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Augmented reality (AR) and virtual reality (VR) are increasingly recognized among technologies potentially driving the digital innovation of small and medium-sized enterprises (SMEs). Nevertheless, the implications of AR/VR adoption in innovation processes have yet to receive much attention to date, with the need to explore the issues facilitating a systematic implementation in the SME context. This study aims to investigate the possible innovation paths of SMEs as a result of AR/VR adoption.

The authors performed a multiple case study research involving six Italian SMEs in manufacturing and service sectors that are investing in AR and/or VR solutions and are digitally innovating thanks to these technologies.

AR/VR solutions lead to different types of innovation in SMEs, i.e. for innovating product or service offerings, business processes or even the business model, when AR/VR extends to the company business logic. SMEs demonstrate being able to leverage internal sources with the essential commitment of top management and low resistance of employees in all kinds of AR/VR-enabled innovations. Conversely, they involve different external innovation sources according to the type of innovation pursued. Organizational issues emerged as more relevant than technological issues.

Results contribute to the literature on digital transformation of SMEs and provide managerial guidance on innovation sources and organizational issues to be considered to effectively deploy AR/VR solutions into specific innovation paths.

This study explores the impact of emergent technologies in the innovation process along with multiple perspectives, degree of complexity, and strategic importance in the SME context.

The literature on public procurement (PP) has increased significantly in recent years, and, to date, several reviews have been conducted to study this relevant subject. Nevertheless, a bibliometric analysis of the PP knowledge domain is still missing. To fill this knowledge gap, a bibliometric review is carried out to investigate the current state of PP research.

A total of 640 journal articles are selected from the Scopus database for the final analysis. The performance indicators of the literature are identified and explained through bibliometric analysis. Furthermore, the conceptual and intellectual structures are studied through a keyword co-occurrence network and bibliographic coupling.

The results of the review indicate that PP research has increased significantly in recent years. The top ten most productive journals, countries, authors and academic institutions are identified. The findings from the keyword co-occurrence network reveal six main research themes including innovation, corruption and green public procurement (GPP). By applying bibliographic coupling, the focus of PP research revolves around seven thematic areas: GPP, corruption, the role of small and medium-sized enterprises (SMEs) in PP, electronic PP, innovation, labour standards and service acquisition. The research potential of each thematic area is evaluated using a model based on maturity and recent attention (RA).

To the best of the authors' knowledge, this is the first study to successfully organise, synthesise and quantitatively analyse the development of the PP domain amongst a large number of publications on a large time scale.

Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.