Search results

Nearly 40 years since they first appeared, there is renewed interest in dashboards, engendered by the diffusion of business intelligence (BI) desktop software, such as Power BI, QlikView and Tableau, denoted collectively as “self-service” BI. Using these commodity software tools, the work to construct dashboards apparently becomes easier and more manageable and no longer requires the intervention of specialists. This paper aims to analyse the implementation of this kind of commodity dashboard in a university, exploring its role in performance management processes and investigating whether the dashboard affects the organisation (or not).

This paper focusses on an action research project developed by the authors, where the objective was to design and implement a dynamic performance measurement tool fitting the needs of department directors. The three authors were all involved in the project, respectively, as project manager, dashboard implementation manager and accounting manager of the studied organisation.

The results reveal a specific but complex change to the procedures and outcomes in the organisation studied, where the dashboard becomes a boundary infrastructure, thereby reviving technical and organisational problems that had been latent for years.

In this paper, the authors contribute to the debate on the digital age and the role of accounting with their exploration into the “revolution” of self-service BI tools. The democratisation and flexibility of these instruments put into discussion two core and somewhat controversial functions of accounting: data integration and personalised reporting.

The purpose of the paper is to explore the meaning of Human Resources (HR) analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.

Conceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.

HR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.

The HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.

This paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

This study aims to investigate the impact of technologies on the knowledge transfer process. In particular, the authors aim to analyze the topic of knowledge brokers and the relationship between broker and digital tools in the knowledge transfer process in the sport context. The study developed, therefore, aims to investigate the creating of this environment for knowledge transfer and knowledge sharing between man and machine, looking to improve the planning of technical sports projects of the clubs.

This paper presents a qualitative approach aimed at analyzing how platforms and the players’ agents can be useful tools in the knowledge transfer process. The research was conducted through a survey with a structured questionnaire via e-mail to 64 managers at the head of clubs playing in the Italian Series B basketball in the 2021–2022 championship. The total number of questions administered is 21.

The results demonstrate how sports directors, for the construction of a technical sports project, in addition to learning off the pitch by interactions with media, fans, pressure management, leadership skills, positive attitude, tolerance, understanding of other opinions, background and cultures, see the athletes’ agents as the main stakeholder of the managers. The research resulted, by the clubs’ managers, in both formal learning and informal-type learning. Informal learning, by far the most frequently used and most important in the general learning process of executives, is identified in the use that executives make of information available on digital platforms and of the fiduciary relationships that management has with players’ agents.

The results demonstrate the valuable opportunities for executives, coaches, managers and clubs to strategically manage learning and knowledge sharing. Improving and managing knowledge-sharing strategies would help increase knowledge, not only of the sports directors but also of the entire club, thus improving the absolute quality of the game within the Italian basketball divisions. The authors have developed an innovative framework regarding the construction of a “typed sports technical project”, and the authors have identified a series of crucial phases capable of determining the creation of a new roster of athletes.

Interruptions are prevalent in knowledge work, and their negative consequences have driven research to find ways for interruption management. However, these means almost always leave the responsibility and burden of interruptions with individual knowledge workers. System-level approaches for interruption management, on the other hand, have the potential to reduce the burden on employees. This paper’s objective is to pave way for system-level interruption management by showing that data about factual characteristics of work can be used to identify interrupting situations.

The authors provide a demonstration of using trace data from information and communications technology (ICT)-systems and machine learning to identify interrupting situations. They conduct a “simulation” of automated data collection by asking employees of two companies to provide information concerning situations and interruptions through weekly reports. They obtain information regarding four organizational elements: task, people, technology and structure, and employ classification trees to show that this data can be used to identify situations across which the level of interruptions differs.

The authors show that it is possible to identifying interrupting situations from trace data. During the eight-week observation period in Company A they identified seven and in Company B four different situations each having a different probability of occurrence of interruptions.

The authors extend employee-level interruption management to the system-level by using “task” as a bridging concept. Task is a core concept in both traditional interruption research and Leavitt's 1965 socio-technical model which allows us to connect other organizational elements (people, structure and technology) to interruptions.

This paper aims to investigate the distinctive economic and social dynamics of ethnic quasi-enclave industrial sub-clusters and to econometrically analyse the main factors affecting the economic performance of Chinese-migrant microentrepreneurs with a specific focus on social capital.

An interpretative framework that encompasses sustainable local human development and mixed embeddedness is applied to a case study of Wenzhounese migrant socioeconomic quasi-enclave leather industrial sub-clusters located adjacent to the industrial district area of Florence, Italy. Given the complexity of the phenomenon, the research study adopted a mixed-method approach encompassing both qualitative and quantitative methods. The econometric analysis was based on data collected via a survey administered to a random sample of enterprises.

Ethnic social capital plays a central role in ethnic entrepreneurship. The results confirm the relevance of social networks in the context analysed and reveal the importance of ethnic and non-ethnic business social capital as one of the main factors affecting enterprise’s economic performance.

The findings propose potential policies to upgrade the ethnic enterprises especially in terms of increasing their formality and inclusion in the Italian social and economic systems of production.

This analysis contributes to existing literature on migrant entrepreneurship and communities, adding new evidence related to ethnic enterprises and the importance of social capital in terms of performance and working conditions of the community of entrepreneurs.

This paper aims to analyse the evolution of Chinese industrial ethnic clusters in Italy, by focusing on the role of social networks and the processes behind the phenomenon of Chinese worker exploitation and entrepreneur “self-exploitation”.

The case study is a sub-cluster of micro and small enterprises owned by Chinese entrepreneurs within the leather industrial district of Florence, Italy. This research adopts the following mixed methods: a small-scale survey to capture the characteristics of the sub-cluster and a social network analysis to describe cluster evolution, complemented by life-course interviews conducted with key informants and entrepreneurs.

Migrant social capital and social networks play a central role in the evolution of the case study sub-cluster. Social networks play a supportive role in migration, job creation, entrepreneurship formation and the creation of business opportunities. Simultaneously, they enhance the phenomenon of worker exploitation and entrepreneur self-exploitation. Furthermore, the more the business community grows, the more the economic performance of ethnic enterprises depends on agglomeration forces produced by the cluster.

The findings suggest a series of potential policies to upgrade the ethnic enterprises' capacities, to increase their formality and inclusion in the Italian social and economic systems and sub-cluster.

To the authors’ knowledge, this paper is the first attempt to examine the evolution of social networks in relation to the phenomenon of Chinese worker exploitation and entrepreneur self-exploitation in an ethnic industrial sub-cluster.

A globalized world demands proactive tactics from organizational supply chains. Companies should be capable of mitigating the impacts of natural and manmade disasters, which requires that they understand their stages of maturity and resilience. This study develops a theoretical model of the relationship between maturity and resilience, seeking to guide decision-making about aligning these two concepts.

A systematic literature review was conducted to identify the constructs that form the basis for our proposed maturity and resilience model.

The authors identified the key constructs related to maturity and resilience by analyzing the existing literature and selected 13 constructs and 3 maturity stages to construct our maturity and resilience model.

This research contributes to the supply chain management literature, especially that involving the themes of maturity and resilience. It can encourage research to develop future empirical research in the field to validate and overcome the limitations of the initial model the authors propose.

The authors’ proposed model supports supply chain managers in establishing strategies to increase resilience based on the maturity of the chains they manage, enabling them to face crises such as the coronavirus disease 2019 (COVID-19) pandemic.

The model presents a holistic view of maturity and resilience in supply chains contributing to supply chain theory by examining the alignment between the two themes.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.