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The agricultural sector has experienced a transformative impact through the adoption of digital technologies, particularly mobile applications designed for farmers. This study investigates the factors influencing smallholder farmers' willingness to adopt digital application tools in Ogun State, Nigeria, focusing on the IITA herbicide calculator and Akilimo mobile applications.

Data were gathered from 572 smallholder farmers participating in the Zero Hunger project. This research contributes to the limited empirical evidence in Nigeria concerning farmers' willingness to adopt digital application tools. The study analyzes the effects of education, training, access to internet services, smartphone ownership, willingness to use paid applications, awareness of application tools and the cost of digital tools on farmers' willingness to adopt. Gender differentials in willingness to adopt were also explored.

The results indicate positive and statistically significant effects of education, training, internet access, smartphone ownership, willingness to use paid applications, awareness of application tools and the cost of digital tools on farmers' willingness to adopt. However, female farmers exhibited a lower willingness to adopt digital application tools.

Policymakers are urged to create supportive policies promoting basic formal education and provide effective extension services to enhance farmers' training. Additionally, efforts should be made to reduce the cost of digital applications and improve internet accessibility in rural areas. Encouraging female farmers to adopt advanced agricultural technologies is essential. Stakeholders are advised to raise awareness of digital application tools to expedite the adoption of agricultural technologies in the country.

This study will be helpful for the government to determine the state’s readiness for digital agriculture, it will help technology developers and agricultural technology startups to understand the factors determining farmers willingness to adopt digital application tools.

This study offers insights into the readiness of Ogun State, Nigeria, for digital agriculture. It provides valuable information for technology developers and agricultural startups to understand the determinants of farmers' willingness to adopt digital application tools, contributing to the advancement of the agricultural technology landscape.

Small farmers in India are collectivized and legalized as Farmer Producer Companies (FPCs) to progress in agri-food value chains as small agribusiness enterprises. FPCs are dependent on timely information for their sustainability and profitability. Mobile apps are a cost-effective form of information and communication technology. Hence, the purpose of this study is to explore the major determinants of mobile apps adoption by FPCs.

Quantitative and qualitative data are collected by administering a semi-structured questionnaire and conducting in-depth interviews with board members of 115 FPCs, with a total membership of 30,405 farmers operating in 14 districts of the state of Kerala, India. The logit model is used for quantitative analysis, while dialog mapping is used for qualitative analysis, based on an integrated technology acceptance model and technology organization environment framework.

Logistic regression results evidence that amongst FPC characteristics, while company size and age are significantly impacting apps adoption, there is no significant association between board size, education level, multiple commodities business or export intention of companies on apps adoption. Digital literacy and technical hands-on training for FPC board members are quintessential to facilitate mobile apps adoption.

The findings are pertinent to policymakers to earmark funds for technical handholding and digital upskilling of FPCs. The need for developing comprehensive, location-centric, farmer-friendly apps by agritech companies is evidenced.

To the best of the authors’ knowledge, this is a pioneering work in the domain of mobile apps adoption from a farmers’ agribusiness enterprise perspective in an emerging market economy using a mixed-methods approach.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

This study aims to validate the impact of waiting staff’s attitudes, subjective norms, and perceived behavioural control on customer dissuasion from over-ordering and identify their antecedents using an extended theory of planned behaviour.

We selected three categories of restaurants (30 in total, including fine dining, casual dining, and fast food) in Macao and Zhuhai (China) for conducting the survey using a purposive sampling approach. The respondents were waiting staff who took customers’ orders in the past three months. In total, 393 valid responses were used for a structural-equation-modelling analysis.

The results show that restaurant waiting staff’s attitudes, subjective norms, and perceived behavioural control have positive effects on their intention to dissuade customers from over-ordering. Our study further reveals that perceived behavioural control is far more influential than attitudes and subjective norms on restaurant employees’ intentions to intervene with over-ordering. We also validate seven antecedents, including environmental concern and communication for attitudes, peer influence, supervisor influence, and organisational support for subjective norms, and self-efficacy and training for perceived behavioural control.

The food-waste literature tends to focus on consumers in home and restaurant settings and has paid scarce attention to the role of restaurant waiting staff in intervening in consumers’ waste behaviours. We fill in this research gap by revealing a formation mechanism for waiting staff’s intention to dissuade over-ordering.

This paper, based on the 2022 Master Class delivered at the 50th National Economic Meeting organized by ANPEC, discusses how post-Keynesian macroeconomics and New Developmentalism complement each other to understand middle-income economies' development in financial globalization. It summarizes my academic reflection about the advance in post-Keynesian thinking to develop macroeconomics for peripheral middle-income economies.

As part of this reflection, I first bring up the idea of a developmental convention and, next, how peripheral financialization impacts the elaboration of this convention. Given the asymmetric configuration of the international financial system and the context of hierarchical currencies, I discuss the challenge of overcoming underdevelopment in peripheral economies. The post-Keynesian macroeconomics and advances in the structuralist debate provide the analytical tools to understand how peripheral economies develop virtuous or vicious growth cycles. At the end of the paper, I present some comments on the stagnation of the Brazilian economy.

The growth strategy with foreign savings does not provide the conditions for middle-income economies to operate with sufficient economic policy autonomy to promote productive transformation. To this end, a developmental convention should replace the neoliberal convention that has dominated since the 1970s.

The dynamics of peripheral, middle-income economies, often influenced by international liquidity flows, are a crucial area of study. This research underscores the importance of understanding these dynamics, as it forms the basis for economic policy recommendations. The paper also highlights the inadequacy of the growth strategy with foreign savings in the current configuration of the international financial system, emphasizing the need for middle-income economies to operate with greater economic policy autonomy to foster productive transformation.

The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.