Purpose: The SECI model (Nonaka, 1994) is the best-known conceptual framework for understanding knowledge generation processes in organizations. To date, however, empirical support for this framework has been overlooked. The present study aims to provide an evidence-based groundwork for the SECI model by testing a multidimensional questionnaire Knowledge Management SECI Processes Questionnaire (KMSP-Q) designed to capture the knowledge conversion modes theorized by Nonaka. Methodology: In a twofold study, the SECI model was operationalized via the KMSP-Q. Specifically, Study One tested its eight-dimensional structure through exploratory and confirmatory factorial analyses on 372 employees from different sectors. Study Two examined the construct validity and reliability by replicating the KMSP-Q factor structure in knowledge-intensive contexts (on a sample of 466 health-workers), and by investigating the unique impact of each dimension on some organizational outcomes (i.e., performance, innovativeness, collective efficacy). Findings: The overall findings highlighted that the KMSP-Q is a psychometrically robust questionnaire in terms of both dimensionality and construct validity, the different knowledge generation dimensions being specifically linked to different organizational outcomes. Research/Practical Implications: The KMSP-Q actualizes and provides empirical consistency to the theory underlying the SECI model. Moreover, it allows for the monitoring of an organization’s capability to manage new knowledge and detect the strengths/weaknesses of KM-related policies and programs. Originality/Value: This paper proposes a comprehensive measure of knowledge generation in work contexts, highlighting processes that organizations are likely to promote in order to improve their performance through the management of their knowledge resources.

The recent COVID-19 pandemic and related social distancing measures have significantly changed worldwide employment conditions. In developed economies, institutions and organizations, both public and private, are called upon to reflect on new organizational models of work and human resource management, which - in fact - should offer workers sufficient flexibility in adapting their work schedules remotely to their personal (and family) needs. This study aims to explore, within a Job Demands-Resources framework, whether and to what extent job demands (workload and social isolation), organizational job resources (perceived organizational support), and personal resources (self-efficacy, vision about the future and commitment to organizational change) have affected workers’ quality of life during the pandemic, taking into account the potential mediating role of job satisfaction and perceived stress. Using data from a sample of 293 workers, we estimate measurement and structural models, according to the Item Response Theory and the Path analysis frameworks, which allow us to operationalize the latent traits and study the complex structure of relationships between the latent dimensions. We inserted in the model as control variables, the socio-economic and demographic characteristics of the respondents, with particular emphasis on gender differences and the presence and age of children. The study offers insights into the relationship between remote work and quality of life, and the need to rethink human resource management policies considering the opportunities and critical issues highlighted by working full-time remotely.

This paper aims to investigate the relationship between Italian teachers' well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers' well-being, we investigate several aspects related to the teachers' working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers' well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers' positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers' job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher's well-being. Findings and implications are discussed.

Purpose: The purpose of this paper is to understand the representation that senior managers of Italian social enterprises have about their organization’s intellectual capital (IC), precisely about the human capital, relational capital and organizational capital. Design/methodology/approach: This paper used a qualitative approach. A total of 81 senior managers were interviewed individually. Interview data were analyzed using different techniques of content analysis, particularly by using the T-Lab software (analysis of word occurrence and co-word mapping, analysis of Markovian sequences). Findings: Findings confirm the divide between theory and practice of IC. The representation of the IC dimensions is rather different from the definition that is found in the academic literature. Limited awareness about IC components and their generative power of knowledge determines a limited exploitation of the social enterprises’ organizational knowledge. Research limitations/implications: The group reached is limited to Italy and is not statistically representative of all Italian social enterprises. Practical implications: Social enterprises are crucial in the development and well-being of societies. However, the findings suggest that many social enterprises managers are not fully aware of the importance of IC and how it may create value for their organizations. This paper stresses that senior managers of social enterprises need to, through various methods, have a better understanding of IC management and knowledge creation if they are to fully utilise the potential of IC in their organizations for survival and growth. Originality/value: This is the first attempt to explore the perception of IC’s components among social enterprises, which represent an important development of non-profit organizations.

Introduction—Decision making (DM) is a fundamental responsibility for managers, with significant implications for organizational performance and strategic direction. The increasing complexity of modern business environments, along with the recognition of human reasoning limitations related to cognitive and emotional biases, has led to a heightened interest in harnessing emerging technologies like Artificial Intelligence (AI) to enhance DM processes. However, a notable disparity exists between the potential of AI and its actual adoption within organizations, revealing skepticism and practical challenges associated with integrating AI into complex managerial DM scenarios. This systematic literature review aims to address this gap by examining the factors that influence managers’ adoption of AI in DM. Methods—This study adhered to the PRISMA guidelines. Articles from 2010 to 2024 were selected from the Scopus database using specific keywords. Eligible studies were included after rigorous screening and quality assessment using checklist tools. Results—From 202 articles screened, a data synthesis of 16 eligible studies revealed seven major interconnected factors acting as key facilitators or barriers to AI integration within organizations. These factors—Managers’ Perceptions of AI, Ethical Factors, Psychological and Individual Factors, Social and Psychosocial Factors, Organizational Factors, External Factors, and Technical and Design Characteristics of AI—were then organized into a complex analytical framework informed by existing theoretical constructs. Discussion—This contribution provides valuable insights into how managers perceive and interact with AI systems, as well as the conditions necessary for successful integration into organizational DM processes.

Nonprofit organizations (NPOs) promote citizens’ participation in community life through several different kinds of organizations: some more informal (such as associations and volunteering groups), others more formal or public (such as charities and foundations). This heterogeneity, as well as the well-known peculiarities of NPOs when compared to profit and public ones, poses new challenges to their management. In the constant need to find balance between financial constraints and social value, a main resource for NPOs is the management of intangible assets, such as knowledge, positive relationships within the organization and with users, external image, loyalty and commitment, and so on. From the literature on for-profit organizations, it is well known that proper management of intangible assets improves an organization’s sustainable competitive advantage, not only by enhancing its members’ affiliation and commitment but even by enhancing their productivity. This is particularly relevant when taking into account the main role of volunteers in the third sector. Volunteers, indeed, show different job attitudes and organizational behaviors than paid employees, as their membership and accountability are less formalized and they frequently lack a proper teamwork, due to the high volunteer turnover. At the same time, from the managers point of view, managing volunteers and paid workers require higher skills and competencies than managing human resources in for-profit organizations. Developing these reflections and considerations, we aim to conduct a systematic literature review on the association between intangible assets and performance in NPOs. The literature will be conducted following the indications from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. It provides an evidence-based minimum set of items to be included in the review, as well as a workflow to properly manage and choose the papers to be included. The authors conducted the research using EBSCO, ProQuest, and Scopus databases.