Business Management

More titles

• 

  ADVANTAGES AND ETHICAL CONSIDERATIONS OF INDUSTRIAL IOT ARTIFICIAL INTELLIGENCE SOLUTIONS USAGE

  The objective of this paper is to research the synergistic interaction between Industrial Internet of Things (IIoT) and the application of Artificial Intelligence (AI) in IIoT ecosystems (AIoT). For achieving this purpose, a method of integrative study of leading scientific publications on the topics ‘Industrial Internet of Things’ and ‘Artificial ... The objective of this paper is to research the synergistic interaction between Industrial Internet of Things (IIoT) and the application of Artificial Intelligence (AI) in IIoT ecosystems (AIoT). For achieving this purpose, a method of integrative study of leading scientific publications on the topics ‘Industrial Internet of Things’ and ‘Artificial Internet of Things’ and the PRIZMA flow chart for filtering the list of found documents are applied. The inability of full coverage of the AIoT topic in one article has necessitated the usage of an approach for its content analysis in several thematic groups. The IIoT and AIoT concepts has been explored by various theoretical and practical aspects in many scientific papers published mainly over the past five years, but continues to arouse the interest of researchers. The novelty of the manuscript could be viewed in the attempts of systematizing the key characteristics, advantages, and risks in the practical use of these technologies in the form of smart manufacturing machines, robotic devices, autonomous vehicles, and software for predictive maintenance, and in outlining the possibility for green and sustainable AIoT platforms creation. It is concluded that a key requirement for the implementation of AIoT solutions is the consideration of the various threats and ethical aspects of their design, which contributes to the correct definition of the ‘social value’ of such business initiatives.

• 

  DRIVING PROFITABILITY THROUGH SOCIAL RESPONSIBILITY: UNVEILING THE SUCCESS STORY OF AUTOMOTIVE PLANT STELLANTIS SLOVAKIA

  This study examines the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP) in Slovakia's unique context of the Stellantis automobile plant. Driven by the growing focus on mitigating business impacts and delivering value to stakeholders, the research explores CSR as a strategic tool to enhance ... This study examines the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP) in Slovakia's unique context of the Stellantis automobile plant. Driven by the growing focus on mitigating business impacts and delivering value to stakeholders, the research explores CSR as a strategic tool to enhance competitiveness. The analysis utilises Stellantis Slovakia's financial reports from 2017 to 2021, incorporating horizontal and vertical analysis of assets. A questionnaire has also been administered to gauge employee perception of the company's CSR initiatives. The survey findings reveal a positive correlation between stakeholder engagement and CSR. Stellantis Slovakia's car plant showcases a commendable CSR commitment, with the potential to set more precise, measurable objectives to drive sustainability efforts. The company's robust financial performance, characterised by significant net profit growth, is in line with industry trends, suggesting that CSR-oriented firms enjoy enhanced CFP. The survey underscores the highly positive employee perception of CSR, related to moral commitment, thereby supporting previous research on CSR-driven employee engagement and productivity. This study deepens our understanding of CSR's impact on financial performance, offering practical recommendations for Stellantis Slovakia. Integrating CSR can improve the company's image, enhance employee motivation, reduce costs and drive revenue growth, thereby highlighting the strategic value of socially responsible practices and encouraging optimism about the potential benefits of these practices.

• 

  METADATA MANAGEMENT FRAMEWORK FOR BUSINESS INTELLIGENCE DRIVEN DATA LAKES

  Data lakes (DL) provide powerful capabilities for processing and utilizing large and diverse data, helping organizations adapt to the modern environment and extract maximum value from the information at their disposal. Effective data analysis provides actionable knowledge which is a competitive advantage for organizations. Metadata management in ... Data lakes (DL) provide powerful capabilities for processing and utilizing large and diverse data, helping organizations adapt to the modern environment and extract maximum value from the information at their disposal. Effective data analysis provides actionable knowledge which is a competitive advantage for organizations. Metadata management in data lakes is a key element in ensuring their full functionality. At the same time, this is a dynamic and under-researched area that reflects the rapid development of information technology and the business needs for effective data management. The research is based on a thorough scientific analysis of existing publications on the chosen topic. For this purpose, up-to-date and relevant open access publications from Scopus and Web of Science that correspond to the keywords "data lake" and "metadata" are identified and are from the last 15 years. Based on a review of the existing literature, the main challenges in data lake metadata management are highlighted. The goal of the research is to summarize the existing models in the field of metadata management in data lakes and to propose a new conceptual framework that can serve as a useful guide for designing and implementing metadata management models in heterogeneous data warehouses, as well as implementation steps. The concept's adoption involves a detailed study of the data management model in a specific organization, a measurement of the level of effectiveness after the model’s implementation, and the use of additional metrics to confirm its feasibility. These tasks are therefore the subject of future research. Another limitation of the proposed framework is that it does not address in depth the rules and standards related to ensuring data security, which would be of the highest priority especially in sectors such as finance, defence and healthcare. In addition, further research could also focus on future analysis of the level of satisfaction with the transformation of metadata management processes.