Adoption of Artificial Intelligence in Industry and Politics: An Analysis Based on Web Mining and Case Studies across Diverse Data Sources - Dumbach, Philipp (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2024)
Artificial Intelligence (AI) and related fields, based on decades of research, have seen a rise in recent years and are expected to have a significant impact on large parts of industry and society. Besides pure technical advancement, there are further social and economic factors, that influence the adoption of AI and need to be considered. There is an interest in detecting trends and analysing their impact from an industrial perspective, which concerns applications of AI but also from a societal and political perspective. In this cumulative dissertation, I demonstrate the existence of research gaps not only in the individual research areas of Web Mining (WM), trend detection, and AI adoption but specifically in the combination and parallel consideration of these areas within industrial and public sectors or organizational entities such as Small andMedium-sized Enterprises (SMEs). These identified gaps led to the following research objectives addressed in the contributed publications. The first objective is to investigate German industrial and political data, from five business magazines and two central political protocols of the German federal parliament, in trend detection and to investigate whether and how industry trends are reflected in political data sources. In the journal publication [P1] I led an effort to address this objective by proposing a WM approach that was utilized for the creation of a novel data set with business and political data sources comprising 1.07 million documents from 1998 to 2020. Using 246 identified AI-related buzzwords the AI trend could be visualized and its development further examined over more than two decades based on the relative occurrences and importance of the words. Within this study, the adoption of AI trends in German business and politics was investigated. The results showed the reflection of industrial trends in political data sources and revealed a faster adoption in business compared to politics. In the last years of the period under review, the analysis showed a notable increase in political discourse regarding the area of AI. In addition, specific and interpretable topics in business and political data sources could be identified using topic clustering. The second objective is to use the emerging digital medium of podcasts for trend detection and to propose a novel data-driven approach in WM based on podcasts. This objective was addressed in the journal article [P2] by examining the AI trend development in healthcare based on podcast data. For this purpose, I proposed aWMapproach together with my co-authors, which was used to create a novel data set of more than 3,400 episodes of 29 healthcare podcasts from 2015 to 2021. Various glossaries and hype cycles were considered for the identification and extraction of 102 AI-related buzzwords. Again, these buzzwords were used to successfully detect an AI trend and to analyze its development. In addition, interpretable topics in healthcare could be identified and examined over time based on the data source of podcasts. The detection of 14 topic clusters led to the visualization of the most dominant topics. The study outcome illustrated the transferability of the approach for trend detection besides AI and showed opportunities for future research using podcasts as a research medium. Finally, the third objective is to examine the AI adoption in industrial organizations based on SMEs in the healthcare sector and to conduct a cross-national comparison of China and Germany. The article [P3] addressed this objective by investigating the development and adoption of AI from the company’s perspective. I conducted together with my co-authors a multiple-case study collecting primary data from 14 SMEs equally distributed from both countries and gave insights regarding the perceived advantages and challenges of AI adoption among healthcare SMEs. Furthermore, the views in both countries on the expected future development of AI were presented and the results showed organizational requirements for AI implementation in healthcare. This cumulative dissertation presents methods to identify, collect and exploit data from new or less-considered sources in an industrial and political context. DifferentWMapproaches including the pipeline are shown to address the challenges of large (public) databases as sources and enable other researchers to recreate the novel data sets as well as generate further insights beyond the described application domains and sectors. In addition, this doctoral thesis contributes to empirical research with a cross-national comparison of the use of AI in the German and Chinese healthcare industries, with a particular focus on the organizational group of SMEs, which has received less attention in the literature to dat
Keywords: Adoption; Artificial Intelligence; Industry; Politics; Web Mining; Case Study

Artificial intelligence trend analysis in German business and politics: a web mining approach - Dumbach, Philipp; Schwinn, Leo; Löhr, Tim; Elsberger, Tassilo; Eskofier, Björn (International Journal of Data Science and Analytics, 2023)
Current research on trend detection in artificial intelligence (AI) mainly concerns academic data sources and industrial applications of AI. However, we argue that industrial trends are influenced by public perception and political decisions (e.g., through industry subsidies and grants) and should be reflected in political data sources. To investigate this hypothesis, we examine the AI trend development in German business and politics from 1998 to 2020. Therefore, we propose a web mining approach to collect a novel data set consisting of business and political data sources combining 1.07 million articles and documents. We identify 246 AI-related buzzwords extracted from various glossaries. We use them to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. This study successfully detects an AI trend and follows its evolution in business and political data sources over the past two decades. Moreover, we find a faster adoption of AI in business than in politics, with a considerable increase in policy discourse in recent years. Finally, we show that the collected data can be used for trend detection besides AI-related topics using topic clustering and the COVID-19 pandemic as examples.<br
Keywords: Artificial intelligence; Web mining; Trend detection; Topic clustering; Business; Politics

Artificial intelligence trend analysis on healthcare podcasts using topic modeling and sentiment analysis: a data-driven approach - Dumbach, Philipp; Schwinn, Leo; Löhr, Tim; Do, Phi Long; Eskofier, Björn (Evolutionary Intelligence, 2023)
Over the past few decades, the topic of artificial intelligence (AI) has gained considerable attention in both research and industry. In particular, the healthcare sector has witnessed a surge in the use of AI applications, as the maturity of these methods increased. However, as the use of machine learning (ML) in healthcare continues to grow, we believe it will become increasingly important to examine public perceptions of this trend to identify potential impediments and future directions. Current work focuses mainly on academic data sources and industrial applications of AI. However, to gain a comprehensive understanding of the increased societal interest in AI, digital media such as podcasts should be consulted, as they are accessible to a broader audience. In order to examine this hypothesis, we investigate the AI trend development in healthcare from 2015 until 2021. In this study, we propose a web mining approach to collect a novel data set consisting of 29 healthcare podcasts with 3449 episodes. We identify 102 AI-related buzzwords that were extracted from various glossaries and hype cycles. These buzzwords were used to conduct an extensive trend detection and analysis study on the collected data using machine learning-based approaches. We successfully detect an AI trend and follow its evolution in healthcare podcasts over several years. Besides the focus area of AI, we are able to detect 14 topic clusters and visualize the trending or decreasing dominant topics over the whole period under consideration. In addition, we analyze the sentiments in podcasts towards the identified topics and deliver further insights for trend detection in healthcare. Finally, the collected data set can be used for trend detection besides AI-related topics using topic clustering.<br
Keywords: Artificial intelligence; Trend analysis; Healthcare podcast; Topic clustering; Sentiment analysis; Web mining

Exploration of Process Mining Opportunities In Educational Software Engineering - The GitLab Analyser - Dumbach, Philipp; Aly, Alexander; Zrenner, Markus; Eskofier, Björn (Proceedings of the 13th International Conference on Educational Data Mining, 2020)
The increasing complexity in software development leads to the necessity for a detailed data analysis. Literature illustrates a stronger research focus on Educational Process Mining (EPM) being applied to the fields of e-learning and professional training. In this work, the opportunities of Process Mining (PM) are further examined by the evaluation of software engineering (SE) courses. The methodology follows the five stages of the L* life cycle model for PM projects using data from software repositories. The event log data was analyzed with the PM tool Disco to examine the students’ work following an agile development process. The new tool GitLab Analyser supports supervisors to visualize educational processes and still extracts event logs for the further analysis and application of PM techniques. <br
Keywords: data mining; educational process mining; software engineering; agile development; Git; GitLab; education; software repositories; Innovation Lab; Scrum

Integration of Artificial Intelligence in the Organizational Adoption – A Configurational Perspective - Laut, Philipp; Dumbach, Philipp; Eskofier, Björn (ICIS 2021 Proceedings, 2021)
The adoption of artificial intelligence (AI) within organizations is experiencing growing interest. Since AI has distinctive characteristics (automation – augmentation), it is unclear how both characteristics influence the adoption. Besides, current research calls for a configurational perspective within the adoption theory. Building on these research gaps, we use the technology-organization-environment framework (TOE) and combine the AI characteristics ‘automation – augmentation’ and ‘product or service – process’ with the organizational and environmental characteristics: differentiation strategy, resources, entrepreneurial orientation, and network support. We collected a sample of 104 questionnaires from top-level and middle-level managers as well as IT experts. Using fuzzy set Qualitative Comparative Analysis (fsQCA), we reveal four different solutions, namely internal integrator, supportive provider, powerful innovator, and rising enthusiast, that explain the adoption of AI. Moreover, we show that organizations adopting AI have an explicit focus on AI characteristics. Furthermore, we underline the importance of configurational thinking within the adoption.<br
Keywords: Artificial Intelligence; Organizational Adoption; Automation; Augmentation; Adoption; Configuration; fsQCA

The Adoption Of Artificial Intelligence In SMEs - A Cross-National Comparison In German And Chinese Healthcare - Dumbach, Philipp; Liu, Ruining; Jalowski, Max; Eskofier, Björn (Joint Proceedings of the BIR 2021 Workshops and Doctoral Consortium co-located with 20th International Conference on Perspectives in Business Informatics Research (BIR 2021), 2021)
Artificial Intelligence (AI) as an emerging technology is increasingly applied in the healthcare sector. Moreover, the AI-related progress and technology application is not only driven by traditional companies but even more by the establishment of small and medium-sized enterprises (SME) in healthcare, the innovation process as well as dynamic product development in the very same organizations. We chose a multiple-case study design using expert interviews with 14 SMEs, equally distributed from China and Germany to analyze the adoption of AI in healthcare SMEs. Our results contribute to current empirical research with a cross-national comparison in Germany and China on the status of AI development and adoption, the perceived advantages and challenges of AI, as well as the expected future development and implementation of AI in healthcare in the upcoming five year
Keywords: Artificial Intelligence; Adoption; Healthcare; SME; Digital Transformation; Germany; China; Benefits; Challenges; Qualitative-Empirical Study; Interview Study