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This study aims to investigate the association between certifications in the Bangladeshi readymade garment (RMG) sector and diverse firm resources, contributing to Bangladesh’s competitive advantage.

The study conducted a quantitative content analysis of 366 Bangladeshi RMG firm websites, using Barney’s (1991) resource-based theory (RBT) framework. Pearson correlation and linear regression analyses were used to explore the research questions.

The findings reveal significant positive impacts of certifications on all firm resource categories (physical, human, organizational knowledge and learning, general organizational and financial) under the RBT framework. Certifications correlate positively with resources, from small to medium, and with various factors, though some negative correlations were identified.

The study improves comprehension of apparel manufacturers’ certifications and their association with firm resources, offering valuable insights for stakeholders on long-term competitive advantages. Yet, limitations should be considered, including size-dependent variations and reliance on self-reported website data.

This study represents a pioneering effort, concentrating on Bangladesh’s RMG sector and offering a unique perspective on the implications of certifications for firm resources within emerging economies.

The aim of this paper was to examine the interrelationships between Maslow’s motivated needs in relation to employees’ personal and workplace spirituality experiences.

Regression analysis using a cross-sectional, convenience sampling approach of 158 US employees responding to survey questions using a Qualtrics survey related to their demographics, motivated needs, daily spirituality experiences, workplace spirituality, work spirituality experiences and work-related flow.

Correlation analyses provided support for Maslow’s hierarchy of needs in relation to employees’ daily spiritual experiences, workplace spirituality, work spirituality experiences and work-related flow. Regression analyses also identified the specific Maslow needs that served as predictive factors in relation to employees’ personal and workplace spirituality. Findings and conclusions are also discussed in relation to employees and organizations.

Currently, there are no correlation studies that have examined workplace spirituality as an ethical behavior in the workplace and Maslow’s hierarchy of needs. This correlation gap is notable because further examination of Maslow’s hierarchy of needs as a theoretical framework in relation to employees’ spirituality can be particularly valuable for contemporary work settings. Consider that today’s work environment is faced with dynamic and unique factors, and each of these factors not only changes the work environment but also they significantly drive or minimize employee motivation. Three such factors include new generations of employees with unique values entering the workforce and the great resignation and quiet quitting.

The study identifies that Maslow’s belonging, esteem and self-transcendence are related positively to employees’ spiritual experiences in the workplace. When these needs are fulfilled in the work environment, employees may be more likely to engage in spiritual practices at work, such as participating in yoga, prayer and meditation and in fulfilling one’s motivated needs and spirituality, employees are able to pursue their true purpose in the workplace.

This study extends the literature regarding understanding the value of workplace spirituality as a positive outcome for the employees and organizations.

This study aims to analyze the export competitiveness of Bangladesh's apparel industry by identifying the specific product categories that help sustain its export comparative advantage.

Compound annual growth rate (CAGR) and market share (MS) are calculated between 2011 and 2020 at the two- and four-digit level apparel product categories within the harmonized system (HS) to analyze the industry’s growth and export dominance. Trade competitiveness (TC) at the four-digit level, revealed comparative advantage (RCA) and normalized revealed comparative advantage (NRCA) at the two-, four- and six-digit-level apparel product categories are computed for the same 10-year period to investigate the industry’s export competitiveness. Major export destinations of the top 5 exporting product categories are identified to understand the factors facilitating the industry’s growth. A non-parametric Spearman rank correlation analysis evaluated the association between the RCA and NRCA indices.

Among the 34 product categories at the four-digit level, 29 consistently demonstrated an export comparative advantage, as did 34 out of 217 six-digit level sub-categories. In contrast, 12 sub-categories at the six-digit level consistently exhibited a comparative disadvantage in Bangladesh's export competitiveness. Furthermore, the TC measure identified 28 categories at the four-digit level with a robust comparative advantage. 30 categories displayed a positive CAGR, and Bangladesh asserted significant market dominance over 26 product categories at the four-digit level.

This study's implications are significant for various stakeholders in Bangladesh and other apparel-exporting industries, encompassing government entities, industry officials, policymakers, investors, researchers and students. Nevertheless, limitations arise from the study's reliance on RCA and NRCA as competitiveness indicators, particularly its adoption of a macro-level approach for measurement without exploring a micro-level perspective. This constitutes a notable constraint in the study's analytical framework.

This study contributed novelty and enrichment to the existing academic literature by identifying distinct apparel product categories that contribute to the industry's growth.

The prospect of Sub-Saharan Africa (SSA) as an apparel-sourcing base for US fashion companies has been a growing heated debate among academia, industry practitioners and policymakers. This study aims to evaluate SSA countries’ readiness to serve as an alternative sourcing destination to Asia for US fashion companies, focusing on comparing the similarities and differences of US apparel imports from these two regions at the product level.

This study was based on a statistical analysis of detailed product features and assortment information of thousands of apparel items at the stock-keeping unit level sold by US retailers between January 2021 and December 2023.

US fashion companies seemed to leverage SSA countries as suppliers of “niche products,” such as those relatively simple and basic apparel categories containing African cultural elements and targeting the luxury and premium market segment. However, the range of apparel products available for US fashion companies to source from the SSA region remained significantly more limited than those from Asia. Also, US apparel imports from SSA countries were primarily made of cotton and polyester, with less use of other fiber types, including nylon, rayon, viscose, wool and those made from recycled textile materials.

The study’s findings provided fresh insights into why US fashion companies sourced from SSA countries and the specific types of products they were sourcing, going beyond existing studies based on macro trade statistics. The results also deepened the understanding of SSA countries’ competitiveness as an apparel-sourcing destination and their potential to serve as an alternative to sourcing from Asia, particularly from a unique product perspective.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

Emotional labour (EL) is a complex phenomenon that has received increasing attention in recent years due to its impact on employee’s well-being and job satisfaction. For a comprehensive understanding of the evolving field of EL, it is important to extract different research trends, new developments and research directions in this domain. The study aims to reveal 13 prominent research topics based on the topic modelling analysis.

Using latent Dirichlet allocation (LDA) method, topic modelling is done on 1,462 journal research papers published between 1999 and 2023, extracted from the Scopus database using the keyword “EL”.

The analysis identifies several emerging trends in EL research, including emotional regulation training and job redesign. Similarly, the topics like EL strategies, cultural differences and EL, EL in hospitality, organizational support and EL, EL and gender and psychological well-being of nursing workers are popular research topics in this domain.

The findings provide valuable insights into the current state of EL research and can provide a direction for future research as well as assist organizations to design practices aimed at improving working conditions for employees in various industries.

Topic modelling on emotional labor is done. The paper identifies specific topics or clusters related to emotional labor, quantifies these topics using topic modeling, adds empirical rigor, and allows for comparisons across different contexts.

Cloud computing, a dominant technology, significantly impacts organizations, necessitating talent management strategies for sustained growth. This study aims to explore the impact of cloud adoption on large French organizations through a “learning organization” perspective.

Interviews were conducted with business and IT stakeholders from 35 multinational organizations in France.

Cloud services have a high impact on large organizations, leading to a demand for cloud-related skills, a power shift from IT to business departments and increased shadow IT activities. Effective utilization requires organizational learning and a change management project, transforming organizations into productive and innovative learning organizations.

This paper contributes to cloud computing, organizational learning and talent management literature, offering managers a novel approach to handling cloud services.

The main goal of this research is to analyze and assess the barriers to Digital Transformation (DT) of the healthcare Supply Chain (SC) in developing countries and evaluate strategies to overcome such barriers. The DT has been related not only to the development of SC performance but also to the expansion of its resilience capabilities in a healthcare setting to overcome the results of unavoidable risk events and return to its previous or new state, which has at least the same or better conditions, after the occurrence of an unpredicted event.

Assessment of the barriers and determination of the importance or effectiveness of proposed strategies to handle these barriers are conducted through a hybrid Multi-Criteria-Decision-Making method consisting of an Analytical Hierarchy Process and multi-attributive border approximation area comparison (MABAC) under picture fuzzy (PiF) environment.

Based on the outcomes of the research, the most important barriers for DT in healthcare SC functions were the organizational and technological issues, including Low support from top managers (O1), Lack of digital culture (O2), and Info/data safety and privacy (T4). To handle these barriers, the prior strategy was selected as “Making breakthroughs and incentives of top managers for the DT in the industry (ST3)”.

The study may be extended to include developed countries and or experts from developed countries. Through this, the study will be generalized to a global level. The most common qualitative criteria, which include subjectivity, are considered. The research may be enhanced by including quantitative criteria in the decision-making process. The barriers related to DT for healthcare SC were considered. Thus, the study may be detailed by looking at the problem in terms of operational failure and determining the digital technology that can overcome such operational failures. The precedence or importance of any barrier may fluctuate with the upgradation of technological tools and improvements.

In terms of practical contribution, this research provides real-world suggestions to facilitate digital transformation in their SCs. Specifically, this study increases the awareness of healthcare and other industries' managers about obstacles that prevents digital transformation in SC and provides managerial path to relieve the effects of barriers.

To the authors' best knowledge, this is the first study to examine barriers of DT required for the promotion of resilient healthcare SCs in this concept. In addition, no previous research applied PiFSs-based AHP and MABAC integration to assess barriers and develop strategies for the DT of healthcare SC. The findings may be references for governmental institutions, policymakers, decision-makers, and stakeholders to develop proper strategies for a successful DT in healthcare SC resilience and the healthcare industry.

This study aims to investigate the impact of job loss on the mental health of individuals in Tunisia during the COVID-19 crisis.

In this research, the authors use the counterfactual decomposition technique and the potential outcome approach. In the first part, the authors calculated mental health indicators for all individuals included in the sample based on the World Health Organization-5 items. The individuals were then grouped into two subpopulations: the first group included those who had lost their jobs and the second group included individuals whose status in the labor market had remained unchanged. In the second part, the authors used the Blinder and Oaxaca decomposition to explain the mean difference in the mental health scores between the two groups and determine the factors contributing to this difference.

The empirical results identified symptoms of depressed mood, decreased energy and loss of interest in several individuals. Based on these three symptoms, the authors were able to classify individuals into three types of depression: mild, moderate and severe. In addition, it appeared that job loss had significantly contributed to the worsening mental health of the individuals.

Although the psychological impact of the COVID-19 outbreak among health-care professionals has been the subject of other studies in health literature on Tunisia, to the best of the authors’ knowledge, no research has addressed the impact of job loss on the mental health of Tunisian workers. Thus, this study fills this gap in the literature.

This study aims to examine the influence of geopolitical uncertainty on cryptocurrency markets (CM).

Utilizing two distinct sets of daily returns data spanning from January 1, 2019, to May 4, 2023, the analysis employs the geopolitical risk (GPR) index formulated by Caldara and Iacoviello (2022), which encapsulates two pivotal events: the COVID-19 pandemic and the Russia–Ukraine conflict. The cryptocurrency market (CM) encompasses Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and Dogecoin (DOGE). Employing the DCC-GARCH model and supplementing it with wavelet coherence analysis to discern perceptual distinctions between short- and long-term market reactions.

The main findings indicate that the GPR index clearly impacts the return of CM in the short-, mid- and long-term periods. BTC exhibited the highest volatility in response to changes in the GPR index. The cryptocurrency market offers a better diversification opportunity, and the impact of geopolitical events varies across time, with their direction and magnitude closely related to the specificity of the CM.

This research is helpful for financial market investors, portfolio and risk managers, make informed decisions about including cryptocurrencies in their investment portfolios to mitigate the risks in uncertainty period.

Cryptocurrency market volatility is treated weakly during the risk period. With advanced statistical method, this study links two important events: the COVID-19 pandemic and the Russia–Ukraine conflict and selects the top four cryptocurrencies constituting 80% of the market. This study examines the impact of geopolitical risk on the cryptocurrency market and shows that this market is considered a safe haven.