The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

The ability to take advantage of new digital solutions and technology will give companies a competitive edge, and operational optimization remains a major concern. A significant area of risk is cyber security because software-based technologies are integral to ship operations. Particular emphasis has been placed on the vulnerabilities of the Global Navigation Satellite System (GNSS), since it is an essential part of many maritime facilities and hence a target for hackers. Presently, research has shown that increased integration of new enabling technologies, like the Internet of Things (IoT) and big data, is driving the dramatic proliferation of cybercrimes. However, most of the attacks are related to ransomware attacks and/or with direct attack to the information technology (IT) and infrastructure. Nevertheless, there is a strong trend toward increased systems integration, which will produce substantial business value by making it easier to operate autonomous vessels, utilizing smart ports more, reducing the need for labour, and improving economic stability and service efficiency. Cybersecurity is becoming more and more important as a result of the quick digital transformation of the offshore and maritime sectors, which has also brought new dangers and laws. The marine sector has started to take cybersecurity seriously in light of the multiple documented instances of cyberattacks that have exposed business or personal data, caused large financial losses, and caused other problems. However, the body of existing research on emerging threats in maritime cyberspace is either inadequate or ignores important variables. Based on the most recent developments in the maritime sector, the article presents a classification of the most serious cyberthreats as well as the risks to cybersecurity in maritime operations and possible mitigation strategies from an educational research perspective.

Increasing the environmental friendliness of production systems is largely dependent on the effective organization of waste logistics within a single enterprise or a system of interconnected market participants. The purpose of this article is to develop and test a methodology for evaluating a data-based waste logistics model, followed by solutions to reduce the level of waste in production. The methodology is based on the principle of balance between the generation and beneficial use of waste. The information base is data from mandatory state reporting, which determines the applicability of the methodology at the level of enterprises and management departments. The methodology is presented step by step, indicating data processing algorithms, their convolution into waste turnover efficiency coefficients, classification of coefficient values and subsequent interpretation, typology of waste logistics models with access to targeted solutions to improve the environmental sustainability of production. The practical implementation results of the proposed approach are presented using the production example of chemical products. Plastics production in primary forms has been determined, characterized by the interorganizational use of waste and the return of waste to the production cycle. Production of finished plastic products, characterized by a priority for the sale of waste to other enterprises. The proposed methodology can be used by enterprises to diagnose existing models for organizing waste circulation and design their own economically feasible model of waste processing and disposal.

Smart cities incorporate fundamental aspects such as sustainability and citizens’ well-being. Therefore, the objective of this study is to analyze the feasibility and effectiveness of the implementation of an evaluation model of the transformation processes towards smart cities as a strategy to improve the state of the transformation processes in Lima, Peru. The research is descriptive and basic. A questionnaire was administered to 80 municipal officials in Lima, focusing on the variable “smart cities evaluation model”, covering three key dimensions: open data, smart public transport and energy efficiency, with a total of 15 questions and the variable “state of the transformation processes”, analysed through the dimensions of educational level of the population and municipal budget, with 10 questions. The results revealed that 48% expressed a gap in terms of the availability and quality of accessible information. 53% argued that stronger energy conservation and sustainability strategies need to be implemented. In addition, 53% felt that the education level needs to focus on improving local education systems. In conclusion, transformation processes drive economic, social and environmental development, improving the quality of life and promoting equality among citizens. This study contributes to a broader understanding of how to address these challenges in order to build more sustainable and liveable cities in the future.

With the rapid development of digital technology, the digital infrastructure enables the rapid formation, modification and refactoring of digital products through continuous experimentation and implementation, reduces the cost of innovation, and facilitates the implementation of digital innovation. To solve the problem that the technical scope of digital innovation is relatively concentrated and the knowledge flow between the achievements of digital innovation is insufficient, this study investigates the impact of digital infrastructure on organizational digital innovation in China. The cross-sectional study was conducted from November 2023 to March 2024 among 384 employees and managers in the core industries of the digital economy, as well as enterprises in traditional industries in China. Data were collected using closed-ended questionnaires adapted from previous literature. Structural equation modelling (SEM) was employed to analyze the data using SPSS 28 and AMOS 28. The results reveal that both the information infrastructure and the innovation infrastructure have a positive and direct effect on organizational digital innovation in China, as well as an indirect effect through data flows. Converged infrastructure has only an indirect impact on organizational digital innovation through the flow of data.