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Aims to present a novel approach for the worst‐case, robust design (RD) of an electromagnetic system able to overcome some discretionary aspects of the available methods.

The proposed methodology is based on a suitable application of the interval arithmetic and considers the width of the interval Taylor extension (WITE) of the performance function (PF) as a valuable robustness index (RI). In particular, the most robust stationary point is obtained by looking for the minimum of a continuous, non‐differentiable function thanks to the properties of WITE.

The method furnishes a reliable classification of the robust solutions with PFs expressed by either mono or multi‐dimensional polynomial forms and provides the over‐bounding of the PF without additional computational efforts.

Limitations concern the necessity of an analytic formulation of the system performance. The IA approach may lead to lose some robust solution.

A RI can be assigned in analytic form and it can be suitably used as a figure of merit in a multi‐objective optimisation problem.

The use of the interval analysis for the worst‐case, RD of an electromagnetic system is original. The system designer may find the most robust stationary solution by means of a continuous, non‐differentiable function.

Firms increasingly rely on both external and internal crowdsourcing to capture ideas more broadly and enhance innovative problem-solving. Especially in internal crowdsourcing, knowledge sharing that contributes to develop or further the understanding of the problem the idea is focused on solving can take place between critical employees, and in that way improve ideas generated by others. This far, most crowdsourcing practices have focused on identifying solutions to proposed problems, whereas much less is known about how crowds can be used to share problem-related knowledge. There is thus an untapped potential in leveraging crowds not just to generate solution-oriented ideas but also to share knowledge to improve ideas and even to reframe problems. This paper aims to explore the effect of problem- and solution-related knowledge sharing in internal crowdsourcing for idea development.

Data on ideas and comments were collected from an idea management system in a Swedish multinational company. The investigation captures the influences of the problem- and solution-related knowledge sharing on ideas based on content analysis and logistic regression analysis.

The results from this study show that sharing knowledge related to solutions in idea development impacts idea acceptance positively, whereas sharing knowledge related to problems in idea development has a negative effect on the likelihood of idea acceptance and these effects of knowledge sharing are moderated by the active author responses.

This research provides managerial implications for firms to deliberately manage knowledge sharing in peer communities in internal crowdsourcing, especially by providing suggestions on problem reframing and solution refining for ideas.

The results contribute to existing theory in terms of extending the view of crowdsourcing in ideation to include how crowds contribute to the development of the problem and the solution during the development of ideas and providing new insights on knowledge sharing in internal crowdsourcing based on problem-solving theory.

The purpose of this paper is to discuss how aesthetic features can be the basis for a virtuous chain that leads to social and economic development.

The aesthetic characteristics of a destination are an important attractive feature. More than that, they can be considered a key determinant for economic and social growth. A conceptual support to this idea is presented here by following a line which links beauty to creativity, innovation and socio-economic development.

A purely qualitative and logical-deductive line shows how beauty, creativity and innovation are connected in a chain that leads to socio-economic well-being.

This paper is conceptual in nature. The authors intend to use these considerations as a basis for future research.

This paper contributes to the literature on destination governance highlighting a number of factors that are often underestimated.

With the proliferation of ideas submitted by users in firm-built online user innovation communities, community managers are faced with the problem of user idea overload. The purpose of this paper is to explore the influencing factors on the idea adoption to identify high quality ideas, and then propose a method to quickly filter high value ideas.

The authors collected more than 110,000 data submitted by Xiaomi community users and analyzed the factors affecting idea adoption using a multinomial logistic regression model. In addition, the authors also used BP neural network to predict the idea adoption process.

The empirical results show that idea semantics, number of likes, number of comments, number of related posts, the existence of pictures and self-presentation have positive impact on idea adoption, while idea length and idea timeliness had negative impact on idea adoption. In addition, this paper calculates the idea evaluation value through the idea adoption process predicted by neural network and the mean value of idea term frequency inverse document frequency (TF-IDF).

This empirical study expands the theoretical perspective of idea adoption research by using dual-process theory and enriches the research methods in the field of idea adoption research through the multinomial logistic regression method. Based on our findings, firms can quickly identify valuable ideas and effectively alleviate the information overload problem of online user innovation communities.

This paper analyzes how the distribution and structure of employees' attention influence idea survival in an organizational internal crowdsourcing session.

Data from an online internal crowdsourcing session carried out within a multinational company with headquarters in Sweden were used to explore how idea attention influenced idea survival.

Our findings indicate that the positive relationship between attention allocation and idea survival is mediated by idea appreciation, i.e. positive comments and suggestions that employees provide in response to ideas. In addition, we find that competition for attention negatively moderates the relationship between idea attention and positive comments. Finally, our results indicate that ideas are more likely to survive if they are submitted earlier in the crowdsourcing process and when the elapsed time since previously posted ideas in the session is longer.

This study provides organizers of internal crowdsourcing sessions with new insights about factors influencing idea survival and about potential systematic biases in idea selection due to timing and competition between ideas.

This paper contributes to the literature highlighting the relevance of attention-based theory in the context of crowd-based creativity and innovation management.

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.

Academics and practitioners are increasingly discussing the role of crowdsourcing initiatives in dealing with the challenges imposed by the COVID-19 pandemic, along with their role in an emergency context in general. However, empirical evidence about the role of crowdsourcing initiatives in an emergency context is still scarce. This paper aims to address this gap in knowledge.

The authors adopted an inductive approach to investigate how companies leveraged crowdsourcing initiatives to address the issues posed by COVID-19. Data were collected through semi-structured interviews. The selection of the interviewees was based on a purposive sampling strategy. Data were analysed through thematic analysis.

The analysis led to the identification and examination of drivers (e.g. preserving relationships with customers), advantages (e.g. increased demand for services) and disadvantages (e.g. increased platform cost) of implementing crowdsourcing initiatives during COVID-19. By leveraging the findings, this paper suggests future research opportunities. Relevant future research opportunities include: (1) quantitatively evaluating the economic impact of crowdsourcing initiatives during a disruptive event and (2) examining how the sector in which the company operates influences the beneficial and detrimental effects of crowdsourcing initiatives on company performance.

The involvement of crowds as innovation partners has provided unique opportunities for companies to innovate and address the challenging scenario. The scientific literature about the role of crowdsourcing during COVID-19 is growing. However, there is still a scarcity of empirical evidence about the mechanisms underpinning the use of crowdsourcing during the pandemic. By leveraging semi-structured interviews, this paper addresses this relevant gap in knowledge.

The purpose of this paper is to identify the key factors for successful creation of social value using the social business model (SBM) as an explanatory framework.

This study follows the Krippendorff and Gioia’s methodology seeking the rigor while examining the selected case study – a social cooperative named La Paranza, in the city of Naples in the south of Italy. In doing so, the SBM canvas is used to present the research’s results in a systematic way.

The existence of a strong “identity” with the local context, the “enthusiasm and the wish to change things”, the presence of a “visionary”, acting as an orchestrator and the ability to provide for responses to the local needs were identified as key factors in the successful creation of social value in the examined case.

The main research limits stem from the single case study methodology approach, which embodies the researchers’ subjectivity. A comparative study based on the collection of multiple successful case studies is therefore suggested to develop a generalization of the key drivers underlying the process of social value creation.

The study’s originality results from its use of the SBM framework in presenting a successful example of social value creation. The examined empirical evidence is also original in itself, mainly because of its inner uniqueness as a new and innovative formula: this allows an in-depth investigation and the inspiration for new ideas in the ground of SBM and, in a broader sense, in the field of social innovation and social value creation.

The purpose of this paper is to present some evidence on the role of management models in the implementation or in the transformation of the business models, highlighting the pivotal role of managerial dynamic capabilities. The analysis provides relevant lines of managerial action both strategic and operational levels.

An innovative conceptual analysis is proposed. Managerial dynamic capabilities play a central role in the coupled link between management model and business model at the organizational level.

The authors propose a highly usable and generalizable conceptual model for management practices, strategic planning and operational assessment.

This paper investigates a new emerging research stream of management innovation theory. The research presents a new and innovative conceptual analysis of management model and business model alignment. This theme has not been explored in prior researches and represents an experiment to pair the management model and the business model evidence.

The aim of the paper is to analyze how actors in foodservice companies organize for inbound open innovation (OI).

This paper conducted a case analysis of a large and successful foodservice company operating in the Dutch market. Furthermore, drawing on 18 interviews and archive data, we identified the main organizational practices involved in the implementation of inbound innovation activities and the ways they are embraced are defined.

The results provide a holistic view of the main organizational practices a foodservice company implemented at different organizational levels, to exploit external knowledge coming from third parties and to promote the sharing and recombination of knowledge resources within the organization. The identified organizational practices reveal the main interaction patterns between relevant internal actors and other external parties in the company network, as well as between actors on different hierarchical organizational levels which allows processing relevant innovation information and make relevant decisions about it.

Implications are provided in terms of both theory and practice. This paper helps foodservice companies to create an internal organizational environment that supports the exploitation of customer knowledge.

There are few studies on how companies organize themselves for OI in general, and especially in the foodservice sector.