demographic characteristics, and has largely ignored considerable individual variation attributable to occupational skills. By introducing novel skill measures derived from work tasks and using theoretical models combined with empirical evidence, I demonstrate the determining role played by occupational skills.
The first paper examines the connection between problem solving and lifecycle wage dynamics. I introduce a model of learning-by-doing which relates the intensity of complex tasks to the growth of problem solving skills and labour productivity. Using German administrative data, I find that workers in complex jobs receive static and dynamic wage premia and acquire relatively more human capital throughout life.
The second paper examines the selection pattern of Mexican migrants to the United States and shows that Mexican migrants have lower cognitive skills and higher manual skills compared to non-migrants. Using an extended version of the Roy-Borjas model, I show that differences in the returns to occupational skills explain the selection pattern better than differences in the returns to education and basic characteristics.
The third paper explores the role that complexity plays in economic development. I develop a regional model of endogenous growth which relates aggregate problem solving skills to the rate of technology adoption. In the model, migration costs and spillovers in technology adoption create persistent differences in regional income. By estimating growth regressions, I find that problem solving skills strongly predict per capita income growth in a sample of European regions.
The results collected in the dissertation have implications for economic development policies focused on human capital, projection and analysis of international migration, and evaluating long-term effects of recessions.
This study explores the distinctive patterns of language use in political discourse across selected outer circle (Cameroon and Ghana) and inner circle (US and South Africa) varieties, using a corpus-based approach. More specifically, the research sets out to investigate the use of two types of linguistic features, namely, personal pronouns and kinship metaphors. In a first analysis, I adopt an alternative approach to investigating the use of personal pronouns in political discourse. The approach essentially draws from the cognitive linguistic concept of ‘frames’ as articulated by the theory of frame semantics (Fillmore, 1976, 1977a, 1982, 1985, 2008; Fillmore & Baker, 2010). I use an automatic frame semantic parsing tool, the SEMAFOR parser (Das et al, 2014), to identify the different types of (semantic) frames and frame roles with which specific personal pronouns are instantiated across the four varieties. I then compare the findings to illustrate instances of universality and variation.
In a second analysis, I examine the types of metaphorical conceptualizations which are made using kinship terms across the varieties. Working top-down from conceptual schemas to linguistic instantiation, I identify and compare the frequencies of metaphors from the kinship field and also describe the types of cross-domain mappings typically involved in each of the varieties. My analysis is mostly informed by mainstream cognitive approaches to the study of metaphors, more especially cultural variations in the use of conceptual metaphors (Kövecses, 2002, 2005). I demonstrate that although there is empirical evidence for the use of a kinship conceptual schema across all four varieties, there are however significant variances in the specific metaphorical mappings used to instantiate this high-level conceptual structure.
Both analyses make a case for the fact that in the field of political discourse especially, language use may be structured and constrained by conceptual schemas which themselves are culturally determined.