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Social practices of work humor among engineering workers are a lesser studied phenomenon. We examine the social practices of an engineering work team through acts of a peculiar form of humorous expression we identify as installation humor. In these cases of installation humor, an anonymous member of the team created a temporary, inappropriate, yet neutral installation of a physical object to amuse the other members of the team. We provide three mini-cases of installation humor; these installations appeared as the team subtly resisted a managerial initiative. We contribute knowledge to the practices of engineers at work and to the practices of resistive humorous expression.

Qualitative, full-participant ethnographic research with multiple data collection methods and utilizing abductive analysis. During the data collection, one of the researchers was a full member of the engineering team.

We identified anonymous, artefact-based enactments of resistive humorous expression, which we named installation humor. We identify and describe installation humor, which occurred at the intersectionality of work and self-expression and served as momentary artefacts symbolic of engineering worker resistance in a high-tech environment.

Managerial awareness of the unfolding forms of worker-led, fleeting signals of resistance, such as acts of installation humor, would provide another dimension of perception for identifying salient signals surrounding the phenomenon of resistance to managerial-led change initiatives. Further research is needed on engineering humor in the R&D workplace to better understand the complexity and dynamics of phenomena such as worker resistance through humorous acts. We suggest future studies on forms of humor in the engineering workplace, including incidences of installation humor as they exist in other professional work environments and organizations, to understand common and shared practices across professional boundaries.

We advance and extend the understanding of humor as a social practice in the context of professional engineers in their R&D workplace and we identify humorous acts serving as a response to negative emotions (Huber, 2022) toward the organization related to a newly instated form of managerial control. This paper contributes to the studies of social practices of humor and emotions (Fine and De Soucey, 2005) in the engineering workplace (Buch and Andersen, 2013; Buch, 2016; Mazzurco et al., 2021) as unsupervised activity at work (Gabriel, 1995), with the social practice of humor adopting a non-verbal form that we identified as installation humor. We named this specific form of humor that we observed as installation humor and defined its specificity and differences from more traditional methods of humor (t. ex. Fine and De Soucey, 2005; Martin and Ford, 2018), shop floor humor (t. ex. Roy 1959), workplace humor (t. ex. Rosenberg et al., 2021) and engineering student humor (Holmila et al., 2007; Bender, 2011; Berge, 2017).The results of this study also suggest that ethnography for studying humor as a social practice is useful in identifying micro-level occurrences of unfolding engineering humor, including humor as a form of resistance.

The study of humor in high-tech engineering settings enhances the literature of engineering work (t. ex. Mazzurco et al., 2021) and emerging humorous phenomena (Jarzabkowski and Lê, 2017). This case study highlights and extends the understanding of the non-technical competencies of engineers and the role of peer-to-peer humor in the engineering workplace as a form of resistance during managerial initiatives within an organization.

The study extends and contributes new knowledge to research on emotions and humor by engineers at work, including the identification of a peculiar form of humor used by the engineers. This study also contributes to nascent research on the social practices of engineers at work. The research material was gathered as a full-member ethnography, increasing methodological knowledge of researching a site from within.

Intensified job demands (IJDs; work intensification, intensified job- and career-related planning and decision-making demands, and intensified learning demands) illustrate the intensification of working life. This study examined relationships between IJDs and work engagement.

Nine diverse samples (n = 7,786) were analyzed separately via regression analysis by estimating linear and curvilinear relationships between IJDs and engagement.

The results showed that certain subdimensions of IJDs, i.e. intensified learning demands, related positively to engagement across several subsamples. Moreover, learning demands showed a curvilinear relationship with engagement in several subsamples; engagement was highest in a moderate level of learning demands whereas low and high levels of learning demands were associated with lower engagement. We also found that other subdimensions of IJDs did not show consistent positive relationships with engagement, and some of them were negatively associated with engagement.

Cross-sectional design.

Organizations should consider what would be the optimal level of learning demands as excessive learning demands can be detrimental to employees’ engagement.

This is a first study focusing on different manifestations of the intensification of working life, operationalized via IJDs, and their curvilinear relationships with engagement by applying a multi-sample design.

To explore the mechanisms of the implementation strategy, “oilcloth sessions” and understand and explain the ripple effects of oilcloth sessions as a strategy to implement a new emergency department.

A qualitative design was used whereby data were collected using field notes from an ethnographic study of the oilcloth sessions and follow-up semi-structured interviews with staff, managers and key employees who participated in the oilcloth sessions. The data analysis was inspired by the realist evaluation approach of generative causality proposed by Pawson and Tilley.

The primary ripple effect was that the oilcloth sessions were used for different purposes than the proposed program theory, including being used as: (1) a stage, (2) a battlefield, (3) a space for imagination and (4) a strategic management tool influencing the implementation outcomes. The results bring essential knowledge that may help to explain why and how a well-defined implementation strategy has unplanned outcomes.

Unintended outcomes of implementation strategies are an underexplored issue. This study may help implementation researchers rethink the activities required to reduce unintended negative outcomes or explore potential unplanned outcomes and, in this way, hinder or enhance outcomes, effectiveness and sustainability. Future studies within implementation research should incorporate attention to unintended outcomes to fully understand the impact of implementation strategies.

Recognizing novel entrepreneurial opportunities arising from a crisis is of paramount importance for firms. Hence, understanding the pivotal factors that facilitate firms in this endeavor holds significant value. This study delves into such factors within a representative empirical context impacted by a crisis, drawing insights from existing literature on opportunity recognition during such tumultuous periods.

The authors conducted a qualitative inspection of 14 Italian firms during the COVID-19 pandemic crisis. The authors collected a rich body of multi-source qualitative data, including 34 interviews (with senior managers and entrepreneurs) and secondary data (press releases, videos, web interviews, newspapers, reports and academic articles) in two phases (March–August 2020 and September–December 2020).

The results suggest the existence of a process model of opportunity recognition during crises based on five entrepreneurial influencing factors (entrepreneurial knowledge, entrepreneurial alertness, entrepreneurial proclivity, entrepreneurial personality and entrepreneurial purpose).

Various scholars have highlighted that, in times of crises, it is not easy and indeed very challenging for entrepreneurs to identify novel entrepreneurial opportunities. However, recent research has shown that crises can also positively impact entrepreneurs and their capacity to identify new entrepreneurial opportunities. Given these findings, not much research has analyzed the process by which entrepreneurs identify novel entrepreneurial opportunities during crises. This study shows that some entrepreneurial influencing factors are very important to identify new entrepreneurial opportunities during crises.

The purpose of this paper is to explore two financial inclusion measures adopted within the local welfare context of the city of Milan, Italy, examining their functioning and underpinning representations. The aim is also to understand how such representations take concrete shape in the practices of local actors, and their implications for the opportunities and constraints regarding individuals' effective inclusion. To this end, this paper takes a wide-ranging look at the interplay between the rise of financial inclusion and the individualisation and responsibilisation models informing welfare policies, within the broader context of financialisation processes overall.

This paper draws on the sociology of public action approach and provides a qualitative analysis of two case studies, a social microcredit service and a financial education programme, based on direct observation and semi-structured interviews conducted with key policy actors.

This paper sheds light on the rationale behind two financial inclusion services and illustrates how the instruments involved incorporate and tend to reproduce, individualising logics that reduce the problem of financial exclusion, and the social and economic vulnerability which underlies it, to a matter of personal responsibility, thus fuelling depoliticising tendencies in public action. It also discusses the contradictions underlying financial inclusion instruments, showing how local actors negotiate views and strategies on the problems to be addressed.

The paper makes an original contribution to the field of sociology and social policy by focusing on two under-researched instruments of financial inclusion and improving understanding of the finance-welfare state nexus and of the contradictions underpinning attempts at financial inclusion of the most vulnerable.

The purpose of this study is to examine how different innovation efforts can support multinational enterprises’ (MNEs’) pursuits of sustainable development goals (SDGs) in emerging markets and under what circumstances they are applied.

The article comprises in-depth case studies on two high-profile Swedish MNEs: a telecom firm and a fast-fashion firm, with data collected both at the headquarter-level and local-market level.

The study shows that MNEs pursue a selection of prioritized SDGs in emerging markets. To overcome challenges related to attaining these goals, we find that MNEs engage in innovation efforts at different levels of commitment. In some instances, they engage in operational innovation aimed at relieving symptoms of sustainability misconduct and ensuring compliance. In other instances, they engage in systemic innovation efforts, which involve the actual market structures underlying sustainability problems.

MNEs are increasingly incorporating the United Nations SDGs into their innovation strategies. The study contributes to international business research on MNEs’ roles in realizing the SDGs by conceptualizing and discussing two pertinent approaches to innovation.

The competition in the academe has always been tough, but today, the academe seems to be more like an industry than an academic community as academics are evaluated through quantified and economic means.

This article leans on Heidegger’s thoughts on the essence of technology and his ontological view on being to show the dangers that lie in this quantification of researchers and research.

Despite the benefits that information systems (ISs) offer to people and research, it seems that technology has made it possible to objectify researchers and research. This has a negative impact on the academe and should thus be looked into especially by the IS field, which should note the problems that exist in its core. This phenomenon of quantified academics is clearly visible at academic quantification sites, where academics are evaluated using metrics that count their output. It seems that the essence of technology has disturbed the way research is valued by emphasising its quantifiable aspects. The study claims that it is important to look for other ways to evaluate researchers rather than trying to maximise research production, which has led to the flooding of articles that few have the time or interest to read.

This paper offers new insights into the current phenomenon of quantification of academics and underlines the need for critical changes if in order to achieve the academic culture that is desirable for future academics.

This study aims to improve a construction company's overall project delivery by utilising lean six sigma (LSS) methods combined with building information modelling (BIM) to design, modularise and manufacture various building elements in a controlled factory environment off-site.

A case study in a construction company utilised lean six sigma (LSS) methodology and BIM to identify non-value add waste in the construction process and improve sustainability.

An Irish-based construction company manufacturing modular pipe racks for the pharmaceutical industry utilised LSS to optimise and standardise their off-site manufacturing (OSM) partners process and leverage BIM to design skids which could be manufactured offsite and transported easily with minimal on-site installation and rework required. Productivity was improved, waste was reduced, less energy was consumed, defects were reduced and the project schedule for completion was reduced.

The case study was carried out on one construction company and one construction product type. Further case studies would ensure more generalisability. However, the implementation was tested on a modular construction company, and the methods used indicate that the generic framework could be applied and customized to any offsite company.

This is one of the few studies on implementing offsite manufacturing (OSM) utilising LSS and BIM in an Irish construction company. The detailed quantitative benefits and cost savings calculations presented as well as the use of the LSM methods and BIM in designing an OSM process can be leveraged by other construction organisations to understand the benefits of OSM. This study can help demonstrate how LSS and BIM can aid the construction industry to be more environmentally friendly.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.