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Greening of the tax code
(2023)
This thesis examines the impact of green fiscal policies, specifically environmental taxes, on firms' decision-making and competitiveness. It aims to contribute to the taxation and financial policy literature by analyzing the influence of environmental taxes on corporate investment decisions and the distribution of the economic burden, the effect on corporate emission levels, and the shift in competitive market dynamics due to a green VAT. The findings suggest that standalone environmental taxes may not be the first best option, but their effectiveness can be improved through combining them with additional policy measures to foster firms' innovativeness. The thesis also shows that the adjustment of traditional forms of taxation, such as the VAT, can promote sector growth. The thesis thereby provides a more nuanced view on the economic consequences of environmental taxes.
This dissertation examines the role tangible resources play in innovation projects. Specifically, it is distinguished between two mindsets when it comes to tangible resource constraints in innovation projects. One mindset regarding the relationship between tangible resources and innovation project performance can be described best as “resource driven”, meaning that innovation project performance is strongly linked to the presence of adequate tangible resource input. Counter-intuitively, a vast number of case examples suggest the exact opposite, though. The underlying “less is more” mindset suggests that innovation project performance could also be achieved because (not despite) of constrained tangible resources. Those views reflect the two extremes of the continuous variable “tangible resource orientation” which expresses the perceived dependency on tangible resources in order to achieve a high level of performance in innovation projects.
Since conceptual and empirical evidence on the antecedents and consequences of varying degrees of tangible resource adequacy in extant literature is inconclusive, this dissertation aims at disentangling the ambiguous tangible resources-innovation project performance relationship. The arguments I put forward in this dissertation are based on conceptual evidence as a result of synthesizing literature from neighboring research fields like cognitive psychology, entrepreneurship, and corporate strategy pointing to mechanisms which cause material resource constraints to facilitate creative and innovative performance in innovation projects. These literature streams are then brought together within the framework of the resource based view (RBV) of the firm. The empirical analyses to test the hypothesized relationships are based on a sample consisting of 121 innovation project teams from the electronics industry.
The results presented in this dissertation show that the ambiguity of the relationship between tangible resources and innovation project performance observed in the literature may be a result of moderating variables that determine the direction and strength of tangible resources’ influence on innovation project performance. Specifically, the analyses reveal an interaction effect in that perceptions of material resource adequacy indeed may entail diverging effects on innovation project outcomes in family and non-family businesses, depending on differences in these firms’ organizational culture regarding the orientation toward material resources. In particular, innovation projects in non-family firms do Extended summary III significantly better when being provided with more adequate material resources, while in family firms innovation projects do not show significant differences in conditions of more or less adequate tangible resources and are shown to perform better (relative to those in nonfamily firms) under conditions of perceived material resource inadequacy.
Moreover the results of this dissertation point to the influence of socio-cognitive factors on a team’s evaluation of material resource adequacy. These socio-cognitive influences are further shown to root both on innovation project team attributes and capabilities, as well as on characteristics of organizational culture. Specifically, regarding team-level factors, it is found that innovation project team members’ perception of material resource adequacy is influenced by a team’s action repertoire, the belief of a team being able to accomplish a task, and the team task’s work volume. Regarding organizational-level factors, this research reveals that innovation project teams in family firms perceived their material resource provisions generally as being more adequate. Further, this relationship is shown to be mediated by an organization’s cultural characteristic of material resource orientation, representing an important link that transmits family influence to innovation project teams’ perceptions of material resource adequacy.
Finally, the results reveal that depending on which outcome dimension is operationalized as the main project focus, i.e., whether the quality or innovativeness of the developed products is used as outcome measure, converse results may emerge. Specifically, it is found that perceptions of material resource adequacy have a positive effect on new product quality and a negative effect on new product novelty. In a similar vein, relative team size is shown to be negatively related with new product novelty while showing a positive relationship with new product quality. Additionally, relative team size is also found to have a positive relationship with project efficiency.
From these findings, key implications for the management of innovation projects are derived in order to ensure the effectiveness of companies’ investments in R&D, showing that adequate tangible resources per se do not have a direct effect (neither positive nor negative) on innovation project performance. Rather, the role of tangible resources in innovation projects is contingent upon how adequate tangible resources are perceived to be, on certain contingency variables, and on the outcome dimension focused on. Thus, sometimes less may indeed turn out to be more and this thesis points to ways how to tap this potential.
This dissertation investigates the value of customer behavior in supply chain management through the application of (big) data analytics in demand forecasting. The use of advanced analytics in supply chain management is not novel. However, the growing expansion of data volumes provides companies with new opportunities to optimize their supply chain. Despite the rising interest from both academia and practice and the recent increase in publications in this area, empirical insights are still limited. At the same time, changes in customer expectations towards instant product delivery require companies to rethink their supply chain, where accurate demand forecasts are often at the core of enabling efficient and flexible processes. This makes the development of demand prediction models that can be used in practice especially relevant. We1 analyze the use of customer behavior in demand forecasting in three separate research papers. Leveraging data from research partners in the online fashion and construction industry, we assess the potential of the developed prediction models in three areas of application in supply chain management, namely order fulfillment, order picking, and inventory planning. In the first paper, we develop a prediction model for anticipatory shipping in the fashion industry, which predicts customers’ online purchases with the aim of shipping products in advance, and subsequently minimizing delivery times. Using various forecasting methods and data on customers’ behavior on the website, we test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The second paper assesses the extent to which clickstream data can improve forecast accuracy for fashion products. Specifically, we assess which clickstream variables are most suitable for predicting demand, and identify the products that benefit most from this. Results indicate that clickstream data is especially useful for forecasting medium- and certain intermittent-demand products. A simulation of order picking for these products shows that using clickstream data in the forecast substantially decreases picking times. The third paper investigates how sequential pattern mining can be used to determine products with correlated demand, and how to leverage this as an input into forecasting for a supplier in the construction industry. We find that sequential pattern mining may be beneficial when used in combination with traditional forecasting methods, and that support vector regression models seem especially suited to forecast intermittent-demand products. An application to inventory planning shows that our developed forecasting model might reduce the company’s costs of inventory holding and lost sales by up to 6.9%. Overall, our research highlights the value of using customer behavior to enhance demand forecasting and the benefit of using improved forecasts in various applications in supply chain management.
1Referring to the authors of the respective chapters as noted at the beginning of each chapter.
Airport slot allocation
(2023)
This dissertation addresses the concept of airport slot allocation as a major regulatory directive in air transport management. In three sequential parts, today’s slot allocation procedure, addressing the assignment of time windows for departure and landing operations at coordinated airports, is being assessed and critically evaluated. The three sections evolve from the evaluation and revision of a suitable criteria set to the development and implementation of a network solution. As a key feature, a carbon emissions price including the air carriers’ CO2 emissions is being provided, serving as the allocation principle. In all three parts, explicit reference to the IATA Worldwide Slot Guidelines, representing today’s regulatory framework, is being provided, highlighting explicit deficits and drawbacks of that solution. In the first part, the Analytic Hierarchy Process (AHP), as a concept of decision making based on a comparison of criteria and alternatives, is being applied guiding the allocation decision at a single airport. In this section, a set of multiple criteria is being proposed and weighted according a set of slot coordinators’ preferences. As a key feature, the concept of Analytic Hierarchy Process (AHP) is being extended by the conduction of a Pairwise Comparison-based Preference Measurement (PCPM) representing one stage of the Analytic Hierarchy Process (AHP). In part two, the perspective of the dissertation changes from a single-point to a multiple-point allocation environment. In this part, a model is being provided that includes the allocation of slots in an airport network. As a key feature, slots are being allocated such that the two complementary cost functions are being minimized. On one hand, the developed carbon cost function includes the minimization of the carbon footprint per traveling passenger. On the other hand, the developed handling cost function is being minimized incorporating a dedicated airport perspective to the solution. In part three, the proposed model is being further extended by the incorporation of a third directive, the minimization of connection cost, related to the application of solution in a hub-and-spoke network. As a result, the study demonstrates how slot allocation can be conducted efficiently in an airport network, and how the consideration of the carbon footprint per traveling passenger serves to calculate the allocation optimum.