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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

Purpose This study aims to extend the discussion on firm profitability to include handloom enterprises in India. Design/methodology/approach 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. Findings 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. Originality/value 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.

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

PurposeThis 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsGiven 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.Social implicationsAlthough 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.Originality/valueNo prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

Purpose 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). Design/methodology/approach 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. Findings 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. Originality/value 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.

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Research limitations/implicationsThe 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.Originality/valueThis 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.