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This study aims to deal with the multi-mode resource-constrained project scheduling problem (MRCPSP) with the ability to transport resources among multiple sites, aiming to minimize the total completion time and the total cost of the project simultaneously.

To deal with the problem under consideration, a bi-objective optimization model is developed. All activities are interconnected by finish-start precedence relations, and pre-emption is not allowed. Then, the ɛ-constraint optimization method is used to solve 24 different-sized instances, ranging from 5 to 120 activities, and report the makespan, total cost and CPU time. A set of Pareto-optimal solutions are determined for some instances, and sensitivity analyses are performed to find the impact of changing parameters on objective values.

Results highlight the importance of resource transportability assumption on project completion time and cost, providing useful insights for decision makers and practitioners.

A novel bi-objective optimization model is proposed to deal with the multi-site MRCPSP, considering both the cost and time of resource transportation between multiple sites. To the best of the authors’ knowledge, none of the studies in the project scheduling area has yet addressed this problem.

Theory of Constraints (TOC), though a well-established process improvement methodology in manufacturing, is still a novel philosophy for healthcare and an exhaustive review of literature is needed to summarize the key findings of various researchers. Such a review can provide a direction to the researchers and academicians interested in exploring the application of TOC in the healthcare sector. This paper aims to review the existing literature of TOC tools and techniques applied to the healthcare environment, and to investigate motivating factors, benefits and key gaps for identifying directions for future research in the domain of healthcare.

In this paper, different electronic repositories were searched using multiple keywords. The current study identified 36 articles published between January 1999 to mid-2021 to conceptualize and summarize the research questions used in the study. Descriptive analysis along with pictorial representations have been used for better visualization of work.

This paper presents a thorough literature review of TOC in healthcare and identifies the evolution, current trends, tools used, nature of services chosen for application and research gaps and recommends future direction for research. A variety of motivating factors and benefits of TOC in healthcare are identified. Another key finding of this study is that almost all implementations listed in literature reported positive outcomes and substantial improvements in the performance of the healthcare unit chosen for study.

This paper provides valuable insight to researchers, practitioners and policymakers on the potential of TOC to improve quality of services, flow of patients, revenues, process efficiency and cost reduction in different health care settings. A number of findings and suggestions compiled in the paper from literature study can be used for diagnosing, learning and making substantial changes in healthcare. The methodologies used by different researchers were analysed and combined to propose a generic step by step procedure to apply TOC. This methodology will guide the practising managers about the appropriate tools of TOC for their specific need.

Good health is always the first desire of all men and women around the globe. The global aim of healthcare is to quickly cure more patients and ensure healthier population both today and in future. This article will work as a foundation for future applications of TOC in healthcare and guide upcoming applications in the booming healthcare sector. The paper will help the healthcare managers in serving a greater number of patients with limited available resources.

This paper provides original collaborative work compiled by the authors. Since no comprehensive systematic review of TOC in healthcare has been reported earlier, this study would be a valuable asset for researchers in this field. A model has been presented that links various benefits with one another and clarifies the need to focus on process improvement which naturally results in these benefits. Similarly, a model has been presented to guide the users in implementation of TOC in healthcare.

This study aims to determine how the applications of blockchain technology (BT) can play a crucial role in managing financial flows in the humanitarian supply chain (HSC) and what benefits and challenges are associated with BT in a humanitarian setting.

The present study used a qualitative research approach, incorporating a systematic literature review and conducting semi-structured interviews with 12 experts in the fields of humanitarian operations, supply chain management, fintech and information technology.

The findings show that the humanitarian sector has the potential to reap significant benefits from BT, including secure data exchange, efficient SCM, streamlined donor financing, cost-effective financial transactions, smooth digital cash flow management and the facilitation of cash programs and crowdfunding. Despite the promising prospects, this study also illuminated various challenges associated with the application of BT in the HSC. Key challenges identified include scalability issues, high cost and resource requirements, lack of network reliability, data privacy, supply chain integration, knowledge and training gaps, regulatory frameworks and ethical considerations. Moreover, the study highlighted the importance of implementing mitigation strategies to address the challenges effectively.

The present study is confined to exploring the benefits, challenges and corresponding mitigation strategies. The research uses a semi-structured interview method as the primary research approach.

This study adds to the existing body of knowledge concerning BT and HSC by explaining the pivotal role of BT in improving the financial flow within HSC. Moreover, it addresses a notable research gap, as there is a scarcity of studies that holistically cover the expert perspectives on benefits, challenges and strategies related to blockchain applications for effective financial flows within humanitarian settings. Consequently, this study seeks to bridge this knowledge gap and provide valuable insights into this critical area.

This paper aims to examine how the rise of financial technology (Fintech) lending affects bank competition. Moreover, this study also identifies the structure of Indonesian commercial banking sector and the different behaviour of competition among bank groups (based on their size, type and ownership) and the joint impact of COVID-19 due to the rise of Fintech lending.

Using an unbalanced panel data set of 118 commercial banks in Indonesia over the period 2018–2022, both static panel and 2SLS/IV data analysis were used and found that random effect model is the best model.

The results show that the Indonesian commercial banking sector can be considered as monopolistic competition. Moreover, using the Lerner index reveals that the entry of the Fintech lenders increases bank competition. Furthermore, there were different responses to the impact of Fintech lending on bank competition among state-owned banks, private banks, regional development banks and foreign banks. Greater efficiency and stability lead to greater market power. In the meantime, higher level of asset growth, capitalisation and cost-to-income ratio increase the competition. Lastly, higher bank credit growth and lower inflation boost overall bank competitiveness.

This study highlights some policy recommendations for commercial banks to be aware of the coming of Fintech lenders because they have started to increase the market competition. The government should create a more collaborative ecosystem between banks and Fintech lending to anticipate unhealthy competition.

This study will contribute to the literature by expanding the determinants of bank competition by considering the rise of Fintech lending in the market.

Much research has explored high levels of son preference in India, finding that parents often report a desire for more sons than daughters. While scholars have noted that a nontrivial portion of respondents claim to have no sex preference, little is known about (1) the characteristics of this group and (2) how such parental preferences relate to child outcomes. We use data from a representative study of rural South Indian households (n = 7,891 adults) to address these gaps. Descriptive results show that a sizable portion of respondents – one in four – indicated that, at the start of their marriage, they had no preference for the number of daughters or sons they wanted. Further, multinomial regression results show that those who reported no sex preference at the time of marriage were more likely to be female, older, and less likely to be sterilized than those who reported equal or son preference, with additional distinctions across educational attainment and religion. Turning to child-level outcomes, we examined whether parents’ sex preferences related to adolescent mental health through ordinary least squares (OLS) regression models (n = 1,245 adolescents). Adolescents whose mothers stated no sex preference reported significantly fewer anxiety and depressive symptoms than their peers. Fathers’ sex preferences were not significantly related to adolescent mental health. These findings suggest that a lack of sex preference may hold meaningful and positive implications for adolescent mental health. Further, although son preference is a widespread phenomenon, singular attention on those with son preference may mask important nuances among Indian families.

This study aims to investigate connections between the development of robotic and artificial intelligence (AI) and green crypto investments. The author also explores the influences of global uncertainty shocks like the COVID-19 pandemic and international conflicts on the role of each channel.

In this research, the author uses a cutting-edge model-free connectedness approach to investigate the relationships between the development of Global X Robotics and AI (BOTZ) and the volatility of green crypto investments from November 9, 2017 to March 24, 2023.

In the sample duration, the findings reveal a two-way link between AI and green/nongreen cryptocurrencies. Throughout the examined period, BOTZ has been a net receiver of shocks as determined by the net total connectedness. Among the main spillover shock carriers in the system, green cryptocurrencies are the most significant. The net pairwise directional connectivity reveals that green cryptocurrencies controlled BOTZ throughout the analyzed time, particularly during the COVID-19 era as well as the Ukraine–Russia crisis. According to the findings, the proposed system is vulnerable to a high level of indication influence.

The results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies.

To the best of the authors’ knowledge, this paper is the first to look at the link between AI, technological advancement and green cryptocurrency investing. Second, this study developed a methodology for examining instability links between various factors that is more appropriate for investigating these linkages. This study investigates the links between AI, technical advancement and green digital currencies using a cutting-edge model-free connectivity method. This work is also the first to examine the interconnection between volatility derived from AI, technological development and green cryptocurrency investments in light of unknown events, such as the COVID-19 pandemic and the Ukrainian–Russian conflict. Finally, this study includes a daily database from the BOTZ fund, which attempts to invest in firms that stand to gain from rising robotics and AI use. Cardano (ADA), IOTA, NANO (XNO), Stellar Lumens and Tron are examples of green cryptocurrencies, whereas Bitcoin is an example of a nongreen cryptocurrency. These virtual currencies are being used to investigate the relationship between investor mood and green and nongreen digital currencies. The data set spans the period from November 9, 2017 to March 24, 2023.

This study examines whether US effective tax rates on foreign income of US multinationals (MNCs) vary according to the favorability of US macroeconomic conditions relative to those of non-US countries. We use the pre-Tax Cuts and Jobs Act of 2017 regime as our setting and present evidence that US effective tax rates on foreign earnings are higher (lower) in periods when macroeconomic conditions in the US are favorable (unfavorable) relative to those elsewhere in the world. These results imply that firms seek to maximize after-tax returns when making asset allocation decisions, even when faced with US repatriation tax costs. We provide further evidence indicating that our primary results vary predictably according to certain firm characteristics, namely the ability to acquire funds for investment through less expensive means than repatriation of foreign profits, high intangible asset intensity, and tax aggressiveness. Finally, we show that economic uncertainty in the US counters the positive effects of favorable US macroeconomic conditions on US effective tax rates on foreign earnings. Our findings have implications for the policy debate around the US taxation of foreign earnings and provide a (partial) explanation for the observed lower-than-expected levels of repatriation activity following the implementation of the Tax Cuts and Jobs Act of 2017.