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Die kumulative Dissertation „The Effectiveness of Segment Disclosures under the Management Approach: Empirical Evidence from Europe” untersucht die Determinanten und ökonomischen Auswirkungen von Segmentberichtsangaben, welche nach International Financial Reporting Standard (IFRS) 8 aufgestellt wurden. Die Dissertation erweitert die bestehende Literatur zur Wirksamkeit des IFRS 8 in Europa und besteht dabei aus drei Beiträgen.
Der erste Beitrag untersucht den Einfluss von Kultur als Determinante für die Quantität und Qualität der Segmentberichtsangaben europäischer Unternehmen. Im Rahmen der empirischen Analyse werden darüber hinaus die ökonomischen Auswirkungen der kulturinduzierten Veröffentlichungsmuster nachgewiesen.
Der zweite Beitrag beantwortet die Forschungsfrage welche Auswirkungen Non-IFRS Segmentdaten auf die Prognosegenauigkeit von Finanzanalysten haben. In der empirischen Analyse wird dieser Wirkungszusammenhang zudem für spezielle Abweichungen von IFRS Rechnungslegungsgrundsätzen untersucht.
Der dritte Beitrag stellt Änderungsvorschläge an IFRS 8 seitens des internationalen Standardsetters vor und untersucht deren potenzielle Auswirkungen aus Perspektive der Jahresabschlussersteller.
The cumulative dissertation consists of three articles.
The first article analyses the use of vertical manager interlocks for tax avoidance purposes.
The second article studies the effect of tax department organization on tax avoidance and tax risk.
The third article contains an analysis of the effects of tax evasion penalties levied on corporations or managers on aggressive tax avoidance.
In this thesis, I evaluate different tax policy reforms or tax policies in general with respect to the three pillars of sustainability: the environmental, the economic, and the social pillar. The cumulative dissertation consists of the following four chapters:
Chapter 1: Towards Green Driving - Income Tax Incentives for Plug-in Hybrids (based on joint work with Henning Giese)
Chapter 2: Corporate Taxation and Firm Performance (based on joint work with Dominika Langenmayr, Valeria Merlo, and Georg Wamser)
Chapter 3: Tax Avoidance Using Hybrid Financial Instruments Among European Countries
Chapter 4: Avoiding Taxes: Banks’ Use of Internal Debt (based on joint work with Franz Reiter and Dominika Langenmayr)
The first chapter relates to the environmental dimension of sustainability. More specifically, I evaluate a specific German tax reform that aimed at reducing greenhouse gas emissions in the transportation sector – a key driver of emissions – by fostering green driving. This essay empirically investigates whether a preferential tax treatment for hybrid plug-in company cars was effective in fostering green driving and whether it was cost-efficient.
The second chapter relates to the economic dimension of sustainability. A subgoal towards more sustainability in this dimension is decent economic growth. Chapter 2 of this thesis contributes to the question of how tax policy can foster firm performance which is an important driver for economic growth. This essay empirically investigates the relationship between corporate taxation and firm performance taking into account firm heterogeneity. As tax policy is an essential instrument for reaching political goals like boosting the economy in crises, this essay may provide valuable insights for policymakers.
The third and fourth chapters relate to the social dimension of sustainability. A subgoal towards more sustainability in this dimension is increased international cooperation. With respect to taxation, the global challenge of multinational firms engaging in tax avoidance made international cooperation in tax policy essential in the last decade. Chapter 3 investigates a specific channel of tax avoidance, i.e., tax avoidance with hybrid financial instruments. This legal analysis reveals tax avoidance opportunities among selected European countries and evaluates countermeasures.
Chapter 4 investigates a different channel of tax avoidance, i.e., intragroup debt finance, specifically for the banking sector. I use administrative data to empirically analyse whether and to what extent multinational banks engage in tax avoidance.
Revenue management has proven successful in service industries. This dissertation tries to answer the question if revenue management can also be applied successfully to manufacturing companies. For this purpose, a survey was conducted which showed that there is significant potential for revenue management in the steel, aluminium and paper industries. Furthermore, a number of mathematical decision models were developed and solved by heuristic procedures which showed that revenue management can improve profits for manufacturing companies substantially.
"Present Bias in Choices over Food and Money: Evidence from a Framed Field Experiment" - this paper presents results from a field experiment investigating dynamically inconsistent time preferences over real food choices and commitment take-up in a natural environment. We implement a longitudinal consumption experiment with a continuous convex budget: College students repeatedly choose and consume lunch menus at their college canteen. We not only allow for a full continuum of healthy or unhealthy foods; we also explicitly design the consumption stage to comply with the consume-on-receipt assumption: utility is created right after handing out the food. The paper also focuses on the implications for effective policy design: It sheds light on consumers' tendency to utilize different types of self-control devices to commit to personal consumption plans. We further compare inconsistent behavior between convex food and money choices to investigate the applicability of monetary reward studies to natural behavior. We find no correlation between time parameters in food vs. money tasks. Utility estimates from food choices suggest that dynamically consistent individuals substitute internal self-control with commitment take-up. Non-committing individuals tend to be present-biased and naive about their inconsistency.
"Dynamic Inconsistencies and Food Waste: Assessing Food Waste from a Behavioral Economics Perspective" - this paper investigates the link between dynamically inconsistent time preferences and individual food consumption and waste behavior. Food waste is conceptualized as unintended consequence of consumption choices along the food consumption chain: Because inconsistent individuals postpone the consumption of healthier food at home, storage time is prolonged and food more likely to be wasted. Capitalizing on a rich data set from a nationally representative survey, the paper constructs targeted measures of time-related food consumption and waste behaviors. In line with the theory, the paper finds that more present-biased individuals waste more food. The mechanism can be empirically supported: Although dynamically inconsistent individuals plan their food consumption, they deviate from their consumption plans more compared to consistent individuals. Deviation behavior is significantly associated with food waste. The paper points to the importance of considering dynamic inconsistencies at different stages of the food consumption process to foster the intended effects of food policies (increase in healthy nutrition) and diminish unintended consequences (increase in food waste).
"Economic Behavior under Containment: How do People Respond to Covid-19 Restrictions?" - this paper investigates the effects of policy interventions on individual decision-making and preferences by looking at a period of unprecedentedly strict regulation: the Covid-19 pandemic. Focusing on Germany, we measure regulatory strictness by calculating a policy stringency index at the federal state level. Capitalizing on exogenous variation in predetermined election cycles across states, we instrument policy stringency with the distance to the next election. Results support a positive relation between weeks to next election and state policy stringency. Leveraging a nationally representative short panel, the paper exploits this exogenous variation in policy stringency across states and over time to assess behavioral changes in different economic domains. The paper finds that government restrictions increase working from home and the provision of childcare at home, while it reduces the number of grocery shopping trips. Containment policies also affect risk preferences and fear of Covid-19: individuals become more risk-averse and afraid of Covid-19 as a consequence of stricter policies. The results are robust against various different model specifications.
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.
Abstract Paper 1:
Cybervetting has become common practice in personnel decision-making processes of organizations. While it represents a quick and inexpensive way of obtaining additional information on employees and applicants, it gives rise to a variety of legal and ethical concerns. To limit companies’ access to personal information, a right to be forgotten has been introduced by the European jurisprudence. By discussing the notion of forgetting from the perspective of French hermeneutic philosopher Paul Ricoeur, the present article demonstrates that both, companies and employees, would be harmed if access to online information on applicants and current employees would be denied. Consistent with a Humanistic Management approach that promotes human dignity and flourishing in the workplace, this article proposes guidance for the responsible handling of unpleasant online memories in personnel decision-making processes, thereby following Ricoeur’s notion of forgetting as “kept in reserve”. Enabling applicants and employees to take a qualified stand on their past is more beneficial to both sides than a right to be forgotten that is questionable in several respects.
Abstract Paper 2:
When hiring new employees, managers often conduct online background checks on applicants. This practice has led many applicants to reach out to legal and technological solutions that render unpleasant online memories forgotten. With this article, we question the effectiveness and social value of approaches aimed at permanently erasing information from the web. Furthermore, we provide practitioners with guidance toward responsible handling of applicant information from the web. In this conceptual article, we review the literature on organizational memory and interpret it through a philosophical lens by turning to the works of the French philosopher Paul Ricoeur and the French sociologist Maurice Halbwachs. We conclude that legally or technologically enforced forgetting on the web fails to provide true protection from a memory in a hiring situation, as remembering in an organization is a complex social process that hardly lets a memory disappear completely. As a result, legal and technological approaches that aim at erasing unpleasant memories from the web represent only an illusion of protection for job applicants. Alternatively, we propose a selection process that makes the erasure of unpleasant memories from the web unnecessary. Previous literature on organizational memory has already criticized a purely mechanistic view of memory in organizations, by which memories are treated as objects that can be retrieved and deleted on demand. To the best of our knowledge, this is the first article that establishes a connection between organizational memory and the use of online information in selection.
Abstract Paper 3:
Companies have started using social media for screening applicants in the selection process. Thereby, they enter a low-cost source of information on applicants, which potentially allows them to hire the right person on the job and avoid irresponsible employee behavior and negligent hiring lawsuits. However, a number of ethical issues are associated with this practice, which give rise to the question of the fairness of social media screening. This article aims to provide an assessment of the procedural justice of social media screening and to articulate recommendations for a fairer use of social media in the selection process. To achieve this, a systematic literature review of research articles pertaining to social media screening has been conducted. Thereby, the benefits and ethical issues relating to social media screening, as well as recommendations for its use have been extracted and discussed against Leventhal’s (1980) rules of procedural justice. It turns out that without clear guidelines for recruiters, social media screening cannot be considered procedurally fair, as it opens up way too many opportunities for infringements on privacy, unfair discrimination, and adverse selection based on inaccurate information. However, it is possible to enhance the fairness of this practice by establishing clear policies and procedures to standardize the process.
Abstract Paper 4:
Social media platforms have presented individuals with sheer endless possibilities for networking, creating and exchanging information. Yet, overwhelming social demands, privacy concerns, and even a lack of access to new technologies have led many users to discontinue social media usage. Simultaneously, companies screen and select applicants on social media. As previous research suggests that employers may view missing social media information with suspicion, we ask if non-users of social media face disadvantages in hiring: Are they discriminated against? In this study, we employ a 2 x 2 experimental survey design to verify whether absence from social media may result in discriminatory behavior towards job applicants. We conduct the study with two samples: The first comprises business students, and the second consists of more senior members of the workforce. Although this study does not confirm discrimination against non-users of social media in the selection process, it adds to the literature in two respects: Firstly, it shows differences in call-back between highly and less qualified candidates as a result of social media information. Secondly, it suggests unintentional, systemic rater biases. Against these findings, we formulate recommendations for applicants and employers and give recommendations for future research in this field.
Modeling ETL for Web Usage Analysis and Further Improvements of the Web Usage Analysis Process
(2006)
Currently, many organizations are trying to capitalize on the Web channel by integrating the Internet in their corporate strategies to respond to their customers’ wishes and demands more precisely. New technological options boost customer relationships and improve their chances in winning over the customer. The Web channel provides for truly duplex communications between organizations and their customers and at the same time, provides the technical means to capture these communications entirely and in great detail. Web usage analysis (WUA) holds the key to evaluating the volumes of behavioral customer data collected in the Web channel and offers immense opportunities to create direct added value for customers. By employing it to tailor products and services, organizations gain an essential potential competitive edge in light of the tough competitive situation in the Web. However, WUA cannot be deployed offhand, that is, a collection of commercial and noncommercial tools, programming libraries, and proprietary extensions is required to analyze the collected data and to deploy analytical findings. This dissertation proposes the WUSAN framework for WUA, which not only addresses the drawbacks and weaknesses of state-of-theart WUA tools, but also adopts the standards and best practices proven useful for this domain, including a data warehousing approach. One process is often underestimated in this context: Getting the volumes of data into the data warehouse. Not only must the collected data be cleansed, they must also be transformed to turn them into an applicable, purposeful data basis for Web usage analysis. This process is referred to as the extract, transform, load (ETL) process for WUA. Hence, this dissertation centers on modeling the ETL process with a powerful, yet realizable model – the logical object-oriented relational data storage model, referred to as the LOORDSM. This is introduced as a clearly structured mathematical data and transformation model conforming to the Common Warehouse Meta-Model. It provides consistent, uniform ETL modeling, which eventually supports the automation of the analytical activities. The LOORDSM significantly simplifies the overall WUA process by easing modeling ETL, a sub-process of the WUA process and an indispensable instrument for deploying electronic customer relationship management (ECRM) activities in the Web channel. Moreover, the LOORDSM fosters the creation of an automated closed loop in concrete applications such as a recommendation engine. Within the scope of this dissertation, the LOORDSM has been implemented and made operational for practical and research projects. Therefore, WUSAN, the container for the LOORDSM, and the LOORDSM itself can be regarded as enablers for future research activities in WUA, electronic commerce, and ECRM, as they significantly lower the preprocessing hurdle – a necessary step prior to any analysis activities – as yet an impediment to further research interactions.
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.
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.
Topic models such as latent Dirichlet allocation (LDA) aim to identify latent topics within text corpora. However, although LDA-type models fall into the category of Natural Language Processing, the actual model input is heavily modified from the original natural language. Among other things, this is typically done by removing specific terms, which arguably might also remove information. In this paper, an extension to LDA is proposed called uLDA, which seeks to incorporate some of these formerly eliminated terms -- namely stop words -- to match natural topics more closely. After developing and evaluating the new extension on established fit measures, uLDA is then tasked with approximating human-perceived topics. For this, a ground truth for topic labels is generated using a human-based experiment. These values are then used as a reference to be matched by the model output. Results show that the new extension outperforms traditional topic models regarding out-of-sample fit across all data sets and regarding human topic approximation for most data sets. These findings demonstrate that the novel extension can extract valuable information from the additional data conveyed by stop words and shows potential for better modeling natural language in the future.
The essays of the cumulative dissertation are concerned with the topic of retail shelf-space optimization.
Increasing product proliferation as well as decreasing space productivity force retailers to efficiently use the limited retail shelf space and assign the right shelf quantities to products they offer to customers. Optimization models support retailers with these decisions.
The first essay analyzes how demand volatility can be integrated into shelf-space optimization models. The developed optimization model furthermore accounts for space- and cross-space elasticities as well as vertical shelf positions. A specialized heuristic solves the model efficiently and returns near-optimal results. The second essay builds on the optimization model developed in the first essay and conducts extensive numerical analyses to examine the impact of cross-space elasticities on optimal shelf quantities. The essay shows that cross-space elasticities have a negligible impact on space decisions. Therefore, the complex measurement as well as the development of corresponding optimization models are of minor relevance for future research.
The third and fifth essay extend the optimization model from the first essay by integrating adjacent decision problems. The third essay accounts for the assortment decision, i.e., supports retailers in selecting the products to offer. Apart from space elasticities, the model accounts for substitution behavior in case items are unavailable and shows that both demand effects reinforce each other, which is why they should be accounted for simultaneously. The fifth essay shows how retails can furthermore guarantee the efficient replenishment of shelves and examines the impact of the availability of a backroom on optimal shelf quantities.
The fourth essay improves the algorithm developed for the optimization model from the third essay. The improved heuristic yields higher retail profits in shorter runtime.
“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.
Specifying or deriving customers' needs into detailed requirements becomes essential for holistic customer-centricity in automotive systems engineering, relying heavily on simulation models. However, with the increase of system complexity, customer diversity, and difficulty of customer data acquisition, it becomes challenging to specify model inputs that represent individual customer usage behavior across the whole product lifecycle. To let these challenges be tackled, this dissertation addresses the problem of customer usage profiling to support decision-making in the context of automotive systems engineering. First, two fundamental data engineering procedures are investigated, including data aggregation for feature reduction and sampling for representing the customer fleets with a few selected reference customers. After data preprocessing, a method for preparing the model inputs from aggregate customer data, i.e., usage profiling, is developed. Usage profiling applies meta-heuristics, synthesizing sufficiently representative from aggregate fleet data. Furthermore, a decision support system is developed to deploy the aggregation, sampling, and usage profiling into model-based automotive system engineering processes. As both data and various models are connected, digital twinning is applied. Using real-world fleet data, an evaluation case study for determining lifetime requirements indicates that the methodology is plausible and capable of consolidating customer-centricity into automotive systems engineering processes.