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The existing literature on linking growers directly with the market mostly overlooks the case of smallholders. They grow commercial–perishable crops and have to rely on the efficacy of the marketing system. The present paper intends to fill this void.

The paper studies the performance of two local markets among 216 pineapple producers and 50 traders using the structure–conduct–performance framework. Following which the authors attempt to unravel the determinants of growers' direct participation in the market and the impact of such involvement on the farm income using the Heckman two-stage treatment effect model.

The study analysis shows that the likelihood of growers’ direct participation in markets, found oligopolistic, increases with education, price information and family labor unit, while decreases with the growers' age, distance from market and the footfall of intermediaries at the farm gate. The second stage of the model has established a positive impact of participation on farm income.

The small sample size could restrict generalization. The authors used only operating efficiency as an indicator of the performance of the marketing system due to the unavailability of district-level time series data on pineapple pricing.

This study shows that local food markets are oligopolistic. Growers fetch very less share in consumers' price and become vulnerable to food insecurity. The study highlights the determinants of growers' direct participation in the local market and the impact of such involvement on farm income.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

Home insurance is widely recognised as a tool for mitigating economic risk associated with natural disasters. This study aims to analyse the influence of homeowners’ home insurance knowledge (both objective and subjective types), perceived benefits (PB) and perceived vulnerability towards disaster loss (PVUL) on their intention to purchase (ITP).

This research makes use of survey data collected from 394 respondents (the homeowners) residing in various parts of India. The structural equation modelling is used to verify 11 hypotheses proposed in the study.

The findings indicate that both objective knowledge (OK) and subjective knowledge (SK) of home insurance have significant influence on homeowners’ benefit perception and PVUL. The homeowners’ PB of home insurance negatively affect PVUL. The OK of home insurance has a stronger influence on homeowners’ ITP home insurance than SK while the homeowners benefit perceptions and PVUL significantly affects homeowners’ ITP home insurance. These findings confirms that if homeowners are knowledgeable about home insurance, they perceive the plans as more beneficial and feel less vulnerable about catastrophic events, resulting in positive intentions towards purchasing them.

To the best of the authors’ knowledge, this is the first comprehensive research that assesses the Indian homeowners’ knowledge, PB and PVUL in influencing their ITP home insurance. The finding of this paper will assist both public and private insurance companies in India and similar markets in designing and implementing effective strategies to sell home insurance policies.

This study aims to extend the discussion on firm profitability to include handloom enterprises in India.

This study uses a random sample of 427 handloom microentrepreneurs from the Indian state of Assam. The seemingly unrelated regression model is used to determine the profitability drivers in India’s handloom enterprises.

The empirical results revealed that human, financial and social capital, along with their control variables such as information and communication technology, firm size and sales distribution, are the main drivers of profitability of Indian handloom enterprises.

To the best of the authors’ knowledge, this study is the first to offer an in-depth insight into what makes profitability in the handloom enterprises in India, the world’s second-largest reservoir of the handloom industry.

Although handloom is a significant source of livelihood for millions of people in India, it performs poorly compared to other sectors of the economy, which may be the root of technical inefficiency. Until now, to measure technical efficiency, no studies have been carried out; therefore, the purpose of this study is to estimate the technical efficiency in the handloom micro-enterprises in India.

This study includes 427 handloom micro-entrepreneurs from the Indian state of Assam. Using bootstrap truncated regression, the data envelopment analysis (DEA) was used to calculate the technical efficiency and identify the factors responsible for inefficiency.

The findings of this study reveal that handloom enterprises are 75% pure technically efficient, suggesting room for input reduction. The bootstrap truncated regression results show that education, prior experience, modern technology, ICT, bank loan, training, gender and location significantly influence the technical efficiency of handloom enterprises.

Despite recent advances in the DEA method, this study used a traditional form of DEA. This study used only one output and a limited set of inputs. Better results could have been obtained by expanding the number of inputs and output. Finally, the data for this study has been obtained from a very narrow geographic area. The production practices of the handloom enterprises in other parts of the region and other states might vary considerably.

Technical efficiency measurement has management implications for businesses because it allows entrepreneurs to determine how much less input is required to produce the same output. A meticulous analysis can pinpoint the causes of inefficiency.

This paper aims to make two significant contributions to the extant literature. First, to the best of the authors’ knowledge, no published document has analyzed the technical efficiency of handloom micro-enterprises anywhere in the world. The authors fill this void by systematically analyzing the technical efficiency of the handloom industry in Assam.

This paper aims to estimate the technical efficiency (TE) and its determinants in the handloom micro-enterprises of Assam (India) using the double-bootstrap data envelopment analysis (DEA) technique.

The study uses a random sample of 340 handloom micro-entrepreneurs from the three districts of Assam in India. The double-bootstrap DEA was used to calculate the TE and its determinants.

The findings reveal that handloom enterprises are only 60% technically efficient, suggesting room for improvement. The bootstrap truncated regression results demonstrate that the handloom firms’ TE is influenced by both entrepreneur-specific and firm-specific factors.

The implication lies in the fact that the management of a firm may figure out how much it can reduce its input utilization to produce the existing amount of output so that it can move along the TE ladder. Moreover, it can crosscheck the factors to weed out inefficiency.

This paper has made two significant contributions to the extant literature. Firstly, it fills the gap by way of accounting the TE of handloom micro-enterprises, which has so far been neglected. Secondly, it used the bootstrap approach, which otherwise is very rare in the discourse on the Indian manufacturing industry, let alone in the micro, small and medium scale enterprises sector.

Despite efforts to reduce environmental pollution and wasteful fossil fuel use, electric vehicles (EVs) are still rare on the road. Why is it so challenging to get widespread EV adoption? One significant factor on which it heavily depends is one's awareness and understanding of EVs. However, due to an absolute lack of knowledge on the part of the populace, this factor becomes a huge impediment to the uptake of EVs. A systematic review of the electronic database Scopus for the years 2003–2022 was carried out on ‘EV awareness and adoption of EV’ while considering the ‘Preferred Reporting Items for Systematic Reviews and Meta-analysis’ (PRISMA) standards. A three-step identification process resulted in the ultimate detection of 41 papers, which were then thoroughly examined. A conceptual framework that encompasses the three key awareness aspects that influence EV adoption is developed. To encourage greater uniformity among EV researchers, this study's conclusions serve as a foundation for operationalising upcoming research efforts within a predetermined framework. The authors must therefore be optimistic that lingering technological, legislative, cultural, behavioural and business-model barriers may be overcome over time through widespread dissemination of knowledge and awareness related to EVs, making it possible for everyone to switch to greener, more economical and more efficient transportation solutions.