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This paper explores digital transformation's impact on the work of owners in entrepreneurial firms. The interplay between working practices and technology is analyzed, taking into account the organizations' specific contexts.

A multiple case study design was applied. Eight cases of entrepreneurial firms, defined as companies that bring new products and services to the market by creating and seizing opportunities, were selected, with the goal to maximize the diversity of cases. The sample includes both small- and medium-sized firms, as well as high- tech and low- tech companies in equal number. Interviews have been used to collect both quantitative and qualitative data, which was analyzed in a structured way.

The digital transformation of entrepreneurial work, that is the daily work of entrepreneurs, is an evolutionary, practice-based phenomenon, rather than the result of rational design. The use of different digital tools is interrelated and depends on the characteristics, and dynamics of the surrounding environment.

The findings of this study are relevant to entrepreneurs interested in understanding the dynamics of their working practice, to software development firms interested in entrepreneurs as customers and to institutions interested in the education of entrepreneurs.

To the best of the authors' knowledge this is the first study which considers the interplay between digital technology and the daily activities of entrepreneurs, considered as a whole. It provides insights on how these interconnected dimensions evolve, thus contributing to understanding the work of entrepreneurs, and as a consequence the dynamics of entrepreneurial firms in the context of digital transformation of organizations.

The article discusses the current relevance of artificial intelligence (AI) in research and how AI improves various research methods. This article focuses on the practical case study of systematic literature reviews (SLRs) to provide a guideline for employing AI in the process.

Researchers no longer require technical skills to use AI in their research. The recent discussion about using Chat Generative Pre-trained Transformer (GPT), a chatbot by OpenAI, has reached the academic world and fueled heated debates about the future of academic research. Nevertheless, as the saying goes, AI will not replace our job; a human being using AI will. This editorial aims to provide an overview of the current state of using AI in research, highlighting recent trends and developments in the field.

The main result is guidelines for the use of AI in the scientific research process. The guidelines were developed for the literature review case but the authors believe the instructions provided can be adjusted to many fields of research, including but not limited to quantitative research, data qualification, research on unstructured data, qualitative data and even on many support functions and repetitive tasks.

AI already has the potential to make researchers’ work faster, more reliable and more convenient. The authors highlight the advantages and limitations of AI in the current time, which should be present in any research utilizing AI. Advantages include objectivity and repeatability in research processes that currently are subject to human error. The most substantial disadvantages lie in the architecture of current general-purpose models, which understanding is essential for using them in research. The authors will describe the most critical shortcomings without going into technical detail and suggest how to work with the shortcomings daily.

Food waste is a severe problem affecting the supply chain due to its significant adverse social and environmental effects. Even if the topic is hotly debated in the literature, there is a lack of research about the success factors influencing food waste prevention initiatives retailers undertake.

The research analyzes how several variables (i.e. product-related variables and technology-enabling variables) might impact the success of the sales of products close to the expiration date that is sold at a discounted price. Data from 390.000 products sold at a discounted price in 2020 and 2021 by a large Italian food retailer were examined with a regression analysis.

The results highlight that both product-related and technology-enabling variables influence the success of food prevention initiatives aimed at selling products close to the expiration date at a discounted price. In particular, the authors stress the importance of digital technologies in supporting food waste prevention initiatives.

The study offers several practical implications for managers in structuring a waste prevention initiative. The introduction of digital technologies, the monitoring of specific variables or the ability to find synergies with other food waste prevention initiatives are discussed to support retailers in reducing food losses.

The paper is focused on the retailer perspective, which is barely investigated due to the difficulty in finding data.