Refine
Year of publication
Document Type
- Doctoral Thesis (61)
- Article (3)
Language
- English (52)
- German (10)
- Multiple languages (2)
Has Fulltext
- yes (64)
Is part of the Bibliography
- no (64)
Keywords
- Digitalisierung (3)
- Marketing (3)
- Armut (2)
- Digitalization (2)
- Entscheidungstheorie (2)
- Geschäftsmodell (2)
- Management (2)
- Social Media (2)
- Subsaharisches Afrika (2)
- Abschlussprüfer (1)
Institute
- Wirtschaftswissenschaftliche Fakultät (64) (remove)
Technological advancements and new legal requirements are continuously changing the online data disclosure landscape in terms of both, the quantity and quality of data that firms can acquire. Nowadays, consumers are required to disclose personal data online multiple times a day and in a variety of different contexts, such as creating user profiles, online payment or using location-based services. Despite consumers’ increasing online privacy concerns, firms rely ever more strongly on consumer data that they convert into a competitive advantage through personalized product recommendations and targeted advertising. In an effort to encourage consumer data disclosure, many firms have focused on building trust as a way to counterbalance privacy concerns and mitigate risk perceptions. Correspondingly, marketing literature has continued to examine the interplay of trust and consumer privacy concerns. While the extant research has considerably advanced our understanding of the role of trust in privacy-related decision-making, the majority of studies has mainly focused on single-stage, dyadic disclosure settings and cognitive decision-making processes.
Against this background, the overarching goal of this thesis is to shed light on under-researched data disclosure contexts involving trust and to explore additional facets of the underlying decision-making processes. For example, considering pre- and post-disclosure stages when evaluating consumers’ data disclosure decisions allows for a more holistic picture of the decision-making process. Similarly, social media and sharing economy settings challenge the traditional assumption of purely dyadic consumer-firm data disclosure, thus extending traditional conceptualizations of trust. Across three independent essays, this thesis addresses the overarching research question of how the peculiarities of multi-stage and multi-actor settings shape consumers’ trust-based decision-making strategies.
Data has become a necessary resource for firm operations in the modern digital world, explaining their growing data gathering efforts. Due to this development, consumers are confronted with decisions to disclose personal data on a daily basis, and have become increasingly intentional about data sharing. While this reluctance to disclose personal data poses challenges for firms, at the same time, it also creates new opportunities for improving privacy-related interactions with customers. This dissertation advocates for a more holistic perspective on consumers’ privacy-related decision-making and introduces the consumer privacy journey consisting of three subsequent phases: pre data disclosure, data disclosure, post data disclosure. In three independent essays, I stress the importance of investigating data requests (i.e., the first step of this journey) as they represent a largely neglected, yet, potentially powerful means to influence consumers’ decision-making and decision-evaluation processes. Based on dual-processing models of decision-making, this dissertation focuses on both consumers’ cognitive and affective evaluations of privacy-related information: First, Essay 1 offers novel conceptualizations and operationalizations of consumers’ perceived behavioral control over personal data (i.e., cognitive processing) in the context of Artificial Intelligence (AI)-based data disclosure processes. Next, Essay 2 examines consumers’ cognitive and affective processing of a data request that entails relevance arguments as well as relevance-illustrating game elements. Finally, Essay 3 categorizes affective cues that trigger consumers’ affective processing of a data request and proposes that such cues need to fit with a specific data disclosure situation to foster long-term decision satisfaction. Collectively, my findings provide research and practice with new insights into consumers’ privacy perceptions and behaviors, which are particularly valuable in the context of complex, new (technology-enabled) data disclosure situations.
In the ongoing 21st century, low- and middle-income countries will face two health challenges that are thoroughly different from what these countries have been dealing with in preceding centuries. First, they are confronted with surging rates of non-communicable diseases (NCDs), and second, climate change will take its toll and is predicted to cause catastrophic health impairments and exacerbate chronic health conditions further. Both will pose a disproportionate health and economic burden on low- and middle-income countries, which are also the countries least able to cope with them. By threatening individual health and socioeconomic improvements, and by putting an immense burden on already constrained health care systems, they impede the progress in poverty reduction and widen health inequities between the rich and the poor.
Against this background, this thesis investigates the potential of NCD prevention and treatment measures in the context of Southeast Asia, with case studies in Indonesia. Specifically, it seeks to understand what kind of health interventions have the potential to be (cost-)effective considering the cultural background, lifestyle, health literacy and health system capacities in the region. Further, this thesis analyzes the interplay between NCDs and climate change and assesses the financial burden that both might pose in the decades to come. Hence, this thesis contributes to a better understanding of how the two health challenges of the 21st century, NCDs and climate change, can be addressed in the context of Southeast Asia and offers insights into what type of health policies and interventions can play a supportive role.
Die vorliegende Dissertation behandelt die Qualität von Prozessmodellen.
Vor diesem Hintergrund haben Experteninterviews mit Forschungs- und Praxispartnern zur Entwicklung und Evaluierung eines Ordnungsrahmens zur Qualitätsbestimmung von Prozessmodellen beigetragen.
Die Ergebnisse zeigen, dass unter anderem die Einsatzzwecke der Prozessmodelle sowie die mit den Prozessmodellen in Berührung kommenden Personengruppen differenziert betrachtet werden müssen, um Auswirkungen auf die Prozessmodellqualität untersuchen zu können.
This dissertation examines the overarching research question of how the suppliers’ brand management in the form of brand identity, brand culture, and brand essence influences buyer-seller relationships in three independent essays.
In Essay 1, I address the structure, capabilities, and outcomes of brand identity from a supplier perspective. Through qualitative interviews with suppliers, I examine how widespread the concept of brand identity is in practice and what exactly practitioners understand by it. Going further, I look at what capabilities and conditions are necessary for brand identity to be successful and what outcomes suppliers hope to achieve. Using an Information-Display-Matrix (IDM) test and a sample of Master of Business Administration (MBA) students, I examine the relevance of brand functions in more detail.
In Essay 2, I use a dyadic dataset with matched buyer-seller dyads to examine the causes and effects of perceptual congruence and incongruence of brand culture strength on the buyer-seller relationship, while considering relationship-specific investments and interaction mechanisms as moderating effects. I show that congruence and incongruence have different effects on customer loyalty and price sensitivity and that these are strongly context-dependent.
In Essay 3, I deal with brand essence strength interactions and their effects on the buyer-seller relationship. I use a dyadic dataset with matched buyer-seller dyads to show how brand essence strength influences customer loyalty and customer profitability, and how it interacts with key customer attitudes and other important buyer-seller relationship closeness indicators.
This dissertation makes a significant contribution to the literature on brand identity, brand culture, and brand essence in buyer-seller relationships. Furthermore, my dissertation offers practical implications for managers at B2B suppliers who (re)shape their brand management with a focus on the inner parts of the brand.
1. IT-Exposure and Firm Value: We analyze the joint influence of a firm’s information technology (IT)-Exposure and investment behavior on firm value. Estimating a firm’s (partial) IT-Exposure allows for distinguishing between firms with a business model that is challenged by IT above and below market average. Hence, we estimate the annual IT-Exposure of a firm using a 3-factor Fama-French model extended by an IT-proxy. Subsequently, we analyze the relationship with Tobin’s Q in a panel data context, accounting for the relationship between IT-Exposure and investments proxied by R&D as well as CapEx. We use more than 48,000 firm-year observations for firms in the Russell 3000 Index covering the period 1990 to 2018. Although IT-Exposure has a negative impact on firm value, this discount can be overcompensated by up to 2.1 times by sufficient investments through R&D and CapEx, giving a firm with an average Tobin’s Q a premium of 14.8% to 19.2%, while controlling for endogeneity.
2. Corporate Social Responsibility, Risk, and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of corporate social responsibility (CSR) on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on CSR scores from Refinitiv and MSCI, using up to 12,013 firm-year observations over the period 2002 to 2019 for all U.S. companies listed on NYSE, NASDAQ, and AMEX. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. For CSR we find a risk-reducing as well as value-enhancing effect. When applying fixed effects OLS, we can just partly confirm the risk-reducing and value-enhancing effect of CSR shown in the literature.
3. Heterogenous Effects of Religiosity on Firm Risk and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of religiosity on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on all U.S. companies listed on NYSE, NASDAQ, and AMEX for the period from 1980 through 2020. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. Overall, the risk-reducing effect of religiosity is more pronounced in the higher quantiles of the distribution. We further observe a value-reducing as well as value-enhancing religiosity effect. When applying fixed effects OLS, we can confirm the risk-reducing and non-existing value effect of religiosity shown in the literature. The robustness of our results is underpinned by a battery of additional tests.
Abstract 1: This paper investigates whether market quality, uncertainty, investor sentiment and attention, and macroeconomic news affect bitcoin price discovery in spot and futures markets. Over the period December 2017 – March 2019, we find significant time variation in the contribution to price discovery of the two markets. Increases in price discovery are mainly driven by relative trading costs and volume, and by uncertainty to a lesser extent. Additionally, medium-sized trades contain most information in terms of price discovery. Finally, higher news-based bitcoin sentiment increases the informational role of the futures market, while attention and macroeconomic news have no impact on price discovery.
Abstract 2: We investigate whether local religious norms affect stock liquidity for U.S. listed companies. Over the period 1997–2020, we find that firms located in more religious areas have higher liquidity, as reflected by lower bid-ask spreads. This result persists after the inclusion of additional controls, such as governance metrics, and further sensitivity and endogeneity analyses. Subsample tests indicate that the impact of religiosity on stock liquidity is particularly evident for firms operating in a poor information environment. We further show that firms located in more religious areas have lower price impact of trades and smaller probability of information-based trading. Overall, our findings are consistent with the notion that religiosity, with its antimanipulative ethos, probably fosters trust in corporate actions and information flows, especially when little is known about the firm. Finally, we conjecture an indirect firm value implication of religiosity through the channel of stock liquidity.
Abstract 3: This study shows that higher physical distance to institutional shareholders is associated with higher stock price crash risk. Since monitoring costs increase with distance, the results are consistent with the monitoring theory of local institutional investors. Cross-sectional analyses show that the effect of proximity on crash risk is more pronounced for firms with weak internal governance structures. The significant relation between distance and crash risk still holds under the implementation of the Sarbanes-Oxley Act, however, to a lower extent. Also, the existence of the channel of bad news hoarding is confirmed. Finally, I show that there is heterogeneity in distance-induced monitoring activities of different types of institutions.
This dissertation uses four studies to examine the context-contingent strategic factors that are critical to the success of digital transformation strategies from the perspectives of capital markets, incumbents, and start-ups. It focuses on a better understanding of (1) digital innovations and their quantitative evaluation, (2) power disruptions in digitally servitized supply chains, (3) strategic measures and dynamics in digital B2B platform markets, and (4) strategizing by data-driven start-ups in digitalized business networks.
Digital platforms consist of technical elements such as software and hardware and associated social elements such as organizational processes and standards. When such social or technical elements seem logical individually but inconsistent when juxtaposed they form tensions. Prior research on platforms often focused on individual elements of digital platforms but neglected possible related and conflicting elements which offers limited insight about underlying tensions. While some studies on platforms considered tensions, they largely assumed that centralized platform owners being responsible for responding to tensions, neglecting collective response mechanisms in blockchain-based decentralized autonomous organizations (DAOs) where decentralized participants typically respond to tensions. The examination of tensions in the context of centralized platforms and decentralized autonomous organizations offers an opportunity to surface conflicting elements that form novel types of socio-technical tensions which require collective and technology-enabled response mechanisms. This thesis explored what tensions exist in centralized and decentralized digital platform contexts and how platform participants can respond to selected tensions. For this purpose, this thesis comprises five essays that employ multiple different research methods including interviews analyzed by using techniques of grounded theory, qualitative meta-analysis of published case studies, and systematic literature reviews. The findings derived from all five essays contribute to a better understanding of tensions in digital platforms. In particular, this thesis (1) offers a lens for analyzing platforms as collective organizations in which tensions arise at the collective meta-organizational level requiring collective responses, (2) identifies new tensions and response mechanisms related to generativity and collectivity, and (3) points to a novel category of socio-technical tensions that are especially salient in digital platforms.
Blockchain technology enables the automated recording of information and execution of contract content – utilizing so-called Smart Contracts – without relying on trusted intermediaries (Beck et al., 2016). A blockchain is best described as a decentralized digital ledger (Atzori, 2015). The decentralized data storage on the blockchain makes the recorded information tamper-proof and creates transparency along the value chain. Therefore blockchain changes fundamentally the way data and information are processed (Al-Jaroodi & Mohamed, 2019; Avital et al., 2016). This has given rise to numerous use cases for blockchain in a wide range of industries.
Fundamentally, blockchain technology can be used at any time when information needs to be stored in an automated and tamper-proof manner (Crosby et al., 2016). In the financial industry, blockchain helps to automate peer-to-peer transactions. This makes middlemen obsolete, which can reduce transaction costs (Cai, 2018). In the public health sector, blockchain is primarily used for decentralized storage of patient records. By using blockchain technology, these patient records are secured against manipulation and unauthorized access by third parties (Mettler, 2016). Another interesting use case can be seen in the electricity market. Blockchain technology makes it possible to integrate micro producers of electricity, such as private households, into the power grid in a cost-efficient way (Cheng et al., 2017). However, blockchain technology also offers several applications in the creative industries to support the daily work of professionals.
The term creative industries encompasses industries and sectors which hold intellectual property at the core of their value creation (Caves, 2000). According to DCMS (1998),creative industries include not only classic art sectors such as fine art, painting or crafting, but also areas such as marketing, game developing, film and video or music. As the main drivers of innovation, the creative industries have a steadily increasing influence on the overall economic impact. Often, ideas, products and services from the creative industries ultimately flow into other areas, such as the automotive sector, and support them in achieving their entrepreneurial goals (Banks, 2010; Jones et al., 2004). In order to continuously maintain the position as an innovation driver, a certain form of organization has prevailed in the creative industries.
For the creative industries to react flexibly to new requirements and a constantly changing environment, work is usually carried out as project-based (DeFillippi, 2015). For this purpose, the teams of the project-based organization are predominantly formed using freelancers who are specialists in the required field. As a result, many recurring organizational activities arise, such as contracting, team finding or onboarding (DeFillippi, 2015; Eikhof & Haunschild, 2006). Therefore, professionals from the creative industries have to spend significant time on activities that do not serve their core task of creating intellectual property. These tasks not only reduce the efficiency of their work, but also hinder their creative flow (Foord, 2009; Hennekam & Bennett, 2016). In this regard, blockchain represents a promising technology to support professionals in the creative industries.
For the creative industries, blockchain is primarily used to automate formal processes and secure intellectual property rights (O’Dair, 2018). Blockchain technology enables artists and creatives more freedom for their own creative activities. By automating repetitive activities, professionals from the creative industries are freed from typical management tasks (Arcos, 2018; Cong & He, 2019). Furthermore, for the first time, intellectual property can be secured in a cost- and time-efficient way by utilizing blockchain technology. This is made possible by the decentralized nature of the blockchain, which makes subsequent manipulation of the contents of the intellectual property impossible (Avital et al., 2016; Beck et al., 2016; Regner et al., 2019). Ultimately, the use of so-called Non-Fungible Tokens (NFTs) create the opportunity for artists and creatives to sell unique digital art (Regner et al., 2019). Thus, blockchain generates entirely new ways for creative industries to organize their projects and opens new markets to sell their work. While several use cases of blockchain technology can be identified in the creative industries, the widespread use of blockchain is still lacking.
So far, no Blockchain service or blockchain application has managed to take a dominant market position in the creative industries. At first sight, this seems surprising since artists and creatives could fundamentally benefit from this technology. At the same time, professionals from the creative industries would not be dependent on middlemen or central entities. This circumstance gave the impulse for the research presented in this thesis. I was able to identify that professionals from the creative industries are still underutilizing blockchain technology for three main reasons: (1) When using blockchain technology, professionals from the creative industries experience strong resistance from their stakeholders, who want to prevent the use of blockchain. (2) The perceived constraints by artists and creatives in using blockchain still deter many from using this technology extensively. (3) Several blockchain applications lack a persuasive design, resulting in many artists and creatives continue to prefer conventional services and products.
Data is an important resource in our economy and society, substantially improving overall business efficiency, innovativeness and competitiveness, and shaping our everyday lives. Yet, to leverage the data's full potential, its access and availability is vital. Thus, data sharing across organizations is of particular importance. This thesis examines the role of data sharing in the digital economy and contributes to a better understanding why data sharing matters, why it is still underutilized, and how data sharing can be encouraged. Thereby, the thesis contributes to the ongoing academic debate as well as the practical and political efforts on how to promote data sharing.
The thesis is comprised of three studies. Study 1 examines personal data sharing among (competing) online services. Particularly, it investigates the consequences of Article 20 in the General Data Protection Regulation (GDPR, May 2018), ensuring the right to data portability. This relatively new right allows online service users to transfer any personal data from one service provider to another. Focusing on a) the amount of data provided by users and b) the amount of user data disclosed to third party data brokers by service providers, the study investigates the right to data portability's effect on competitiveness and consumer surplus. Study 2 and Study 3 focus on non-personal data sharing among competing firms. Study 2 examines the literature to identify and classify barriers to non-personal, machine-generated data sharing. The study explains firms' reluctance to sharing data and discusses policy and managerial implications for overcoming the data sharing barriers. Study 3 focuses on data sharing via platforms. It investigates the Business-to-Business (B2B) data sharing platform design implications for promoting industrial data sharing. In particular, Study 3 investigates the dimensions control and transparency regarding their effect in eliciting cooperation and encouraging data sharing among firms.
In summary, this thesis examines and reveals how access and availability of data can be increased through creating beneficial data sharing conditions in B2B relationships. Particularly, the thesis contributes to the understanding of a) the implications of data sharing laws, defined in the GDPR for personal data and b) the challenges and measures of the not yet successfully established, non-personal data sharing.
Over the last decades, ongoing advancements in information technology (i.e., Internet and mobile devices) have expanded a firm’s ability to communicate and interact with consumers and hence, create the potential of building sustainable relationships. Tailoring offerings through (1) consumer-initiated customization and (2) firm-initiated personalization is considered a key driver of long-term consumer relationships. As technologies continue to evolve, the opportunities for tailored marketing expand and enable new technology-driven business models that help to leverage customization and personalization and strengthen customer relationships in the era of the digital economy.
Across three independent essays, the purpose of this dissertation is to answer the overarching research question of how innovative technology-driven business models versus traditional business models in the domains of customization and personalization influence consumer behavior. Thereby, this dissertation contributes to an understanding of challenges and opportunities of innovative customization and personalization business models with the ultimate goal of enabling their successful diffusion in the marketplace.
Specifically, in Essay 1 and Essay 2, I investigate an innovative business model located in the realm of customization, that is, internal product upgrades (i.e., offering fee-based access to originally built-in, but deliberately restricted, optional features). Using a conceptual approach, Essay 1 provides a framework for understanding how internal product upgrades will likely influence consumers’ responses. As such, it outlines evolving challenges and opportunities of internal product upgrades and derives questions for future research. In Essay 2, I use an empirical approach to examine pitfalls of internal product upgrades in the product usage phase. Drawing on research on normative expectations and perceived ownership, this essay reveals that consumers respond less favorably to internal (vs. external) product upgrades and investigates managerially relevant boundary conditions.
Finally, Essay 3 creates novel insights into a business model in the domain of personalization. This essay examines how the increasingly prevalent data disclosure practice of firms engaging in a network with other firms to exchange consumer data, which we denote as Business Network Data Exchange (BNDE), influences consumers’ privacy-related decision-making. In particular, this essay shows that consumers are less likely to disclose personal data in BNDE (vs. traditional dyadic) data exchange settings and that immediate affective reactions are crucial in explaining consumers’ privacy-related decision-making.
Within this dissertation, I make substantial contributions at a more general level to literature on customization and personalization by comparing innovative business models to established ones. At the individual essay level, I extend existing research in the domains of product feature modifications, norm violations, and privacy-related decision making. Moreover, this dissertation provides actionable implications for managers who are facing the decision to transform their established business model into an innovative technology-driven one.
A firm's entrepreneurial orientation (EO) is its propensity to act proactively, innovate, take risks, and engage in competitive and autonomous behaviors. Prior research shows that EO is an im-portant factor for new ventures to overcome barriers to survival and fostering growth, measured by annual sales and employment growth rates. In particular, individual-level EO (IEO) is an important driver of a firm’s EO. The firm’s ability to exploit opportunities appearing in the mar-ket and to achieve superior performance depends on the employees’ skills and experiences to act and think entrepreneurially. The main objective of this dissertation is to investigate how and when employees engage in entrepreneurial behaviors at work. Building on three essays, this dissertation takes an interdisciplinary approach to employee entrepreneurial behaviors in new ventures, encompassing both entrepreneurship and gamification research. The first main contri-bution proposed in this field is a more nuanced understanding of how employee entrepreneurial behaviors help young firms cope with growth-related, organization-transforming challenges (i.e., changes in organizational culture that accompany growth, the introduction of hierarchical structures, and the formalization of processes). When new ventures grow, employees’ IEO tends to manifest in introducing technological innovations and business improvements rather than in actions related to risk-taking. Second, this dissertation reveals the relevance of self-efficacy for entrepreneurial behaviors and explores how gamification can enhance employee entrepreneurial behaviors in new ventures. Based on these findings, this dissertation contributes to EO research by highlighting the role of IEO as a building block for EO pervasiveness. This research further develops our knowledge on the use of gamification in new ventures. This cu-mulative dissertation is structured as follows. Part A is an introduction to the study of entrepre-neurial behaviors. Part B contains the three essays.
In three essays, this dissertation examines the past, present and future of branding in an international context, contributing to the research area of global/local brands, while also offering managers valuable insights for their branding strategies.
The first essay provides scholars and practitioners a detailed state of the art of global/local brand research and proposes promising angles for future research, especially considering major
challenges for our societies.
The second essay incorporates the segment of cosmopolitan consumers into perceived brand globalness/localness research. Theoretically grounded in the concepts of social identity theory and complexity, the essay builds on perceived brand globalness/localness to analyze how cosmopolitans arrange both their global and local orientations. Aside offering scholars a new theoretical lens regarding consumer cosmopolitanism, managers can benefit from the gained insights, if cosmopolitans are a particular target group in their business strategy.
The third and final essay meta-analytically investigates how the variables perceived brand globalness and localness materialize on various key outcome variables. At heart of this essay is a comparison of both perceived brand globalness and localness, offering scholars and practitioners valuable empirical insights on similarities and differences between their effects on outcomes such as brand quality.
The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.
The described secondary data provide a comprehensive basis for modeling conditional mean nitrogen dioxide (NO2) concentration levels across Germany. Besides concentration levels, meta data on monitoring sites from the German air quality monitoring network, geocoordinates, altitudes, and data on land use and road lengths for different types of roads are provided. The data are based on a grid of resolution 1 × 1 km, which is also included. The underlying raw data are open access and were retrieved from different sources. The statistical software R was used for (pre-)processing the data and all codes are provided in an online repository. The data were employed for modeling mean annual NO2 concentration levels in the paper "Agglomeration and infrastructure effects in land use regression models for air pollution - Specification, estimation, and interpretations" by Fritsch and Behm (2021).
Due to the advances of digitalization, firms are able to collect more and more personal consumer data and strive to do so. Moreover, many firms nowadays have a data sharing cooperation with other firms, so consumer data is shared with third parties. Accordingly, consumers are confronted regularly with the decision whether to disclose personal data to such a data sharing cooperation (DSC). Despite privacy research has become highly important, peculiarities of such disclosure settings with a DSC between firms have been neglected until now. To address this gap is the first research objective in this thesis. Another underexplored aspect in privacy research is the impact of low-cognitive-effort decision-making. This is because the privacy calculus, the most dominant theory in privacy research, assumes for consumers a purely cognitive effortful and deliberative disclosure decision-making process. Therefore, to expand this perspective and examine the impact of low-cognitive-effort decision-making is the second research objective in this thesis. Additionally, with the third research objective, this thesis strives to unify and increase the understanding of perceived privacy risks and privacy concerns which are the two major antecedents that reduce consumers’ disclosure willingness.
To this end, five studies are conducted: i) essay 1 examines and compares consumers’ privacy risk perception in a DSC disclosure setting with disclosure settings that include no DSC, ii) essay 2 examines whether in a DSC disclosure setting consumers rely more strongly on low-cognitive-effort processing for their disclosure decision, iii) essay 3 explores different consumer groups that vary in their perception of how a DSC affects their privacy risks, iv) essay 4 refines the understanding of privacy concerns and privacy risks and examines via meta-analysis the varying effect sizes of privacy concerns and privacy risks on privacy behavior depending on the applied measurement approach, v) essay 5 examines via autobiographical recall the effects of consumers’ feelings and arousal on disclosure willingness.
Overall, this thesis shines light on consumers’ personal data disclosure decision-making: essay 1 shows that the perceived risk associated with a disclosure in a DSC setting is not necessarily higher than to an identical firm without DSC. Also, essay 3 indicates that only for the smallest share of consumers a DSC has a negative impact on their disclosure willingness and that one third of consumers do not intensively think about consequences for their privacy risks arising through a DSC. Additionally, essay 2 shows that a stronger reliance on low-cognitive-effort processing is prevalent in DSC disclosure settings. Moreover, essay 5 displays that even unrelated feelings of consumers can impact their disclosure willingness, but the effect direction also depends on consumers’ arousal level.
This thesis contributes in three ways to theory: i) it shines light on peculiarities of DSC disclosure settings, ii) it suggests mechanisms and results of low-effort processing, and iii) it enhances the understanding of perceived privacy risks and privacy concerns as well as their resulting effect sizes.
Besides theoretical contributions, this thesis offers practical implications as well: it allows firms to adjust the disclosure setting and the communication with their consumers in a way that makes them more successful in data collection. It also shows that firms do not need to be too anxious about a reduced disclosure willingness due to being part of a DSC. However, it also helps consumers themselves by showing in which circumstances they are most vulnerable to disclose personal data. That consumers become conscious of situations in which they are especially vulnerable to disclose data could serve as a countermeasure: this could prevent that consumers disclose too much data and regret it afterwards. Similarly, this thesis serves as a thought-provoking input for regulators as it emphasizes the importance of low-cognitive-effort processing for consumers’ decision-making, thus regulators may be able to consider this in the future.
In sum, this thesis expands knowledge on how consumers decide whether to disclose personal data, especially in DSC settings and regarding low-cognitive-effort processing. It offers a more unified understanding for antecedents of disclosure willingness as well as for consumers’ disclosure decision-making processes. This thesis opens up new research avenues and serves as groundwork, in particular for more research on data disclosures in DSC settings.
This collection of three chapters responds to today’s energy challenges. It explores innovative policy aimed to equip the energy poor with access to improved cooking energy and electricity, looking both at the demand and supply side of modern energy technologies. Concretely, it discusses mechanisms to increase uptake of off-grid solar electricity in rural Rwanda based on experimental demand measurements (Chapter 1), it studies how to diffuse improved cooking technologies in rural Senegal via supply-side mechanisms (Chapter 2), and it identifies the need to target cooking technologies in consideration of the broader household context in rural Senegal and beyond (Chapter 3).
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.