Kooperativer Bibliotheksverbund

UID:

Format: 119 S. , überw. Ill., Kt., graph. Darst.

ISBN: 377012801X

Language: German

Subjects: Art History

Keywords: Deutschland ; Dorfkirche ; Bildband ; Bildband ; Bildband ; Bildband

Author information: Monheim, Florian 1963-

UID:

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

Language: Undetermined

Keywords: Academic theses

URL: FULL

UID:

Format: 36 pages : , illustrations ; , 21 × 29.7 cm.

Content: AUTHOR-SUPPLIED ABSTRACT: Abstract: E-governance was developed so that the government could directly interact with the citizens. As the nation's government, communicating with the citizens is an unavoidable duty. This can lead to better security and accuracy in the services, reduced corruption rates, better comfort, and higher transparency. Information Systems Management is regarded as an application of information technologies such that the major operations and functions of the public or private sectors can be managed. Previously, in the traditional systems, the organisations had to emphasise the management of the resources like employees, capital, raw materials and others. However, at present, one of the most important assets of the organisation is data. Global usage of the technologies has helped public and private organisations utilise the technologies to reach out to more people. The usage of the e-governance models have improved the processes considerably and have an immense impact on the operations of the government of the countries. However, it is not enough to address the issues which the countries face due to the utilisation of private information. The research explores how implementing information system management can help improve the effectiveness of the e-government models in Denmark. Denmark has been selected as a country for conducting the research as it is considered to be one of the most technologically sound countries making use of the technologies for the benefit of the people of the nation. The research developed with the help of Denmark as an example shall help in understanding how the implementation of the information system management processes shall improve the usage of the e-government models in other nations as well and how the same can be implemented for the improvement of the same. Keywords: e-governance, government, citizens, security, accuracy, services, corruption rates, comfort, transparency, Information Systems Management, public sectors, private sectors, resources, data, technologies, e-government models, operations, global usage, private information, implementation, Denmark, technologically sound countries, research, information system management, effectiveness, improvement.

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Data Science & Business, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Executive Summary..............................................................................................i Chapter 1: Introduction...........................................................................................1 1.1 Background.................................................................................................1 1.2 Research Problem......................................................................................2 1.3 Research Aim, Objectives, and Research Question........................................3 Chapter 2: Research Methodology..........................................................................3 2.1 Research Philosophy...................................................................................4 2.2 Research Approach.....................................................................................4 2.3 Research Design........................................................................................5 2.4 Data Sources and Data Collection Method..................................................6 2.5 Data Analysis Technique.............................................................................6 2.6 Ethical Consideration..................................................................................7 Chapter 3: Literature Review...................................................................................7 3.1 E-Government Models................................................................................8 3.2 E-Government Models through Information Systems Management...............9 3.2.1 Layne and Lee E-Government Model.....................................................11 3.2.2 Public Sector Process Rebuilding (PPR) Model......................................12 3.2.3 The Manchester E-Government Maturity Model.....................................13 3.3 Advantages of the E-Government Models.................................................13 3.4 Disadvantages of the E-Government Models.............................................14 3.4.1 Budgetary Challenges............................................................................14 3.4.2 Infrastructure Challenges.....................................................................15 3.4.3 Organisational Challenges..................................................................15 3.5 Theoretical Underpinning..........................................................................16 3.5.1 Broadcasting Model..............................................................................16 3.5.2 Comparative Analysis Model.................................................................17 3.6 Literature Gap............................................................................................17 3.7 Summary..................................................................................................18 Chapter 4: Results and Discussion of the Data Collection......................................18 Chapter 5: Conclusion and Recommendations.......................................................21 5.1 Conclusion.................................................................................................21 5.2 Recommendation......................................................................................23 5.3 Research Limitations.................................................................................25 References..........................................................................................................26 Declaration Page..................................................................................................ix

Language: Undetermined

Keywords: Academic theses

URL: FULL

UID:

Format: 144 S. : überw. Ill.

ISBN: 3770128214

UID:

Format: 158 S. : überw. Ill.

ISBN: 3774303290

UID:

Format: 31 S. : zahlr. Ill., graph. Darst.

Edition: 1. Aufl.

Series Statement: DKV-Kunstführer

UID:

Format: 38 pages : , illustrations ; , 21.59 × 27.94 cm.

Content: AUTHOR-SUPPLIED ABSTRACT: Abstract Companies are increasingly interested in finding ways to attract and retain talent, which, in today’s competitive landscape, is the safest bet that can be made to reach a competitive advantage. However, the increasing amount of data available and the changing needs within the workforce make the traditional approaches to Talent Acquisition (TA) outdated. To address the current challenges of TA, specialists should leverage Data Analytics to gain valuable insights. Based on existing literature reviews and the specific case study of Zalando, the purpose of this research is to understand what the current main challenges of TA are and to analyze how Zalando approaches these challenges in terms of Data Analytics. Through interviews with Zalando’s Talent Acquisition experts, results indicate that, while TA specialists are progressively intrigued in implementing these solutions, there are still many deficiencies in the company as a whole that do not allow for the full integration of Data Analytics. Zalando is not yet at a level of analytics maturity that allows Talent Acquisition to completely leverage Data Analytics. However, solving operational challenges first can lead to the development of models of advanced Data Analytics that could differentiate Zalando from the competition. Keywords: Data Analytics, Talent Acquisition, talent, challenges, data, Zalando

Note: DISSERTATION NOTE: Bachelor of Arts thesis in Business Administration - International Management & Marketing, Berlin International University of Applied Sciences, 2022. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents Abstract Introduction 2. Literature Review 2.1 Data Science & Analytics 2.2 Talent Acquisition 2.2.1 Challenges of Talent Acquisition 2.4 Talent Analytics 2.5 Zalando 3. Methodology 3.1 Research Purpose 3.2 Research Approach 3.3 Research Design 3.4 Data Collection 3.4.1 The Interviews 3.5 Data Analysis 4. Results and Analysis 4.1 Theme 1: Process 4.1.1 Lead Time 4.1.2 Hiring Bottlenecks 4.1.3 Candidate Sources 4.1.4 Diversity 4.1.5 Attrition 4.2 Theme 2: People 4.2.1 Biases 4.2.2 Support 4.2.3 Interviewer Accuracy 4.3 Theme 3: Business 4.3.1 Future Employment Needs 4.3.2 Alignment 4.3.3 Costs 5. Discussion 6. Conclusion References Appendices

Language: Undetermined

Keywords: Academic theses

UID:

Format: 51 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: Context: User segmentation is an effective tool to understand your users’ needs and make data-driven decisions. With limited resources many startups have. Would user clustering with unsupervised machine learning models be effective? Objectives: The thesis consists of a literature review, two interviews, and data analysis. It evaluates how effective the different unsupervised models are in application to user segmentation in a mobile application. Methods: a convenience sample interview was conducted with the chief executives of a startup to get an understanding of primary goals and objectives. Data were analyzed with the application of dimension reduction (PCA, t-SNE, UMAP, correlation coefficient, variance threshold), clustering (K-Means, DBSCAN), and supervised models for predictive analysis (Random Forest, Lasso, Logistic Regression). Results: It was possible to identify 4 different clusters of users within the app with unique behavior. Conclusion: The application of PCA, K-Means, and Random Forest was the most effective for a highly dimensional dataset. This user segmentation was valuable, but not new to the chief executives to the company. Meaningful insights were drawn from data analysis. Keywords: user segmentation, unsupervised learning, machine learning, mobile application, PCA, K-Means clustering, Random Forest, data analysis, startups, user behavior clustering.

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..........................................................................................................2 introduction....................................................................................................3 literature Review..........................................................................................6 2.1 Non-machine Learning User Segmentation Techniques..................7 2.2 Supervised Versus Unsupervised Machine Learning.........................7 2.2.1 Supervised Learning................................................................8 2.2.2 Unsupervised Learning..............................................................8 2.3 Customer Segmentation With Machine Learning...........................12 2.4 User Segmentation in Social Media Platforms..............................13 2.4.1 Case of Pinterest.................................................................13 2.4.2 Case of Instagram.................................................................13 2.5 Methodological Considerations..............................................14 methodology...............................................................................................15 the Research Hypotheses.........................................................................15 Research Design.....................................................................................15 Literature Review...................................................................................16 Interview No1.........................................................................................16 Data Analysis........................................................................................17 Data Collection......................................................................................17 Data Cleaning and Dimension Reduction..........................................17 Clustering Algorithms...........................................................................20 Predictive Models................................................................................22 Interview 2............................................................................................22 results...........................................................................................................23 Evaluation of Interview No1...................................................................23 Data Analytics.......................................................................................24 Cluster Analysis...................................................................................25 Predictive Models................................................................................28 Evaluation of Interview No2.................................................................29 discussion....................................................................................................30 conclusion...................................................................................................33 references...................................................................................................35 appendix......................................................................................................40 Section A.............................................................................................40 Section B.............................................................................................49 appendix 2..................................................................................................50

Language: Undetermined

Keywords: Academic theses

UID:

Format: 68 pages : , illustrations ; , 21 × 29.7 cm.

Content: AI-GENERATED ABSTRACT: Abstract: This thesis explores the integration of business process management (BPM) and data visualization tools to optimize performance marketing campaigns. The study looks at the present status of performance marketing, the benefits of BPM in improving strategies, the importance of data visualization tools like Tableau, and the challenges and opportunities connected with their integration. The survey results show that BPM and data visualization tools are widely used in performance marketing, with moderate efficacy for BPM integration and good perceptions for data visualization. Data integration complexity, learning curves, communication and collaboration issues, implementation complexities, data accuracy and privacy concerns, time and resource constraints, and skill development in data visualization are identified through thematic analysis of interviews. Organizations may improve their performance marketing strategies and get greater results by tackling these issues and capitalizing on the advantages. This study adds to a full understanding of the integration of BPM and data visualization tools in performance marketing and gives useful insights for practitioners looking to optimize their campaigns. Keywords: Performance Marketing, Business Process Management (BPM), Data Visualization, Integration, Optimization

Note: DISSERTATION NOTE: Master of Business Administration thesis, Berlin International University of Applied Sciences, 2023. , MACHINE-GENERATED CONTENTS NOTE: Table of Contents table of Contents.....................................................................................i list of Tables.........................................................................................ii table of Figures......................................................................................iii abstract...................................................................................................iv 1. Introduction.......................................................................................1 2. Literature Review and Theoretical Framework..........................................4 2.1. Current State of Performance Marketing and Challenges Faced...............4 2.2. Business Process Management (bpm)..................................................9 2.2.1. Importance of Business Process Management in Organizational Performance...............................................................................10 2.2.2. Role of Business Process Management.....................................11 2.3. Tableau and Its Significance in Data Visualization...............................15 2.4. Theoretical Basis for Investigating the Integration of Bpm and Tableau in Performance Marketing Strategies................................................18 3. Methodology.....................................................................................20 4. Findings............................................................................................22 4.1. Analysis of Survey Data on the Usage of Bpm and Data Visualization in Performance Marketing...................................................................22 4.1.1. Results of Survey...........................................................................24 4.2. Analysis of Interviews on the Usage of Bpm and Data Visualization in Performance Marketing.................................................................36 5. Integration of Bpm and Tableau in Performance Marketing.........................42 6. Discussion..........................................................................................51 7. Conclusion..........................................................................................53 8. References.........................................................................................56 9. Appendix...........................................................................................62

Language: Undetermined

Keywords: Academic theses

URL: FULL

UID:

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

Language: Undetermined

Keywords: Academic theses

URL: FULL