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Format: 42 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: Purpose: During the COVID-19 pandemic, social media platforms such as TikTok have played a significant role, drawing in content makers and performers alike. This research seeks to answer whether individuals' exposure to a song on TikTok has a different effect on their interest and degree of liking with music than exposure to the same song solely on other music streaming platforms. The research also expects to show how TikTok might be a marketing tool for musicians. Research Design and Methodology: The study uses a quantitative research approach to determine whether, or not, watching TikTok videos increases viewers' interest in and preference for a song. Quantitative information is gathered from two groups of respondents using structured surveys with PANAS questions and Likert scale questions. The research uses the reliable and accurate Music Receptivity Scale (MRS) to measure the participants' degree of liking a specific song. Findings: The results of this research examine how viewing a TikTok video effects Gen Z listeners' degree of liking a piece of new music. Participants in the baseline group were only asked to listen to a song on Spotify. The participants in the second group, who first saw a TikTok video showed more interest and liking in the song than those in the baseline group. A statistically significant difference in average scores between the two groups indicates that participants' interest and impressions of the music were affected by their exposure to TikTok. Value, originality: These results add to the expanding body of literature on TikTok as a music marketing tool, suggesting future directions for the independent music industry and general music industry. The study provides insights into the potential of TikTok as a vital tool for enhancing the degree of liking for a song, which illuminates the role of TikTok in changing music tastes and emotional reactions among Generation Z. Keywords: TikTok, degree of liking, music consumption, social media, Generation Z, music marketing, Music Receptivity Scale

Note: DISSERTATION NOTE: Bachelor of Arts thesis in International Management & Marketing, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents 1. Introduction.................................................................1 2. Literature Review............................................................4 2.1. the Music Industry......................................................4 2.2. the Paradigm Shift in Unveiling Musical Talents: Accepting a New Era of Artist Discovery................................................7 2.3. Music Marketing........................................................8 2.3.1. Marketing of Emerging, Lesser-known, or Independent Artists......8 2.3.2. Overview of Tiktok as a Marketing Tool..........................10 2.3.3. Going Viral on Tiktok - Lizzo’s Case............................13 2.4. Theoretical Framework on Music Marketing and Social Media.............15 2.5. Previous Research on the Impact of Tiktok on Music Consumption and Engagement..........................................................16 3. Research Design and Methodology.............................................18 3.1. Sampling Method.......................................................19 3.2. Procedure.............................................................19 3.3. Data Collection and Analysis..........................................21 4. Findings...................................................................21 4.1. Presentation of the Data..............................................22 4.2. a Comparative Analysis of Baseline Group and Tiktok Group.............25 5. Discussion................................................................28 5.1. Study Implications....................................................28 5.2. Limitations of the Study..............................................29 5.3. Future Directions.....................................................31 6. Conclusion................................................................31 7. References................................................................33

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Format: 42 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: Purpose: During the COVID-19 pandemic, social media platforms such as TikTok have played a significant role, drawing in content makers and performers alike. This research seeks to answer whether individuals' exposure to a song on TikTok has a different effect on their interest and degree of liking with music than exposure to the same song solely on other music streaming platforms. The research also expects to show how TikTok might be a marketing tool for musicians. Research Design and Methodology: The study uses a quantitative research approach to determine whether, or not, watching TikTok videos increases viewers' interest in and preference for a song. Quantitative information is gathered from two groups of respondents using structured surveys with PANAS questions and Likert scale questions. The research uses the reliable and accurate Music Receptivity Scale (MRS) to measure the participants' degree of liking a specific song. Findings: The results of this research examine how viewing a TikTok video effects Gen Z listeners' degree of liking a piece of new music. Participants in the baseline group were only asked to listen to a song on Spotify. The participants in the second group, who first saw a TikTok video showed more interest and liking in the song than those in the baseline group. A statistically significant difference in average scores between the two groups indicates that participants' interest and impressions of the music were affected by their exposure to TikTok. Value, originality: These results add to the expanding body of literature on TikTok as a music marketing tool, suggesting future directions for the independent music industry and general music industry. The study provides insights into the potential of TikTok as a vital tool for enhancing the degree of liking for a song, which illuminates the role of TikTok in changing music tastes and emotional reactions among Generation Z. Keywords: TikTok, degree of liking, music consumption, social media, Generation Z, music marketing, Music Receptivity Scale

Note: DISSERTATION NOTE: Bachelor of Arts thesis in International Management and Marketing, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents 1. Introduction.................................................................1 2. Literature Review............................................................4 2.1. the Music Industry......................................................4 2.2. the Paradigm Shift in Unveiling Musical Talents: Accepting a New Era of Artist Discovery................................................7 2.3. Music Marketing........................................................8 2.3.1. Marketing of Emerging, Lesser-known, or Independent Artists......8 2.3.2. Overview of Tiktok as a Marketing Tool..........................10 2.3.3. Going Viral on Tiktok - Lizzo's Case............................13 2.4. Theoretical Framework on Music Marketing and Social Media.............15 2.5. Previous Research on the Impact of Tiktok on Music Consumption and Engagement..........................................................16 3. Research Design and Methodology.............................................18 3.1. Sampling Method.......................................................19 3.2. Procedure.............................................................19 3.3. Data Collection and Analysis..........................................21 4. Findings...................................................................21 4.1. Presentation of the Data..............................................22 4.2. a Comparative Analysis of Baseline Group and Tiktok Group.............25 5. Discussion................................................................28 5.1. Study Implications....................................................28 5.2. Limitations of the Study..............................................29 5.3. Future Directions.....................................................31 6. Conclusion................................................................31 7. References................................................................33

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Format: 32 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: The COVID-19 crisis has had a profound impact on various industries worldwide, including the airline and movie sectors. Machine learning models, which play a crucial role in predicting outcomes and optimizing operations in these industries, have also been affected by the pandemic. This comparative literature review aims to explore and analyze the performance of machine learning models in the airline and movie industries during and after the COVID-19 crisis. By conducting a comprehensive analysis of relevant scholarly articles, conference papers, and industry reports, this review aims to provide insights into the challenges, adaptations, and advancements made in machine learning models pre- and post-pandemic. The relevant papers and sources were selected based on being pre- and post-pandemic. These two categories of sources were then compared for both industries to illustrate the effects of the pandemic on machine learning models in both industries, and how they have developed since this global event. The findings demonstrated that machine learning has been in use for decades in both industries. In the movie industry, the algorithms were mainly used for forecasting revenue or predicting movie success pre-pandemic, while in the airline industry, machine learning models predicted flight patterns/delays or ticket prices. While the algorithms and models in both industries struggled initially in the new dynamic environments, key differences can be synthesized between the developments since. While the airline industry continues to grow and utilizes ML as a globally demanded and necessary industry, the movie industry has still not fully recovered since COVID-19 as many consumers move to digital alternatives like streaming platforms. The findings of this review will contribute to a deeper understanding of the implications of the COVID-19 crisis on machine learning applications and provide insights for researchers, practitioners, and decision-makers in these industries. Keywords: COVID-19 Impact, Machine Learning Models, Airline Industry, Movie Industry, Predictive Analytics, Pandemic Adaptations, Comparative Analysis, Algorithm Performance, Industry Recovery, Digital Transformation.

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business & Management, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Abstract..................................................................................................iii Introduction 1.1 Background and Limitations of ML in a Non-Static Environment.....................................4 1.2 Effects of COVID-19 on ML Models in the Airline and Movie Industries........................5 1.3 Academic Contribution.......................................................................................................5 Methodology 2.1 Research Design and Approach..........................................................................................6 2.2 Selection Criteria................................................................................................................6 2.3 Data Collection and Analysis.............................................................................................7 Literature Review 3.1 Machine learning in static and dynamic environments......................................................7 3.2 Types of Machine Learning: Predictive and Descriptive Learning....................................8 3.3 Machine Learning Applications in the Movie Industry: Past Developments...................12 3.4 Advancements in Natural Language Processing Algorithms for Movie Industry Analysis...................................................................................................................................13 3.5 Machine Learning in the Movie Industry: Post-COVID Shifts and Trends.....................15 3.6 Machine Learning Applications in the Airline Industry: Pre-COVID Insights................16 3.7 ML’s Applications in the Airline Industry Post-COVID........................................18 3.8 Implications of Machine Learning Techniques in Dynamic Environments..................19 3.9 Impact of COVID-19 on Machine Learning Applications in the Movie and Airline Industries............................................................................................................21 Results and Discussion 4.1 Impact of COVID-19 on the Movie Industry: Shifting Trends and Environments........22 4.2 Challenges of ML Models in the Movie Industry................................................23 4.3 Airline Industry during the COVID-19 Crisis: Challenges and Adaptations...............25 4.4 Impact of COVID-19 on Flight Operations Post-Covid.......................................25 Conclusion 5.1 Summary of the main findings of the study and outlook..................................................26 References..............................................................................................................................28

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Format: 32 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: The COVID-19 crisis has had a profound impact on various industries worldwide, including the airline and movie sectors. Machine learning models, which play a crucial role in predicting outcomes and optimizing operations in these industries, have also been affected by the pandemic. This comparative literature review aims to explore and analyze the performance of machine learning models in the airline and movie industries during and after the COVID-19 crisis. By conducting a comprehensive analysis of relevant scholarly articles, conference papers, and industry reports, this review aims to provide insights into the challenges, adaptations, and advancements made in machine learning models pre- and post-pandemic. The relevant papers and sources were selected based on being pre- and post-pandemic. These two categories of sources were then compared for both industries to illustrate the effects of the pandemic on machine learning models in both industries, and how they have developed since this global event. The findings demonstrated that machine learning has been in use for decades in both industries. In the movie industry, the algorithms were mainly used for forecasting revenue or predicting movie success pre-pandemic, while in the airline industry, machine learning models predicted flight patterns/delays or ticket prices. While the algorithms and models in both industries struggled initially in the new dynamic environments, key differences can be synthesized between the developments since. While the airline industry continues to grow and utilizes ML as a globally demanded and necessary industry, the movie industry has still not fully recovered since COVID-19 as many consumers move to digital alternatives like streaming platforms. The findings of this review will contribute to a deeper understanding of the implications of the COVID-19 crisis on machine learning applications and provide insights for researchers, practitioners, and decision-makers in these industries. Keywords: COVID-19 Impact, Machine Learning Models, Airline Industry, Movie Industry, Predictive Analytics, Pandemic Adaptations, Comparative Analysis, Algorithm Performance, Industry Recovery, Digital Transformation.

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business and Management, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Abstract..................................................................................................iii Introduction 1.1 Background and Limitations of ML in a Non-Static Environment.....................................4 1.2 Effects of COVID-19 on ML Models in the Airline and Movie Industries........................5 1.3 Academic Contribution.......................................................................................................5 Methodology 2.1 Research Design and Approach..........................................................................................6 2.2 Selection Criteria................................................................................................................6 2.3 Data Collection and Analysis.............................................................................................7 Literature Review 3.1 Machine learning in static and dynamic environments......................................................7 3.2 Types of Machine Learning: Predictive and Descriptive Learning....................................8 3.3 Machine Learning Applications in the Movie Industry: Past Developments...................12 3.4 Advancements in Natural Language Processing Algorithms for Movie Industry Analysis...................................................................................................................................13 3.5 Machine Learning in the Movie Industry: Post-COVID Shifts and Trends.....................15 3.6 Machine Learning Applications in the Airline Industry: Pre-COVID Insights................16 3.7 ML's Applications in the Airline Industry Post-COVID........................................18 3.8 Implications of Machine Learning Techniques in Dynamic Environments..................19 3.9 Impact of COVID-19 on Machine Learning Applications in the Movie and Airline Industries............................................................................................................21 Results and Discussion 4.1 Impact of COVID-19 on the Movie Industry: Shifting Trends and Environments........22 4.2 Challenges of ML Models in the Movie Industry................................................23 4.3 Airline Industry during the COVID-19 Crisis: Challenges and Adaptations...............25 4.4 Impact of COVID-19 on Flight Operations Post-Covid.......................................25 Conclusion 5.1 Summary of the main findings of the study and outlook..................................................26 References..............................................................................................................................28

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Format: 75 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract Various analyses have confirmed that the concept of “being agile” has grown considerably with increasing digitalization. During times of rapid and uncertain events like the COVID-19 pandemic, agility is an efficient methodology that helps keep a company “flexible”, and, at the same time, resilient. Companies, therefore, are more prepared for possible spontaneous changes that come their way, both internally and externally. Moreover, “New Work”—the approach and concept—enables growth in organizational agility by offering flexible choices for a workforce with elements of self-organization and greater autonomy. Not every company that calls itself “agile” is alike in its approach to agility. Multiple factors, such as the techniques in individual teams or individuals, play a role. Different frameworks—like “Scrum” or “Kanban”—need to be implemented so that the company is ready to become adaptable. Furthermore, some companies appear to be “agile”, but this is a myth and not really true; it is propagated simply because it is better for the companies’ reputation. The aim of this research is to uncover meanings and detect the importance of agility in companies, which anticipates, in particular, flexibility, adaptability, and speed. Agility is also described as a contributor to better development and success along with increasing digitalization, even as a necessity when changes occur, viz. unexpected events like the coronavirus crisis. Advanced technology in terms of, for instance, Artificial Intelligence (AI), contributes to problem-solving and the assistance of teams. Related to this topic, “New Work” has been an important and essential development for years, but has acquired even more significance on account of the coronavirus pandemic. This is associated, among other things, with the leadership of the top management and the corporate culture, values, and work practices of the employees, and, hence, offers more “freedom” and opportunities. To provide a practical case study, the focus in the relevant section in this thesis is on the digital data-driven company ImmoScout24, an online marketplace that is run as an operating online platform. Agility in the company is thereby measured and identified with the help of self-observation, interviews, and a company survey that is distributed among all its employees. Keywords: Business agility, Digital Transformation, COVID-19, New Work, Scrum, Kanban, Project Management

Note: DISSERTATION NOTE: Master of Business Administration thesis, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Introduction................................................................................1 Main Problem Statement........................................................1 Need and Justification for the Study..........................................1 Research Aim.....................................................................2 Research Questions..............................................................2 Structure of the Thesis...........................................................3 Literature Review.........................................................................3 Definitions of Business Agility..................................................3 Business Agility—Digitalization and COVID-19..............................5 Business Agility and New Work...............................................9 Limitations of current studies...................................................10 Conceptual Framework.........................................................11 Method....................................................................................12 Results.....................................................................................13 Business Agility—General Analysis, Identification, and Evaluation....13 Business Agility—Influence of Digitalization and COVID-19...........20 Digitalization...........................................................20 COVID-19..............................................................21 Business Agility—Relationship to New Work..............................23 Business Agility at ImmoScout24............................................25 Interviews...............................................................27 Survey....................................................................30 Discussion.................................................................................35 Business Agility—Digitalization, COVID-19 & New Work.............35 ImmoScout24....................................................................38 Conclusion................................................................................42 References................................................................................45 Appendices...............................................................................54 Appendix A—Survey Questions/Statements & Results...................54 Appendix B—Domains of Business Agility................................73 Appendix C—Agile Transformation.........................................74 Appendix D—2022 Global Business Agility Survey IS24................75

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Keywords: Academic theses

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Format: 75 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract Various analyses have confirmed that the concept of "being agile" has grown considerably with increasing digitalization. During times of rapid and uncertain events like the COVID-19 pandemic, agility is an efficient methodology that helps keep a company "flexible", and, at the same time, resilient. Companies, therefore, are more prepared for possible spontaneous changes that come their way, both internally and externally. Moreover, "New Work"-the approach and concept-enables growth in organizational agility by offering flexible choices for a workforce with elements of self-organization and greater autonomy. Not every company that calls itself "agile" is alike in its approach to agility. Multiple factors, such as the techniques in individual teams or individuals, play a role. Different frameworks-like "Scrum" or "Kanban"-need to be implemented so that the company is ready to become adaptable. Furthermore, some companies appear to be "agile", but this is a myth and not really true; it is propagated simply because it is better for the companies' reputation. The aim of this research is to uncover meanings and detect the importance of agility in companies, which anticipates, in particular, flexibility, adaptability, and speed. Agility is also described as a contributor to better development and success along with increasing digitalization, even as a necessity when changes occur, viz. unexpected events like the coronavirus crisis. Advanced technology in terms of, for instance, Artificial Intelligence (AI), contributes to problem-solving and the assistance of teams. Related to this topic, "New Work" has been an important and essential development for years, but has acquired even more significance on account of the coronavirus pandemic. This is associated, among other things, with the leadership of the top management and the corporate culture, values, and work practices of the employees, and, hence, offers more "freedom" and opportunities. To provide a practical case study, the focus in the relevant section in this thesis is on the digital data-driven company ImmoScout24, an online marketplace that is run as an operating online platform. Agility in the company is thereby measured and identified with the help of self-observation, interviews, and a company survey that is distributed among all its employees. Keywords: Business agility, Digital Transformation, COVID-19, New Work, Scrum, Kanban, Project Management

Note: DISSERTATION NOTE: Master of Business Administration thesis, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Introduction................................................................................1 Main Problem Statement........................................................1 Need and Justification for the Study..........................................1 Research Aim.....................................................................2 Research Questions..............................................................2 Structure of the Thesis...........................................................3 Literature Review.........................................................................3 Definitions of Business Agility..................................................3 Business Agility-Digitalization and COVID-19..............................5 Business Agility and New Work...............................................9 Limitations of current studies...................................................10 Conceptual Framework.........................................................11 Method....................................................................................12 Results.....................................................................................13 Business Agility-General Analysis, Identification, and Evaluation....13 Business Agility-Influence of Digitalization and COVID-19...........20 Digitalization...........................................................20 COVID-19..............................................................21 Business Agility-Relationship to New Work..............................23 Business Agility at ImmoScout24............................................25 Interviews...............................................................27 Survey....................................................................30 Discussion.................................................................................35 Business Agility-Digitalization, COVID-19 and New Work.............35 ImmoScout24....................................................................38 Conclusion................................................................................42 References................................................................................45 Appendices...............................................................................54 Appendix A-Survey Questions/Statements and Results...................54 Appendix B-Domains of Business Agility................................73 Appendix C-Agile Transformation.........................................74 Appendix D-2022 Global Business Agility Survey IS24................75

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Format: 47 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: Few businesses operate in todays’ environment without planned and purposeful Enterprise Architecture (EA). As organizations continue to transform and move toward digital, EAs must work with the business to define priorities and align business requirements to IT strategies. Utilizing the outcomes of Koc et al. (2021), this paper will use topic modeling to identify, analyze, and report patterns within a dataset (Gillies et al., 2022). This study investigates the application of topic modeling techniques to analyze the goals and priorities of enterprise architects. The analysis below employs LDA, CTM, and BTM models, but there are other topic modeling techniques that could be explored in future research, such as Hierarchical Dirichlet Process (HDP) or Structural Topic Model (STM). Findings from this study provide a foundation for future research and further refinement of topic modeling techniques. Keywords: Enterprise Architecture, digital transformation, business alignment, IT strategy, topic modeling, LDA, CTM, BTM, HDP, STM

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business & Management, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents abstract........................................................................iv i. Introduction.............................................................1 ii. Literature Review..................................................2 A. Enterprise Architecture and 4em...........................................2 B. Previous Studies in Enterprise Architecture.................................4 C. Nlp and Topic Modeling...................................................5 1. Latent Dirichlet Allocation (lda)......................................5 2. Correlated Topic Model (ctm).......................................6 3. Biterm Topic Model (btm)............................................6 iii. Methodology........................................................7 A. Data Collection...............................................................8 1. Importing the Basic Libraries.........................................8 2. Extracting the Goals....................................................9 B. Data Preprocessing..........................................................9 1. Tokenization.............................................................9 2. Processing With Spacy...............................................10 3. Custom Stop Word Removal Function.................................10 4. Removal of Underscore Character..................................11 5. Lemmatization..........................................................11 6. Applying the Preprocessing Function.................................11 7. Training a Bigram Model............................................11 8. Execution of Final Preprocessing Function..........................12 C. Setting up Lda Model....................................................12 1. the Gensim/corpora Module.......................................12 2. Lda Model Configuration...........................................12 3. Lda Model Stability Test............................................14 4. Lda Model Topic Visualizations..................................14 D. Setting up Ctm Model....................................................15 1. the Tomotopy Module................................................15 2. Ctm Model Configuration..........................................15 3. Ctm Model Stability................................................16 4. Ctm Topics Charts...................................................16 E. Setting up Btm Model....................................................16 1. the Biterm Modules and Preparation.................................16 2. the Btm Configuration..............................................16 3. the Btm Visualizations..............................................17 4. Btm Nan/zero-sum Checks........................................17 iv. Results...................................................................18 A. Modeled Topics............................................................18 1. Lda Output.............................................................18 2. the Ctm Results.......................................................21 3. Btm Results............................................................22 B. Cumulative Findings......................................................23 v. Discussion.................................................................25 A. Validation of the Results................................................25 B. Methodological Reflections..............................................26 C. Suggestions for Future Research.......................................27 vi. Conclusion..............................................................28 vii. References............................................................29 viii. Appendices............................................................33 A. Pyldavis Outputs........................................................33 1. Cluster 1.................................................................33 2. Cluster 2.................................................................33 3. Cluster 3.................................................................34 4. Cluster 4.................................................................34 5. Cluster 5.................................................................35 B. Complete Jupyter Notebook...........................................36 C. Thesis Declaration Page.................................................43

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Format: 47 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: Few businesses operate in todays' environment without planned and purposeful Enterprise Architecture (EA). As organizations continue to transform and move toward digital, EAs must work with the business to define priorities and align business requirements to IT strategies. Utilizing the outcomes of Koc and others (2021), this paper will use topic modeling to identify, analyze, and report patterns within a dataset (Gillies and others, 2022). This study investigates the application of topic modeling techniques to analyze the goals and priorities of enterprise architects. The analysis below employs LDA, CTM, and BTM models, but there are other topic modeling techniques that could be explored in future research, such as Hierarchical Dirichlet Process (HDP) or Structural Topic Model (STM). Findings from this study provide a foundation for future research and further refinement of topic modeling techniques. Keywords: Enterprise Architecture, digital transformation, business alignment, IT strategy, topic modeling, LDA, CTM, BTM, HDP, STM

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Digital Business and Management, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents abstract........................................................................iv i. Introduction.............................................................1 ii. Literature Review..................................................2 A. Enterprise Architecture and 4em...........................................2 B. Previous Studies in Enterprise Architecture.................................4 C. Nlp and Topic Modeling...................................................5 1. Latent Dirichlet Allocation (lda)......................................5 2. Correlated Topic Model (ctm).......................................6 3. Biterm Topic Model (btm)............................................6 iii. Methodology........................................................7 A. Data Collection...............................................................8 1. Importing the Basic Libraries.........................................8 2. Extracting the Goals....................................................9 B. Data Preprocessing..........................................................9 1. Tokenization.............................................................9 2. Processing With Spacy...............................................10 3. Custom Stop Word Removal Function.................................10 4. Removal of Underscore Character..................................11 5. Lemmatization..........................................................11 6. Applying the Preprocessing Function.................................11 7. Training a Bigram Model............................................11 8. Execution of Final Preprocessing Function..........................12 C. Setting up Lda Model....................................................12 1. the Gensim/corpora Module.......................................12 2. Lda Model Configuration...........................................12 3. Lda Model Stability Test............................................14 4. Lda Model Topic Visualizations..................................14 D. Setting up Ctm Model....................................................15 1. the Tomotopy Module................................................15 2. Ctm Model Configuration..........................................15 3. Ctm Model Stability................................................16 4. Ctm Topics Charts...................................................16 E. Setting up Btm Model....................................................16 1. the Biterm Modules and Preparation.................................16 2. the Btm Configuration..............................................16 3. the Btm Visualizations..............................................17 4. Btm Nan/zero-sum Checks........................................17 iv. Results...................................................................18 A. Modeled Topics............................................................18 1. Lda Output.............................................................18 2. the Ctm Results.......................................................21 3. Btm Results............................................................22 B. Cumulative Findings......................................................23 volume Discussion.................................................................25 A. Validation of the Results................................................25 B. Methodological Reflections..............................................26 C. Suggestions for Future Research.......................................27 vi. Conclusion..............................................................28 vii. References............................................................29 viii. Appendices............................................................33 A. Pyldavis Outputs........................................................33 1. Cluster 1.................................................................33 2. Cluster 2.................................................................33 3. Cluster 3.................................................................34 4. Cluster 4.................................................................34 5. Cluster 5.................................................................35 B. Complete Jupyter Notebook...........................................36 C. Thesis Declaration Page.................................................43

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Format: 113 pages : , illustrations ; , 21.59 × 27.94 cm.

Content: AUTHOR-SUPPLIED ABSTRACT: Abstract: Since the business environment experiences constant change, integrating Environmental Social Governance (ESG) into a business’s strategy presents a potential opportunity for a business to become more progressive, resilient, and sustainable. To effectively integrate and implement an ESG strategy/initiatives into their business, companies are increasingly turning to digital technologies. With a focus on the retail industry, this thesis investigates the influence of digital transformation on business's ESG initiatives. This will be accomplished by performing a framework analysis on ESG initiatives from 10 representative businesses in the retail industry. This examination will help gain insight into how digital technology influences ESG initiatives in businesses. The research finds that digital technology influences ESG initiatives by enhancing or creating value. These findings provide insights to develop better approaches to implement ESG initiatives, accelerate and encourage businesses to adopt ESG initiatives, decide the type of ESG initiative and method businesses pursue, and potentially formulate ESG strategy. Keywords: business environment, Environmental Social Governance (ESG), digital transformation, retail industry, framework analysis, digital technology, ESG initiatives, value creation, sustainability.

Note: DISSERTATION NOTE: Master of Business Administration thesis, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Table of Figures ................................................................................................................. iii Abstract ............................................................................................................................. v Introduction ...................................................................................................................... 1 Research Question ........................................................................................................... 4 Literature Review ............................................................................................................ 5 ESG Overview .............................................................................................................. 5 ESG Drivers .................................................................................................................. 7 Customer Trends .......................................................................................................... 7 UN Sustainable Development Goals ............................................................................ 8 Innovation ................................................................................................................... 10 ESG Risk & Benefits: ................................................................................................ 11 Benefit .................................................................................................................... 11 Risk ....................................................................................................................... 13 Shareholder and Stakeholder Theory ........................................................................ 15 Digital Transformation ............................................................................................... 17 Methodology .................................................................................................................. 20 Framework Analysis .................................................................................................. 21 Result and Analysis ....................................................................................................... 22 Data Familiarization ................................................................................................. 22 Ahold Delhaize ......................................................................................................... 23 Amazon .................................................................................................................... 24 Apple ....................................................................................................................... 26 Best Buy .................................................................................................................. 27 Carrefour ................................................................................................................. 28 Costco Wholesale ..................................................................................................... 29 The Home Depot ...................................................................................................... 30 Metro AG ................................................................................................................ 32 Walgreens Boots Alliance (WBA) ........................................................................... 33 Walmart .................................................................................................................. 34 Framework Identification ........................................................................................ 36 Digital Transformation Framework .......................................................................... 36 Business Model Canvas .......................................................................................... 38 Indexing .................................................................................................................. 40 Customer ................................................................................................................... 40 Competition ............................................................................................................... 41 Data .......................................................................................................................... 42 Innovation ................................................................................................................ 43 Value ........................................................................................................................ 44 Charting ...................................................................................................................... 46 Mapping and Interpretation ........................................................................................ 47 CC-DIV Framework Analysis Result .......................................................................... 47 BMC Framework Analysis Results ............................................................................. 53 Discussion ................................................................................................................... 55 Notes on the Data Collected ...................................................................................... 57 Conclusion ................................................................................................................... 58 Reference .................................................................................................................... 60 Appendix ...................................................................................................................... 68 Appendix 2 ................................................................................................................... 72 Appendix 3 ................................................................................................................... 74

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Format: 113 pages : , illustrations ; , 21.59 × 27.94 cm.

Content: AUTHOR-SUPPLIED ABSTRACT: Abstract: Since the business environment experiences constant change, integrating Environmental Social Governance (ESG) into a business's strategy presents a potential opportunity for a business to become more progressive, resilient, and sustainable. To effectively integrate and implement an ESG strategy/initiatives into their business, companies are increasingly turning to digital technologies. With a focus on the retail industry, this thesis investigates the influence of digital transformation on business's ESG initiatives. This will be accomplished by performing a framework analysis on ESG initiatives from 10 representative businesses in the retail industry. This examination will help gain insight into how digital technology influences ESG initiatives in businesses. The research finds that digital technology influences ESG initiatives by enhancing or creating value. These findings provide insights to develop better approaches to implement ESG initiatives, accelerate and encourage businesses to adopt ESG initiatives, decide the type of ESG initiative and method businesses pursue, and potentially formulate ESG strategy. Keywords: business environment, Environmental Social Governance (ESG), digital transformation, retail industry, framework analysis, digital technology, ESG initiatives, value creation, sustainability.

Note: DISSERTATION NOTE: Master of Business Administration thesis, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Table of Figures ................................................................................................................. iii Abstract ............................................................................................................................. v Introduction ...................................................................................................................... 1 Research Question ........................................................................................................... 4 Literature Review ............................................................................................................ 5 ESG Overview .............................................................................................................. 5 ESG Drivers .................................................................................................................. 7 Customer Trends .......................................................................................................... 7 UN Sustainable Development Goals ............................................................................ 8 Innovation ................................................................................................................... 10 ESG Risk and Benefits: ................................................................................................ 11 Benefit .................................................................................................................... 11 Risk ....................................................................................................................... 13 Shareholder and Stakeholder Theory ........................................................................ 15 Digital Transformation ............................................................................................... 17 Methodology .................................................................................................................. 20 Framework Analysis .................................................................................................. 21 Result and Analysis ....................................................................................................... 22 Data Familiarization ................................................................................................. 22 Ahold Delhaize ......................................................................................................... 23 Amazon .................................................................................................................... 24 Apple ....................................................................................................................... 26 Best Buy .................................................................................................................. 27 Carrefour ................................................................................................................. 28 Costco Wholesale ..................................................................................................... 29 The Home Depot ...................................................................................................... 30 Metro AG ................................................................................................................ 32 Walgreens Boots Alliance (WBA) ........................................................................... 33 Walmart .................................................................................................................. 34 Framework Identification ........................................................................................ 36 Digital Transformation Framework .......................................................................... 36 Business Model Canvas .......................................................................................... 38 Indexing .................................................................................................................. 40 Customer ................................................................................................................... 40 Competition ............................................................................................................... 41 Data .......................................................................................................................... 42 Innovation ................................................................................................................ 43 Value ........................................................................................................................ 44 Charting ...................................................................................................................... 46 Mapping and Interpretation ........................................................................................ 47 CC-DIV Framework Analysis Result .......................................................................... 47 BMC Framework Analysis Results ............................................................................. 53 Discussion ................................................................................................................... 55 Notes on the Data Collected ...................................................................................... 57 Conclusion ................................................................................................................... 58 Reference .................................................................................................................... 60 Appendix ...................................................................................................................... 68 Appendix 2 ................................................................................................................... 72 Appendix 3 ................................................................................................................... 74

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Keywords: Academic theses

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