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XAIR: A Systematic Metareview of Explainable AI (XAI) Aligned to the Software Development Process
(2023)
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) aims to make black-box AI models more transparent and comprehensible for humans. Fortunately, plenty of XAI methods have been introduced to tackle the explainability problem from different perspectives. However, due to the vast search space, it is challenging for ML practitioners and data scientists to start with the development of XAI software and to optimally select the most suitable XAI methods. To tackle this challenge, we introduce XAIR, a novel systematic metareview of the most promising XAI methods and tools. XAIR differentiates itself from existing reviews by aligning its results to the five steps of the software development process, including requirement analysis, design, implementation, evaluation, and deployment. Through this mapping, we aim to create a better understanding of the individual steps of developing XAI software and to foster the creation of real-world AI applications that incorporate explainability. Finally, we conclude with highlighting new directions for future research.
This thesis deals with four topical issues of strategic management within insurance companies: the future of mobility, digital applications, customer satisfaction and collaborations with MGA-InsurTechs.
Firstly, a review of academic and practitioner-oriented literature on the future of mobility and its impact on the automobile insurance industry is presented, in which we take into account mobility concepts incorporating autonomous, shared and electric vehicles. To comprehensively assess the future of mobility and its consequences, we moreover derive a “mobility ecosystem” that additionally addresses demographic change, digitalization, the increasing societal focus on sustainability and environmental awareness, individualization and flexibilization, as well as urbanization. As a result, we contribute to the literature by identifying the risk and opportunity landscape, along with a set of strategic measures, so as to react to transforming risk exposures and insurance demand.
Secondly, we study how digitalization as a wide-ranging trend affects insurance companies with a general overview of the opportunities and challenges resulting from implementing digital technologies (e.g., artificial intelligence or cloud computing). By creating a sample of 102 academic articles, industry studies and publications of supervisory authorities, we review and examine functionalities and use cases of digital technologies, as well as the requirements for an insurance company’s IT. We find a multitude of interdependencies between the digital technologies that substantiate the relevance of holistically managing the IT of insurers.
Thirdly, we take an integrated perspective on digital transformation and customer satisfaction and concentrate on assessing a set of digital applications for insurance companies when managing customer satisfaction. The impact of these is clustered around four customer touch points (contract conclusion, contract modifications, event of damage and further contacts). The main findings reflect strategic measures to strengthen the insurers’ position for marketing and sales, to simplify standard processes and to create intuitive processes for customers along with a positive impact on efficiency and an increase in customer interaction.
Fourthly, there is a focus on the strategic importance of collaborating with digital partners. By applying a qualitative research approach, the study examines the strategic opportunities and challenges when collaborating with MGA-InsurTechs from an insurer’s perspective and shows that the strategic opportunities for insurers, functioning as risk carriers and investors, go considerably beyond generating revenue streams or returns. Selecting adequate partners, installing steering and monitoring mechanisms, as well as regulatory requirements rank amongst the severe challenges of collaborations that are moreover presented in detail.
This cumulative dissertation sheds light on the construction of cultural identities during CCT (Chapter 2 and 3) and in the context of cross-border acquisitions (Chapter 4 and 5). The four main chapters (2-5) represent individual articles that each cover particular facets of the dissertation project.
Chapter 2 describes the development of an encompassing conceptual model of CCT effectiveness based on social cognitive theory. Relevant moderators and mediators of CCT success are identified through an extensive literature review, which yields 20 journal articles and three doctoral dissertations published between 1966 and 2015. The examined empirical studies allow for the specification of the relations between CCT, environmental and personal moderators, as well as behavioral and cognitive outcomes. The study extends prior literature by showing that CCT success does not solely depend on training design but is highly context-dependent. The comprehensive, systematic categorization of CCT moderators and mediators goes beyond previous research that only focuses on a few specific determinants of CCT success.
Chapter 3 specifies the effects of an intercultural simulation with artificial cultures. Based on ELT and social identity theory, hypotheses on participants’ learning are derived. The assumptions are tested through two separate quasi-experimental studies involving 152 master students in business economics from a Danish university and 190 bachelor students in international business from a German university. Paired sample t-tests prove that the simulation enhances the ability to modify behavior depending on cultural context. The characteristics of the assigned artificial culture, as well as general and culture-specific international experience of the participants are confirmed as moderators. The study extends prior research focused on affective outcomes by examining the improvement of behavioral skills.
Chapter 4 sheds light on organizational members’ sensemaking processes in the wake of a cross-border acquisition and specifies the salient motives and logics underlying their organizational identification. Based on the analysis of 28 semi-structured interviews with members of a German company that was recently acquired by a Chinese competitor, three successive phases in members’ sensemaking are distinguished. The study constitutes a dynamic extension of prior articles on social identification. The applied interpretive perspective allows examining interaction effects between members’ identification with diverse targets in the workplace as they are confronting an identity-threatening event.
Chapter 5 extends the preceding chapter as it analyzes the sensemaking processes of the same employees yet focuses on their consideration of discursive influences. In addition to the first round of 28 interviews, another ten interviews help specify employees’ integration of information from diverse sources on the cross-border acquisition. The study clarifies the role of diverse sources for employees’ meaning-construction. It determines the functions discursive cues fulfil in employees’ sensemaking and shows how they use particular sources to satisfy these functions. The holistic perspective reveals cross-source effects in employees’ sensemaking, which have been missed by prior one-sided research.
Chapter 6 summarizes the findings of this dissertation and highlights major contributions. Moreover, promising avenues for future research are presented with regard to a fruitful topical area, an alternative theoretical approach, and a discussion of the researcher’s role in studies on cultural identity construction.
Gegenstand der vorliegenden Arbeit ist die Erforschung der Ursachen und Mechanismen der Entstehung von Bildungsarmut in Form fehlender Zertifikate und Kompetenzen. Letztendlich geht es auch darum, nach den Determinanten sozialer Ungleichheit von Bildungschancen und der Verteilung von Bildungsergebnissen nach leistungsfremden Kriterien, z.B. der sozialen Herkunft zu suchen. Ein weiterer Ansatzpunkt ist die Frage nach den bildungspolitischen Herausforderungen zur Reduktion von Bildungsarmut und der Zahl von Schulabgängern ohne Abschluss nach der Sekundarstufe I in Deutschland.
For thousands of years, people have depicted the real world through geographic maps or portraits of people, landscapes, and machines for different reasons, like representing complex environments simply and understandably or depicting detailed aspects of reality. Due to the increasing digitalization and connectivity over the last years, it is possible to continuously collect more and more data describing the real world, including interactions between environments, objects, and people, and to gain more precise, comprehensive, and completely new insights. Activity trackers for the collection and analysis of health data, smart home systems for the monitoring and energy-efficient control of light, temperature, and household appliances, or the collection and analysis of production data in industrial manufacturing for the monitoring, prediction, and optimization of production processes are used. An emerging approach in this context for creating virtual data-based representations of real objects is the Digital Twin Concept, examined by the research presented in this thesis. Digital Twins are virtual models that mirror physical objects throughout their lifecycle, and real-time connections between the physical and virtual worlds allow Digital Twins to monitor and control physical objects from any location. Physical objects can be any living or non-living object, such as aircraft, manufacturing equipment, cars, buildings, wind turbines, food, or even people.
Companies see great potential in Digital Twins, and more and more organizations are using Digital Twins or plan to do so. In addition to the interest of practitioners, Digital Twins are being intensively studied as research objects. However, the discussion on Digital Twins in the scientific literature shows that there is currently no consolidated understanding of Digital Twins or the Digital Twin Concept. Nevertheless, the interest in Digital Twins has grown strongly in business and research in recent years. Therefore, specific findings and solutions from research are becoming increasingly important to enable organizations to apply Digital Twins in meaningful and efficient ways. This thesis aims to contribute to the understanding and application of Digital Twins by addressing two research areas. In the context of the first research area, a rigorous multi-level literature review was conducted to contribute to the understanding of Digital Twins. Two studies were conducted in the second research area to contribute to the application of Digital Twins.
The first research area contains a rigorous multi-level literature review consisting of three studies. First, a preliminary study was conducted to develop a search and selection strategy for scientific literature and, based on this, a review sample. In the second study, a review of reviews was conducted based on the identified literature reviews from the review sample. As a result, it could be shown that the number of literature reviews has increased significantly since 2018 and that the research is distributed across various outlets and disciplines, with a clear focus on the manufacturing domain. Furthermore, a content analysis was conducted that revealed four research areas containing different aspects of the research on Digital Twins, which are examined more closely. Since the literature reviews did not cover a large part of the publications in the review sample, a third study was conducted. In the third study of this research area, a scoping review, an analysis of the Digital Twin research community, and a citation analysis were conducted to analyze the primary studies of the review sample. The literature analysis showed that the research could be well structured according to seven different research topics, which are examined and characterized in detail.
Two studies were conducted on applying Digital Twins in the second research area. In the first study, an analysis of Digital Twin application cases was conducted to provide a comprehensive and structured overview. Based on a set of application cases retrieved from previous scientific literature, the study identified six dimensions with a total of 24 characteristics to classify Digital Twin application cases. Furthermore, a comprehensive overview of selected Digital Twin application cases is presented. In the second study, an IT system based on the Digital Twin Concept was implemented to gain detailed insights into the design and implementation of Digital Twin systems in an enterprise setting. Therefore a Digital Twin introductory project of an industry partner was accompanied over two years according to scientific principles. Within this project, an IT system based on the Digital Twin Concept was implemented, and its validity and utility were demonstrated and evaluated. Five application cases were realized and applied to production settings for the demonstration and evaluation. The study presents a detailed description of the artifact's conceptual architecture and technical implementation.
In summary, this thesis provides valuable insights into two specific research areas in the field of Digital Twins. It provides a comprehensive picture of the research landscape and presents detailed insights into the application of Digital Twins. Therefore this thesis provides a solid basis for further research on the possibilities and challenges of converging real and virtual worlds.
The three essays in this dissertation contribute to research on organizational resilience by addressing aspects of technological innovations, collaboration with partners, and the influence of the individual personalities in decision making under uncertainty. Valuable insights for academics and practitioners alike are generated via systematic literature reviews, content analysis, and an embedded multiple-case study. Results of the essays in combination support decision makers of organizations and as part of supply chains in preparing, coping, and finally adapting to disturbances and crises, thus making their companies resilient. Several aspects of what to consider and how to engage with technology and partners are provided that contribute to superior performance when facing a crisis.
The first article “On the current state of combining human and artificial intelligence for strategic organizational decision making” uses a systematic literature review combined with content analysis to analyze the role artificial intelligence has in networks when making decisions under uncertainty, defined as strategic decisions. Analyzing a sample of 55 articles based on the framework of traditional decision theory, the possible division of tasks in the resulting human-machine relationship is discussed and the effect on the role definitions of both partners analyzed. The article provides an overview of what current research sees as possible use cases for implementing AI into the strategic decision-making process. This is followed by an analysis of challenges, pre-conditions, and consequences that should be taken into account when opting for AI supported decision making under uncertainty. Findings illustrate that organizational structures, the choice of the specific AI application, and the possibilities to use it for knowledge management are analyzed by research thoroughly. The ethical aspect remains rarely discussed, although most authors mention it to be a crucial foundation for deciding how and for what to use AI in this process. Results also demonstrate that AI has the potential to increase challenges inherent in strategic decision making, implying that the human responsibility and human part becomes even more crucial. Using AI for decisions under uncertainty thus means education for the people involved and a thorough awareness of their own responsibility.
The second article “How to successfully mitigate a pandemic as part of a global supply chain? A case study on German small and medium-sized enterprises” expands the focus on the human aspect. It concentrates on the role of collaboration for strategic decision making in times of crisis and with the goal of staying resilient. Using an embedded multiple-case study approach, eight German small and medium-sized enterprises are analyzed over a timeframe of two years. The first interview round took place long before COVID-19 was known in June 2018, while the second and third were executed during the first and second lockdown in 2020. The different touchpoints with interview partners make it possible to analyze which measures such small companies took to become resilient and how they performed during one of the biggest crises the world has seen. The focus on small companies is chosen as they are often claimed to be the driver of economic growth, but at the same time can become dangerous for a supply chain’s resilience when they struggle. They often lack financial resources and access to crucial information, which is why they have to rely on their partners for compensation. The right combination of contractual and relational aspects is thus key for mutually beneficial partnerships and network resilience. According to the interview findings, relational investments are higher with suppliers, while partnerships with customers can also be organized mainly in contracts. Based on transaction cost theory, a framework is developed that demonstrates the possible combinations. This offers insights for practitioners and academics alike, as it demonstrates that even with the best technology, processes, and contracts, the human aspect dominates and plays an important role, especially in uncertain times.
This aspect is then further analyzed with the third article “CEO Ignorans: How personality influences strategic decision making and information behavior”. Using a systematic literature review, a sample of 56 articles is clustered into six categories, following the five-factor model of personality and adding narcissism. Databases are analyzed from 2006 to 2021. Based on the assumption that it is economically irrational to deliberately neglect or misinterpret information, especially in times of uncertainty, the six personality types are evaluated according to their tendency to do so. The systematic literature review helps to combine findings from various academic disciplines, thus providing a thorough overview of how a type of personality can either aid making an organization resilient or completely work against it. This is also influenced by the tendency to make decisions intentionally, and thus fact-based, or rather intuitively. Findings show that CEOs who are open to experience or emotionally stable are the ones supporting resilience best, while the others are rather “CEO Ignorans”. For the agreeable CEO, no recommendation can be given, implying that such a personality type should also not be the one making decisions when disturbance is ahead. The results support advisory boards in choosing the right people for the decision-making position in companies, but also further add to the understanding of combining technology and humans with the goal of resilience.
The advent of deepfakes - the manipulation of audio records, images and videos based on deep learning techniques - has important implications for science and society. Current studies focus primarily on the detection and dangers of deepfakes. In contrast, less attention is paid to the potential of this technology for substantive research - particularly as an approach for controlled experimental manipulations in the social sciences. In this paper, we aim to fill this research gap and argue that deepfakes can be a valuable tool for conducting social science experiments. To demonstrate some of the potentials and pitfalls of deepfakes, we conducted a pilot study on the effects of physical attractiveness on student evaluations of teachers. To this end, we created a deepfake video varying the physical attractiveness of the instructor as compared to the original video and asked students to rate the presentation and instructor. First, our results show that social scientists without special knowledge in computer science can successfully create a credible deepfake within reasonable time. Student ratings of the quality of the two videos were comparable and students did not detect the deepfake. Second, we use deepfakes to examine a substantive research question: whether there are differences in the ratings of a physically more and a physically less attractive instructor. Our suggestive evidence points toward a beauty penalty. Thus, our study supports the idea that deepfakes can be used to introduce systematic variations into experiments while offering a high degree of experimental control. Finally, we discuss the feasibility of deepfakes as an experimental manipulation and the ethical challenges of using deepfakes in experiments.
Zusammenfassung. Die Digitalisierung und Globalisierung fordern von Unternehmen vermehrte
Flexibilität, was sich in der Gestaltung von Bürokonzepten niederschlägt. Es entstehen
häufig Activity-Based Flexible Offices, die sich durch ein offenes und flexibles Raumkonzept auszeichnen.
Dabei befindet sich ein Großteil der Arbeitsplätze in offenen Bereichen ohne Zwischenwände und
ohne fest zugewiesene Arbeitsplätze. Dieses Konzept ist für den Austausch ausgelegt, bietet aber
auch Rückzugsmöglichkeiten wie etwa Konzentrationszellen. In drei international agierenden
Unternehmen wurde eine webbasierte Tagebuchstudie durchgeführt, die den Einfluss der aufgabenbezogenen
Konzentrationserfordernisse und des Arbeitsortes auf die wahrgenommene Passung zwischen Arbeitsaufgabe und
Arbeitsort sowie das psychische Wohlbefinden untersuchte. Die Ergebnisse zeigen, dass offene Arbeitsbereiche
bei Aufgaben mit hohen Konzentrationserfordernissen als nicht passend wahrgenommen werden, jedoch nicht im
Homeoffice. Generell geht die Passung zwischen Arbeitsaufgabe und Arbeitsort mit dem psychischen Wohlbefinden
der Beschäftigten einher. Zusammenfassend sollte das Activity-Based Flexible Office als ganzheitliches
Konzept mit Rückzugsmöglichkeiten innerhalb als auch außerhalb des Unternehmens verstanden
werden.
Remarkable advances in Deep Learning, a subfield of Artificial Intelligence (AI), have
attracted considerable attention in recent years. One prominent example is DeepMind,
a company working on the development of a general-purpose AI. After AI systems
outperformed professional players in games such as Go and chess, DeepMind recently
achieved another breakthrough in predicting protein folding. Using Deep Learning,
protein structures can now be predicted with over 90 percent accuracy, replacing
laboratory experiments for the first time in history.
Becoming aware of the successful application of Deep Learning, numerous industrial
companies started pilot projects to gain insights. Manufacturing companies, in particular,
are faced with the question of how Deep Learning can be leveraged to realize a
competitive advantage and what challenges need to be considered. In production
environments, quality control is a core task often relying on visual techniques. One of the
world's leading German multinational automotive suppliers has been using Automatic
Optical Inspection (AOI) for quality assurance in electronics production for decades.
Since Computer Vision with Deep Learning has the potential to improve visual quality
inspection, the company intends to support its AOI systems with suitable Deep Learning
approaches.
In this context, the present dissertation aims to contribute to research and gather general
knowledge about the application of Deep Learning in an AOI environment. To this end,
several studies are conducted. Extensive structured literature reviews form the
foundation for selected Deep Learning experiments, which represent the main focus of
this thesis. The experiments are based on a dataset provided by the company, containing
images of Printed Circuit Boards (PCBs) captured by an AOI camera. The characteristics
of the real-world dataset affect both, the experimental design as well as the results.
Contributing to debates on architecture selection and Transfer Learning for operational
use, the experiments provide significant insights into factors influencing the performance
of deep neural networks in machine vision tasks for defect detection on PCBs.
Despite recent advances in Computer Vision through Vision Transformers, the results
for the case at hand show that the inductive bias inherent in established Convolutional
Neural Networks (CNNs) is better suited for inspection tasks on compartmentalized
PCBs and that Transfer Learning can accelerate the training-to-production cycle. All
studies reveal in different ways that Deep Learning can make a substantial contribution
to the industry in the field of optical inspection.
Uncovered workers in plants covered by collective bargaining: Who are they and how do they fare?
(2022)
Abstract
In Germany, employers used to pay union members and non‐members in a plant the same union wage in order to prevent workers from joining unions. Using recent administrative data, we investigate which workers in firms covered by collective bargaining agreements still individually benefit from these union agreements, which workers are not covered anymore and what this means for their wages. We show that about 9 per cent of workers in plants with collective agreements do not enjoy individual coverage (and thus the union wage) anymore. Econometric analyses with unconditional quantile regressions and firm‐fixed‐effects estimations demonstrate that not being individually covered by a collective agreement has serious wage implications for most workers. Low‐wage non‐union workers and those at low hierarchy levels particularly suffer since employers abstain from extending union wages to them in order to pay lower wages. This jeopardizes unions’ goal of protecting all disadvantaged workers.
Some Technical Remarks on Negations of Discrete Probability Distributions and Their Information Loss
(2022)
Negation of a discrete probability distribution was introduced by Yager. To date, several papers have been published discussing generalizations, properties, and applications of negation. The recent work by Wu et al. gives an excellent overview of the literature and the motivation to deal with negation. Our paper focuses on some technical aspects of negation transformations. First, we prove that independent negations must be affine-linear. This fact was established by Batyrshin et al. as an open problem. Secondly, we show that repeated application of independent negations leads to a progressive loss of information (called monotonicity). In contrast to the literature, we try to obtain results not only for special but also for the general class of ϕ-entropies. In this general framework, we can show that results need to be proven only for Yager negation and can be transferred to the entire class of independent (=affine-linear) negations. For general ϕ-entropies with strictly concave generator function ϕ, we can show that the information loss increases separately for sequences of odd and even numbers of repetitions. By using a Lagrangian approach, this result can be extended, in the neighbourhood of the uniform distribution, to all numbers of repetition. For Gini, Shannon, Havrda–Charvát (Tsallis), Rényi and Sharma–Mittal entropy, we prove that the information loss has a global minimum of 0. For dependent negations, it is not easy to obtain analytical results. Therefore, we simulate the entropy distribution and show how different repeated negations affect Gini and Shannon entropy. The simulation approach has the advantage that the entire simplex of discrete probability vectors can be considered at once, rather than just arbitrarily selected probability vectors.
As a sustainable alternative to conventional cast-in-situ construction, modular construction (MC) offers several promising benefits concerning energy and waste reduction, shorter construction times, as well as increased quality. In addition, given its high degree of prefabrication, MC offers ideal conditions to solve the industry’s long-lasting productivity problem by implementing manufacturing concepts such as lean production and automation. However, in practice, the share of automation and robotics in the production process is still relatively low, which is why the potential of this construction method is currently far from being fully exploited. An overview of the particular barriers to implementing automation in the context of MC is still lacking. Therefore, a qualitative study was conducted including eight MC manufacturers from Germany, Austria, and Switzerland. Following a comprehensive literature review, expert interviews were conducted based on an academically proven framework. Thereby, seven barrier dimensions with 21 sub-categories could be identified. The findings of this study contribute to the understanding of current barriers to implementing automation in prefabrication and how they can be overcome most effectively. Additionally, recommendations for future research are proposed within a research agenda.
The fast-growing, market-driven demand for cryptocurrencies worries central banks, as their monetary policy could be completely undermined. Central bank digital currencies (CBDCs) could offer a solution, yet our understanding of their design and consequences is in its infancy. This non-technical paper examines how The Bahamas has designed the Sand Dollar, the first real-world instance of a retail CBDC. It contrasts the Sand Dollar with definition-based specifications. The author then develops a scenario analysis to illustrate commercial bank risks. In this process, the central bank becomes a deposit monopolist, leading to high funding risks, disintermediation risks, and solvency risks for the commercial banking sector. This paper argues that restrictions and caps will be the new specifications of a regulatory framework for CBDCs if disintermediation in the banking sector is to be prevented. The anonymity of CBDCs is identified as a comparative disadvantage that will affect their adoption. These findings provide insight into governance problems facing central banks and coherently lead to the design of the Sand Dollar. This paper concludes by suggesting that combating cryptocurrencies is a task that cannot be solved by a CBDC.
Abstract
Job loss expectations were widespread amongst workers in East Germany following reunification with West Germany. Though experiencing a large negative employment shock, East German workers were nevertheless overpessimistic immediately after reunification with respect to their job loss risk. Over time, job loss expectations fell and converged to West German levels, which was driven by a stabilizing economic environment and by an adaptation of the interpretation of economic signals with workers learning to distinguish individual risk from firm‐level risk. In fact, conditional on actual job loss risk, East German workers quickly caught up to West Germans regarding the share of correctly predicted job losses.
Abstract
In this paper, we first construct a cyber risk consciousness score using a text mining algorithm, applied to annual reports of large‐ and mid‐cap US banks and insurers from 2011 to 2018. We next categorize the firms' cyber risk management based on keywords to study determinants and value‐relevance. Our results show an increasing cyber risk consciousness, regardless of the industry. In addition, for the entire sample we find that firms belonging to the banking industry, with a higher cyber risk consciousness score and a higher general risk awareness are more likely to implement cyber risk management, which also holds for both industries separately. We find the opposite in the case of profitable firms for the entire sample and the insurer subsample. Finally, we observe a significant positive relationship between cyber risk management and firm value measured by Tobin's Q for the entire sample and the subsamples of banks and insurers.
Abstract
Joint Institutional Frameworks governing the EU's relations with third countries often fail to address important issues of sectoral governance. Non‐EU countries benefit from access to EU sectoral bodies, but this is limited, and alternative avenues of co‐operation are therefore needed. This article contributes to existing research on EU bilateral relations, which has thus far not paid sufficient attention to the external face of sectoral governance. The qualitative case comparison studies the well‐established, yet increasingly politicized bilateral co‐operation with Switzerland in order to draw insights for UK–EU relations, and contrasts two strategically important areas of market integration, namely electricity and financial markets. The findings show that politicization and (external) disintegration have repercussions for allegedly ‘technical’ areas of co‐operation where formalized requirements for EU sectoral bodies, public and private, become more stringent and less permissive to accommodate informal modes of co‐operation that in the past facilitated external participation.
Purpose
Clinical abundance of artificial intelligence has increased significantly in the last decade. This survey aims to provide an overview of the current state of knowledge and acceptance of AI applications among surgeons in Germany.
Methods
A total of 357 surgeons from German university hospitals, academic teaching hospitals and private practices were contacted by e-mail and asked to participate in the anonymous survey.
Results
A total of 147 physicians completed the survey. The majority of respondents (n = 85, 52.8%) stated that they were familiar with AI applications in medicine. Personal knowledge was self-rated as average (n = 67, 41.6%) or rudimentary (n = 60, 37.3%) by the majority of participants. On the basis of various application scenarios, it became apparent that the respondents have different demands on AI applications in the area of “diagnosis confirmation” as compared to the area of “therapy decision.” For the latter category, the requirements in terms of the error level are significantly higher and more respondents view their application in medical practice rather critically. Accordingly, most of the participants hope that AI systems will primarily improve diagnosis confirmation, while they see their ethical and legal problems with regard to liability as the main obstacle to extensive clinical application.
Conclusion
German surgeons are in principle positively disposed toward AI applications. However, many surgeons see a deficit in their own knowledge and in the implementation of AI applications in their own professional environment. Accordingly, medical education programs targeting both medical students and healthcare professionals should convey basic knowledge about the development and clinical implementation process of AI applications in different medical fields, including surgery.
This paper develops a multi-dimensional Dynamic Time Warping (DTW) algorithm to identify varying lead-lag relationships between two different time series. Specifically, this manuscript contributes to the literature by improving upon the use towards lead-lag estimation. Our two-step procedure computes the multi-dimensional DTW alignment with the aid of shapeDTW and then utilises the output to extract the estimated time-varying lead-lag relationship between the original time series. Next, our extensive simulation study analyses the performance of the algorithm compared to the state-of-the-art methods Thermal Optimal Path (TOP), Symmetric Thermal Optimal Path (TOPS), Rolling Cross-Correlation (RCC), Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW). We observe a strong outperformance of the algorithm regarding efficiency, robustness, and feasibility.
Design eines Academic Analytics Systems zur Unterstützung des Qualitätsmanagements an Hochschulen
(2022)
Der Logik des Design Science Research folgend, wird in der vorliegenden Arbeit im Rahmen
eines mehrzyklischen Design Prozesses ein Academic Analytics System entwickelt und in ei-
nem konkreten Kontext erprobt. Die Umsetzung erfolgt dabei an einem wirtschafts- und sozi-
alwissenschaftlichen Fachbereich. Die Evaluationen werden mit Hilfe der Verantwortlichen der
Studiengänge, sowie über technischen Experimente durchgeführt. Auf einer höheren Abstrak-
tionsebene wird ein theoretisches Modell zur Gestaltung von Academic Analytics Systemen
generiert und im Rahmen der Zyklen evaluiert.
The four essays of this dissertation contribute to the use of satellite big data analytics for decision intelligence. Utilizing established methodological foundations in case studies, we study scenarios in various sectors such as energy, humanitarian disaster relief operations, and agriculture where satellite big data analytics can provide decision intelligence. We specifically look at some of the real-life settings in these sectors in India and assess how satellite big data analytics can provide to answer select research questions. The research results support decision-makers in coping with Volatility, Uncertainty, Complexity, and Ambiguity (VUCA) supply chain environments.
The first research paper, “Open Innovation using Satellite Imagery for Initial Site Assessment of Solar Photovoltaic Projects”, studies the adoption of solar energy in countries like India which is propagating mainly through the development of energy producing photovoltaic farms. The realization of solar energy producing sites involves complex decisions and processes in the selection of sites whose knowhow may not rest with all the stakeholders supporting (e.g. banks financing the project) the industry value chain. In this paper, we use the region of Bangalore in India as the case study to present how open innovation using satellite imagery can provide the necessary granularity to specifically aid in an independent initial assessment of the solar photovoltaic sites. We utilize the established analytical hierarchy process over the information extracted from open satellite data to calculate an overall site suitability index. The index takes into account the topographical, climatic and environmental factors. Our results explain how the intervention of satellite imagery based big data analytics can help in buying the confidence of investors in the solar industry value chain. Our study also demonstrates that open innovation using satellites can act as a platform for social product development.
The second essay, “Management of Humanitarian Relief Operations using Satellite Big Data Analytics: The Case of Kerala Floods”, discusses how disasters lead to breakdown of established Information and Communication Technology (ICT) infrastructure. ICT breakdown obstructs the channel to gather real-time last mile information directly from the disaster-stricken communities and thereby hampers the agility of humanitarian supply chains. This creates a complex, chaotic, uncertain, and restrictive environment for humanitarian relief operations, which struggles for credible information to prioritize and deliver effective relief services. In this paper, we discuss how satellite big data analytics built over real-time weather information, geospatial data and deployed over a cloud-computing platform aided in achieving improved coordination and collaboration between rescue teams for humanitarian relief efforts in the case of 2018 Kerala floods. The analytics platform made available to the stakeholders involved in the rescue operations led to timely logistical planning and execution of rescue missions. The developed platform improved the accuracy of information between the distressed community and the stakeholders involved and thereby increased the agility of humanitarian logistics and relief supply chains. This research proves the utility of fusing data sources that are normally sitting as islands of information using big data analytics to prioritize humanitarian relief operations.
The third essay, "Satellite Big Data Analytics for Ethical Decision Making in Farmer’s Insurance Claim Settlement – Minimization of Type-1 & Type-II errors", investigates the crop failure claims to insurance providers when affected by sowing/planting risk, standing crop risk, post-harvest risk, and localized calamities risk. Decision making for settlement of claims submitted by farmers has been observed to comprise of type-1 and type-II errors. The existence of these errors reduces confidence on agri-insurance providers and government in general as it fails to serve the needy farmers (type-I error) and sometimes serve the ineligible farmers (type-II error). The gaps in currently used underlying data, methods and timelines including anomalies in locational data used in crop sampling, inclusion of invalid data points in computation, estimation of crop yield, and determination of the total sown area create barriers in executing the indemnity payments for small and marginal farmers in India. In this paper, we present a satellite big data analytics based case study in a region in India and explain how the anomalies in the legacy processes were addressed to minimize type-I and type-II errors and thereby make ethical decisions while approving farmer claims. Our study demonstrates what big data analytics can offer to increase the ethicality of the decisions and the confidence at which the decision is made, especially when the beneficiaries of the decision are poor and powerless.
The fourth essay, "Digitalisation of decision making using satellite big data analytics: a case study from India’s agri-insurance sector", explores how digitalisation based on satellite big data analytics can help India’s agriculture sector. The sector suffers from uncertainty in performance of farms due to weather fluctuations and other risks is tackled by providing insurance cover. However, policymaker’s choice of administrative measures for estimating crop loss has resulted in inaccurate data collection, opened vulnerability to politicization of the process and created bottlenecks to operate at scale. These problems have led to skewed timelines for data collation, lack of confidence of the data produced by the agri-insurance providers and caused long drawn delays in settling claims made by farmers. In this paper, we present a case study on how digitalization using satellite big data analytics deployed in the Northern Indian district of Bhiwani has attempted to solve the aforementioned problems between the stakeholders in the agri-insurance claim settlement process. This essay is an extension to the third essay and puts into limelight how satellite big data based analytics provides an independent data source and digitalization assisted decision-making platform for the agri-insurers to conduct an unbiased assessment into the total acreage of the crop as well as the total yield of the crop which are the two main parameters for calculating the indemnity payments. The third essay looks at the effect on farmers and that motivated us to look at the root causes. This essay explores the root causes from the perspective of reviewing the data collection, dissemination in agri-insurance operations. The results showcase how transparency brought in by digitalization using satellite big data analytics curbs the plausible exploitation of claim settlement process and leads to provisioning increased efficiency and efficacy in settling claims for small and marginal farmers.