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The purpose of this study is to improve the magnetron quality in Company T by identifying the nonconforming defect, adjusting the factors affecting the leakage of the magnetron tube core, and determining the optimal parameter values of these factors.

A case study method is used to present the quality improvement of magnetron tube core. The define, measure, analyze, improve, and control framework is applied in the case study as well as several Six Sigma tools.

The results show that Ag–W thickness, Ag–W installation state and furnace entry interval are significant factors on the leakage of magnetron tube core, and the optimum settings for these factors are 0.055 mm, offset by 1 mm from the outer edge and 5 cm, respectively.

The main limitation of this study is that it was carried out on a small number of production processes. The authors would like to analyze more case studies on the improvements of after-sales quality and supplier quality.

This research could be used in magnetron manufacturing process as a tool for managers and engineers to improve product quality, which can also be extended to similar manufacturing systems.

In this case study, the Six Sigma approach has been applied for the first time to solve magnetron manufacturing problems by improving the quality of magnetron production process. It can help the quality engineers be more familiar with the deployment of Six Sigma and effective tools.

With the increase in uncertainty around the globe, an intensifying interest is seen in Economic Policy Uncertainty (EPU) as a topic of research. Researchers worldwide understand the significance of the impact of EPU on the country's development. EPU has a far-reaching impact as uncertainty shocks in one part of the world resonate worldwide due to the level of interconnectivity, globalization and quick communication. In order to facilitate these researchers, this study presents a bibliometric analysis of the existing research in this field using VOS viewer software, by consolidating all the studies from Scopus indexed journal articles, conference proceedings and review papers published in English language from 2006 to 2022. Bibliometric analysis on EPU has rarely been performed. The analysis identifies the publication trends, journal-wise citation, most influential authors, countries, institutions, keyword co-occurrence and authors of different countries who have collaborated for the research in the field. Finally, 1,055 papers were used for bibliometric analysis. The findings depicted that the most cited article on EPU is ‘Measuring economic policy uncertainty’ by Baker et al. (2016) and the most prolific author appears to be Rangan Gupta from University of Pretoria which as an institution also has the maximum publications on this topic. The Journal Finance Research Letters has published the greatest number of researches on EPU. This chapter also summarizes the limitations of the study along with new areas of research.

The purpose of the study is to find out the impact of Digital Financial Inclusion (DFI) on economic growth [(Industrial Production Index (INDP)] of Bangladesh.

Using the monthly data over the period 2018 M12 to 2021 M12, this study applied the Auto-regressive Distributed Lag (ARDL) model to assess the effect of DFI indicators on INDP. The secondary data was collected from the Bangladesh Bank and CEIC Global Economic Data.

The study found that the majority of DFI indicators are positively associated with INDP. From the short-run ARDL, it is seen that one unit positive increase in Point of Sales Transactions (POST) can increase the INDP by 0.055 units. From the long-run ARDL, it is seen that POST and e-commerce transactions (ECOMT) have a significant positive impact, while Automated Teller Machine Transactions (ATMT) have a significant negative effect on INDP. One unit increase in POST and ECOMT increases INDP by 0.13544 and 0.11611 units, respectively.

During the era of the fourth industrial revolution, the findings will be beneficial for policymakers, financial technology service providers, manufacturers, consumers, corporations and investors as they pave the way for a more inclusive approach to financial transactions for economic growth.

The study’s novelty is that it explored the influential DFI indicators and shed light on both short-run and long-run relationships between the indicators and macro-economy from the context of a developing nation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0306

This study tries to examine the effect of artificial intelligence (AI) drivers on the willingness to adopt the human capital supply chain (HCSC) in manufacturing firms (MFs) in developing countries (DCs) including Jordan, Saudi Arabia, Bahrain, Qatar and the United Arab Emirates, which are listed in the Chambers of Industry of these countries.

The quantitative methodology with a simple random sampling method was adopted using a questionnaire survey-based approach to collect data from 233 out of 1,055 participants (human resource (HR) managers and information technology (IT) senior managers) from various MFs (private and commercial), representing a 22% response rate. Covariance-based structural equation modeling (CB-SEM) was used to analyze the raw data using Amos V.25.

The results of this study showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs. At the same time, there is no significant effect on a firm’s infrastructure readiness (FIRs), in addition to the indirect effect of UC in the relationship between CPs and FIRs on the willingness to adopt AI in HCSC.

Such findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive AI drivers' effect on willingness to adopt the HCSC in the MFs in DCs as emerging economies. Additionally, the managers might focus on the existence of a significant positive indirect effect of UC as a mediating factor in the relationship between FIRs and willingness to adopt AI and its applications in HCSC systems and departments.

The successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.

Based on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.

The study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.

This study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

The world of investing has changed drastically. Investors are willing to invest the companies that give high priority to environmental, social and governance issues (ESG). This study delves into the performance of the BSE CARBONEX index in comparison to the BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas. It seeks to examine the impact of calendar anomalies, particularly focusing on the day-of-the-week effect, on these indices. To accomplish this, daily closing prices of the BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas were gathered from the BSE official website. The study period was divided into three segments: the full period, period I (2017–2020) and period II (2020–2022). The study's findings reveal that throughout the full period, period I and period II, BSE Energy exhibited the highest mean daily return compared to the other selected indices. There appears to be a discernible Tuesday effect on the daily average mean returns of BSE CARBONEX, BSE 100, BSE Sensex, BSE Energy and BSE Oil & Gas in both the full sample period and period II. Results from ordinary least squares (OLS) analysis by day indicate a notably high positive and statistically significant daily return on Tuesdays, particularly during the full sample period and period II. Furthermore, the GARCH (1,1) model suggests a significant Tuesday effect on the BSE Energy and BSE Oil & Gas indices.

Due to excessive carbon dioxide emissions, the world is facing environmental devastation. Energy and environmental innovations are considered to be critical tools in combating the growing CO₂ emissions. Developing these innovations requires extremely high investments in research and development processes, where knowledge is generated as one of the important outputs. This knowledge serves as a basis for innovation development and raising awareness among all relevant stakeholders about excessive environmental degradation. One of the significant sources of knowledge is scientific publications. Therefore, the aim of this research is to examine whether increased CO₂ emissions stimulate the scientific community to publish a greater number of papers, as well as whether the knowledge contained in these publications is utilized in reducing CO₂ emissions. The sample consists of G7 member countries. The time frame of the research is 1996–2019. The dynamic properties of the vector autoregression (VAR) models were summarized using impulse response function and variance decomposition forecast error. In most G7 countries, it has been determined that an increase in scientific production in environmental science and energy leads to a reduction in CO₂ emissions. On the other hand, increased CO₂ emissions affect higher scientific productivity in environmental science and energy only in Canada.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

Multiple chronic conditions (MCCs) have attracted significant public policy and clinical attention. Whether MCCs determine other important outcomes, or are themselves the outcomes of health-producing activities or interventions, metrics based thereon have potential to be useful indicators of the health of populations and of differences between and among the health of subpopulations. While the attention MCCs are attracting in various policy circles is impressive, MCCs' potential roles as indicators of population health and of how health determinants influence population–health outcomes have received less attention. The purpose of this chapter is to direct attention towards questions that involve considerations of chronic condition (CC) patterns as health outcomes; specifically, this paper hopes to advance the consideration of patterns of MCCs as indicators of individual and population health. Using data from the United States (US) Behavioural Risk Factor Surveillance System (BRFSS), the chapter explores whether both the ‘intensity’ (i.e. the number or count) of CCs as well as their ‘composition’ (i.e. the patterns of particular CCs) might be jointly of interest when considering the prevalence of MCCs in populations and how the nature of MCCs may vary across subpopulations of interest. It is seen that information about intensity tells an incomplete story about MCC health outcomes.