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This paper explores how the International Accounting Standards Board (IASB) has dealt with the emerging issue of accounting for cryptocurrencies by investigating its constituents' expectations and the motivations underlying its regulatory response.

The theoretical lens of regulatory space is used to analyse the four-year debate around cryptocurrency holdings and informs the extensive thematic analysis of public documents, meetings recordings and comment letters on the topic.

Facing national standard setters' initiatives to regulate accounting for cryptocurrency, the IASB defended its position in the regulatory space through an agenda decision based on ewct 2xisting standards, which was finalised by the International Financial Reporting Standards Interpretation Committee (IFRS IC) despite criticism from constituents and Board members.

The paper provides insights into the IASB approach to a regulatory vacuum regarding a new class of items, which derive from a new and rapidly-evolving technology. Disruptive technology impacts the contested arena of accounting regulation, in which the constituents ask for new solutions and the IASB tries to resist such pressures, while defending its position.

The paper sheds light on the growing importance of agenda decisions in the IFRS environment and on the limits of the IASB long regulatory process in the circumstance of emerging accounting issues deriving from rapidly-evolving technology.

This investigation is timely and relevant as it considers the regulatory issues arising from disruptive technological innovations (i.e. cryptocurrency), shedding light on the limits of regulatory processes in times of technological change.

This paper aims to analyze factors based on organizational knowledge management (KM; transactional memory systems and knowledge-oriented leadership [K-OL]) that help firms to mitigate conflicts based on task management at work, with the aim to improve their innovation capabilities (IC). The knowledge-based view of the firm, conflict management theory and cognitive collective engagement theory have been used to build a model of relationships that connects the development of positive KM contexts and management of dysfunctional conflict with IC improvement.

Data survey collected from inland hotel establishments in Spain is used to test seven hypotheses by means of structural equations modeling, applying the partial least squares technique. Direct, indirect and mediating relationships between variables are examined from the structural path model.

The results confirm that, as expected, IC improve when K-OL and transactive memory systems (TMSs) are properly implemented by hotel establishments, which leads them to reduce negative effects of task management conflict (TMC). Significant direct effects are found between the key variables of the study and also a significant indirect effect between K-OL and IC through TMS reinforcement and the mitigation of TMC.

This paper provides useful ideas for hotel managers about how to improve KM contexts in their establishments while avoiding TMC. Efforts devoted to creating those contexts by hotel establishments are shown to be effective to improve their IC and create competitive advantages.

The analysis of IC improvement by studying TMC mitigation had not been researched to date by the KM literature. The consideration and testing of a model that integrates KM-related tools such as K-OL and TMS to avoid TMC in the hotel industry is the main contribution of this study.

This paper aims to assess whether the current eResearch Knowledge Centre’s (eRKC) research support practices align with researchers’ requirements for achieving their research objectives. The study’s objectives were to assess the current eRKC research support services and to determine which are adequate and which are not in supporting the Human Sciences Research Council (HSRC) researchers.

This study uses interviews as part of the qualitative approach. The researcher chose to use interviews, as some aspects warranted further explanation during the interview. The interviews were scheduled using Zoom’s scheduling assistant. The interviews were semi-structured, guided by a flexible interview procedure and supplemented by follow-up questions, probes and comments. The research life cycle questions guided the interviews. The data obtained were coded and transcribed using MS Excel. The interview data were analysed, using NVivo, according to the themes identified in the research questions and aligned with the theory behind the study. Pre-determined codes were created in line with the six stages of the research life cycle and applied to group the data and extract meaning from each category. Interviewee responses were assigned to groups in line with the stages of the research life cycle.

The current eRKC research support services are aligned with the needs of HSRC researchers and highlight services that could be expanded or promoted more effectively to HSRC researchers. It proposes a new service, data analysis, and suggests that the eRKC could play a more prominent role in research impact, research data management and fostering collaboration with HSRC research divisions.

This study is limited to assessing the eRKC’s support practices at the HSRC in Pretoria, South Africa. A more comprehensive study is needed for HSRC research services, capabilities and capacity.

Assessment of eRKC followed a comprehensive interviewee schedule that followed Raju and Schoombee’s research life cycle model.

Zoom’s scheduling assistant may have generated Zoom fatigue and reduced productivity. Technical issues, losing time, communication gaps and distant time zones may have affected face-to-face interaction.

eRKC research support practices are rare in South Africa and most parts of the world. This study bridges the gap between theory and practice in assessing eRKC research support practices.

The aim of the study is to explore the genesis of entrepreneurial ecosystems (EE) and highlight the role played by intellectual capital (IC) in that process. Specifically, the paper adopts the collective intelligence approach, and the study shows how human capital (HC), structural capital (SC) and relational capital (RC) interact to create an entrepreneurial ecosystem.

The paper adopts a single case study of an Italian EE. The data analysis is based upon the collection of different sources of data: semi-structured interviews with representatives of each actor of the ecosystem; email correspondence; meetings report; a 24-months period of direct observation. Given the novelty of the topic, the qualitative method seems well suited for studying innovation-based EE since the method offers rich data about a phenomenon in real-life context.

The case is a top-down, innovation-based EE in which all main components of the IC play a crucial role from the initial stage. Findings show how the constant interchange between IC components occurs at two different levels: the micro and the meso level. HC and RC play major roles at both levels, whilst SC only occurs at a meso level, representing the environment in which the whole ecosystem takes place. Additionally, the use case, a new intangible asset integrating all three components of IC, emerged as one of the main outcomes of this innovation-based EE.

The paper contributes to a rather unexplored topic in the existing literature on EE and IC, namely the formation process of EE and the role played by IC within that process. Additionally, through the application of the collective intelligence approach, the authors shed light on the need to manage IC at both micro and meso level in the creation of an EE.

Independent component analysis (ICA) is a widely-used blind source separation technique. ICA has been applied to many applications. ICA is usually utilized as a black box, without understanding its internal details. Therefore, in this paper, the basics of ICA are provided to show how it works to serve as a comprehensive source for researchers who are interested in this field. This paper starts by introducing the definition and underlying principles of ICA. Additionally, different numerical examples in a step-by-step approach are demonstrated to explain the preprocessing steps of ICA and the mixing and unmixing processes in ICA. Moreover, different ICA algorithms, challenges, and applications are presented.