Transportation is one of the most important functions of logistics management because of its impact on both the level of customer service provision and the organization’s cost structure. The study explores the transportation cost optimization strategies and how they affect the supply chain performance of Shea nuts in the Agro-processing industries in Ghana. The quantitative study design was adopted and utilized to solicit data. With a sample size of 204 respondents. Data was analyzed using multiple linear regression in Statistical Package for Social Sciences (SPSS v20). The results revealed interesting findings in the supply chain within the institution as it seeks to assert that transportation is seen to be effective and contribute highly to firms output when Agro companies use outsourcing and pooling as means of transport. From the results, the supply chain correlated positively and significantly with outsourcing and pooling as transport optimization practices in Agro firms supply chain. This paper adds to the existing literature on the supply chain systems of Agro processing firms but departs from previous studies which seek to concentrate not on the transport optimization of the Agro processing firms but rather on the challenges, production, and profit maximization of these firms. Our study is unique unlike any other research previously authored as it seeks to reveal the option of transport optimization that is cost effective and efficiently productive for Agro processing firms to adopt.

Sustainability is getting preference now-a-days in most of the countries in the world. This implies industrial and other developments for economic growth, and of course civilization, but certainly not at the cost of environmental degradation. Environmental conservation is a must. One of the major components of industrial sectors is their electrical energy inputs through electrical substation. Numerous research addresses the issue of energy pollution, cantering on fossil fuel. A very few research can be found which address the issue of pollution generated from electrical energy. This research addresses this issue, with special attention to electrical substations. In the last two or three decades, several electrical equipment manufacturers have started producing this equipment in Bangladesh, which were previously import-oriented. This equipment use various types of oils, acids, zinc and other metals, which are all environmentally hazardous. Thus, any industrial company, buying such equipment from substation manufacturers, should consider green aspects. As such, now it is of prime importance to select green suppliers of substation equipment. Since selection of right or green supplier involves multi-criteria evaluation, Multi-criteria Decision Making (MCDM) technique is essential. Additionally, both qualitative and quantitative criteria require fuzzy analysis to make it more precise and realistic. This research addressed the issue of environmental pollution, its sources, nature and severity, and their mitigation through selecting green suppliers.

With increasing economic instability coupled with climate change and the Covid-19 pandemic the subject of economic resilience in agripreneurship value chains in rural farming has gained prominence. Despite the constant engagements on the subject, there still is a missing piece on the determinants of economic resilience and sustainability for agripreneurship in tobacco value chains. The objective of this study was to characterize rural farming typologies which will assist in determining resilience and sustainability for tobacco value chains. The research adopted a pragmatic research approach, where quantitative results were triangulated with qualitative results to test the robustness of the research findings. A total of 250 questionnaires were administered to small scale tobacco farmers in Gokwe and interviews were held with selected 8 Agri-tex-officers. STATA version 12 was used to analyse quantitative data and thematic analysis using vignettes for qualitative data. The research findings showed a significant positive relationship between sustainable agripreneurship and economic resilience in tobacco farming in Gokwe district. The study recommends that the government prioritise the development of sustainable tobacco infrastructure throughout the value chain and training of tobacco farmers on resilient farming methods. This study offers theoretical contributions on how to achieve economic resilience and sustainable agripreneurship in tobacco value chains in Gokwe district.

Making rational decisions in times of uncertainty is an important capability required of marketing managers. However, choosing the precise quantitative decision-making technique or a combination of such tools usually presents managers with a score of challenges. To lessen this challenge, this single case research demonstrates how the choice of three Bayesian decision models (Laplace criterion, Savage minimax regret, and Hurwicz coefficient of realization) can reduce uncertainty in content marketing, search engine optimization (SEO), and referral marketing decision. We employed the case study approach to enable direct access to the decision-making process of the case organization, and to collect relevant data relating to the three marketing decision variables. Results show that, although the Laplace criterion may not have a direct effect on content marketing decisions, it influences the decision-making process leading to the creation and promotion of content. The Savage minimax regret had a direct bearing on SEO decision-making. Hurwicz's coefficient of realization yielded no direct impact on referral marketing but shows the possibility of influencing decision-making processes that led to the development and implementation of referral marketing campaigns. The implications are discussed.

The uprising of blockchain technology has recently become a trend owing to its prowess to reform the governance of supply chains, offering improved levels of data transparency, traceability, and efficient workflow. The objective drawn on this research is to examine the key determinants that impact the adoption as well as applicability of blockchain technology within the supply chain management context. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) model is tested in this study to inspect and fathom the complex relationships between these components, thereby uncovering their respective significance and interrelationships. The results derived from this study contribute vital insights into the ever-changing landscape of blockchain adoption, with a particular focus on key factors comprising technical competencies, IT infrastructure, interoperability, compatibility, and complexity. The research based on DEMATEL methodology reveals both direct and indirect influences, facilitating a more comprehensive comprehension of these crucial variables. This study is an invaluable resource for industry stakeholders, policymakers, and organizations seeking to embrace blockchain technology into supply chain operations. This study’s original intent is to frame an extensive analysis of the key aspects and their interrelationships, offering valuable insights for making well-informed decisions. The overarching aim is to facilitate the seamless assimilation of blockchain architecture into supply chain management systems, promoting effectiveness along with harmony.

Global pandemic has provoked industries with unprecedented challenges. Stratagems to squash the COVID-19 bow like communal lockdown, social isolation, work at home, containment zones and all restraints sited on travel with stay home orders issued by the authorities led to sharp failure in revenues of service and manufacturing industries. The COVID-19 pandemic has shattered the transportation links with supply chain amongst suppliers, production amenities and consumers. Mostly the business executives are penetrating about passable strategies and plans for restoring production lines to encounter customer mandates. In this paper the pandemic effect of Indian business is considered which are affected by critical barriers of supply chains in the Indian scenario. These critical barriers are identified on a priority basis using the MADM approach. Furthermore, the study will help the scholars to grow conceptual models for maintaining a better supply chain to overwhelm this world-wide problem.

This research aims to explore the significance of supply chain (SC) resilience by integrating SC-Resilience and SC-Ambidexterity concepts. SC-Ambidexterity refers to the simultaneous application of SC-Adaptability and SC-Alignment capabilities within the supply chain. In line with the dynamic capabilities view (DCV) of the firm, this research adopts a quantitative approach to investigate the relationship between variables in the context of manufacturing and production companies in Pakistan, specifically in Karachi. The results of this case study have revealed a strong positive impact of SC-Ambidexterity on SC-Resilience, confirming the significance of adopting concurrent and synchronized supply chain capabilities. Furthermore, the analysis indicated that SC-Agility plays a crucial role as a mediator in the relationship between SC-Ambidexterity and SC-Resilience. The findings suggest that organizations that proactively invest in developing both ambidextrous capabilities and agility are more likely to achieve a higher level of supply chain resilience, enabling them to effectively navigate turbulent business environments.

Access to safe drinking water is essential for human health. In Abbottabad, tap and bore water are commonly used, but there has been a recent increase in bottled water consumption. This study aimed to compare tap and bottled water quality in Jinnahabad, Abbottabad. Physicochemical and bacteriological analysis was conducted on water samples collected from various sources. Surveys and interviews were also conducted to assess consumer perceptions and costs. The study found that, on average, bottled water had better physicochemical quality, although both alternatives met WHO limits. Tap water had higher levels of E. coli due to a weak sanitation system. Interestingly, despite perceiving bottled water as safer, most respondents still consumed tap water daily. Shopkeepers reported higher bottled water purchases for travel but lower daily consumption. Tap water was the main source, according to the Cantonment Board Abbottabad, though resources were insufficient. Doctors confirmed tap water-related diseases. The study suggests further research into consumer behavior and recommends monitoring measures, staff evaluations, and penalties to reduce costs and waste.

This study delved into the repercussions of economic instability on household food security while aiming to construct a robust framework for enhancing the resilience of the food supply chain. It thoroughly analyzed diverse factors, including government policies, economic conditions, environmental variables, and social dynamics, to gauge their reciprocal impact on food security. The research employed a meticulously chosen probability-based sample to ensure the representativeness of findings within the population, specifically focusing on residents in the Tanjung Kupang region of Johor. The study holds paramount significance as it provides novel insights for researchers, academic practitioners, and policymakers, shedding light on the far-reaching consequences of economic uncertainty on household purchasing power and its pivotal role in upholding food security. Moreover, it aspires to devise a tailored and sustainable food supply chain framework for Malaysia's unique context. By employing a quantitative approach enriched with robust statistical analysis and insights gathered through a meticulously designed questionnaire, this study sought to illuminate the intricate dynamics at play. The findings underscore the profound impact of economic instability on diminishing the income of B40 households, thereby curtailing their purchasing power. These findings align with comprehensive literature reviews from authoritative sources, underscoring factors such as inflation, soaring prices of essential commodities, and stagnant incomes contributing to the decline in income among B40 households. The implications of this research extend to policymakers, offering invaluable insights and promoting public awareness of potential challenges, along with plausible solutions.

Nomophobia is a term describing a growing fear in today’s world, the fear of being without a mobile device or beyond mobile phone contact. It is the biggest non-drug addiction of the 21st century and is mainly affected by teen-aged students. Those experiencing nomophobia may feel a sense of panic, anxiety, or distress when they are separated from their mobile phones. This work uses different statistical tools to identify the risk factor of nomophobia and machine learning techniques to propose a fresh way to measure and understand nomophobia. To create a predictive model for nomophobia, we gathered information from a broad sample (n = 357) of smartphone users and used a variety of machine learning methods. Using a questionnaire on 17 different factors and performing a statistically significant test (p<0.05) and ordinal logistic regression analysis on respondents age, level of education, CGPA, self-evaluation, per-day mobile phone usage, and use of media, we can recognize the most important features causative of nomophobia. The context of maximum phone usage is an important feature that highly affects nomophobia. About 201 respondents are at a moderate level. To develop a predictive model, decision tree (DT), random forest (RF), Gaussian Naïve Bayes (NB), and support vector machine (SVM) are utilized in this study for recognition of nomophobia addiction. Proposing an ensemble method to refine the predictive performance. From the analysis, we have found that the SVM feature selector with ensemble algorithm has classified the extent of smartphone addiction with a 57% accuracy rate. Our findings show that nomophobia tendencies can be accurately captured and predicted by machine learning approaches. The model distinguished between students who had symptoms of nomophobia and those who did not with remarkable accuracy. This study of machine learning-based methods presents a viable tool for diagnosing and treating nomophobia in students, eventually assisting in the creation of focused interventions and preventive measures.