Entrepreneurship is one of the driving forces behind the development of the national economy. The effect of entrepreneurship in the economic dimension is a business entity, most often a small or medium enterprise. The SME sector plays a significant role in every national economy. Traditionally, people perceived entrepreneurship as a factor measured by the earned profit. Increasing it was the primary goal of enterprises operating on the market. The operating conditions and competitiveness level have meant that enterprises beside economic goals also pursue social and environmental goals. Entrepreneurship is perceived as a determinant of social and ecological development, thus referred to as sustainable entrepreneurship. This article aims to identify and evaluate attitudes towards sustainable entrepreneurship among Polish enterprises. We compare the obtained results with their self-assessment and distinguish different approaches to business activity. The cluster analysis of survey results shows that the Polish SME sector can be divided into five separate groups characterized by a different approach and level of implementation of sustainable development. We also concluded that the Polish enterprises are more focused on social than environmental aspects.

In recent years, Mobility-as-a-Service (MaaS) has attracted much attention in the context of smart city development. One of the models of intelligent mobility is car-sharing, a modern and convenient form of renting vehicles through a mobile application. Car-sharing is a solution that can help to mitigate the effects of excessive traffic congestion, noise, and air pollution in cities. In Poland, car-sharing has developed in recent years. To increase its popularity, it is necessary to look at the barriers from the user’s perspective. The presented study is a diagnosis of car-sharing problems based on customer reviews. The reviews were obtained from the Google Play store and cover the applications of Poland’s three largest car-sharing service providers. Descriptive statistics and sentiment analysis were used to identify the problems. The study of users’ comments made it possible to establish that car-sharing has gained tremendous popularity in recent years, reflected in the number of reviews. Moreover, car-sharing services are assessed positively by the majority of users. Women perceive services more positively than men, although they are a minority among car-sharing users. The main problems described in the comments relate to the operation of the application, in particular, the registration of new users and the unclear payment policy.

In recent years, online shopping has become a more accessible way of purchasing. In 2019, more than half Poles (53.9%) made this kind of shopping, and it can be assumed that this percentage will steadily be increasing. The speed, ease of making it, and very often lower prices than in stationery stores undoubtedly influenced this type of shopping. However, as in the case of traditional shopping, also in the case of online shopping, there are many factors determining the perception of the online shopping quality level of services. However, the perception of the quality of service may vary depending on the criterion used. Thus, the purpose of the article is to identify significant differences in the perception of online shopping quality level, depending on online shopping frequency. Research conducted among the people who purchase via the Internet showed that there are many significant differences between the outlined groups. However, based on the tests, it is possible to carry out another isolation based on the same criterion.

Purpose: This research explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in brand management. It aims to understand how AI technologies can optimize brand awareness, enhance personalized communication, and improve brand equity measurement, thereby redefining brand management strategies in the digital era. Design/methodology/approach: A systematic literature review was conducted, analyzing research studies published between 2014 and 2024. The methodology focused on articles that delve into the integration of AI and ML within brand management contexts. This approach was chosen to consolidate and synthesize a broad spectrum of findings on AI’s impact on brand management practices. Findings: The review identified significant enhancements in brand management facilitated by AI, particularly in consumer engagement and customization of consumer interactions through data-driven insights. AI's capability to analyze big data has enabled more precise consumer segmentation and targeting, thereby influencing brand loyalty and overall brand equity positively. Research limitations/implications: The primary limitation of this study is its reliance on data from articles indexed only in Scopus, potentially omitting relevant studies published in other languages or databases. Future research should expand the scope to include these sources and explore empirical validations of the proposed theoretical impacts of AI on brand management. Practical implications: This research highlights the practical applications of AI in improving brand management strategies, offering insights into effective AI integration into marketing practices. Businesses are encouraged to adopt AI-driven tools for better market segmentation, consumer behavior predictions, and enhanced customer relationship management. Social implications: The findings could influence public attitudes towards brand interaction by fostering greater acceptance of AI in consumer relations. The research supports enhanced corporate social responsibility through AI's ability to provide more targeted and meaningful consumer interactions. Originality/value: This paper contributes to the academic and practical understanding of AI’s role in brand management by synthesizing existing research and identifying future research directions. It is valuable to academicians, marketing professionals, and policymakers interested in the implications of AI technology in brand strategies. Keywords: Brand Management, Artificial Intelligence, Machine Learning, Consumer Engagement, Marketing Strategies. Category of the paper: Literature review.

This paper presents the idea of open government data along with the benefits and threats resulting from using open data. We describe the results of our research study on availability of the open data on the example of Poland with particular emphasis on Central Repository for Public Information (CRPI). The comparison of CRPI in Poland and other countries has been discussed. The review of accessible public information has been made with particular focus on data formats. Data formats are an important aspect of open data as they facilitate or impede the reuse of data. The insights from our participant observation in the projects of computerization of public administration are also presented. Although the Open Government Data (OGD) movement can provide a number of benefits, recent study has shown that in Poland it has not achieved its full potential yet.

Theoretical background: The rapid and exponential technological advancements have far-reaching impacts on management information systems, management practices, and human life. The promising outcomes in Artificial Intelligence and cutting-edge research on semantic networks and natural language processing have motivated the authors to envision the future of management technology. Purpose of the article: Our paper focuses on the new communication facilities and artificial intelligence models used to process management-type queries in natural language. Research methods: The article discusses recently developed technologies, proposed by Google and Microsoft, notably Google Bard and Bing integrated with ChatGPT-4. Both chatbots use Generative AI methods and large language models to understand domain-based queries and generate answers. Main findings: The practical and social implications of new models in management practice are discussed. To illustrate the qualities and weaknesses of the features of new technologies, four examples of management decision-making are discussed. The case studies also show differences between these two technologies. Finally, the paper concludes by summarizing the expectations and limitations of Generative AI applications in management. The paper is one of the first publications describing and demonstrating the idea of interfaces in natural language in business-oriented applications.