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This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

This study aims to explore the resource development process implemented by a small consulting firm, active in a traditional industrial context, pursuing the innovation path to develop solutions within the Industry 4.0 (I4.0) domain.

This study undertakes a single qualitative case study of Sinergia, an Italian innovative small consulting firm. The case study is analyzed through critical events and adopting the 4 R model, developed within the industrial marketing and purchasing (IMP) approach.

The analysis highlights a transition from knowledge broker to solution provider, based on a process of networking, with a relevant strategizing effort, and of assembling internal, external and shared resources. Three patterns in the evolution of the company’s innovation path emerge: resource-oriented networking, hybrid resource development and resource assembly.

The empirical study provides novel empirical evidence over localized innovation processes in I4.0 by exploring the innovation path pursued by a small consulting firm in connection with the local business. The study represents a theoretical development in terms of the 4 R model as it suggests the need to further conceptualize the category of technical resources – including products and facilities – in the increasingly complex I4.0 domain and provides insights on the changing role of actors in networks underpinned by emerging resource structures.