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This paper aims to present a dual-perspective framework for maintenance service delivery that should be used by manufacturing companies to structure and manage their maintenance service delivery process, using aggregated historical and real-time data to improve operational decision-making. The framework, built for continuous improvement, allows the exploitation of maintenance data to improve the knowledge of service processes and machines.

The Dual-perspective, data-based decision-making process for maintenance delivery (D3M) framework development and test followed a qualitative approach based on literature reviews and semi-structured interviews. The pool of companies interviewed was expanded from the development to the test stage to increase its applicability and present additional perspectives.

The interviews confirmed that manufacturing companies are interested in exploiting the data generated in the use phase to improve operational decision-making in maintenance service delivery. Feedback to improve the framework methods and tools was collected, as well as suggestions for the introduction of new ones according to the companies' necessities.

The paper presents a novel framework addressing the data-based decision-making process for maintenance service delivery. The D3M framework can be used by manufacturing companies to structure their maintenance service delivery process and improve their knowledge of machines and service processes.

Sustainability is increasingly at the forefront of the public debate in Europe and the world. However, despite this increased interest, research seems to have partially ignored the importance of its social dimension and the issues related to social equity, people care, protection and personal development at all stages of society and, consequently, of business. Accordingly, this paper aims at investigating the “soft” dimensions of sustainability, integrating its mainstream “technical storyline” with a “human/social storyline”.

In this paper a taxonomy of the main key drivers of the soft dimension of sustainability is proposed and tested on a sample of Italian companies. Through interviews with their managers, actions and needs in terms of sustainability soft drivers are identified.

The achieved results demonstrated that the case companies differently integrated the soft dimensions of sustainability within their companies. All the sample companies are aware of the role of social sustainability. According to the proposed taxonomy, the systemic drivers of soft sustainability are the main shared ones.

The paper provides new insights into the essence of the organizational soft dimensions and their centrality in the overall achievement of sustainability for companies. It also offers managerial insights into how to effectively manage these dimensions and policy implications about the need for clearer consideration.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

This paper aims to provide a comprehensive view of the different competition-based approaches that policymakers can exploit to foster external knowledge search and their positioning among innovation policy measures. A growing number of companies have implemented initiatives to access external knowledge to increase their innovativeness, consistently with the open innovation paradigm. Competition-based approaches have received increasing attention by the private sector as a way to access external knowledge. However, despite their potential role as innovation policy measures, a limited attention has been devoted so far to investigate them from the policymakers’ perspective.

To this aim, a two-stage empirical analysis has been carried out to develop a taxonomy of competition-based approaches. The first stage leveraged a multiple case study methodology including a sample of 20 competition-based approaches, while the second one leveraged interviews with Italian and European key informants.

This paper proposes a novel taxonomy including eight competition-based approaches, which differ among each other in terms of policy strategy, scope breadth and output required. Moreover, this paper enriches a well-established taxonomy of innovation policy instruments with the identified competition-based approaches.

This study contributes to the current debate on innovation policy by providing a taxonomy that includes eight competition-based approaches that can be exploited by policymakers to foster external knowledge search as well as their positioning among the innovation policy instruments. The taxonomy will hopefully support policymakers in identifying of the most suitable instruments in the light of their policy strategy and objectives.

This paper aims to propose a framework investigating the diffusion and adoption process of big data (BD) in the supply chain (SC) as a tool to manage process innovation at technological, operational and strategical levels.

A comprehensive systematic literature methodology is used to develop the theoretical conceptual framework, which comprehensively describes and captures the innovative stages of BD technology adoption process in SC with a multilevel perspective.

Results show that BD has modified the supply network concept, starting from the dyadic relationships, triads up to the creation of a streamlined and integrated network. These changes are reflected in a novel integrated vision including both benefits and barriers.

The proposed framework supports companies in redesigning the processes affected by the adoption of BD, helping them in identifying the critical elements, barriers, benefits and expected performance. One limitation is the focus of the study on the analysis of the processes of adoption of BD technology in the SC considering a particular structure of SC characterized by only two levels of supply and by a reduced number of members.

Although the role of BD in supply chain operations management (SCOM) is well acknowledged in the literature, its adoption and diffusion process from an interorganizational perspective is still missing. Specifically, the adoption stages of BD in SC have been defined at a strategic level, and successively the SC operations and technological perspective have been integrated to depict the operationalization of BD implementation and diffusion.

This study aims to highlight the impact of altruistic-self and hunger awareness on socially responsible food consumption through the lens of self-awareness and self-congruity theories due to the great challenge of Sustainable Development Goal 2: Zero Hunger.

A survey was conducted with a sample of 812 respondents. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm each variable's structure through the measurement model and test the hypothesis to support a structural model.

The results highlight that the combination of altruistic-self and hunger awareness (AS-HA congruence) drives consumers to execute socially responsible food consumption. Meanwhile, consumers' food-saving attitude mediation translates to the attitude towards responsible and ethical use increasing socially responsible food consumption, a contextual development in the theory of congruence. Conversely, hunger awareness is not confirmed as significantly influencing socially responsible food consumption.

This research provides valuable insights for academicians and practitioners in developing food waste management strategies that can be implemented to reduce food wastage.

Food waste is a global concern and is challenging for many manufacturing, distribution and individual wastage levels. However, food wastage by consumers is one of the most critical problems which can be minimised with awareness and attitudinal changes in behaviour as a form of socially responsible consumption.

The purpose was to present a developed, tested and evaluated methodology for assessing teamwork and sustainable quality culture, focusing on top management teams (TMTs).

The developed methodology was based on a convergent mixed-method design, including two data collection methods: questionnaire and focus group discussion. Two pilot tests were performed with two TMTs. This design involved analysing, merging and interpreting data, first separately by data collection method and theme and then in a meta-interpretation. Lastly, there was a follow-up meeting for evaluating results.

Findings from the study were that the methodology can be used to assess teamwork and sustainable quality culture, and the results also showed the strength of using two data collection methods to provide a broader picture of teamwork and sustainable quality culture. A follow-up meeting validated the results and provided additional value to the two TMTs in the form of suggestions on how to improve their teamwork and sustainable quality culture.

Applying this methodology can guide TMTs in how to improve their teamwork and sustainable quality culture within their organisations.

This is a new methodology, containing a developed questionnaire and an interview guide, aiming to assess and evaluate teamwork within TMTs and sustainable quality culture. The practice of the methodology adds value to both TMTs and their organisations, as well as provides a theoretical and methodological contribution to research on teamwork and sustainable quality culture.

The aim of the paper is to ethnographically detail the poverty-shame nexus in contemporary Spain, and to highlight the contradictions of the newly adopted consumption-based models of inclusion led by charities.

Drawing on 39 cases out of a sample of 78 gathered through two long-term research projects, the paper employs a mixed-methods approach that mainly draws on a multi-sited ethnographic approach and interviews.

The paper ethnographically documents major contradictions that shed light on the complex relationships between poverty, shame, work and consumption in modern societies.

This paper analyses the sources of shame in the experience of poverty and downward mobility, but also it opens new ground for understanding the complex poverty–shame nexus and lets some questions unanswered.

The contradictions highlighted shed light on the complex relationships between poverty, shame, work and consumption that may inform modern policies to fight poverty. Ethnography gives voice to these individuals that currently experience an increasingly precarious and unequal modern world.

The paper contributes to a better understanding of the processes that underlie modern poverty and downward social mobility and points out the contradictions generated by consumption-based models of inclusion.

While the poverty-shame nexus has been already analyzed from the point of view of stigma and exclusion from the labor market, the links between a growing consumerism and the neo-liberal values that underlie our modern societies are largely unexplored. The ethnographic contribution and the detailed case studies are also original in the case of Spain.