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The challenges seafarers face are unique due to the nature of their work. Individuals in this profession face unique stressors such as limited interaction with people, isolation from society, confined workspaces, unpredictable weather conditions and exposure to hazardous situations. Quantitative methods used in studies on seafarers’ well-being fail to capture the individual experiences, emotions and motivations that make up their rich tapestry. The prevailing reliance on close-ended questionnaires is a hindrance to seafarers’ expression of subjective experiences and difficulties. It is essential to prioritize their well-being, both from a humanitarian standpoint and an economic perspective.

For this study, the authors coted qualitative data using open-ended semi-structured questionnaires from 35 (35) merchant navy sailors. The drive of the study was clearly stated to the respondents by the researchers. The study focuses on the emotional detachment of seafarers and follows a two-phase sampling process: criterion sampling and snowball sampling. The sampling methods are chosen in accordance with the purpose of the study.

The in-depth qualitative inquiry and detailed analysis of interview excerpts generated three broad themes that explain the occurrence of emotional detachment among seafarers. The qualitative study advances the knowledge base of the intricate phenomena of mariner’s emotional detachment. The themes illuminate the causes and outcomes of emotional detachment, also necessitating the need for intervention.

This study highlights the issue of seafarers at work, which tends to affect their personal and professional lives. The lived experiences of seafarers help us understand the issue of emotional detachment from a better and more in-depth perspective. This certainly helps companies and policymakers customize their interventions to seafarers’ needs.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization. A modest attempt has been made in the study to capture the relationship between the sales promotion, price discount and the batch procurement strategy of a particular product category to maximize sales volume and profitability.

Time series data relating to sales have been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products that considerably impact the sales promotion and intelligent pricing decisions. A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic.

The model captures the lag effect of sales promotion and price discounting strategy; other strategies have been formulated based upon the sales forecast that was done for taking the lot sizing decisions regarding procurement of products in the selected category. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization.

Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions.

A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

In part-I of this review series, research from Afghanistan, Bangladesh, Bhutan, Maldives, Nepal and Sri Lanka was reviewed. The purpose of this paper which is part-II of the series, is to review management research from India and Pakistan over a 25-year period from 1990 to 2014.

A systematic review approach was adopted for this research. As a quality standard for inclusion, articles were restricted to journals rated A*, A, or B by the Australian Business Deans Council in 2013 and either Q1 or Q2 in the Scopus/Imago classification system. The divisions and interest groups of the Academy of Management were used as framework to organize the search results.

A total of 1,039 articles related to India (n = 930) and Pakistan (n = 112) emerged from the search process, with three articles being related to both countries. The research was published in 163 different journals that met the quality criteria. The period under review coincides with the advent of economic liberalization in India and this emerged as a major theme in the India-related research. Other context-specific insights for these two countries are also derived from an ecological and institutional theory perspective.

This research represents the first comprehensive and systematic review of management research in India and Pakistan. As in part-I, the unique review approach allows for strict adherence to a predetermined quality standard while including a wide variety of journals and research traditions.

The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

The purpose of this paper is to examine the growth and characterization of the two different compound semiconductors, namely, n-zinc oxide (ZnO) and p-gallium antimonide (GaSb). In this paper, fabrication and characterization of n-ZnO/p-GaSb heterojunction diode is analyzed.

Thermo vertical direction solidification (TVDS) method was used to synthesize undoped GaSb ingot from high purity Ga (5N) and Sb (4N) host materials. Thermal evaporation technique is used to prepare a film of GaSb on glass substrate from the pre-synthesized bulk material by TVDS method. Undoped ZnO film was grown on GaSb film by sol–gel method by using chemical wet and dry (CWD) technique to fabricate n-ZnO/p-GaSb heterojunction diode.

The formation of crystalline structure and surface morphological analysis of both the GaSb bulk and film have been carried out by x-ray diffraction (XRD) analysis and scanning electron microscopy analysis. From the XRD studies, the structural characterization and phase identification of ZnO/GaSb interface. The current–voltage characteristic of the n-ZnO/p-GaSb heterostructure is found to be rectifying in nature.

GaSb film growth on any substrate by thermal evaporation method taking a small piece of the sample from the pre-synthesized GaSb bulk ingot has not been reported yet. Semiconductor device with heterojunction diode by using two different semiconductors such as ZnO/GaSb was used by this group for the first time.

This study investigates the various themes around the demand-driven approach of vocational education and training (VET). The study focuses on investigating two major aspects of VET. Firstly, the extant literature has been systematically classified based on seven parameters: Geography, Focus on Vocational Education, Research Methods, Economic Sector, Origin of Study, Type of Training and Level of Skill. Secondly, this study has explored various themes around the VET implementation, performance gaps and road ahead.

Thematic analysis with the systematic literature review (SLR) method is applied to 50 research papers on VET, published between 2011 and 2022.

This systematic review has found that experiential compliments VET and VET is the medium of skill development. Further, it was extracted through themes that VET may help ensure alternate employment in rural areas and contribute to micro- and macro-level economic development. Also, there is a requirement to adopt a demand-driven approach to VET by localizing VT content and delivery.

The study's findings are directed towards the need for demand-driven and customized VET. This study also explores many potential areas for further empirical research through various themes.

This study is a novel effort that extracts the themes constituting the effect of VETs along with the descriptive analysis of the extant literature using the SLR approach. The study has rationalized the findings by providing due coding to various parameters in the previous studies under investigation.

Digital financial services (DFS) have substantial prospect to offer a number of reasonable, appropriate and secure banking services to the underprivileged in developing countries through pioneering technologies such as mobile phone based solutions, digital platforms and electronic money models. DFS allow unbanked people to obtain access to financial services through digital technologies. However, DFS face tough challenges of adoption. Realising this, the purpose of this paper is to identify such challenges and develop a framework.

The authors developed a framework of challenges by utilising interpretive structural modelling (ISM) and fuzzy MICMAC approach. The authors explored 18 such unique set of challenges culled from the literature and further gathered data from two sets of expert professionals. In the first phase, the authors gathered data from 29 professionals followed by 18 professionals in the second phase. All were pursuing Executive MBA programme from a metropolitan city in South India. The implementation of ISM and fuzzy MICMAC provided a precise set of driving, linkage and dependent variables that were used to derive a framework.

ISM model is split in eight different levels. The bottom level consists of a key driving challenge V11 (i.e. high cost and low return related problem), whereas the topmost level consists of two highly dependent challenges namely V1 (i.e. risk of using digital services) and V14 (i.e. lack of trust). The prescribed ISM model shows the involvement of “high cost and low return related problem (V11)”, which triggers further challenges of DFS.

None of the existing research has explored key challenges to DFS in detail nor formulated a framework for such challenges. To the best of the authors’ knowledge, this is the first paper on DFS that attempts to collate its challenges and incorporate them in a hierarchical model using ISM and further divide them into four categories of factors using fuzzy MICMAC analysis.

Big data analytics (BDA) is becoming a strategic tool to harness data to achieve business efficiencies. While business-to-customer organizations have adopted BDA, its adoption in business-to-business (B2B) has been slow, raising concerns about the lack of understanding of the need to adopt BDA. Little knowledge exists on the subject and the purpose of this study is to examine BDA adoption needs among B2B organizations.

A systematic literature review (SLR) following the six-step SLR guidelines of Templier and Paré (2015) involved 1,051 articles, which were content analyzed.

The authors offer two-pronged findings. First, on the basis of the SLR, the authors develop a new four-category classification scheme of needs to adopt BDA and present a consolidated review of the current knowledge base along with these categories (i.e. innovation, operational efficiency, customer satisfaction and digital transformation). Second, underpinned by the theory of organizational motivation and literature evidence, the authors develop propositions and a corresponding model of BDA adoption needs. The authors show that BDA adoption among B2B organizations is driven by the need to augment customer lifetime value, champion the change, improve managerial decision cycle-time, tap into social media benefits and align with market transformation.

The results facilitate theory development as the study creates a new classification scheme of needs and a model of needs to adopt BDA in large B2B organizations.

The findings will serve as a guideline framework for managers to examine their BDA adoption needs and strategize its adoption.

The study develops a new four-category classification scheme for understanding B2B organizations’ needs to adopt big data analytics. The study also develops a new model of needs which will serve as a stepping stone for the development of a theory of needs of technology adoption.