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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.

Nowadays manufacturers companies are increasingly compelled to navigate towards servitization. Different methods and approaches were proposed in literature to support them to switch from traditional product-based business model to product service systems (PSSs). However, new knowledge, capabilities and skills were needed to consistently develop PSSs, since they need a joint focus on both customer’s perspective and company’s internal performance and at the same time a proper support for the integration of product and service design. The purpose of this paper is to propose the Product Service System Lean Design Methodology (PSSLDM), a structured methodology to develop PSSs along their entire lifecycle.

Retrieving concepts from interpretative, interactive and system development research traditions, and strongly reminding the design research methodology framework, the adopted research methodology is composed of three main phases (observation and conceptualization, theory building and tool development, validation) and involved three heterogeneous companies.

This paper provides an overview of the PSSLDM, explaining how the different methods supporting its conduction should contribute to properly design an integrated PSS. Moreover, companies highlighted several benefits in the different stages along the PSS lifecycle deriving by the adoption of the PSSLDM.

The development of a platform based on the PSSLDM methodology raises a discussion on the possible changes needed by current Product Lifecycle Management (PLM) models and systems when they have to do with PSSs.

The PSSLDM enriches the already proposed SErvice Engineering Methodology, introducing new several components linked by lean rules in each of its phases (starting from customer analysis, going through solution concept and detailed design, until the offering analysis) and better supprting the deatil design of both prodcut and service components.

The purpose of this response is to refute Voelpel et al.'s five proposed explanations about why the Balanced Scorecard fails to support innovation and employee empowerment.

Provides quotes from published articles and books by Kaplan‐Norton that directly contradict the positions taken by Voelpel et al.

Voelpel et al. failed to read or systematically ignored the relevant literature before writing their article.

This response confirms that enterprise leaders can continue to adapt and use the Balanced Scorecard to facilitate the implementation of their new, innovative strategies.

This article proposes a conceptual framework and a research methodology for transferring marketing technology to developing countries to address important societal problems. The methodology developed by the author is described and illustrated with an empirical investigation. Guidelines for implementation of this methodology in developing countries are also provided.

The role of macrolevel factors in entrepreneurial intention remains as an underexplored issue in the literature. The purpose of this study is to reduce this gap by testing the effect of economic development on entrepreneurial intention.

This study adopts a quantitative approach that formally tests for a quadratic relationship between economic development measured by the gross domestic product (GDP) and the Global Competitiveness Index (GCI)) and entrepreneurial intention based on longitudinal data covering 72 countries over the 2010–2016 period. Data are gathered from the Global Entrepreneurship Monitor (GEM), the International Monetary Fund (IMF) and the World Economic Forum (WEF).

The results reveal the existence of a U-shaped relationship between the country's GDP per capita and individuals' entrepreneurial intention. The results also support a similar relationship between GCI and entrepreneurial intention. These findings suggest that individuals' entrepreneurial intentions differ between countries depending on the level of economic development.

To the authors' knowledge, this article presents the first attempt to investigate the role of economic development on entrepreneurial intention based on longitudinal data covering a large sample of countries. Moreover, by providing evidence of a U-shaped relationship between economic progress and individuals' propensity to attempt an entrepreneurial career, this study enhances the understanding of the macrolevel determinants of entrepreneurial intention.

Performance in virtual teams, which faces cultural and demographic differences, is a relevant phenomenon that has been widely investigated in recent decades, but with opportunities in exploring other levels of analysis as individual and project. This current research aims to understand how multicultural virtual teams affect individual, team and project performance.

The authors conducted a systematic literature review (SLR) and bibliometric analysis to capture 273 papers from the Web of Science (WoS) database using a snowball approach. In a second approach, the authors selected 130 papers to conduct a content analysis.

The authors presented a longitudinal overview regarding the adoption of virtual teams in project management (PM) literature. A conceptual framework was proposed to explore the relationship between multicultural virtual teams and performance with three levels of analysis: individual, teams and project. The authors contributed with research hypotheses to be explored in future empirical studies not only at the team perspective but also at the project and individual levels. The thematic analysis suggested that the literature focus has shifted from hard to soft aspects faced by virtual teams. Social identity/categorization theory was the most prominent theory in this body, but it is not fully explored in PM literature. Other opportunities of future studies are to understand the impact of cultural diversity, the sense of belongingness, the project life cycle and the development of a knowledge management program.

The authors developed a 3-level conceptual framework for future empirical studies and demonstrated that cultural differences are mainly approached at the national level in the literature, bringing suggestions for future empirical research.

The control role of the Board of Directors is aimed at monitoring the decisions and actions undertaken by managers in order to protect stockholders’ interests. Considerable theoretical and empirical research has analyzed whether directors’ behavior is consistent with their fiduciary responsibility, but this research has reported inconsistent findings. This paper offers a comprehensive review of both theoretical and empirical literature on the control role of the board and suggests several guidelines for future research.

This paper aims to present a focus for identifying the convergence between the theory of the intellectual capital of nations and that of the National Systems of Innovation from the literature of innovation systems, with the aim of providing a more robust theoretical framework to explore the drivers of intangibles and the policies which foster competitiveness through the development of the national intellectual capital platforms.

Selecting 28 indicators which best fit the analyzed theoretic principles and taking into account the member countries of the European Union as a sample (15) with the data taken from the IMD, a simple analysis of the seven‐year (2000‐2006) window was carried out to compare the intangibles pointed out in the two aforementioned perspectives.

The homogeneity of the results using models of measurement of intellectual capital of countries with those obtained through the models of the National Systems of Innovation prove the considerable convergence between these two theoretic fields, validating the hypotheses proposed in the study.

The system provided is not an exhaustive use of all the available measures and countries. A more comprehensive practical application on more countries and indicators would be necessary to validate the model.

The study has implications for the business, politicians and academia. The study opens new lines of research in the sense that it advocates a theoretical approximation and the integration of the abundant literature on the National Systems of Innovation as basic drivers to explain the intangibles at a macro level, their management and politics related to the maintaining and renewal of said assets of intellectual capital.

To the best of the authors' knowledge, so far no study analyzing this convergence has been designed or published. This paper extends and adds robustness to the national IC measurement model to help policymakers and scholars.

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.