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The purpose of this paper is to develop a software-library in the R programming language that implements the concepts of the interpretive coordinate, interpretive axis and interpretive plane. This allows for the automatic and reliable interpretation of results from the multiple correspondence analysis (MCA) as previously proposed and published. Consequently, the users can seamlessly apply these concepts to their data, both via R commands and a corresponding graphical interface.

Within the context of this study, and through extensive literature review, the advantages of developing software using the Shiny library were examined. This library allows for the development of full-stack applications for R users without the need for knowledge of the corresponding technologies required for the development of complex applications. Additionally, the structural components of a Shiny application were presented, leading ultimately to the proposed software application.

Software utilizing the Shiny library enables nonexpert developers to rapidly develop specialized applications, either to present or to assist in the understanding of objects or concepts that are scientifically intriguing and complex. Specifically, with this proposed application, the users can promptly and effectively apply the scientific concepts addressed in this study to their data. Additionally, they can dynamically generate charts and reports that are readily available for download and sharing.

The proposed package is an implementation of the fundamental concepts of the exploratory MCA method. In the next step, discoveries from the geometric data analysis will be added as features to provide more comprehensive information to the users.

The practical implications of this work include the dissemination of the method’s use to a broader audience. Additionally, the decision to implement it with open-source code will result in the integration of the package’s functions by other third-party user packages.

The proposed software introduces the initial implementation of concepts such as interpretive coordination, the interpretive axis and the interpretive plane. This package aims to broaden and simplify the application of these concepts to benefit stakeholders in scientific research. The software can be accessed for free in a code repository, the link to which is provided in the full text of the study.

There is a strong consensus among scholars that the international competitiveness of companies strongly depends on the support of institutions, which reduces uncertainty in transactions by giving form to economic interactions, while less attention was paid to the role of international standards within this context. This study intends to propose its contribution by deepening the role of process certifications in the competitiveness and internationalization strategies of companies, with specific reference to the wood-furniture sector.

Data were collected using a questionnaire survey distributed via computer-assisted web interviewing (CAWI) methodology and sent to a sample of 2,845 Italian companies which operate in the wood-furniture industry, using simple random sampling. Thanks to the survey administration, 228 companies participated to the survey.

The study shows that it is companies operating in international markets that define this tool as relevant; this underlines how certification is seen as a kind of business card for entering international markets. In this context, the role of business leadership emerges as fundamental in the practical definition of the objectives to be set by adopting a quality management systems and in the subsequent commitment to obtain them.

Up to now, the literature has taken these elements into analysis mainly considering the consumers' perspective. In sectors with a higher content of innovation, technology and design, such as the wood-furniture sector, the literature appears to be poor in terms of contributions.

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

The sharing economy (SE) is significantly affecting traditional companies, which have felt a need to adapt their business model. The aim of this study is to identify the different types of adaptation developed by companies within a SE context, and to examine how they relate to their characteristics.

A content analysis involving 149 real-world adaptation cases was carried out, after which a Kruskal–Wallis test and a multiple correspondence analysis were used to explore the relationships between the types of adaptation identified, the business characteristics and the strategic decisions taken for these adaptations.

Through the analyses proposed in the study, the main conclusions suggest that the way companies adapt to SE is related to business characteristics and the strategic decisions taken for these actions, demonstrating throughout the article what types of adaptations are made depending on variables such as sector of activity or business orientation.

This study is the first to examine the variables affecting the decisions among traditional companies in response to the SE. In addition, this work explores the SE from the business point of view, shedding light on the participation in SE by traditional companies.

共享經濟現時正顯著地影響著感到需要改變它們的商業模式的傳統公司。本文旨在確定在共享經濟的背景裡, 公司為適應有關的環境而進行的各種改變; 研究亦擬探討這些改變與公司特徵之間的關係。

研究人員對149個真實世界的改變個案進行內容分析, 繼而進行克拉斯卡 - 瓦立斯檢定 (Kruskal-Wallis test) 和多重應對分析 (Multiple Correspondence Analysis) , 以探究被確定的改變的種類、企業的特徵與採用這些改變的策略性決策之間的關係。

研究人員、透過本研究建議的分析取得結論; 主要的結論似顯示、企業為應對共享經濟所作的改變、與它們的企業特徵和採用哪些行動的策略性決策是有關聯的。整篇論文, 顯示了企業所採用的改變種類、均取決於像活動領域和企業經營理念等的變數。

本研究為首個研究、去探討影響傳統公司回應共享經濟所作的決策的變數。再者, 本研究探究了以商業理念的觀點來考慮的共享經濟, 這使我們更容易理解傳統公司參與共享經濟的課題。

Evaluating existing literature can lead to a better understanding of a scientific journal's state of the art. In this sense, this study aims to analyze the global research evolution of the Revista Europea de Dirección y Economia de la Empresa (REDEE) and the European Journal of Management and Business Economics (EJMBE).

A bibliometric analysis was conducted to acknowledge the most contributing authors, impactful articles, publication trends, keyword analysis, co-occurrence networks and collaboration networks. A total of 454 articles published between 2006 and 2022 were analyzed.

The results suggest that the international strategy set in 2014 has resulted in a steadily growing number of publications and a significant increment in citations. Relationship marketing and the connections between innovation, performance and entrepreneurship are topics of interest for the EJMBE.

Mapping existing EJMBE research through identifying the contributing authors, most impactful articles, publication trends, keyword analysis, co-occurrence networks and collaboration networks is missing to encourage new research projects.

The purpose of this paper is to find out which research topics and methods in information science (IS) articles are used in other disciplines as indicated by citations.

The study analyzes citations to articles in IS published in 31 scholarly IS journals in 2015. The study employs content analysis of articles published in 2015 receiving citations from publication venues representing IS and other disciplines in the citation window 2015–2021. The unit of analysis is the article-citing discipline pair. The data set consists of 1178 IS articles cited altogether 25 K times through 5 K publication venues. Each citation is seen as a contribution to the citing document’s discipline by the cited article, which represents some IS subareas and methodologies, and the author team's disciplinary composition, which is inferred from the authors’ affiliations.

The results show that the citation profiles of disciplines vary depending on research topics, methods and author disciplines. Disciplines external to IS are typically cited in IS articles authored by scholars with the same background. Thus, the export of ideas from IS to other disciplines is evidently smaller than the earlier findings claim. IS should not be credited for contributions by other disciplines published in IS literature.

This study is the first to analyze which research topics and methods in the articles of IS are of use in other disciplines as indicated by citations.

In a dynamic, continuously evolving sports landscape, social media have become an indispensable tool for sports organizations to cultivate meaningful connections with fans. The rapid pace of technological advancements has elevated these digital platforms from a supplementary role to a pivotal position within strategic management frameworks. The existing literature explores how football clubs can utilize social media, but analyzing social media strategies within the context of football leagues is lacking. The absence of comparative studies benchmarking clubs across different geographical regions while simultaneously analyzing multiple social media platforms is especially noteworthy. In this study, a comprehensive analysis of social media engagement is undertaken within esteemed football leagues spanning Europe, South America and North America.

Drawing on relationship marketing and employing content analysis as a methodological tool, the study examined 10,772 posts from the official accounts of eight football leagues on Facebook, Twitter and Instagram.

Across the leagues, the findings reveal that content quality drives engagement more than frequency. In addition, several format combinations were identified that facilitate engagement and Instagram emerged as the top social media platform for generating fan engagement.

This is one of the first empirical studies focusing on optimizing the use of social media to amplify fan engagement across various geographies and social media accounts and formats simultaneously.

Given the general consensus that creativity is a crucial driving force for innovation and progress, understanding how to promote it would benefit individuals, companies, society and academia. Therefore, this paper aims to analyze the independent and contingent impact of individuals’ personality traits, team-member exchange (TMX) and gender on stimulating creativity.

The study uses a survey-sample of 639 university students (51.96% women) between the ages of 17 and 50. Confirmatory factor analysis (CFA) demonstrated reliability and validity of its measures. To test hypotheses, using structural equation modeling, hierarchical regression analyses were performed.

Results show that four of the five personality traits (extraversion, agreeableness, conscientiousness and openness to experience) and TMX positively influence creativity. There are no significant differences between men’s and women’s creativity. High TMX reinforces the influence of extraversion on creativity, while low TMX harms this relationship. High extravert women are more creative than high extravert men, while low extravert men are more creative than low extravert women. Low emotionally stable women are more creative than low emotionally stable men, while high emotionally stable men are more creative than high emotionally stable women. There are differences in how women and men take advantage of their openness to experience when TMX is considered. That is, while women take advantage of openness to experience for any value of TMX, men only increase their creativity as openness to experience increases for low values of TMX.

Like all studies, ours has some limitations that provide opportunities for future research. First, care should be taken when generalizing these findings to other contexts. We use data from Spanish individuals, specifically university students. While they are suitable for testing our hypotheses, future studies should establish whether the general tendencies that we observe hold true for other kinds of people from Spain and other countries. Even more, this paper’s perspective might be biased by the authors’ country of origin (south Europe) in terms of gender. According to Hofstede (2001) south European and South American countries are more masculine than other cultures (Mensa and Grow, 2022). Therefore, analyzing these questions in different cultures (countries and settings) would facilitate the generalization of the results. Second, the data we use is mainly cross-sectional so strict causality cannot be inferred. The theory we use assumes specific causal directions, but alternative causal relationships cannot be ruled out. Finally, ideally, we should have controlled for additional variables that might influence the relationships in our model.

This paper has practical implications, as it demonstrates that neither gender is more creative than the other. It goes a step further, explaining how men and women can leverage their personality traits to be more creative. Moreover, since TMX could reinforce the impact of personality traits on creativity, this paper could help managers better organize teams and companies that want to be more innovative by taking into account the personality traits of their employees and how to get the best out of women and men.

Traditionally, women have considered creativity to be a man’s thing. The results of this work favor society, demonstrating that women are just as creative as men and that through personality traits and TMX, both men and women can be more creative. These results help to reduce the gender gap and may favor women’s place in today’s society.

This work offers academic and practical implications. The main contributions to the creativity and gender literatures are the following: (1) Women are as creative as men. (2) High extravert and low emotionally stable women are more creative than men. (3) High open to experience women with high TMX are more creative than men at any level of TMX. For practitioners, the understanding of what personality traits have higher impact on creativity depending on the levels of TMX for women and men could help companies and politicians in hiring the most suitable people, especially for those positions where creativity is needed. This would increase the quality of their human capital, allowing them to get the most out of their human resources, from the very beginning of the employment relationship.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This chapter compares research management and administration (RMA) associations worldwide and the existing professional development frameworks (PDFs) for RMAs. The comparison is based on a study of 22 national, European Union (EU), and international RMA associations/networks which was carried out between April and June 2020 and revised in 2022; it aims at providing a comprehensive overview of skills and competences of RMAs as a profession to enable worldwide benchmarking and analysis.

The benchmarking analysis could provide useful information for those working on the development of professional frameworks training targeted at RMAs, or the recognition of RMA as a profession.