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Citizenship behaviours are used in organisation studies to explain their effectiveness and to capture the role of unsolicited, extra-role behaviours. Recent extensions towards the interorganisational context provide evidence that Interorganisational Citizenship Behaviours (ICBs) are important in facilitating supply chain operations and cluster innovation activities. This study took stock of citizenship behaviours literature within and among organisations, in order to develop and validate a measurement scale for ICBs useful in the food industry and beyond.

Departing from a literature driven 10-dimensional construct, Th authors proceeded with validating our scale in the South African craft brewing industry. The authors ran a face validation, followed by confirmatory and exploratory factor analyses on a sample of 108 expert informants. The authors ran nomological tests through correlation and regression analysis. Finally, logical analysis was done to ensure the theoretical integrity of the mathematical indications.

The authors found support that the model consisting of interorganisational magnanimity, interorganisational advancement and interorganisational constructiveness, is relevant for value creation within coopetitive networks. The purified scale included three psychometrically distinct ICBs dimensions, measured by 31 items.

Managers can benefit from adequately identifying ICB in their environment, and among their competitors in particular. The study’s measurement scale offers a useful tool for an initial monitoring of firms in their competitive environment.

The authors extended and advanced prior research on the ICB construct by providing a rigorously validated, parsimonious measurement scale of ICBs. The authors bridged the ICB and coopetition literature to better understand spontaneous extra-role coopetitive network facilitating behaviours.

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.