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The purpose of this paper is to fill the research and cognitive gap by comparative analyzing of the cluster supply chain (CSC) and supply chains not belonging to the clusters to examine the relational embeddedness as the differentiator of supply chains operating in the clusters.

The conceptual model was tested with data collected from 475 industrial companies cooperating with their partners within supply chains, including 135 CSC. To identify the livraisons between different indicators, the correspondence analysis was applied.

The division of enterprises participating in this study into groups allows for the determination of relatively clear boundaries between enterprises belonging to the cluster and those that do not declare such affiliation. The obtained results confirmed that the relational embeddedness is the differentiator of the CSC collaboration.

The main limitations are referred to as the static character of the data.

The paper contains implications for cluster facilitators, as well as for cluster policy decision makers, to better design support for cluster organizations.

This research is a contribution to the literature on inter-organizational structures, such as clusters and supply chains, and in particular, contributes to the creation of the scientific ground of SCS theory. The research allowed to better understand the nature of collaboration taking into consideration the fact of the relational embeddedness of the companies operating within supply chains located in clusters. It proves the existence of a new type of inter-organizational form that is CSC.

The purpose of the paper is to present a service design (SD)-based methodology developed to help small and medium enterprises (SMEs) undertake organisational change.

This research used the design science research methodology, which enabled the creation of the Service Design for Organisational Change (SD4OCh) methodology. A real case study of a small service company specialised in neuropsychological disorders was used for the definition and validation of SD4OCh.

The main outcome of this study is the SD4OCh methodology, which is based on three key stages: diagnosis (knowing where to begin by detecting the organisation's strengths and weaknesses), innovation (improving the structure/processes and designing/redesigning services by employing a customer-centric approach), and implementation (enabling the definition of the route towards organisational change). There is also a transversal evaluation stage, which quantifies the organisational changes.

This study adds valuable knowledge to the service science research field and contributes to the awareness of the usefulness of SD theory within companies, especially those which are small and medium-sized, since those companies lack the tools and methods required to tackle organisational change, signifying that the challenges the companies confront are different to those of larger companies.

Although this is a SD-based research, the SD4OCh methodology was developed in order to enable companies to make holistic changes, namely, to innovate their services, structure, and processes, thus supporting and guiding organisational change.

This paper aims to appraise the effectiveness of gender quotas in breaking the glass ceiling for women on boards (WoBs) in companies that are legally obliged to comply with quotas (listed companies and state-owned companies, LP) and in those that are not (unlisted companies and nonstate-owned companies, NLNP). Furthermore, it investigates the glass cliff phenomenon, according to which women are more likely to be appointed to apical positions in underperforming companies.

A balanced panel data of the top 116 Italian companies by total assets, which are present in both 2010 and 2017, is used for estimating ANOVA tests across sectors and fixed-effects panel regression models.

WoBs significantly increased in both the LP and the NLNP companies, and this increase was greater in the financial sector. Furthermore, the relationship between the percentage of WoBs and firm performance is not linear but depends on the financial corporate health. Specifically, the situation in which a woman ascends to a leadership position in challenging circumstances where the risk of failure is high (glass cliff phenomenon) is only present in companies with the lowest performance in the sample, in other words, when negative values of Roe and negative or zero values of Roa occur together.

These findings have relevant policy implications that encourage the adoption of gender quotas even in specific top positions, such as CEO or president, as this could lead to a “double spillover effect” both vertically, that is, in other job positions, and horizontally, toward other companies not targeted by quotas. Practical interventions to support women in glass cliff positions, on the other hand, relate to the extent of supervisor mentoring and support to prevent women from leaving director roles and strengthen their chances for career advancement.

The authors explore the ability of gender quotas to break through the glass ceiling in companies that are not legally obliged to do so, and to the best of the authors’ knowledge, for the first time, the glass cliff phenomenon in the Italian context.

This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).

In the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.

Using a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.

This study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.

This study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.