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The Covid-19 pandemic has placed health-care systems and their facilities throughout the world under immense pressure. The pandemic has highlighted the crucial role of health-care facilities design in looking beyond the ongoing crisis and considering how hospitals can better prepare for unexpected future health situations. This study aims to investigate how hospitals reacted to the crisis in terms of their physical spaces, which architectural features permitted the necessary transformations, and how this data can inform hospital design research in the future.

The research adopted a qualitative and multi-method approach to case studies. Data was collected directly (field survey and interviews) and indirectly (literature, periodicals, specialised websites, webinars, conferences and forums), and a strengths, weaknesses, opportunities, threats analysis supported the data evaluation.

Hospitals’ responses to the crisis were guided by a host of variables depending on the specific intervention context and risk scenario. Some key issues emerged as particularly meaningful to drive future research in hospital design, namely, architectural typology, layout and spatial proximities, technological systems, the quality of care spaces, the role of public spaces, facility management tools to drive the transformation, territorial health care networks and new technologies.

The paper suggests that the current crisis can be transformed into an opportunity, in terms of research and innovation, to rethink and improve the quality and efficiency of health-care spaces, restoring their crucial role of promoting health by design.

Since the first definition of Total Quality Management (TQM) was established, its inseparable relationship with Human Resource Management (HRM) has been undeniable. However, many years have passed since this initial definition was made. During this time, not only have TQM and HRM evolved, but their relationship has also adapted in order to accompany and follow the environmental and technological changes that have ultimately changed the ways the authors work and how the authors perceive quality. This study provides a detailed map of the knowledge evolution of the TQM–HRM relationship. It also identifies interesting gaps for future researchers to consider to enable us to better understand emerging challenges and identify the next steps in the progression of TQM literature.

The study adopted a bibliometric analysis approach, using a systematic literature review of a data set that included 132 peer-reviewed articles published between 1991 and 2021 to identify thematic topics. In exploring each of these, the authors created a framework to spotlight future explorative and exploitative research questions.

The results show that literature on the relationship between HRM and TQM focuses on six topics: a different performance appraisal (PA); a different role for the human resources (HR) department; HRM practices, such as different bridges between JIT and TQM; the integration of job satisfaction into TQM goals; different TQM healers; and the different effects of HRM practices on firm performance. This paper provides clear interpretations of these topics and offers some recommendations for how HRM studies could contribute towards the development of TQM research, thus forming a clear agenda for the future of TQM–HRM research.

This study represents the first attempt to systematize, analyze and critically interpret TQM–HRM studies, promoting a collective reflection on the state of the art and stimulating further discussions on this topic.

This paper explores the field of human resource management (HRM) in family firms, assessing the evolution of this research through a four-domain model that reflects the relationships between cognitions, actions and performances associated with organizational choices.

The authors have performed a bibliometric review of 363 peer-reviewed papers from over the past four decades (1976–2016) in order to provide activity indicators for the overall phenomenon and systemize the entire body of literature into specific HRM strategy domains or practices, using a double-entered pivot table.

The study framework provides managerial implications with regard to the HRM decisions made when attempting to improve human capital in family firms. Accordingly, the authors view HRM-centered decisions and strategies in family firms as ways to scan for and appraise contingent dimensions, make sense of the current environment, make good choices and achieve high performance levels.

The authors offer this four-domain theoretical scheme as a framework through which the field can be interpreted, proposing some potential avenues for moving forward.

Previous research focused on open innovation (OI) suggests that enterprises benefit from adopting the journey; however, the relationship among OI, marketing journey and knowledge-intensive innovation marketing activities (KIIMA) remains unclear. The present study proposes a conceptual model of the marketing journey linking heterogeneous modes of marketing collaboration to knowledge-intensive activities.

The conceptual model was tested via ordinary least squares (OLS) linear regression based on a sample of data drawn from the Eurostat database.

The results indicate that strategies are a robust proxy for evaluating KIIMA, and partnerships, heterogeneous sources of knowledge and different marketing modes for collaboration among European knowledge-intensive firms are core antecedents of KIIMA, such as new-product development and marketing innovation, as well as firms' sustainable competitive advantage.

This study fills the gap by tracking the role of the journey within marketing collaborations on KIIMA, and it intervenes in the debate about interactive marketing innovation mechanisms. The study contributes to OI, knowledge management and the marketing literature by identifying the heterogeneous modes for marketing collaborations under which the marketing journey enhances knowledge-intensive activities such as those for marketing innovation.

Regulatory initiatives and related technological shifts have been imposing restrictions on data-driven marketing (DDM) practices. This paper aims to find the main restrictions for DDM and the key management theories applied to investigate the consequences of these restrictions.

The authors conducted a unified bibliometric analysis with 104 publications retrieved from both Scopus and Web of Science, followed by a qualitative, in-depth systematic literature review to identify the management theories in literature and inform a research agenda.

The fragmentation of the research outcomes was overcome by the identification of 3 main clusters and 11 management theories that structured 18 questions for future research.

To the best of the authors’ knowledge, this paper sets for the first time a frontier between almost three decades where DDM evolved with no significative restrictions, grounded on innovations and market autoregulation, and an era where data privacy, anti-trust and competition and data sovereignty regulations converge to impose structural changes, requiring scholars and practitioners to rethink the roles of data at the strategic level of the firm.

As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP.

This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis.

The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP.

The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company’s collaboration and for effective management to improve NPP in the face of global competition.

This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC.