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Various models and approaches are implemented to provide technical assistance and support to improve cocoa farmers' welfare in Ghana. The Farmer Business School (FBS), which is analogous to Farmer Field School (FFS), is one of the few initiatives of GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit) and Ghana Cocoa Board (COCOBOD). The main aim of the initiative is to train smallholder cocoa farmers to perceive cocoa production as a business. However, there is limited or conflicting evidence as to the effect of FBS on productivity and food security, especially in Ghana. This study assessed FBS participation and the participation's impact on productivity and food security of cocoa farmers.

The study used primary data collected from 542 cocoa farmers in Central and Western North regions of Ghana and employed descriptive statistics, perception index and Endogenous Switching Regression (ESR) as analytical tools.

The results, which reported an overall perception index of 0.7, indicated that the farmers had a strong positive perception on the FBS program. The results also showed that sex of a farmer, number of years of formal education, farm size, extension contact, perception, distance to extension outlet and membership of farmer-based organizations (FBOs) significantly influenced the decision to participate in FBS program. Also off-farm income, years of education and household size significantly influenced farm productivity and household food security. The results further showed that participation in FBS improved productivity and food security of cocoa farmers.

The study used data from two regions of Ghana, namely the Central region and the Western North region. Findings from studies using data covering all cocoa growing areas of Ghana could be more informative in formulating policies aimed at encouraging participation in FBS and consequently help improve cocoa productivity and food security.

This article offers insights into the welfare effects of FBS on cocoa farmers as previous similar studies are without this information.

Blockchain technology enhances information management in healthcare supply chains by securing healthcare information and providing medical resource traceability. However, there is no decision framework to support blockchain implementation for managing information, especially in emerging economies’ healthcare supply chains. This paper develops a hierarchical decision model for implementing blockchain technology for information management in emerging economies’ healthcare supply chains.

This study uses 20 health supply chain experts in Ghana to rank 17 decision criteria for implementing blockchain for healthcare information management using the best-worst method (BWM) multi-criteria decision technique.

The results show that “security” and “privacy,” “infrastructural facility” and “presence of training facilities” are the top three critical factors impacting blockchain adoption in the health supply chain for healthcare information management. Other sub-factors are prioritized.

To implement blockchain effectively to enhance information management in the healthcare supply chain, health institutions, blockchain technology providers and state authorities should concentrate on the highly critical factors extracted from the study.

This is the first study that develops a hierarchical decision model for implementing blockchain technology in emerging economies' health supply chains.

The study examines the intricate interplay between supplier relationship management (SRM), procurement performance, supply chain responsiveness (SCR) and competitive advantage. Additionally, the study examines the mediating role of procurement performance and SCR in the link between SRM and competitive advantage.

A research model grounded in the resource-based view and dynamic capabilities theory (DCT) was developed and tested using partial least squares structural equation modeling (PLS-SEM). Data were obtained from 122 firms in Ghana.

The study revealed that SRM has a positive and significant effect on procurement performance, SCR and competitive advantage. Additionally, SCR has a positive and significant effect on competitive advantage; however, procurement performance has a negative and insignificant effect on competitive advantage. It was also revealed that SCR partially mediates the relationship between SRM and competitive advantage but fully mediates the relationship between procurement performance and competitive advantage. Also, it was also revealed that procurement performance does not mediate the relationship between SRM and competitive advantage.

The study contributes to literature by highlighting the mediating role of SCR in influencing the effect of SRM and procurement performance on competitive advantage.

Practically, the study findings highlight the need for firms to seek, build and manage meaningful relationships with their suppliers in order to enhance their competency and capability to influence their competitive position in the marketplace.

To the best of the researchers' knowledge, no prior study has examined the effect of SRM on procurement performance and SCR. Additionally, no previous study has examined the mediating role of procurement performance and SCR on the link between SRM and competitive advantage.

Building supply chain (SC) resilience has become a priority for many organizations, following a global increase in disruptive events. While management accounting and control (MAC) systems play a supportive role in supply chain management (SCM) decisions, little is known about the contributions offered to resilience decisions in service organizations. The purpose of this study is to examine the performance implications of MCS's impact on proactive and reactive resilience of healthcare supply chains.

This study conducted a survey of 127 public health managers via structural equation modeling. The partial least squares version 3.3.3 was used.

The results show a statistically positive impact of MAC dimensions on proactive and reactive resilience, which in turn impacts the quality, delivery speed and cost effectiveness of the health SC. However, the integration dimension had an insignificant effect on reactive resilience but a positive effect on proactive resilience.

This study examined the performance implications of MAC system dimensions and proactive and reactive resilience on operational performance in health SCs, using empirical data from only one country. Thus, generalizing the findings to include other jurisdictions may be impossible.

Healthcare managers in public health facilities should embrace the four MAC dimensions (except the integrated dimension in reactive resilience) to support information generation in SC resilience decisions.

Perhaps, the first to provide preliminary empirical evidence on the interactive effect of proactive and reactive resilience and MAC dimensions in terms of broad scope, timeliness, integration and aggregation on health SC operational performance under disruption, in the context of an emerging economy.

This study aims to develop an entrepreneurial business model capable of addressing and preventing the exploitation and inequality that traditionally have resulted from entrepreneurship, particularly in emerging economies.

The research uses systems thinking, the first law of cybernetics, and the principles of harmony to formulate a systemic solution to the problem, which it exemplifies via six purposefully selected short cases drawn from diverse industry sectors and economies.

This paper demonstrates how the conventional model of entrepreneurship, often associated with colonial exploitation and resultant inequalities, can be transformed into a triple bottom line model—harmonious entrepreneurship – that integrates the traditional economic, eco-, humane, and social approaches and creates a synergy where profit, planet, and people are in harmony. The model challenges the profit maximisation/shareholder value doctrine of business success.

Only six cases are presented here, and there is a need for further research in different political-economic contexts and industry sectors. Also, the way entrepreneurship is taught needs to change so that it addresses the sustainability challenge in general and the problem of inequality in particular.

There needs to be a change in the entrepreneurial mindset and the way entrepreneurship is taught and potential entrepreneurs are trained if entrepreneurship is to address the sustainability challenge in general and the problem of inequality in particular.

This is a novel approach to the study of entrepreneurship and its impact on inequality that shows how it can ameliorate and/or prevent inequality, particularly in emerging economies, by adopting a more holistic approach to business success and supplanting “having and needing” with “being and caring”.

While the literature has highlighted the impacts of COVID-19, there is limited evidence on the gendered determinants of the impact of COVID-19 among small-scale rural traders in developing and emerging economies.

Cross-border fish traders who had operated before and during the COVID-19 pandemic were interviewed in a survey conducted in Zambia and Malawi. Logistic regressions among male and female traders were employed to assess the gendered predictors.

Heterogeneous effects in geographical location, skills, and knowledge were reported among male cross-border traders. Effects of household structure and composition significantly influenced the impact of COVID-19 among female traders. Surprisingly, membership in trade associations was associated with the high impact of COVID-19.

Due to the COVID-19 pandemic and the migratory nature of cross-border fish traders, the population of cross-border fish traders at the time of the study was unknown and difficult to establish, cross-border fish traders (CBFT) at the landing sites and market areas were targeted for the survey without bias.

This paper addresses a gap in the literature on understanding gendered predictors of the impacts of COVID-19 among small-scale cross-border traders.

This paper aims to identify the aspects that social actors consider in constructing shared futures in communities. In their application in emerging countries, especially in the Global South, the socio-cultural particularities of communities and actors are often overlooked, generating friction or social conflicts. This paper presents two critical elements contributing to the debate: the importance of understanding Social Actors within a model of generating community futures in emerging countries; and the relevant factors that influence the actors in an exercise of building futures in communities.

From qualitative research, a case study of community foresight of the future was used: the future of Puerto Gaitán 2037 (Meta, Colombia). A method of information collection was applied from observation of the participants and analysis of documentation. The analysis method was the deductive qualitative analysis (DQA).

The participation of the social actors presents a model of five relevant elements that influence the actors for the successful construction of futures in communities. The first four factors, revealed from theory, are presented in real life. Likewise, a fifth factor is proven, Long-term thinking, which is evidenced by a model of application of futures studies for the specific context, applicable to the case of communities in countries of the Global South.

Although there are isolated examples of recommendations regarding studies to generate the future of communities, to the best of the author’s knowledge, this is the first study that presents concrete factors that contribute to guiding the construction of community futures from social actors, especially in countries of the Global South such as Colombia. It is also one of the first studies to use the DQA as a method of analysis in a topic of futures studies.

As one of the world's most valuable traded commodities, the market for coffee beans has grown enormously in recent years. The paper aims on analyzing the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia.

Data collected in this paper through reliable channels include nominal import value, exchange rate, production of total industry, etc. Independent and dependent variables are obtained through conversion. Since the nonlinearly adjusted exchange rate differs significantly from the linearly adjusted one for the export trade of Brazilian coffee beans, this paper develops the autoregressive distributed lag (ARDL) and nonlinear ARDL frameworks and demonstrates their application through asymmetric cointegration and error correction models.

The results of this paper show that imports of Brazilian coffee bean exhibit a more dramatic asymmetry compared to Colombia's coffee bean imports. The results of this study contribute to the import trade of non-oil commodities in developing countries, particularly Brazil, and enrich the existing literature on nonlinear exchange rate adjustments.

The export of Colombian coffee beans is not as old as Brazil, and it was not until much later that Colombia began to export coffee beans to the rest of the world.

The present study is an addition to the literature of agricultural trade. The authors analyze the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia. Different from the current mainstream research on oil commodity trade, this paper focuses on international trade from the perspective of coffee beans, which can enlighten the practice in this field.

This study examines the dynamics of financial institution development among economies in sub-Saharan Africa (SSA) and how volatility in forex-adjusted price of key globally traded, commodities and macroeconomic risk influence such development.

The study is based on data collected from the period starting 2001 to 2019 for relevant variables; and the empirical test was performed using the two-step system generalized method of moments (TSS-GMM) estimation method.

Empirical estimates suggest that volatility in forex-adjusted prices of crude oil and cocoa are inimical to development of financial institutions among economies in the sub-region. On the other hand, volatility in the price of gold is found to have a significant positive effect on development of financial institutions. Additionally, political instability is found to exacerbate the adverse effect of volatility in the price of globally traded commodities on the development of financial institutions in the sub-region.

The study verifies how volatility in forex-adjusted prices of key traded commodities on the global market influence development of financial institutions in the sub-region. Additionally, the study examines the impact of macroeconomic risk, a principal component analysis (PCA) constructed index on the development trajectory of financial institutions. Finally, the authors examine the moderating role of institutional quality and political instability in the relationship in question.

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.