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Sustainability has become a priority for companies due to pressure from multiple stakeholders. In an overly competitive market, shareholders push for economic results, allowing lean manufacturing to establish itself as dominant paradigm in manufacturing. However, concerns grow regarding how lean implementation can allow companies to achieve sustainable development goals, or, if the resources required for a successful lean implementation can result in a detriment of environmental and social performance. This paper intends to help close the knowledge gap regarding the effects of lean manufacturing on sustainable performance from a triple bottom line perspective, and how operational, environmental and social outcomes interact between themselves.

Two models for the interaction between lean and sustainability were proposed. The first is called the “sand-cone” model, which poses that performance improvements derived from lean are cumulative on each one of the sustainability dimensions. The second is called the “trade-offs” approach. In this case, the resources required to improve one dimension of sustainability clash with those required by the others. Data were gathered from a sample of 133 Colombian metalworking companies and processed using structural equations models.

The results support the cumulative “sand-cone”, which follows a sequence of operational-environmental-social improvement in the presence of lean. For the “trade-offs” model, partial evidence suggests that they can occur in detriment of social performance.

The “sand-cone” and “trade-offs” are empirically tested for the first time in the context of sustainability, providing further knowledge into its interaction with lean manufacturing. The models’ results contribute to practitioners by providing a tested path for companies to improve their performance in a cumulative sequence that will provide better long-term results.

The purpose of this paper is to integrate the findings of articles appearing in European Journal of Marketing’s special section on covariance-based versus composite-based structural equations modeling (SEM).

This is an editorial which uses literature review to draw conclusions regarding areas of agreement, areas for further research, and changing the discussion around composite-based SEM methods.

There are now four new areas of agreement regarding composite-based SEM. Researchers should adopt a toolbox approach to their methods and know the strengths and weaknesses of the research tools in their toolbox. Partial least squares (PLS) SEM and covariance-based SEM are not substitutes, and it is inappropriate to use the language of confirmatory factor analysis (CFA) in reporting measurement estimates from PLS SEM. Measurement matters and researchers need to devote effort to using reliable and valid multi-item measures in their investigations.

This postscript article outlines recommendations for authors, reviewers and editors regarding the analysis of data and reporting of results using structural equations models.

This paper aims to conduct a fuzzy discrete time cost quality risk in the ambiguous mode CO_{2 tradeoff problem} (FDTCQRP*TP) in a megaproject based on fuzzy ground.

A combinatorial evolutionary algorithm using Fuzzy Invasive Weed Optimization (FIWO) is used in the discrete form of the problem where the parameters are fully fuzzy multi-objective and provide a space incorporating all dimensions of the problem. Also, the fuzzy data and computations are used with the Chanas method selected for the computational analysis. Moreover, uncertainty is defined in FIWO. The presented FIWO simulation, its utility and superiority are tested on sample problems.

The reproduction, rearrangement and maintaining elite invasive weeds in FIWO can lead to a higher level of accuracy, convergence and strength for solving FDTCQRP*TP fuzzy rules and a risk ground in the ambiguous mode with the emphasis on the necessity of CO₂ pollution reduction. The results reveal the effectiveness of the algorithm and its flexibility in the megaproject managers' decision making, convergence and accuracy regarding CO₂ pollution reduction.

This paper offers a multi-objective fully fuzzy tradeoff in the ambiguous mode with the approach of CO₂ pollution reduction.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

Globalization and the importance of emerging markets have increased the pressure of high-cost manufacturing locations to sustain operations. However, there are still some countries in which manufacturing is prospering despite high costs (like Germany, Sweden and Switzerland). This study examines seven competitive priorities through 24 different capabilities, using a case survey of four manufacturing companies located in Sweden. This study aims to develop a contemporary understanding from vital priorities and capabilities.

A case survey was conducted in four different-sized manufacturing companies in Sweden during the autumn of 2018. In total, the survey attracted 89 responses. Respondents were mainly middle managers and other management team members.

In general, companies assess the importance of manufacturing capabilities higher than performance and improvement. The authors’ analysis shows that quality priority through product and process capabilities is ranked highest in terms of importance, performance and improvement. In addition, delivery capability shows a similarity with quality. At the other end, being lowest ranked are typically different flexibility and advertising capabilities. This study demonstrates with correlation analysis that most often capabilities have a positive correlation in terms of their importance, performance and improvement needs. Some capabilities show potential correlations across importance, performance and improvement.

This research is limited to one high-cost environment and to four companies within that environment. Further research should examine the impact of the pandemic era on manufacturing priorities and capabilities.

In general, case surveys have relatively rarely been used in management studies. This research offers an alternative and deeper perspective from high-cost country manufacturing, as the responses are from numerous persons in management positions.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

To meet the rapidly increasing demand for medical treatment during the outbreak of COVID-19, Huoshengshan and Leishenshan Hospital are rapidly built (9–12 days) in Wuhan. These two urgent emergency projects are unprecedented. In general, substantial literature suggests that the possibility of shortening a schedule by more than a quarter of its original duration is implausible. By contrast, the two projects had successfully compressed the schedules from months and years to about ten days. This study aims to investigate how this was done and provide references for future projects.

The study uses qualitative case study techniques to analyze the project practices in two urgent emergency projects. Data were gathered through semi-structured interviews and archival research. During interviews, interviewees were asked to describe the project practices adopted to overcome the challenges and freely share their experiences and knowledge.

The results illustrate that a high degree of schedule compression is achievable through tactful crashing, substitution and overlapping applications. The successful practices heavily rely on the high capacity of participants and necessary organization, management and technology innovations, such as three-level matrix organizational structure, reverse design method, site partition, mock-up room first strategies and prefabricated construction technology. For instance, the reverse design method is one of the most significant innovations to project simplification and accelerate and worthy of promotion for future emergency projects.

The empirical findings are significant as they evoke new thinking and direction for addressing the main challenges of sharp schedule compression and provide valuable references for future emergency projects, including selecting high-capacity contractors and replacing the conventional design methods with reverse design.

Substantial studies indicate that the maximum degree of schedule compression is highly unlikely to exceed 25%, but this study suggests that sharp compression is possible. Although with flaws in its beauty (i.e. compressing schedule at the expense of construction cost and quality), it is also a breakthrough. It provides the building block for future research in this fertile and unexplored area.

Time-cost trade-off is normal conduct in construction projects when projects are expectedly late for delivery. Existing research on time-cost trade-off strategic management mostly focused on the technical calculation towards the optimal combination of activities to be accelerated, while the managerial aspects are mostly neglected. This paper aims to understand the managerial efforts necessary to prepare construction projects ready for an upcoming trade-off implementation.

A preliminary list of critical factors was first identified from the literature and verified by a Delphi survey. Quantitative data was then collected by a questionnaire survey to first shortlist the preliminary factors and quantify the predictive model with different machine learning algorithms, i.e. k-nearest neighbours (kNN), radial basis function (RBF), multiplayer perceptron (MLP), multinomial logistic regression (MLR), naïve Bayes classifier (NBC) and Bayesian belief networks (BBNs).

The model's independent variable importance ranking revealed that the top challenges faced were the realism of contractual obligation, contractor planning and control and client management and monitoring. Among the tested machine learning algorithms, multilayer perceptron was demonstrated to be the most suitable in this case. This model accuracy reached 96.5% with the training dataset and 95.6% with an independent test dataset and could be used as the contingency approach for time-cost trade-offs.

The identified factor list contributed to the theoretical explanation of the failed implementation in general and practical managerial improvement to better avoid such failure. In addition, the established predictive model provided an ad-hoc early warning and diagnostic tool to better ensure time-cost implementation success.

The purpose of this study is to examine, review and analyse the current literature on building and construction quality and determine the related themes and gaps.

A systematic review approach was adopted on the building quality literature, and keywords such as “construction” or “building”; “defect” and “quality” or “rework” were searched through the Scopus, Web of Science and Google Scholar databases. A number of articles were found and filtered by title, abstract and keyword relevancy. Further articles were cross-referenced from these and again filtered by title, abstract and keyword relevancy. The time period for the search was 2000–2020. A total of 97 articles were found and analysed on the topic.

A number of recurring themes were found throughout the literature. They are safety, occupier satisfaction, cost, time, licensing, culture, training, software and building quality frameworks, classifications and recommendations. These themes were linked to display relationships between them from the literature. Based on the project lifecycle stages, an amalgamated classification system was developed and is presented here. The gaps in the current literature have been analysed and reported on.

A comprehensive descriptive, thematic and gap analysis was conducted on the available literature of building and construction quality. The emerging themes were discovered, their relationships were demonstrated and the research gaps were identified. A new classification system positioned in the project lifecycle stages is presented.

This paper aims to test the empirical validity of the dynamic trade-off theory in its symmetric and asymmetric versions in explaining the capital structure of a panel of publicly listed US industrial firms over the period from 2013 to 2019. It analyzes the existence of an adjustment of leverage toward its target level and whether the speed of this adjustment is influenced by the debt measure, the model specification or/and the fact that the actual debt ratio is higher or lower than its long-term target level.

This paper uses a quantitative research methodology using panel data analysis under the partial adjustment model and the error correction model using the generalized moment method in first differences and in systems to explore the dynamic nature of firms’ capital structure behavior.

The results show that the effects of the conventional determinants of leverage are globally consistent with the trade-off theory predictions. The dynamic versions confirm that firms exhibit leverage-targeting behavior. Although this speed of adjustment (SOA) depends on the debt and model specifications, it is around 60% on average. The estimated SOA is higher for the market leverage measure compared to the book leverage. The asymmetric adjustment model reveals that firms are more sensitive to reducing leverage than increasing it when they are away from their target; overleveraged firms exhibit approximately 5% faster adjustment than underleveraged firms when book leverage is used.

The originality of this research paper lies in its development and test of an asymmetric model to allow the leverage adjustment speed to vary depending on whether the firm’s debt ratio is above or below its target level and the methodological approach as well as the different model specifications used and the insights generated through the application of rigorous econometric techniques.