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Durch den Einsatz immer größerer Containerschiffe zum Transport containerisierter Güter über den Seeweg gewinnt die Produktivität der zur Be- und Entladung der Containerschiffe eingesetzten Containerbrücken in Seehafencontainerterminals immer mehr an Bedeutung. Einen erheblichen Einfluss auf die Produktivität der Containerbrücken hat die Containerbrückeneinsatzplanung. Gegenstand dieser Arbeit ist das im Rahmen der Containerbrückeneinsatzplanung auftretende Container Sequencing Problem, welches hier erstmals unter Berücksichtigung von Ladelukendeckeln und Rehandlecontainern verschiedener Containerkategorien untersucht wird. Die Problemstellung wird als ganzzahliges lineares Optimierungsmodell formuliert. Zur Lösung des Problems werden verschiedene heuristische Verfahren vorgeschlagen. Deren Leistungsfähigkeit wird anhand numerischer Experimente analysiert.
This dissertation deals with geostatistical, time series, and regression analytical approaches for modelling spatio-temporal processes, using air quality data in the applications. The work is structured into four essays the abstracts of which are given in the following.
The first essay is titled 'Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany'. It is written in co-authorship by Prof. Dr. Harry Haupt and Dr. Angelika Schmid and published in 2018 in Spatial Statistics 28, pp. 331-351 (https://doi.org/10.1016/j.spasta.2018.04.004).
Abstract
Short-term predictions of air pollution require spatial modelling of trends, heterogeneities, and dependencies. Two-step methods allow real-time computations by separating spatial detrending and spatial extrapolation into two steps. Existing methods discuss trend models for specific environments and require specification search. Given more complex environments, specification search gets complicated by potential nonlinearities and heterogeneities. This research embeds a nonparametric trend modelling approach in real-time two-step methods. Form and complexity of trends are allowed to vary across heterogeneous environments. The proposed method avoids ad hoc specifications and potential generated predictor problems in previous contributions. Examining Belgian and German air quality and land use data, local trend patterns are investigated in a data driven way and are compared to results computed with existing methods and variations thereof. An important aspect of our empirical illustration is the heterogeneity and superior performance of local trend patterns for both research regions. The findings suggest that a nonparametric spatial trend modelling approach is a valuable tool for real-time predictions of pollution variables: it avoids specification search, provides useful exploratory insights and reduces computational costs.
The second essay is titled 'Predictability of hourly nitrogen dioxide concentration'. It is written in co-authorship with Prof. Dr. Harry Haupt and published in 2020 in Ecological Modelling 428, 109076 (https://doi.org/10.1016/j.ecolmodel.2020.109076).
Abstract
Temporal aggregation of air quality time series is typically used to investigate stylized facts of the underlying series such as multiple seasonal cycles. While aggregation reduces complexity, commonly used aggregates can suffer from non-representativeness or non-robustness. For example, definitions of specific events such as extremes are subjective and may be prone to data contaminations. The aim of this paper is to assess the predictability of hourly nitrogen dioxide concentrations and to explore how predictability depends on (i) level of temporal aggregation, (ii) hour of day, and (iii) concentration level. Exploratory tools are applied to identify structural patterns, problems related to commonly used aggregate statistics and suitable statistical modeling philosophies, capable of handling multiple seasonalities and non-stationarities. Hourly times series and subseries of daily measurements for each hour of day are used to investigate the predictability of pollutant levels for each hour of day, with prediction horizons ranging from one hour to one week ahead. Predictability is assessed by time series cross validation of a loss function based on out-of-sample prediction errors. Empirical evidence on hourly nitrogen dioxide measurements suggests that predictability strongly depends on conditions (i)-(iii) for all statistical models: for specific hours of day, models based on daily series outperform models based on hourly series, while in general predictability deteriorates with exposure level.
The third essay is titled 'Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations'. It is written in co-authorship with Dr. Markus Fritsch and published in 2021 in Atmospheric Environment 253, 118337 (https://doi.org/10.1016/j.atmosenv.2021.118337).
Abstract
Established land use regression (LUR) techniques such as linear regression utilize extensive selection of predictors and functional form to fit a model for every data set on a given pollutant. In this paper, an alternative to established LUR modeling is employed, which uses additive regression smoothers. Predictors and functional form are selected in a data-driven way and ambiguities resulting from specification search are mitigated. The approach is illustrated with nitrogen dioxide (NO2) data from German monitoring sites using the spatial predictors longitude, latitude, altitude and structural predictors; the latter include population density, land use classes, and road traffic intensity measures. The statistical performance of LUR modeling via additive regression smoothers is contrasted with LUR modeling based on parametric polynomials. Model evaluation is based on goodness of fit, predictive performance, and a diagnostic test for remaining spatial autocorrelation in the error terms.
Additionally, interpretation and counterfactual analysis for LUR modeling based on additive regression smoothers are discussed. Our results have three main implications for modeling air pollutant concentration levels: First, modeling via additive regression smoothers is supported by a specification test and exhibits superior in- and out-of-sample performance compared to modeling based on parametric polynomials. Second, different levels of prediction errors indicate that NO2 concentration levels observed at background and traffic/industrial monitoring sites stem from different processes. Third, accounting for agglomeration and infrastructure effects is important: NO2 concentration levels tend to increase around major cities, surrounding agglomeration areas, and their connecting road traffic network.
The fourth essay is titled 'Outlier detection and visualisation in multi-seasonal time series and its application to hourly nitrogen dioxide concentration'. It is written in single authorship and has not been published yet.
Abstract
Outlier detection in data on air pollutant recordings is conducted to uncover data points that refer to either invalid measurements or valid but unusually high concentration levels. As air pollutant data is typically characterised by multiple seasonalities, the task of outlier detection is associated with the question of how to deal with such non-stationarities. The present work proposes a method that combines time series segmentation, seasonal adjustment, and standardisation of random variables. While the former two are employed to obtain subseries of homoskedastic data, the latter ensures comparability across the subseries. Further, the standardised version of the seasonally adjusted subseries represents a scaled measure for the outlyingness of each data point in the original time series from its mean and therefore forms a suitable basis for outlier detection. In an empirical application to data on hourly NO2 concentration levels recorded at a traffic monitoring site in Cologne, Germany, over the years 2016 to 2019, the common boxplot criterion is used to examine each standardised seasonally adjusted subseries for positive outliers. The results of the analyses are put into their natural temporal order and displayed in a heatmap layout that provides information on when single and sequential outliers occur.
Poverty, underemployment, lack of infrastructure, low agricultural productivity, degradation of natural resources, climate change, and eroding social cohesion are among the biggest challenges that many low and lower-middle income countries are facing. Objectives linked to addressing these pressing challenges have been ascribed to public works programmes (PWPs). These are social protection instruments which offer remuneration (in cash or kind) for vulnerable people in exchange for temporary work on labour-intensive low-skill activities with social benefits. PWPs are being implemented in around two out of three developing countries. Given the substantial amounts spent on PWPs, it is critical to know to what extent the expectations towards them are backed by evidence. This dissertation sheds light on this overarching question with three self-contained essays. The first essay synthesises the evidence from PWPs in Sub-Saharan Africa, guided by three questions: First, what can we infer from the available impact evaluations regarding the effectiveness of PWPs as a social protection instrument? Second, what do we know about the role of the wage vector, asset vector, and skills vector in this respect? Third, what can we infer about the role of design features in explaining differences in outcomes? The other two essays use empirical evidence from Malawi to address more specific questions regarding the potential of PWPs to strengthen climate resilience and the relationship between PWPs and social cohesion.
What sets the evidence synthesis in my first essay apart from existing reviews of PWPs is that it accounts for their heterogeneity by systematically differentiating results by PWP type and outcome area (income, consumption and expenditures, labour supply, food security, nutrition, asset holdings, agricultural production and techniques, and education). Programmes that offer short-term ad-hoc employment (Type 1) are distinguished from programmes that offer more predictable employment over longer periods (Type 2). For the review of impacts, this paper relies solely on (quasi-)experimental studies, but for the analysis of the role of design factors also on other literature. In line with existing reviews, my results suggest that Type 1 programmes can effectively enable consumption smoothing in the wake of acute crises, whereas in contexts of chronic poverty, Type 2 programmes perform, on balance, better. Offering complementary access to extension services in Type 2 programmes can boost impacts further. However, in all cases, evidence is too scant and mixed to safely conclude whether the higher benefits of costlier PWP types justify the cost premium.
The second essay investigates the potential of PWPs to strengthen climate resilience. Among the main social protection instruments, the biggest potential to strengthen climate resilience is often ascribed to PWPs if they create climate-smart community assets and transfer knowledge of climate-smart practices. Yet, there is a lack of evidence whether design changes to this end can indeed enhance the contribution of an existing PWP to climate resilience. I use a difference-in-differences approach based on two-period panel data to analyse how a modified PWP model performs compared to the standard model of Malawi’s largest PWP after 24 months. The key modification is to embed public works in a communal watershed management plan with a strong emphasis on collective action and capacity building. I find that the modified approach considerably increased communal watershed management activities through voluntary labour contributions on top of the paid public works labour. While this increase was mainly driven by PWP participants, non-participants also made substantial contributions. I also find a small increase in the adoption of soil and water conservation practices on respondents’ private land, especially by non-PWP participants. These findings imply that such modest changes can make PWPs climate-smarter. In particular, they can broaden the engagement in and adoption of climate-smart activities beyond the group of PWP participants.
The co-authored third essay investigates the relationship between Malawi’s MASAF PWP and social cohesion, specifically within-community cooperation for the common good. Like the existing studies, we face the challenge that neither the assignment of the programme to communities nor the selection of individual participants is randomised. We try to mitigate the endogeneity concerns by triangulating fixed effects panel analyses for a set of outcomes and sectors using two datasets with different units of analysis (households and communities). We find that public works are positively associated with coordination activities and voluntary (unpaid) contributions to public goods, along both vertical ties (between community members and local leaders) and horizontal ties (among community members). Especially for school-building activities, voluntary inputs in the form of labour and other in-kind contributions are higher in the presence of the public works programme. Our results contribute to a better understanding of the link between social protection programmes with community-driven features and social cohesion.
Overall, the findings of the three essays in this dissertation contribute to the knowledge base regarding effectiveness and potential of PWPs across a broad range of outcome areas. Specifically, they offer new insights how to harness the potential of PWP to strengthen climate resilience and into the seemingly positive relationship between PWPs and social cohesion. The findings can help researchers and policy makers who are interested specifically in PWPs or in any of the many objectives that can be pursued through PWPs.
Nowadays, consumers are often required to disclose private data in various contexts such as while surfing the internet, downloading a mobile application, or engaging in a business relationship with a firm. Privacy-related decision-making research has so far mainly investigated data disclosure as a cognitive risk-benefit trade-off analysis. While this cognitive approach might be appropriate for situations where consumers have the opportunity for cognitive evaluations, there are many situations in the modern landscape where consumers cannot or do not want to engage in cognitive processing. Decision-making under stress or data disclosure to a business network of collaborating firms, for example, constitute challenges to purely cognitive decision-making approaches, calling for an extension of the established paradigm of cognitive privacy-related decision making. This dissertation advocates for the crucial role of affective processing in many modern data disclosure situations, where consumers do not engage in purely cognitive processing due to external hindrances or a lack of personal involvement in the data disclosure situation.
Fundamental changes in business-to-business (B2B) buying behavior confront B2B supplier firms with unprecedented challenges. On the one hand, a rising share of industrial buyers demands digitalized offerings and processes from suppliers. Consequently, suppliers are urged to implement digital transformations by expanding the range of both digital offerings and processes. On the other hand, B2B buyers increasingly expect suppliers to provide individually tailored solutions to their idiosyncratic needs. Hence, suppliers are also required to implement non-digital transformations by providing offerings and processes that are customized to each customers’ specific requirements.
The rise of these digital and non-digital transformations calls established knowledge into question. Thus, B2B marketing research and practice are urged to create a comprehensive understanding of digital and non-digital transformations by means of novel and empirically grounded insights and derive actionable response strategies. In respond, my dissertation addresses the overall research question of how B2B supplier firms can successfully implement both digital and non-digital transformations in three individual essays.
In Essay 1, I offer a broader perspective on both digital and non-digital transformations by investigating digital service customization (i.e., the tailoring of digital B2B services to customers’ individual needs). Through a systematic literature review and bibliometric analysis, I outline a comprehensive set of factors that favor the application of distinct digital service customization strategies. Essay 2 represents a deep dive into digital transformations of sales processes. By making use of two rich sets of qualitative interview material from supplier and buyer firms, I identify the challenges resulting for B2B salespeople from the introduction of digital sales channels into personal selling. Moreover, I uncover facilitating mechanisms that sales managers can employ to support salespeople in coping with digital sales channels. Finally, Essay 3 constitutes a deep dive into non-digital transformations. Based on qualitative interview material and survey data from matched sales manager–salesperson dyads, the essay explores how configurations of individual salespeople’s personal and procedural competencies facilitate success at selling customer solutions (i.e., highly customized, performance-oriented offerings comprising products and/or services). The essay shows that successfully selling customized offerings like solutions hinges on salespeople’s unique configurations of present and absent competencies.
In a nutshell, these essays provide three major insights on how B2B suppliers can successfully implement digital and non-digital transformations. First, they underscore that a comprehensive understanding of the origins and spillover effects of transformations is a key prerequisite to successfully implementing them. Second, they unveil that digital and non-digital transformations impact on multiple organizational levels. Third, they point out important resources and capabilities that help suppliers to successfully implement transformations, be they digital or non-digital.
With this dissertation, I make substantial contributions to the broader literature on digital and non-digital transformations in B2B contexts. At the same time, my dissertation provides hands-on implications for managers in B2B supplier firms that are facing fundamental transformations in the marketplace—both digital and non-digital in nature.
This dissertation sets out to deepen our understanding on the causes and manifestations of digital inequality through the lens of technology adoption research. To this day, digital inequality remains an important and relevant societal issue. With the rapid proliferation of digital ICT over the last 20 years the nature of the phenomenon may have evolved from an access-based to an appropriation-based issue, yet its significance has not diminished. Again this backdrop, this dissertation aims to explore which mechanisms and factors influence why and how individuals use ICT in the context of digital inequality, and, in particular, what role social influence, socio-cognitive processes, and socio-economic determinants play. In a series of essays, this dissertation first develops a theoretical understanding of the concept of social influence, which plays an important role in determining whether and how individuals use a technology. Next, a process lens is adopted to explore the underlying mechanisms that drive individuals to disengage from a new technology and may lead to digital exclusion. Building on that, this dissertation examines how digital inequality manifests itself in the specific realm of e-commerce. This dissertation concludes with a practical perspective on the issue of digital inequality aimed at policy makers seeking to bridge the gap between digitally advantaged and disadvantaged users.
Job Sequencing and Tool Switching Problems with a Generalisation to Non-Identical Parallel Machines
(2020)
Manufacturing tools have been dominating the manufacturing process since the 1960s. The job sequencing and tool switching problem is an NP-hard combinatorial optimization that has first been introduced in the context of flexible manufacturing systems in the late 1980s. Since then, production systems have undisputedly changed and improved but manufacturing tools still dominate manufacturing processes. Production and system operation processes are continuously adjusted and optimised to changing customer requirements. If the product variety requires an increasing number of tools for processing that exceeds the local tool magazine capacity of the manufacturing system, tool switches become necessary. Although tool changing times within a manufacturing centre or cell may nowadays be very small due to the high degree of automation, tool switching within a dynamic production environment is still a time consuming process that must be avoided. In order to minimize the total tool setup time to enhance productivity, the objectives of the basic job sequencing and tool switching problem are to sequence a set of jobs and simultaneously to determine the best tool loading. Therefore, job sequencing and tool switching problems are gaining considerable attention.
Several solution approaches to the standard problem and related versions of the problem exist. The first part of this dissertation assesses the current state-of-the-art of the job sequencing and tool switching problem and provides a classification scheme for literature on the job sequencing and tool switching problem and its variations. Only few authors consider generalisations of the problem because the level of complexity of extended problems is high. A general approach of the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times is described in this dissertation. A novel mathematical model based on time periods is presented and analysed which can be adapted to different objective functions. The last part of this dissertation is a quantitative evaluation of fast and effective construction heuristics as well as of an iterated local search algorithm tested on a new set of benchmark instances. As such this dissertation provides a broad basis for future evaluations of solution approaches to the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times as well as a basis for further generalisations of the problem like for example tool availability constraints or tool-size dependent variations.
With recent progresses in the field of artificial intelligence (AI), algorithms can support more complex tasks (Frey and Osborne, 2017) that seemed safe from automation a few years ago (Brynjolfsson and McAfee, 2012). They can even outperform human decision-making in many task domains (Meehl, 1954; Dawes et al., 1989). The prominent defeat of Garry Kasparov, the world champion in chess, in 1997 is a milestone for the potentials of algorithms, increasing the value of IBM’s stock increased by $18 billion (Norvig and Russell, 2010). More recently, universal chatbots such as ChatGPT and the Bing Chatbot Sydney can influence millions of users and provide them answers to a broad range of tasks.
To gain a competitive edge, companies invest heavily in algorithmic systems (Kappelman et al., 2021), which are considered as one of the most important drivers of today’s economy (Brynjolfsson and McAfee, 2011; Hogharth and Makridakis, 1981; Önkal et al., 2019). Particularly in management accounting, an algorithmic data analysis offers the potential to support a high decision quality, making their usage a key factor for success.
However, algorithms can not only support, but also manipulate human decision-making (Roose et al., 2023). Algorithmic advice can lead to overtrust, i.e. to the heuristic replacement of vigilant information seeking and processing in favor of the advice provided (Mosier et al., 2001). The implementation of algorithmic decision support systems is thus no panacea to improve human decision-making. In contrast, they can entail new risks, such as the uncritical use of these algorithms (Boden, 2016). Yet, this ‘dark side’ of algorithms is not sufficiently investigated in the field of management accounting. With advances in machine learning, algorithmic support can become even more of a black box, blurring the reference points for evaluating its advice quality and thus increasing the risk of overtrust. Stephen Hawking even warns that AI will be “either the best, or the worst thing, ever to happen to humanity” (Alex Hern in: The Guardian: Stephen Hawking: AI will be 'either best or worst thing' for humanity, 19th October 2016). Interacting with such algorithmic systems therefore requires data literacy skills for an appropriate use. They seem to be a crucial prerequisite in today’s data-driven world to reap of the benefits of algorithmic systems. If no reflective and critical interaction between human and algorithm can be ensured, the trend towards a digitalized world will be a race against the algorithm (Brynjolfsson and McAfee, 2011).
This dissertation takes this understudied critical perspective on the increasing implementation of algorithmic systems and sheds light on different aspects of human-algorithm interaction, ranging from the ability to use data visualization tools to trust calibrations and critical evaluations of algorithmic advices. Study I investigates an adequate information representation of subjective multiple criteria decision problems, an important task in management accounting, to support human decision-making. Study II distinguishes different designs of the human-algorithm interaction investigated in the trust in automation literature and compiles factors influencing the calibration of trust and behavior toward the true capabilities of the algorithm. Study III examines the role of performance feedback on trust and advice usage in a forecasting task for a better understanding of the recently observed phenomenon ‘algorithm aversion’. Finally, Study IV focusses on the risk of overtrust in advice in repeated interactions with a forecasting advisor and investigates the calibration of advice usage. Each study thereby represents an independent contribution and contains all information relevant to the respective research questions asked in the study.
Advocated by leading consulting firms, the concept of value-based management has enjoyed popularity and found its way into corporate practice in the mid 1990s. But the large breakthrough of value-based management held off, especially in the German landscape. The following four documents first analyze the status quo of value-based management in board compensation, second analyze performance effects of value-based management implementations, third investigate the reasons why inconsistent implementations of value-based management might exist and fourth, the effects of investor relations activities on corporate stock.