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The present dissertation investigates the management of RFID implementations in retail trade. Our work contributes to this by investigating important aspects that have so far received little attention in scientific literature. We therefore perform three studies about three important aspects of managing RFID implementations. We evaluate in our first study customer acceptance of pervasive retail systems using privacy calculus theory. The results of our study reveal the most important aspects a retailer has to consider when implementing pervasive retail systems. In our second study we analyze RFID-enabled robotic inventory taking with the help of a simulation model. The results show that retailers should implement robotic inventory taking if the accuracy rates of the robots are as high as the robots’ manufacturers claim. In our third and last study we evaluate the potentials of RFID data for supporting managerial decision making. We propose three novel methods in order to extract useful information from RFID data and propose a generic information extraction process. Our work is geared towards practitioners who want to improve their RFID-enabled processes and towards scientists conducting RFID-based research.
In an Arrow-Debreu world of unrestricted access to perfect and competitive financial markets, there is no need for accounting information about the financial situation of a firm. Because information is costless, share- and stakeholders are then indifferent in deposits and securities (e.g., Holthausen & Watts 2001; Freixas & Rochet 2008). How-ever, several reasons exist indicating a rejection of the assumptions for an Arrow-Debreu world, hence there is no perfect costless information. Moreover, the distribu-tion of information is asymmetric, causing follow-through multi-level agency prob-lems, which are the main reasoning for the variety of financial and non-financial ac-counting standards, regulatory and advisory entities and the auditing and rating agency profession. Likewise, these agency problems have been at the heart of the accounting literature and raised the question of whether and how accounting information can help resolve these problems. ...
Advanced Analytics in Operations Management and Information Systems: Methods and Applications
(2019)
The digital transformation of business and society presents enormous potentials for companies across all sectors. Fueled by massive advances in data generation, computing power, and connectivity, modern organizations have access to gigantic amounts of data. Companies seek to establish data-driven decision cultures to leverage competitive advantages in terms of efficiency and effectiveness. While most companies focus on descriptive tools such as reporting, dashboards, and advanced visualization, only a small fraction already leverages advanced analytics (i.e., predictive and prescriptive analytics) to foster data-driven decision-making today. Therefore, this thesis set out to investigate potential opportunities to leverage prescriptive analytics in four different independent parts.
As predictive models are an essential prerequisite for prescriptive analytics, the first two parts of this work focus on predictive analytics. Building on state-of-the-art machine learning techniques, we showcase the development of a predictive model in the context of capacity planning and staffing at an IT consulting company. Subsequently, we focus on predictive analytics applications in the manufacturing sector. More specifically, we present a data science toolbox providing guidelines and best practices for modeling, feature engineering, and model interpretation to manufacturing decision-makers. We showcase the application of this toolbox on a large data-set from a German manufacturing company.
Merely using the improved forecasts provided by powerful predictive models enables decision-makers to generate additional business value in some situations. However, many complex tasks require elaborate operational planning procedures. Here, transforming additional information into valuable actions requires new planning algorithms. Therefore, the latter two parts of this thesis focus on prescriptive analytics. To this end, we analyze how prescriptive analytics can be utilized to determine policies for an optimal searcher path problem based on predictive models. While rapid advances in artificial intelligence research boost the predictive power of machine learning models, a model uncertainty remains in most settings. The last part of this work proposes a prescriptive approach that accounts for the fact that predictions are imperfect and that the arising uncertainty needs to be considered. More specifically, it presents a data-driven approach to sales-force scheduling. Based on a large data set, a model to predictive the benefit of additional sales effort is trained. Subsequently, the predictions, as well as the prediction quality, are embedded into the underlying team orienteering problem to determine optimized schedules.
Business process modeling is one of the most crucial activities of BPM and enables companies to realize various benefits in terms of communication, coordination, and distribution of organizational knowledge. While numerous techniques support process modeling, companies frequently face challenges when adopting BPM to their organization. Existing techniques are often modified or replaced by self-developed approaches so that companies cannot fully exploit the benefits of standardization. To explore the current state of the art in process modeling as well as emerging challenges and potential success factors, we conducted a large-scale quantitative study. We received feedback from 314 respondents who completed the survey between July 2 and September 6, 2017. Thus, our study provides in-depth insights into the status quo of process modeling and allows us to provide three major contributions. Our study suggests that the success of process modeling projects depends on four major factors, which we extracted using exploratory factor analysis. We found employee education, management involvement, usability of project results, and the companies’ degree of process orientation to be decisive for the success of a process modeling project. We conclude this report with a summary of results and present potential avenues for future research. We thereby emphasize the need of quantitative and qualitative insights to process modeling in practice is needed to strengthen the quality of process modeling in practice and to be able to react quickly to changing conditions, attitudes, and possible constraints that practitioners face.
The present dissertation includes three research papers dealing with the following banking topics: (dis-) incentives and risk taking, earnings management and the regulation of supervisory boards.
„Do cooperative banks suffer from moral hazard behaviour? Evidence in the context of efficiency and risk“:
We use Granger-causality techniques to evaluate the intertemporal relationships among risk, efficiency and capital. We use two different measures of bank efficiency, i.e., cost and profit efficiency, since these measures reflect different managerial abilities. One is the ability to manage costs, and the other is the ability to maximize profits. We find that lower cost and profit efficiency Granger-cause increases in liquidity risk. We also identify that credit risk negatively Granger-causes cost and profit efficiency. Most importantly, our results show a positive relationship between capital and credit risk, thus displaying that moral hazard (due to limited liability and deposit insurance) does not apply to our sample of cooperative banks. On the contrary, we find evidence that banks with low capital are able to improve their loan quality in subsequent periods. These findings may be important to regulators, who should consider banks’ business models when introducing new regulatory capital constraints.
„Earnings Management Modelling in the Banking Industry – Evaluating valuable approaches“:
Accounting research has separately studied the field of Earnings Management (EM) for non-financial and financial industries. Since EM cannot be observed directly, it is important for every research question in any setting to find a verifiable proxy for EM. However, we still lack a thorough understanding of what regressors can add value to the estimation process of EM in banks. This study tries to close this gap and analyses existing model specifications of discretionary loan loss provisions (LLP) in the banking sector to identify common pattern groups and specific patterns used. Thereupon, we use an US-dataset from 2005-2015 and apply prevalent test procedures to examine the extent of measurement errors, extreme performance and omitted-variable biases and predictive power of the discretionary proxies of each of the models. Our results indicate that a thorough understanding about the methodological modelling process of EM in the banking industry is important. The currently established models to estimate EM are appropriate yet optimizable. In particular, we identify non-performing asset patterns as the most important group, while loan loss allowances and net charge offs can add some value, though do not seem to be indispensable. In addition, our results show that non-linearity of certain regressors can be an issue, which should be addressed in future research, while we identify some omitted and possibly correlated variables that might add value to specifications in identifying non-discretionary LLP. Results also indicate that a dynamic model and endogeneity robust estimation approach is not necessarily linked to better prediction power.
„Board Regulation and its Impact on Composition and Effects – Evidence from German Cooperative Bank“:
This study employs a system GMM framework to examine the impact of potential regulatory intervention regarding the occupations of supervisory board members in cooperative banks. To achieve insights the study proceeds in two different ways. First, the author investigates the changes in board structure prior and following to the German Act to Strengthen Financial Market and Insurance Supervision (FinVAG). Second, the author estimates the influence of Ph.D. degree holders and occupational concentration on bank-risk changes in consideration of the implementation of FinVAG. Therefore, the sample consists of 246 German cooperative banks from 2006-2011. Regarding bank-risk the author applies four different measures: credit-, equity-, liquidity-risk and the Z-Score, with the former three also being addressed in FinVAG. Results indicate that the implementation of FinVAG results in structural changes in board composition, especially at the expense of farmers. In addition, the implementation affects all risk-measures and relations between risk-measures and supervisory board characteristics in a risk-reducing and therefore intended way.
To disentangle the complex relationship between board characteristics and risk measures the study utilizes a two-step system GMM estimator to account for unobserved heterogeneity, and simultaneity in order to reduce endogeneity problems. The findings may be especially relevant for stakeholders, regulators, supervisors and managers.
This paper provides a critical analysis of the subadditivity axiom, which is the key condition for coherent risk measures. Contrary to the subadditivity assumption, bank mergers can create extra risk. We begin with an analysis how a merger affects depositors, junior or senior bank creditors, and bank owners. Next it is shown that bank mergers can result in higher payouts having to be made by the deposit insurance scheme. Finally, we demonstrate that if banks are interconnected via interbank loans, a bank merger could lead to additional contagion risks. We conclude that the subadditivity assumption should be rejected, since a subadditive risk measure, by definition, cannot account for such increased risks.
Purpose The purpose of this paper is to enhance consistent partial least squares (PLSc) to yield consistent parameter estimates for population models whose indicator blocks contain a subset of correlated measurement errors. Design/methodology/approach Correction for attenuation as originally applied by PLSc is modified to include a priori assumptions on the structure of the measurement error correlations within blocks of indicators. To assess the efficacy of the modification, a Monte Carlo simulation is conducted. Findings In the presence of population measurement error correlation, estimated parameter bias is generally small for original and modified PLSc, with the latter outperforming the former for large sample sizes. In terms of the root mean squared error, the results are virtually identical for both original and modified PLSc. Only for relatively large sample sizes, high population measurement error correlation, and low population composite reliability are the increased standard errors associated with the modification outweighed by a smaller bias. These findings are regarded as initial evidence that original PLSc is comparatively robust with respect to misspecification of the structure of measurement error correlations within blocks of indicators. Originality/value Introducing and investigating a new approach to address measurement error correlation within blocks of indicators in PLSc, this paper contributes to the ongoing development and assessment of recent advancements in partial least squares path modeling.