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This study examines the intricate interplay of blockchain, public governance and the circular economy (CE), aiming to assess the potential of blockchain technology (BT) in addressing challenges associated with the adoption of CE principles, particularly in the public sector.

Focused on public governance, the research employs in-depth interviews with Finnish policymakers actively engaged in CE initiatives. Qualitative analysis is applied to derive insights and patterns from the gathered data, providing a nuanced understanding of blockchain’s transformative role.

The study uncovers key dimensions for leveraging blockchain in the CE within the public sector. Notable findings include the significance of contextual transparency, the use of incentivization as a regulatory tool, the role of standardization through strategic autonomy and the importance of public engagement and participation.

This research contributes a unique framework that illuminates the transformative potential of blockchain within the CE, emphasizing its relevance to public governance. The identified dimensions offer practical insights for policymakers and practitioners seeking to navigate the complexities of circular transitions in the public sector.

This study aims to investigate the connection between the circular economy and sustainable operations management to identify the challenges and opportunities in platform organizations. The study looks at how the stated circular economy strategies (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) are integrated across different industries, emphasizing how they align with the e-business model. The research evaluates their contribution to achieving Sustainable Development Goal 12, which focuses on responsible consumption and production.

A literature review has analyzed CE frameworks, business models and the role of sustainable operations management practices. This study utilized secondary data analysis of platform organizations and insights from case studies to identify patterns, strategies and outcomes. The study also involved practical examinations within organizations, specifically focusing on innovative start-ups.

The analysis uses the 6R framework (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) to uncover valuable insights into organizational practices and highlight the role of platform organizations in promoting and achieving circular economy objectives. The research findings focus on the central importance of data regulation and governance while showcasing sustainable business practices through platform organizations.

This research is significant as it connects circular economy with platform organizations’ business models, emphasizing data regulation, resource efficiency, waste reduction and aligning business practices with Sustainable Development Goal 12.

This study aims to explore the asymmetric effects of macroeconomic factors on the profitability of large-cap companies in an emerging country like Colombia, using the Morgan Stanley Capital International (MSCI) Colombia index as the basis.

We employ a combination of singular spectrum analysis (SSA) and principal component analysis (PCA) to identify and estimate four key macroeconomic factors that account for approximately 47.8% of Colombia's macroeconomy. These factors encompass indicators related to inflation and cost of living, foreign trade and exchange rate, employment and labor force and trade and production in Colombia. We utilize the distributed lag nonlinear model (DLNM) to analyze the asymmetric relationships between these factors and corporate profitability, considering different scenarios and lags.

Our analysis reveals that there are indeed asymmetric relationships between the identified macroeconomic factors and corporate profitability. These relationships exhibit variability over time and lags, indicating the nuanced nature of their impact on corporate performance.

This study contributes to the existing literature by applying a novel methodology that combines SSA and PCA to identify macroeconomic factors within the Colombian context. Additionally, our focus on asymmetric relationships and their dynamic nature in relation to corporate profitability, using DLNM, adds original insights to the research on this subject.

Lean Manufacturing Systems (LMS) gained popularity among manufacturers globally. However, their efficacy in developing and least-developed countries remained noticeably understudied. Motivated by this research gap, the researchers of this study designed a quantitative study with a structured survey technique to investigate its context-specific impact on the apparel industry of a developing country. Hence, this study aimed to examine the relationship between LMS and elimination of waste (EOW) and operational performance (OP) and comprehend how the EOW mediates the relationship between an LMS and OP within the apparel industry of a developing economy.

The researchers collected data from 227 garment companies in Bangladesh. These organization-level data were then analyzed using the structural equation modeling approach with AMOS 20.0 software to examine the direct and indirect effects among EOW, LMS and OP.

The findings of this study suggest that EOW has a direct and significant effect on OP. This research also revealed that EOW has a partial mediating effect on the relationship between LMS and OP.

This research focused on a single industry administering self-reported data and cross-sectional design, limiting generalizability and causal inference.

LMS and directing efforts towards EOW can significantly improve the operational performance of apparel companies by reducing lead times and costs, improving quality and increasing productivity.

These findings can provide useful insight to managers, practitioners and future researchers to understand the relationship between EOW, LMS and OP to optimize their production processes and improve OP in the apparel industry.

Lean construction (LC) consists of very effective techniques; however, its implementation varies considerably from one industry to another. Although numerous lean initiatives do exist in the construction industry, the research topic related to LC implementation is still unexplored due to the scarcity of validated assessment frameworks. This study aims to provide the first attempt in developing a structural model for successful LC implementation.

This study developed a Lean construction model (LCM) by critically reviewing seven previous LC frameworks from different countries, defining 18 subprinciples grouped into 6 major principles and formulating testable hypotheses. The questionnaire was pre-tested with 12 construction management experts and revised by 4 specialized academics. A pilot study with 20 construction units enhanced content reliability. Data from 307 Moroccan construction companies were collected to develop a measurement model. SPSS V. 26 was used for Exploratory Factor Analysis, followed by confirmatory factor analysis using AMOS version 23. Finally, a structural equation model statistically assessed each construct's contribution to the success of LC implementation.

This work led to the development of an original LCM based on valid and reliable LC constructs, consisting of 18 measurement items grouped into 6 LC principles: Process Transparency, People involvement, Waste elimination, Planning and Continuous improvement, Client Focus and Material/information flow and pull. According to the structural model, LC implementation success is positively influenced by Planning and Scheduling/continuous improvement (β = 0.930), followed by Elimination of waste (β = 0.896). Process transparency ranks third (β = 0.858). The study demonstrates that all these factors are mutually complementary, highlighting a positive relationship between LC implementation success and the holistic application of all LC principles.

To the best of the authors’ knowledge, this study is the first attempt to develop a statistically proven model of LC based on structural equation modelling analysis, which is promising for stimulating construction practitioners and researchers for more empirical studies in different countries to obtain a more accurate reflection of LC implementation. Moreover, the paper proposes recommendations to help policymakers, academics and practitioners anticipate the key success drivers for more successful LC implementation.

Variations due to errors and mistakes have caused cost overruns in building projects. Therefore, it is undeniable that the gravity of such variations will be a critical factor in deciding the success of any building project. In addition, the design stage of a building project is considered the most suitable stage to identify and mitigate the causes of potential variations. However, there are no proper mechanisms to minimise the frequency or gravity of variations. Many researchers experienced the promising essence of Poka-Yoke, a mistake-proofing method aimed at increasing efficiency by early detection and eradication of the causes of potential errors. However, less attention has been paid so far to implement Poka-Yoke principles to minimise variations in the building project. Therefore, this study aims to develop a framework to minimise variations in building projects through the integration of the Poka-Yoke principles.

An interpretivism stance is adopted, and a qualitative research approach is used. The data collection technique adopted is semi-structured interviews with ten experts, and the data is analysed using code-based content analysis through NVivo12.

Research findings revealed 23 causes of variations, categorised under client-originated, consultant-originated, contractor-originated and other variations. The identified causes were then mapped with the Poka-Yoke principles to develop the framework. The research findings could prove useful to researchers, academics, government agencies and construction professionals in developing nations that have demographic/cultural and socioeconomic characteristics such as Sri Lanka.

The findings benefitted the Sri Lankan construction sector by minimising the causes of variations. To the best of the authors’ knowledge, this study will be the first of its kind in the Sri Lankan construction industry, leading to a better understanding of the “Poka-Yoke” principle within the building construction context.

This study aims to improve the market competitiveness of iron and steel manufacturers in developing countries by reducing their production costs.

The research methodology relies on a case study-based approach. The study relies on six steps. The first is the preparation, then the five steps of the six-sigma – define, measure, analyze, improve, control. The qualitative and quantitative data were considered. The qualitative analysis relies on the experts’ judgment of internal status. The quantitative analysis uses the job floor data from three iron and steel manufacturers. After collecting, screening and analyzing the data, the root causes of the different wastes were identified that increase production costs. Consequently, lean manufacturing principles and tools are identified and prioritized using the decision-making trial and evaluation laboratory method, and then implemented to reduce the different types of waste.

The main wastes are related to inventory, time, quality and workforce. The lean tools were proposed with the implementation plan for the discovered root causes. The performance was monitored during and after the implementation of the lean initiatives in one of the three companies. The obtained results showed an increase in some performance indicators such as throughput (70.6%), revenue from by-products (459%), inventory turnover (54%), operation availability (45%), and plant availability (41%). On the other hand, results showed a decrease of time delay (78%), man-hour/ton (52.4%) and downgraded products (63.3%).

The current case study findings can be utilized by Iron and Steel factories at the developing countries. In addition, the proposed lean implementation methodology can be adopted for any other industries.

The current work introduces an original and practical road map to implement the lean six-sigma body of knowledge in the iron and steel manufacturers.

This work introduces an effective and practical case study-based approach to implementing the lean six-sigma body of knowledge in the iron and steel manufacturers in one of the underdevelopment countries. The consideration of the opinion of the different engineers from different sectors shows significant identification of the major problems in the manufacturing and utility sectors that lead to significant performance improvement after solving them.

The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

This study aims to examine the impact of Lean 4.0 practices on value co-creation in the dairy ecosystem.

Data collection were carried out through a questionary application with 126 professionals linked to the dairy ecosystem, including milk producers, milk cooperatives and milk transporters. The data were analyzed using Cluster Analysis, Mann-Whitney test and Chi-Square test.

A strong relation was found between the use of Lean 4.0 tools and the increase in operational performance, in addition to milk quality. Moreover, it can be noted that the use of digital technologies from Industry 4.0 has a strong relation with dairy production optimization, in other words, it is possible to be more efficient in the dairy process via Lean 4.0 adoption.

The study is limited to analyzing the Brazilian dairy ecosystem. The results presented may not reflect the characteristics of the other countries.

Once the potential empirical impacts of the relation between Lean 4.0 and value co-creation are elucidated, it is possible to direct strategies for decision-making and guide efforts by researchers and professionals to deal with the waste mitigation present in the dairy sector.

Lean 4.0 proves to be a potential solution to improve the operational performance of the dairy production system. Lean 4.0, linked to value co-creation, allows the integration of the production sector with consumers, through smart technologies, so new services and experiences can be provided to the consumer market. Additionally, the consumer experience can be stimulated based on Lean 4.0, once the quality specification is highlighted based on data science and smart management control.

To the best of the authors’ knowledge, this is the first study that analyzes the interrelationship between the Lean 4.0 philosophy and the value co-creation in the dairy ecosystem. In this sense, the study reveals the main contributions of this interrelation to the dairy sector via value co-creation, which demonstrates a new perspective on the complementarity of resources, elimination of process losses and new experiences for the user through digital technologies integrated with the Lean Thinking approach.