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The health crisis linked to COVID-19 has made digitization a significant issue for companies regardless of their form or geographical location. Our research focuses on the financial technology (Fintech) revolution in the context of international development. Its theoretical framework lies in both the fields of Fintech and factoring. As innovative start-ups that combine finance with new technologies, Fintechs have been able to disrupt the banking world in a few years by challenging its traditional practices. We use the case method to analyze two Fintech in-depth, and our results highlight the upheaval of Fintechs in the factoring sector. The supply of the analyzed Fintech is a limited working capital requirement. Furthermore, Fintechs may threaten classical banks due to the innovation of their offers and business models. However, the Fintech revolution focused on corporate finance is still in its infancy.

Those who care for people with schizophrenia and substance use disorders (PLS-SUD) are faced with the complex demands of a long journey to recovery. For the carers, this translates into specific needs related to various areas of their lives. However, few studies have contributed to the understanding of these carers’ needs. The purpose of this qualitative evaluative study is to identify, understand and prioritize the needs of PLS-SUD carers in the context of intervention design from the viewpoint of carers themselves (n = 9), those they were accompanying (n = 5) and other key actors involved (n = 10).

A design of action research was employed. Data analysis was done in three phases: concept map analysis, thematic analysis and transversal analysis of the results from two focus groups, 28 interview transcriptions and a logbook.

Over 60 needs were identified. After review, 39 of those were selected for prioritization. For needs related to the carers’ role as clients of the health-care system, the committee prioritized the needs for support, sharing with other carers and improving their own well-being. For the role of supporter, knowledge about substance use disorders and their interactions with psychotic disorders as well as skills such as communication and problem resolution were considered priorities. Needs to be prioritized relating to the role of partner were fewer.

The results of this study highlight the diversity and complexity of the needs experienced by carers.

This is among the first needs surveys carried out by stakeholders to describe the needs of PLS-SUD carers.

Non-verbal communication (NVC) remains largely understudied despite its importance in today's fast-paced and cross-cultural management and research landscape. This article is significant because it reveals valuable insights into NVC, which represents 65–93% (Mehrabian, 1981) of communication and has the potential to considerably increase management effectiveness and efficiency by providing leaders and researchers with the knowledge they need to understand and handle diversity with competence.

This article draws on social identity theory (SIT) (Tajfel and Turner, 1979) and rapport management theory (RMT) (Brown and Levinson, 1987) to analyse illustrative interview extracts of co-occurring verbal and NVC from an interpretative phenomenological analysis (IPA) study focussed on understanding how London-based Romanian migrant entrepreneurs experience acculturation.

Romanian migrant entrepreneurs use a variety of verbal and non-verbal communication approaches in their acculturation narratives, providing depth and occasionally shifting meaning. These tactics include repeating verbal discourse with non-verbal clues, replacing verbal communication with non-verbal gestures, complementing verbal communication and juxtaposing non-verbal cues with verbal descriptions.

This study makes a valuable contribution to the fields of qualitative organisational management and entrepreneurial studies by addressing the lack of methodological tools available for analysing non-verbal language in interpretative research. This study presents a systematic technique for assessing non-verbal language symbols that has been developed through face-to-face interviews. The article utilises the first-hand interview experience of a Romanian co-researcher to demonstrate the significance of NVC in the transmission of meaning and the formation of identities amongst Romanian migrant entrepreneurs. These findings contribute to a better understanding of organisational management and research practices, particularly about this understudied entrepreneurial minority of Romanian businesses in London, by helping researchers and managers better grasp the cultural and contextual meanings communicated non-verbally. The article holds significance in the context of cross-cultural and organisational management practices.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.