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In recent years, the public sector has faced the challenge of digitalisation. This has significantly impacted the relationships between citizens and public organisations and, thus, it widely affects participatory processes, such as participatory budgeting (PB); in fact, digital tools (DTs) have emerged as a solution, increasing citizen engagement whilst improving efficiency, reducing costs and saving time. This contribution analyses PB in Rome, which is also implemented with DTs, seeking to understand how DTs impact citizens’ role in creating public value.

The study is based on a qualitative approach, precisely by analysing a descriptive and exploratory single case study of PB’s first adoption in Rome in 2019. The information is obtained from multiple sources and examined through document analysis.

In the Roman context, DTs in PB primarily facilitated cost-effective information sharing, offering citizens basic participation. Unfortunately, the potential for more interactive DTs was overlooked, failing to enhance citizen engagement in critical phases like deliberation, evaluation or monitoring. Therefore, the tools did not fully support citizens becoming co-creators of public value instead of just users in governance.

The novelty of this study lies in exploring the difference between the use of DTs that assist citizens/users in improving service quality and those that support citizens in creating a public and shared value. It ventures further to assess various tiers of participation, meditating on the digital elements that stimulate active engagement and value creation instead of simply expanding the participant pool or process efficiency.

This study applies the target similarity model to examine the effects of servant leadership on supervisor commitment and supervisor citizenship behavior. The mediating role of supervisory commitment is explored to determine the relationship between servant leadership and supervisor citizenship behavior. The difference in supervisor gender is examined in the linkage between servant leadership and supervisory commitment.

The data were collected through a survey of 478 salespeople in the retail industry. Structural equation modeling (SEM) techniques were used to verify the hypotheses of this study.

The findings showed significant support for the direct and indirect effects of servant leadership on supervisor commitment and supervisor citizenship behavior. Furthermore, the positive relationship between servant leadership and supervisory commitment was stronger among female supervisors than male supervisors.

Due to the scarcity of studies conducted on the linkages of servant leadership, supervisory commitment and supervisory citizenship behavior, this study theoretically and empirically contributes to the leadership literature as it is the first study to investigate these direct and indirect relationships. Similarly, this study examined gender differences in servant leadership to fill the gap in the research field.

Engaged employees assure organizational competitiveness and sustainability. The purpose of this study is to explore the relationship between job resources and employee turnover intentions, with employee engagement as a mediating variable.

Data were collected from 934 employees of eight wholly-owned pharmaceutical industries. The proposed model and hypotheses were evaluated using structural equation modeling. Construct reliability and validity was established through confirmatory factor analysis.

Data supported the hypothesized relationship. The results show that job autonomy and employee engagement were significantly associated. Supervisory support and employee engagement were significantly associated. However, performance feedback and employee engagement were nonsignificantly associated. Employee engagement had a significant influence on employee turnover intentions. The results further show that employee engagement mediates the association between job resources and employee turnover intentions.

The generalizability of the findings will be constrained due to the research’s pharmaceutical industry focus and cross-sectional data.

The study’s findings will serve as valuable pointers for stakeholders and decision-makers in the pharmacuetical industry to develop a proactive and well-articulated employee engagement intervention to ensure organizational effectiveness, innovativeness and competitiveness.

By empirically demonstrating that employee engagement mediates the nexus of job resources and employee turnover intentions, the study adds to the corpus of literature.

Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.

The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.

It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.

This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

Firms embedded in networks of relations are theorized through Gnyawali and Madhavan’s (2001) (G&M) structural embeddedness model to gain competitive advantage from topological characteristics. Empirical studies to support their theory have never been executed in full. Our study provided a full empirical test of their model in a digital trading network to achieve a higher degree of certainty that those network structural characteristics can have a major impact on the degree to which certain firms lead to competitiveness in a digital trading network environment.

To examine how firms respond in competitive situations, we chose the hyper-active digital trading network, eBay as our empirical context. We used eBay auction data to analyze how the network characteristics of eBay resellers impact their competitive behaviors.

Our study found strong support for the G&M model of competitiveness. We offer explanations for where support was not as strong as the Gynawali and Madavan theory proposes.

Our research is limited by our chosen context and findings in support of part of G&M model. Future studies in other digital contexts are needed to enhance the modeling of network topologies and further study the impacts of network density and structural autonomy on competitive action.

Our study suggests that managers proceed cautiously in forming partnerships, weighing circumstances where the firm can find itself with increased information power and avoiding, to the greatest extent possible, situations where the playing field is roughly equal.

Theory-making in this domain has begun as well as initial empirical testing. Much more needs to be accomplished, though, before embeddedness modeling can be thought of as being well established.

The G& M Model of competitiveness is an SNA explanation of why some competitive units succeed and others do not. Our study is the first, full blown empirical analysis of the theory.