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This research paper aims to integrate the employee-related factors that empirical literature considers antecedents of performance (skills, work motivation, personal characteristics) into a multiple linear regression model, and to test such a model in order to measure the level of each individual factor on the performance.

Quantitative, multisource research approach. After testing the validity of the model with a Confirmatory Factor Analysis, this research applies the multiple linear regression model Work performance = a(Skills) + b(Work Motivation) + c(Personal Characteristics) + e(constant) to two different samples of workers: chemical technicians (N = 63) and salespeople (N = 61).

This study confirms the factorial structure of the antecedents of work performance, showing that skills, motivation, and personal characteristics are three general employee-related factors underlying work performance. The statistical analysis highlights a variance in performance between 40 and 65% explained by employee-related factors, hence leaving 35–60% as due to factors outside the model (firm/environment-related and/or job-related factors, or other skills and personal characteristics not considered in the model). The study also highlights that employee-related factors sometimes affect performance differently than job designers' expectations, and sometimes even negatively.

The equation was tested on two case studies, so further explorations are needed. Furthermore, the approach adopted is inductive thus describing performance as it is, not as it should be. Therefore, it explains the best actual performance of workers, not the ideal performance.

The equation tested here represents a simple and valid tool to guide many Human Resource Management practices, such as; selection, training, development, and career orientation.

Findings provide a valid indication for designing and managing human resource management systems more even-handedly, from an organizational and employee point of view. In doing so, it drives organizations towards a better Person/Job fit.

The study represents one of the first attempts to take into consideration multiple factors simultaneously in explaining work performance.

The socio-emotional wealth (SEW) framework and the resource-based theory (RBT) offer two valuable perspectives on innovation dynamics within family firms. Through the SEW lens, family firms may prioritize preserving their traditions and core values, potentially resisting the adoption of new technologies and modern business methods. In contrast, RBT suggests that family firms can gain a competitive edge by harnessing and nurturing their unique resources and capabilities, such as collective experiences, market reputation, and established networks, to foster innovation and secure a strong market position. By integrating these two perspectives, family firms can achieve sustainable competitive advantages by balancing the tensions between tradition and innovation. This comprehensive approach allows them to innovate while preserving their SEW, ensuring long-term success in the ever-evolving business landscape.

The aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.

The methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.

After analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.

Enhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.

The novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.

The present study aims to identify the practices employed to bring heirs into family businesses as successors.

We conducted an exploratory, qualitative investigation using a case study approach. Semi-structured face-to-face interviews were conducted with external consultants and with incumbent leaders, next-generation heirs working in the firm (and likely to become successors) and employees from three family firms from different industries and under ownership and control of different generations of their respective families (first, second and third and fourth generations). In addition to surveying their general perceptions of the succession processes in their firms, each informant was asked to rate the degree of importance of 12 succession practices identified in the literature and the extent to which they exist in their respective firms.

Our results showed that heirs typically enter the family business after a development process outside of the family business, which we have termed as coming back to the nest. This process was enacted through practices that we allocated to the following categories: continued development of heirs, developing relationships in the succession process, separation of roles and attitude of the successor heirs. Overall, 8 of the 12 practices derived from the theoretical framework were endorsed as important by representatives of the family businesses and 9 were endorsed by the consultants, 7 of which coincided in both groups. However, only 5 of the practices were identified as present in the firms’ succession processes by the representatives of the family businesses, while the consultants did not identify any of the 12 practices as present.

We present additional important practices, the adoption of which would be beneficial for family business succession, such as adapting external learning to the family business, acquiring leadership skills and experience and developing emotional intelligence. Our study advances the prior literature since we do not merely discuss succession planning but analyze in an applied manner how succession actually takes place in family businesses.

The study delves into the role played by cutting-edge data analytics, machine learning, and innovative technologies in reshaping traditional insurance practices. The primary goal of this review is to juxtapose findings from the literature sources, enabling a comprehensive analysis of the current state of implementation.

Systematic narrative review methodology has been applied to the present study. Scopus database has been used for the manuscripts ranging from year 2020 to 2024 considering the 5-year rule. 74 manuscripts were reviewed to navigate the landscape of data-driven revolution, unlocking the potential to elevate insurance operations to new heights. Two research questions about the impact of data alchemy on operational efficiency and insights and its contribution to reshaping the future landscape of insurance practices have been answered.

This approach captured the interplay between the theoretical potential for insurance and the practical realities of implementation of advanced practices, drawing upon the collective expertise within the field. By doing so, the article discerned the trajectory of the insurance sector concerning the advanced data alchemy observed in the industry.

The current research contributes to the broader area of data alchemy in the insurance industry. The transformative power of big data analytics lies in its capacity to turn vast and diverse datasets into valuable insights, driving innovation, informed decision-making, and improved business outcomes across various sectors. Notably, the research extends the body of literature exploring the impact of data alchemy on operational efficiency and insights, area where limited studies have been conducted.

The empirical study of factors related to digital transformation (DT) in the banking sector is still limited, even though the importance of the topic is universally evident. To bridge that gap, this paper aims to explore the role of digital leadership (DL), innovative culture (IC) and technostress inhibitors (TI) to support engagement for improved digital innovation (DI). Based on the literature, these variables are crucial aspects of digitalisation, even though there is no agreement on their conclusiveness.

This quantitative study tested a new conceptual model using survey data from five major banks in Libya. Partial least squares structural equation modelling was used to analyse the data from the 292 usable responses.

The results showed that DL and IC positively affect DI. Techno-work engagement (TE) mediated the relationship between leadership, culture and innovation. TI played a significant moderating role in leadership, culture and engagement relationships.

The research findings highlight critical issues about how leadership style and fostering organisational support in the banking sector can enhance DT. Leaders must demonstrate a commitment to long-term resource allocation to avoid possible negative effects from digital stress while pursuing DI through work engagement.

The study suggests that fostering organisational support can enhance DT in retail banks, potentially leading to improved customer experiences and increased access to financial services. These programs will help banks contribute to societal and economic development.

This timely study examines predictor mechanisms of innovation in retail banking that resonate within the restrictions of organisational and DI frameworks and the social exchange theory. Exploring the intervening effect of TE in the leadership, culture and innovation associations is unprecedented.

This study highlights the significance of knowledge management in the relationship between organizational ambidexterity and organizational performance within the context of Indonesian SMEs.

The study adopts a quantitative research approach, employing a survey questionnaire to collect data from a sample of SMEs operating in Indonesia. Structural Equation Modeling using Partial Least Square is used to investigate the relationship between variables.

The findings of this study show that in the context of small and medium enterprises, the variables of environmental and technological uncertainty are not driving factors in organizational ambidexterity. In contrast, market uncertainty has a significant effect on organizational ambidexterity. Moreover, it is also found that knowledge management does not support the variables of environmental, market, and technological uncertainty on organizational ambidexterity in small and medium enterprises. The results show that knowledge management plays a significant role in organizational ambidexterity. It also shows that knowledge management could support a significant way between organizational ambidexterity and organizational performance.

The findings of this study give insights for SMEs to attain a sustainable competitive edge in a dynamic business landscape by implementing efficient knowledge management techniques that bolster their ambidextrous capacities.

Using a dynamic capability view, this study examined the relationships between big data analytics capability (BDAC), entrepreneurial orientation (EO) and sustainable supply chain performance (SSCP) by exploring the moderating role of trust among supply chain partners.

Questionnaires were collected from 300 manufacturing organizations using snow sampling. The moderating connections and direct relationships were examined using Hays' process macro and structural equation modeling.

BDAC was positively related to EO and SSCP. When supply chain partners experienced low levels of trust, an increase in BDAC did not enhance SSCP. As trust increased, the relationship between BDAC and SSCP became more positive, underpinning the moderating effects of trust. Moreover, trust did not moderate the relationship between BDAC and EO. The moderating effect of trust on the relationship between EO and SSCP showed a positive relationship between EO and SSCP when trust was low; however, the relationship became negative when trust was high.

Developing technology alone may not be sufficient, as supply chain managers need to establish a strong business relationship based on mutual trust. However, they also need to be aware of the dangers of high levels of trust because these may negatively affect performance. Therefore, supply chain managers need to achieve an optimal level of trust that is neither excessive nor insufficient.

Advances in technology and entrepreneurial drive for supply chain sustainability make it pertinent to examine trust levels among supply chain partners and the varying impact on BDAC, EO and SSCP. The current study shows the negative aspects of too much trust among supply chain partners.

Evaluation and evaluative design aim to assess the impact of programmes, services and interventions. Underpinned by programme logics and theories of change, evaluation aims to assess intervention effectiveness and to determine an intervention’s capacity to produce the intended change and achieve ‘success’. This chapter is focused on evaluative data and the stories that data and its production make (in)visible and the excess data that gets left behind. I document the ways that health interventions use evidence and the shifts in evaluation towards making sense of the complex contexts and systems where interventions are embedded. Taking digital health interventions as an example of a critical contemporary shift in health, I examine the ways digital data is used to offer ‘evidence’ of interventions and how data excess emerges in evaluative research where potentially useful data is not collected or is ignored as seemingly irrelevant. Here, I situate excess in two ways. The first is in relation to the broadening of data that emerges with new digital technologies and what it promises. The second form of excess is data about social life, complexity and practices, which can get left behind when there is a focus on the ‘digital’. I argue that continuing to interrogate the use(s) of digital data is critical for situating health within complex contexts and social practices of everyday life. Excess offers a useful framing to make sense of data and its (non)uses and the implications of such actions in evaluative research.