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This study aims to investigate how to identify and address production levelling problems in assembly lines utilising an intensive manual workforce when higher productivity levels are urgently requested to meet market demands.

A mixed-methods approach was used in the research design, integrating case study analysis, interviews and qualitative/quantitative data collection and analysis. The methodology implemented also introduces to the literature on operational performance a novel combination of data analysis methods by introducing the use of the Natural Language Understanding (NLU) methods.

First, the findings unveil the impacts on operational performance that transportation, limited documentation and waiting times play in assembly lines composed of an intensive workforce. Second, the paper unveils the understanding of the role that a limited understanding of how the assembly line functions play in productivity. Finally, the authors provide actionable insights into the levelling problems in manual assembly lines.

This research supports industries operating assembly lines with intensive utilisation of manual workforce to improve operational performance. The paper also proposed a novel conceptual model prescriptively guiding quick and long-term improvements in intensive manual workforce assembly lines. The article assists industrial decision-makers with subsequent turnaround strategies to ensure higher efficiency levels requested by the market.

The paper offers actionable findings relevant to other manual assembly lines utilising an intensive workforce looking to improve operational performance. Some of the methods and strategies examined in this study to improve productivity require minimal capital investments. Lastly, the study contributes to the empirical literature by identifying production levelling problems in a real context.

This paper aims to fulfil a need to identify assembly interfaces from existing products based on their Assembly Process Planning (APP). It proposes a tool to identify assembly interfaces responsible for reused components integration. It is integrated into a design for mixed model final assembly line approach by focusing on the identification of assembly interfaces as a generic tool. It aims to answer the problem of interfaces’ identification from the APP.

A tool is developed to identify assembly interfaces responsible for reused component integration. It is based on the use of a rule-based algorithm that analyses an APP and then submits the results to prohibition lists to check their relevance. The tool is then tested using a case study. Finally, the resulting list is subjected to a visual validation step to validate whether the identified interface is a real interface.

The results of this study are a tool named ICARRE which identify assembly interfaces using three steps. The tool has been validated by a case study from the helicopter industry.

As some interfaces are not contained in the same assembly operations and therefore, may not have been identified by the rule-based algorithm. More research should be done by testing and improving the algorithm with other case studies.

The paper includes implications for new product development teams to address the difficulties of integrating reused components into different products.

This paper presents a tool for identifying interfaces when sources of knowledge do not allow the use of current methods.

This study aims to develop and test a model of the antecedents, mediators and consequences of the glass cliff phenomenon through public sector service organizations in Iran to explore more insights on gender inequality in managerial positions.

The current research was conducted based on a mixed-method approach, using both qualitative and quantitative research designs. First, the qualitative method includes content analysis by conducting semi-structured interviews with 20 university professors and expert managers working in public sector service organizations in Iran. The outcomes of the qualitative phase lead to designing the conceptual framework and research hypothesis. Then, through a quantitative phase, 384 female managers working in public sector service organizations in Iran are selected using stratified random sampling and fill out the research questionnaire. The exploratory factor analysis was used to verify the model. Moreover, structural equation modeling, using AMOS 24, was used to test the research hypothesis.

The findings of the qualitative phase were represented in three categories including antecedents (e.g. the characteristics of women’s leadership, the selection of women based on meritocracy criteria, women’s preferences and organizational factors), mediation effect (e.g. succession planning, personal development planning and support networks) and consequences of the glass cliff phenomenon (e.g. positive and negative consequences). The results of the exploratory factor analysis show there are ten components, explaining 88.5% of variances. Moreover, the test of the structural model supports the direct effect of antecedents on the glass cliff phenomenon. The results also show the effect of the glass cliff phenomenon on consequences through mediation effects.

There are some limitations that can be addressed by other researchers. Accordingly, the limited number of female managers in Iran prevented larger quantitative research. Moreover, the current research only found casual and mediation consequences of the glass cliff phenomenon, and potential moderators were not considered in this study.

The present study’s innovations may include using a mixed-method approach to investigate the antecedents, mediators and consequences of the glass cliff phenomenon in this study and examining the model constructs in some public sector service organizations. This research may provide a deep understanding of the antecedents, mediators and consequences of the glass cliff phenomenon by finding new factors using a mixed-method approach.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

By combining manifold approaches from migrant entrepreneurship and family business studies, the purpose of the paper is to shed some light upon the contextual features of motivation, resources, generational pathways of Turkish migrant family entrepreneurs in Berlin – through the lens of a mixed and multiple embeddedness approach.

An explorative research design, based on an eclectic theoretical framework and on purposive sampling, combines qualitative in-depth interviews/content analysis and on-site observation resulting in an almost ethnographic assessment of selected case studies of Turkish migrant family entrepreneurs (concerning age (min. 20 years), size (15+ employees) and currently at a stage of succession).

The results show that despite specific strategies vary – four circumstances hold true for all cases: (1) firm trajectories were characterized by little strategic planning and mostly trail-and error processes in the past and business survival is highly dependent on owner families; (2) owner families heavily relied on personal, family and collective resources, not benefiting from promotion programmes or micro-funding measures for SMEs; (3) owner families have actively developed their (mixed) embeddings during the growth of their migrant business beyond the single ethnic group at various spatial scales; (4) succession adds another layer of context – what we call here multiple embeddedness – with ambivalent effects: emerging potentials and conflicts between the preceding and succeeding generation.

Results have shown that is it necessary to set up both: customized funding opportunities for migrant start-ups in general and succession consulting for migrant family entrepreneurs in particular. Given the magnitude of family migrant entrepreneurs and the accelerating migration patterns in most Western European countries, there is urgent need for such measures.

Family entrepreneurship has been often discussed without a migration perspective, neither taking a systematic look at pertinent motivation, resources, and future trajectories nor context. Migrant entrepreneurship studies barely take the family or family-specific issues (e.g. succession) into account, and mainly deal with the integration or economic aspects. Our mixed and multiple embeddedness approach allows for a holistic view on transgenerational migrant family entrepreneurship by integrating both socio-spatial (actor, family, network, micro, meso, macro) and multi-generational contexts (preceding, succeeding).

The Internet of Things-based digital twin (IoT-DT) technologies offer a transformative approach to building retrofitting for reducing operational carbon (ROC) emissions. However, a notable gap exists between the potential and adoption of the two emerging technologies, further exacerbated by the nascent state of research in this domain. This research aims to establish the best practices that innovatively strengthen the identified enablers to decisively tackle challenges, ensuring the efficient implementation of IoT-DT for ROC emissions in buildings.

This study adopted a mixed-method approach. Questionnaire data from 220 multidiscipline professionals were analysed via structural equation modelling analysis, while interview data obtained from 18 stakeholders were analysed using thematic content analysis. The findings were triangulated for cohesive interpretation.

After the analysis of questionnaire data, a structural model was established, depicting the critical challenges (inadequate data security, limited technical expertise and scalability issues) and key enablers (robust data security measures, skill development and government incentives) of implementing IoT-DT for ROC. Sequentially, analysis of in-depth interview data revealed the IoT-based DT best practices (safeguarding data, upskilling and incentivization). Upon triangulating the questionnaire and interview findings, this study explicitly highlights the potential of the established best practices to strategically strengthen enablers, thereby mitigating challenges and ensuring the successful implementation of IoT-based DT for ROC emissions in buildings.

This study provides practical guidance for stakeholders to effectively implement IoT-DT in ROC in buildings and contributes significantly to climate change mitigation.

This study aims to analyze the impact of Artificial Intelligence (AI) and Machine Learning (ML) on heritage conservation and preservation, and to identify relevant future research trends, by applying scientometrics.

A total of 1,646 articles, published between 1985 and 2021, concerning research on the application of ML and AI in cultural heritage were collected from the Scopus database and analyzed using bibliometric methodologies.

The findings of this study have shown that although there is a very important increase in academic literature in relation to AI and ML, publications that specifically deal with these issues in relation to cultural heritage and its conservation and preservation are significantly limited.

This study enriches the academic outline by highlighting the limited literature in this context and therefore the need to advance the study of AI and ML as key elements that support heritage researchers and practitioners in conservation and preservation work.

Focusing on the social influence processes in the context of massively multiplayer online role-playing games (MMORPGs), this study aims to investigate the nomological network of social influence factors, a topic seldom explicitly articulated in the literature in this unique context.

This study adopts a mixed-methods approach to develop and test a context-specific model of social influence processes in MMORPGs. First, the authors conducted qualitative interviews with MMORPG players to identify the drivers shaping players' perceptions of social influences. Second, the authors formulated and tested a research model with quantitative data collected from 450 respondents of an online survey.

Through the qualitative study, the authors identify leader enthusiasm, social support and social presence as the critical drivers of social influence factors. The result of the quantitative study validates the influences of the critical drivers and demonstrates the impact of social influences on MMORPG players' we-intention to continue playing games.

This research extends the social influence theory by identifying contextualized drivers that shape MMORPG players' perception of social influences determining their we-intention to continue playing games. MMORPG service providers can draw on these drivers to leverage social influences to increase players' we-intention of continuance.

Positive experiences with working from home (WFH) during the Corona pandemic (COVID-19) have motivated many employees to continue WFH after the pandemic. However, factors influencing employees' WFH intentions against the backdrop of experiences during pandemic-induced enforced working from home (EWFH) are heterogeneous. This study investigates factors linked to information technology (IT) professionals' WFH intentions.

This mixed-methods study with 92 IT professionals examines the effects of seven predictors for IT professionals' WFH intentions. The predictors are categorized according to the trichotomy of (1) characteristics of the worker, (2) characteristics of the workspace and (3) the work context. Structural equation modeling is used to analyze the quantitative survey data. In addition, IT professionals' responses to six open questions in which they reflect on past experiences and envision future work are examined.

Quantitative results suggest that characteristics of the worker, such as segmentation preference, are influencing WFH intentions stronger than characteristics of the workspace or the work context. Furthermore, perceived productivity during EWFH and gender significantly predict WFH intentions. Contextualizing these quantitative insights, the qualitative data provides a rich yet heterogeneous list of factors why IT professionals prefer (not) to work from home.

Reasons influencing WFH intentions vary due to individual preferences and constraints. Therefore, a differentiated organizational approach is recommended for designing future work arrangements. In addition, the findings suggest that team contracts to formalize working patterns, e.g. to agree on the needed number of physical meetings, can be helpful levers to reduce the complexity of future work that is most likely a mix of WFH and office arrangements.

This study extends literature reflecting on COVID-19-induced changes, specifically the emerging debate about why employees want to continue WFH. It is crucial for researchers and practitioners to understand which factors influence IT professionals' WFH intentions and how they impact the design and implementation of future hybrid work arrangements.

Contemporary entrepreneurial ecosystem models and frameworks advocate that culture is a criterion for entrepreneurial intention and central to entrepreneurship discourse. However, there is limited research from resource-constrained economies, such as sub-Saharan Africa and at a sub-national level. Responding to calls for bottom-up perspectives hinged on local context and heterogeneous nature, this paper aims to provide an in-depth understanding from multiple perspectives about the effect that culture and entrepreneurial intention have on the entrepreneurship process and performance in Nelson Mandela Bay, South Africa.

A mixed-method research design followed a sequential independent process consisting of two phases. Phase 1 included the dissemination of questionnaires to economically active participants, and 300 responses were statistically analysed. In Phase 2, 15 semi-structured interviews with influential economic development agents were conducted.

The results indicated that social legitimacy towards entrepreneurship existed and self-employment was viewed positively. However, self-employment endeavours were mainly necessity driven, and the systemic low levels of innovation, poor business competitiveness and the inability to scale were highlighted. The findings indicated that individuals venturing into business had a culture of being dependant on the government, lacking a risk appetite, fearing failure, with disparate groups suffering from a poor legacy of entrepreneurship.

Despite research done on the role of culture and entrepreneurial intention on entrepreneurial ecosystems, there are few case studies showing their influence at a sub-national level. This study responds to calls for studies on a sub-national level by exploring the influence that culture and entrepreneurial intention have on entrepreneurship in a resource-constrained metropole.