In this paper, we investigate how institutional conditions shape individual-level resilience in healthcare organizations which in turn facilitates the intrapreneurial behavior of healthcare professionals. Using a critical literature review methodology, we identify that the current literature centers around three dominant clusters defined by intrapreneurial outcomes: organizational performance, innovation and knowledge creation, and intrapreneurial agency. Across these clusters, both formal and informal institutions play a complementary role in shaping resilience. This resilience, enables professionals to navigate uncertainty, cope with institutional barriers, and drive bottom-up change within hierarchical healthcare organizations, fostering intrapreneurship predominantly related to internal innovation and organizational improvement. Based on these insights, we propose a conceptual model illustrating how institutions jointly foster resilience, which acts as a mediating mechanism between institutional conditions and intrapreneurial behavior in healthcare. The study offers theoretical contributions to research on intrapreneurship and resilience, as well as managerial implications for healthcare leaders and policymakers.

PurposeThis study aims to examine how collective dimensions manifest and operate in worker cooperatives with strong collaborative orientations, analyzing the dynamic tension between structured processes and emergent adaptations in their collaborative practices.Design/methodology/approachA qualitative two-phase design was implemented with ten Aragonese cooperatives from Spain's Alternative and Solidarity Economy Network (REAS). Phase one comprised semistructured interviews with cooperative representatives, while phase two involved a focus group with 6 participants to validate and deepen initial findings.FindingsFour collective dimensions were identified operating as adaptive systems: collective intelligence, collective leadership (reconceptualized as shared governance), collective action and collective impact. These dimensions enable organizations to navigate between formal structures and emergent responses while maintaining collaborative principles. The study reveals how cooperatives develop specific organizational capabilities to manage inherent tensions in collaborative processes.Research limitations/implicationsThe sample is limited to worker cooperatives in Arag & oacute;n, Spain, potentially restricting generalizability. However, findings contribute to interorganizational collaboration theory by demonstrating how collective dimensions operate as integrated systems rather than isolated elements.Practical implicationsThe research documents specific strategies for navigating structure-emergence tensions, developing shared governance systems and creating impact evaluation frameworks that recognize both external results and internal transformations.Social implicationsThe study demonstrates how alternative organizational forms can maintain economic viability while preserving transformative principles, offering insights for social economy development.Originality/valueThis research provides empirical evidence of how collective dimensions operate in practice, contrasting theoretical frameworks with organizational realities. It identifies the critical importance of contextual factors like territorial embeddedness and emotional-relational aspects previously underexplored in interorganizational collaboration literature.

Problem: The adoption of Big Data and Business Intelligence is often limited by the lack of leadership with the necessary skills to manage the information provided by the data. This is compounded by the absence of an organization with an established data-driven culture. Objective: The objective of this study was to analyze how the role of leadership influences the development of a data-driven organizational culture and how this relates to Big Data management. Methodology: A qualitative approach with a theoretical-exploratory focus was used, based on a systematic literature review of high-impact databases such as Scopus and Web of Science regarding leadership, organizational culture, Big Data, and data culture. and decision-making processes published between 2015 and 2025. Results: The study identified four key drivers: data analysis, data democratization, data-driven leadership, and ethics in data-driven decision-making. Conclusion: Leadership is a fundamental factor in building a data-driven organizational culture. Its role is not limited to the adoption of technologies but extends to guiding cultural change processes that allow for the strategic and ethical use of Big Data for decision-making and the achievement of strategic objectives . Proper alignment between leadership, culture, and data management is required to build competitive organizations that are better prepared for digital transformation in dynamic environments.

Crowdlending is becoming an increasingly viable financing alternative for companies and individuals. This paper analyses the combined use of reward-based crowdfunding and crowdlending to finance ventures. It also examines the use of crowdlending to secure funding. Specifically, the paper studies the cases of two companies from different sectors (watchmaking and quality wine) and at different stages of the business life cycle (early years and maturity). The crowdlending projects, platforms and types of loans used by these two companies also differ. Analysis based on the case study method indicates that, for early-stage ventures, the combination of crowdfunding for business creation and crowdlending to support business operations is an effective way of securing funding an entrepreneurial venture. Moreover, crowdlending is the most suitable way to secure funding for a project at an existing firm. Using a crowdlending platform with a large community of investors ensures that borrowers achieve the funding they seek, as long as the loan offers high returns and low risk or the business idea has a positive impact on the planet or society.

This research presents a successful implementation of a Project-Based Learning (PBL) methodology to foster the acquisition of Transversal Competencies (TCs) and incorporate Sustainable Development Goals (SDGs) into higher education curricula. This was carried out in the framework of an Educational Innovation and Improvement Project (EIIP) carried out at the Universitat Politècnica de València (UPV) in Spain during the academic years that span from 2023 to 2025. Specifically, it was conducted within the “Dynamics of Mechanical Systems” course of the “Mechatronic Engineering” master’s program of the at the Higher School of Aerospace Engineering and Industrial Design (HSAEID), which involves an average of 40 students. Quantitative and qualitative techniques were used to collect evidence regarding the successful implementation of the PBL methodology. The EIIP project offers practical insights and strategies for efficiently integrating global competencies and sustainability into technical education. In this sense, the positive outcomes indicate that PBL not only enhances technical competencies and motivation but also cultivates critical thinking, collaboration, problem-solving abilities, acquisition of learning goals, and preparing students to address complex global challenges of the labor market and. However, ongoing evaluation and continuous improvement processes, supported by robust feedback mechanisms, are crucial for sustaining and enhancing these educational innovations.

Background Digital transformation is increasingly recognized as a cultural challenge, yet there is a lack of robust theoretical frameworks and validated instruments to systematically assess digital culture within organizations.Objective This study aims to develop and validate a psychometric measurement scale for digital culture, addressing a significant gap in the literature and providing a practical tool for assessing cultural readiness for digital transformation.Methods We first establish a clear conceptual definition of the digital culture construct. Based on this definition, we design a measurement scale and test it on a representative sample of 183 firms. To assess the scale's psychometric properties, we employ confirmatory factor analysis (CFA) and partial least squares structural equation modeling (PLS-SEM).Results The results confirm the reliability and validity of the proposed digital culture scale. The instrument demonstrates strong psychometric properties and consistent measurement performance across diverse organizational contexts.Conclusions This study contributes to academic research by introducing a theoretically grounded and empirically validated scale for digital culture. It also provides a practical tool for organizations to evaluate cultural alignment with digital transformation efforts and identify areas for cultural development.

PurposeThis study aims to explore the factors that influence the development of big data analytics capabilities (BDAC) in organizations, an area that has received limited attention in the academic literature.Design/methodology/approachEmploying partial least squares structural equation modeling, this research scrutinizes the interconnections between various antecedents and BDAC. Notably, it examines the mediating roles of organizational culture (OC) and digital maturity (DM) in the nexus between managerial data orientation and BDAC.FindingsAnalysis indicates that OC and DM play crucial roles in enhancing the efficacy of managerial data orientation on BDAC. The sequential mediation by these factors underscores the importance of nurturing an appropriate OC and advancing DM to optimize the benefits of managerial data orientation towards BDAC.Practical implicationsThe findings bear significant implications for organizational practice. They underscore the necessity of enhancing managerial analytical skills and commitment to digital transformation. Furthermore, the study highlights the critical need for aligning OC with strategic objectives and the digital context. The formulation of a cultural strategy that advocates for a data-driven mindset and champions digital initiatives is essential for fostering BDAC development, thereby bolstering organizational performance and competitiveness in the big data era.Originality/valueThis study enriches the body of literature by illuminating the overlooked antecedents of BDAC. It extends the discourse on the human-centric aspects of digital transformation, offering insights into how managerial data orientation can be effectively translated into improved BDAC. This innovative angle deepens our comprehension of the strategies through which organizations can leverage big data technology for value creation and informed decision-making, emphasizing the pivotal role of OC and the requisite digital competencies and resources.

Purpose - This study aims to estimate the relationship between collaboration and social entrepreneurship organizations (SEOs), identifying the current and emerging collaborative scenarios in which SEOs are involved to provide an integral understanding of SEOs' collaborative behavior and development. Design/methodology/approach - Based on a dual methodological approach (bibliometric analysis and systematic literature review [SLR]), the authors identify key research clusters, co-citation patterns and emerging trends in the field. The authors' keyword clusters, derived from bibliometric analysis, guide the SLR and allow the identification of collaborative scenarios. Combining both methodologies enriches the paper by bridging the collaborative scenarios through thematic clusters and current and emerging research associations. Findings - The analysis reveals four key scenarios: inter-organizational collaboration; cross-sector collaboration; collective action; and collaborative learning and networks. These scenarios shape the collaborative behavior of SEOs in the existing literature, which reconfigures their collaborative orientation, generating tensions, actions and strategies for archiving social mission and impact. Research limitations/implications - This study's timeframe (2003-2023) limits temporal scope. Reliance on Scopus may introduce bias, despite cross-checking with Web of Science. Our theoretical propositions require empirical validation through future research. Practical implications - With the identification of the scenarios or collaborative dynamics in which SEOs are involved, the study proposes collaboration as an opportunity and capacity to be developed at the SEOs that can allow the scaling of its impacts to a systemic level. Social implications - Understanding how collaboration shapes organizational behavior helps SEOs develop capabilities to address complex social challenges. The findings suggest policymakers can design regulatory environments facilitating interorganizational or cross-sector collaboration, potentially amplifying impact. Originality/value - By identifying the collaborative scenarios that SEOs establish on an ongoing basis, as well as the features that characterize them, and by understanding the nature and emerging impact of these collaborative arrangements, this study provides a significant advancement in both theories and practice in social entrepreneurship, offering valuable guidance for future studies in SEO collaboration, particularly in delineating the intricate endeavors of collaboration at the organizational level.

The contemporary university has evolved into a multifaceted institution, serving not only as a hub for research, professional training, and knowledge dissemination, but also as a pivotal component of economic machinery and societal advancement, especially within the context of globalization and intense competition. These institutions are now expected to significantly contribute to the socio-economic development of their surroundings, striving for excellence and competing within an increasingly interconnected global framework. To meet these heightened expectations, universities have undergone profound transformations over recent decades. This period of change is particularly notable within the Spanish context, mirroring the broader societal shifts since the onset of the democratic transition in 1975 through to the present day in 2024. This era has necessitated substantial reforms in university governance, equipping these institutions with the requisite tools to navigate and address emerging challenges effectively. A pivotal moment in this transformative journey was the enactment of the University Reform Law of 1983. Grounded in constitutional principles, this law marked a significant milestone in updating and revitalizing university education in Spain. After this, the Organic Law on Universities of 2001 further aligned the Spanish university framework with the broader European educational landscape, reinforcing the integration and competitiveness of Spanish institutions on a continental scale. However, to comprehensively grasp the magnitude and implications of these changes, it is essential to extend our analysis beyond the immediate past and explore the historical trajectory of Spanish universities over previous centuries. By examining this extensive historical context, we can better understand the foundational elements that have shaped the current state of higher education. This article aims to meticulously analyze the evolution of the Spanish university system, delineating the chronological progress, identifying persistent challenges, and engaging in a critical discourse on potential solutions. Through this examination, the article seeks to provide a nuanced understanding of the interplay between historical legacies and contemporary reforms, offering insights that are essential for shaping the future trajectory of higher education in Spain.

Objective: The objective of this article is to describe the relationship between service innovation and ICTs and how they have influenced the hotel sector. Methodology: We used exploratory factor analysis (EFA) to describe the causal relationships. A total of 280 surveys were conducted with managers of Spanish 4 and 5-star hotels to test the 3 hypotheses using partial least squares structural equation modelling. This non-parametric tool exempts the sample from conforming to the normal distribution. Results: The authors have shown how the relationship between service innovation and ICTs is established through the ability to learn so that hotels can increase their productivity and the results obtained. The incorporation of new technologies in the customer experience and the digitisation of internal processes are mainly highlighted here. Limitations: Of interest for the hotel sector is the causality of information technologies as a driver of innovative development, as well as the mediation of the learning capacity of organisations as an influential aspect in their development. Practical implications: This article describes the influence of ICTs and learning capacity as an enabler of service innovation capacity in hotels.

Digital transformation (DT) and Big Data Analytics Capabilities (BDAC) enable SMEs to adapt to rapidly changing markets, innovate, and maintain relevance in the digital age. This research explores the impact of DT on SME performance through the lens of BDAC and innovation, from a multi-methods approach and applying the dynamic capabilities view. It asserts that simply investing in DT doesn't ensure enhanced performance. Analyzing 183 Spanish SMEs from various sectors, the study highlights the need for creating specific conditions that enable DT to positively impact performance. The integration of PLS-SEM and fsQCA methodologies provides a comprehensive analysis of BDAC as pivotal in optimizing SME performance through DT, emphasizing the necessity of strategic alignment with innovation. This nuanced approach, combining the predictive power of PLS-SEM and the configurational insights of fsQCA, demonstrates that investment in DT alone is insufficient without fostering conditions conducive to innovation. Our empirical insights offer actionable guidance for managers utilizing BDA or contemplating technological investments to elevate firm performance which go in the direction of increasing their innovation capabilities. Additionally, these findings equip policymakers with a nuanced understanding, enabling the design of tailored measures promoting DT in SMEs anchored in the nuances of BDAC and innovation capabilities.

This study examines the antecedent role of organizational culture and the mediating role of digital transformation when promoting big data analytics capabilities. Employing the Competing Values Framework, we scrutinize the influence of various cultural typologies, including digital culture on the successful deployment of digital transformation and the enhancement of big data analytics capabilities. Our analysis utilizes Partial Least Squares Structural Equation Modeling on a dataset of 183 firms to evaluate our hypotheses. The findings reveal that adhocratic, digital and hierarchical cultures significantly foster big data analytics capabilities mediated by digital transformation, which is a dynamic process that needs supportive digital and innovative values. In contrast, market and clan cultures exhibit weaker linkages. By providing empirical evidence and practical implications, this study highlights how organizations with a strong adhocratic and digital cultures outperform those with traditional cultures in their digital transformation and big data analytics capabilities efforts.

Purpose In the last decade, the hospitality sector has undergone numerous changes in the organization and structure of its business models. Specifically, the adoption of new digital technologies has initiated transformative changes toward circular economy and sustainability. The present study aims to analyze whether the use of the digital reservation system in circular entrepreneurship businesses has an impact on entrepreneurs' satisfaction and trust in the in circular economy. Design/methodology/approach The data collected via a survey of 317 entrepreneurs who use a circular economy strategy were analyzed using SEM in a proposed model based on circular entrepreneurship businesses and the adoption of digital reservation systems. Findings The results showed a positive relationship between usability and perceived ease of use and user satisfaction and trust in using digital reservation systems to boost circular entrepreneurship in hospitality. Therefore, it was identified that adopting a digital reservation system can increase the efficiency of entrepreneur resources, as well as lead to creation of sustainable knowledge, augment the use of new sources of user information, improve prediction of services and demand and, consequently, boost sustainability and circular economy. Research limitations/implications Future research can use the results of the present study to investigate how digital technologies work and affect user trust, satisfaction, and use of these systems in entrepreneurs' projects in hospitality. In addition, it would be interesting to explore how these factors influence hospitality in different business models that support circular economy in tourism. To this end, researchers can use the variables used in the present study, along with other variables, to extend the proposed model and deepen the authors' research. In summary, this study contributes to the literature on the use of applications in hospitality sector and offer useful insight on how the adoption and use of new technologies can drive the management of knowledge and technology development, decision making and acquisition of new data sources that improve the experience of both entrepreneurs and users that interact with their services to enable circular entrepreneurship. Practical implications Hospitality entrepreneurs can use the results of the present study to better evaluate how using these applications can affect the communication protocols with their employees and stakeholders. Furthermore, entrepreneurs operating in the hospitality sector can use the results to develop action plans focused on the circular economy, new knowledge creation, as well as development and adoption of new digital technologies that enable circular entrepreneurship. In this way, hospitality companies will be able to effectively combine both more traditional offline channels and new technologies, such as mobile applications or the Internet. Social implications The authors' prediction of a strong positive link between perceived usefulness and user trust was also supported by the results of data analysis. The finding that perceived utility increases user trust in entrepreneurs when making adopting these technologies is also consistent with other similar studies. Finally, the results of this study also confirmed the hypothesized link between the perceived utility of digital technologies and user satisfaction. Taken together, the results also highlight the relevance of analyzing the use of digital reservation systems in the hospitality sector to enable circular entrepreneurship. Originality/value Taken together, the results highlight the relevance of analyzing the use of digital reservation systems in the hospitality sector to enable circular entrepreneurship and increase the perceived usefulness of new digital technologies so that to improve sustainable actions and the circular economy globally.

In a digital ecosystem where large amounts of data related to user actions are generated every day, important concerns have emerged about the collection, management, and analysis of these data and, according, about user privacy. In recent years, users have been accustomed to organizing in and relying on digital communities to support and achieve their goals. In this context, the present study aims to identify the main privacy concerns in user communities on social media, and how these affect users’ online behavior. In order to better understand online communities in social networks, privacy concerns, and their connection to user behavior, we developed an innovative and original methodology that combines elements of machine learning as a technical contribution. First, a complex network visualization algorithm known as ForceAtlas2 was used through the open-source software Gephi to visually identify the nodes that form the main communities belonging to the sample of UGC collected from Twitter. Then, a sentiment analysis was applied with Textblob, an algorithm that works with machine learning on which experiments were developed with support vector classifier (SVC), multinomial naïve Bayes (MNB), logistic regression (LR), random forest, and classifier (RFC) under the theoretical frameworks of computer-aided text analysis (CATA) and natural language processing (NLP). As a result, a total of 11 user communities were identified: the positive protection software and cybersecurity and eCommerce, the negative privacy settings, personal information and social engineering, and the neutral privacy concerns, hacking, false information, impersonation and cookies data. The paper concludes with a discussion of the results and their relation to user behavior in digital environments and an outline valuable and practical insights into some techniques and challenges related to users’ personal data.

The last decade has witnessed an increase in the number of extreme weather events globally. In addition, the economic output around the world is at all-time high in terms of production and profitability. However, global warming and extreme weather are modifying the natural ecosystem and the human social system, leading to the appearance of extreme climate events that have an adverse impact on the world economy. To address this challenge, the present study identifies the main impacts of extreme weather on production economics based on the analysis of user-generated content (UGC) on the social network Twitter. Methodologically, a sentiment analysis with machine learning is developed and applied to analyze a sample of 1.4 m tweets; in addition, computing experiments to calculate the accuracy with Support Vector Classifier, Multinomial Naive Bayes, Logistic Regression, and Random Forest Classifier are conducted. Second, a topic modeling known as latent Dirichlet allocation is applied to divide sentiment-classified tweets into topics. To complement these approaches, we also use the technique of textual analysis. These approaches are used under the framework of computer-aided test analysis system and natural language processing. The results are discussed and linked to appraisal theory. A total of 7 topics are identified, including positive (Sustainable energies and Green Entrepreneurs), neutral (Climate economy, Producer's productivity and Stock market), and negative (Economy and policy and Climate emergence). Finally, the present study discusses how the recent trend of an increase in extreme weather conditions has significantly impacted international markets, leading companies to adapt their business models and production systems accordingly. The results show that the climate economy and policy, producers' productivity, and the stock market are all heavily influenced by extreme weather and can have significant effects on the global economy.

Purpose Technological advances in the last decade have caused both business and economic sectors to seek for new ways to adapt their business models to a connected data-centric era. Family businesses have also been forced to leave behind traditional strategies rooted in family stimuli and ties and to adapt their actions in digital environments. In this context, this study aims to identify major online marketing strategies, business models and technology applications developed to date by family firms. Methodology: Upon a systematic literature review, we develop a multiple correspondence analysis (MCA) under the homogeneity analysis of variance by means of alternating least squares (HOMALS) framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters. Design/methodology/approach Upon a systematic literature review, we develop an MCA under the HOMALS framework programmed in the R language. Based on the results, the analyzed contributions are visually analyzed in clusters. Findings Relevant indicators are identified for the successful development of digital family businesses classified in the following three categories: (1) digital business models, (2) digital marketing techniques and (3) technology applications. The first category consists of four digital business models: mobile marketing, e-commerce, cost per click, cost per mile and cost per acquisition. The second category includes six digital marketing techniques: search marketing (search engine optimization and search engine marketing (SEM) strategies), social media marketing, social ads, social selling, websites and online reputation optimization. Finally, the third category consists of the following aspects: digital innovation, digital tools, innovative marketing, knowledge discovery and online decision making. In addition, five research propositions are developed for further discussion and future research. Originality/value To the best of our knowledge, this study is the first to cover this research topic applying the emerging programming language R for the development of an MCA under the HOMALS framework.

The COVID-19 pandemic has caused many entrepreneurs and small and medium enterprises (SMEs) to adapt their business models and business strategies to the consequences caused by the pandemic. In order to identify the main innovations and technologies adopted by SMEs in the pandemic, in the present study, we used a database of 56,941 tweets related to the coronavirus to identify those that contained the hashtag #SMEs. The final sample was analyzed using several data-mining techniques such as sentiment analysis, topic modeling and textual analysis. The theoretical perspectives adopted in the present study were Computer-Aided Text Analysis, User-Generated Content and Natural Language Processing. The results of our analysis helped us to identify 15 topics (7 positive: Free support against Covid-19, Webinars tools, Time Optimizer and efficiency, Business solutions tools, Advisors tools, Software for process support and Back-up tools; 4 negative: Government support, Payment systems, Cybersecurity problems and Customers solutions in Cloud, and and 4 neutral: Social media and e-commerce, Specialized startups software, CRMs and Finance and Big data analysis tools). The results of the present study suggest that SMEs have used a variety of digital tools and strategies to adapt to the changing market conditions brought on by the pandemic, and have been proactive in adopting new technologies to continue to operate and reach customers in a connected era. Future research should be directed towards understanding the long-term effects of these technologies and strategies on entrepreneurial growth and value creation, as well as the sustainability of SMEs in the new era based on data-driven decisions.

Technological development of the last several decades has driven open innovation towards organizational, business, social, and economic change. Open innovation has emerged as the main driver of change in a business sector that needs to be flexible and resilient, rapidly adapting to change through innovation. In this context, the present study aimed to explore the limits of open innovation by extracting evidence from user-generated content (UGC) on Twitter using social media mining. To this end, in terms of the methodology, we first applied machine learning Sentiment Analysis algorithm texted using Support Vector Classifier, Multinomial Naive Bayes, Logistic Regression, and Random Forest Classifier to divide the sample of n = 586.348 tweets into three groups expressing the following three sentiments: positive, negative, and neutral. Then, we used a mathematical topic modeling algorithm known as Latent Dirichlet allocation to analyze the tweet databases. Finally, Python was used to develop textual analysis techniques under the theoretical framework of Computer-Aided Text Analysis and Natural Language Processing. The results revealed that, in the tweets dataset, there were eight topics. Of these topics, two contained tweets expressing negative sentiments (Culture and Business Models/Management), three topics contained tweets expressing positive sentiments (Communities, Creative projects and Ideas), and three topics contained tweets expressing neutral sentiments (Entrepreneurship, Teams and Technology). These topics are discussed in the context of limitations, risks, and characteristics of open innovation according to the UGC on Twitter. The paper concludes with the formulation of 20 limits of open innovation and 27 research questions for further research on open innovation, as well as a discussion of theoretical and practical implications of the study.

EDITORIAL article Front. Psychol., 19 January 2023Sec. Organizational Psychology Volume 14 - 2023 | https://doi.org/10.3389/fpsyg.2023.1123236

In the rapidly evolving tourism industry, technology and social media play a crucial role in the success of hotels' sustainable practices. In today's interconnected society, a company's online reputation and the spread of information through electronic word-of-mouth are crucial to its success. The present study investigates the role of travellers in the success of sustainable strategies implemented by hotels through their social media activity. By analysing a sample of user-generated content (UGC) from 22 hotels selected by the TripAdvisor Traveler's Choice Ranking, this study employs a Latent Dirichlet Allocation (LDA) algorithm for topic modelling and a Supervised Vector Machine (SVM) algorithm for sentiment analysis. The results identify 11 indicators related to sustainable tourism and 7 topics classified by sentiment from the traveller's perspective, including positive indicators such as loyalty, nature, and sustainability; neutral indicators such as customer satisfaction and location; and negative indicators like pollution and dirt, and disappointment. These findings offer practical implications for hotel managers, including the importance of considering travellers' perceptions in the development of sustainable tourism strategies and the value of monitoring and analyzing UGC to manage online reputation and foster customer loyalty.