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The purpose of the study is to understand what impact the first COVID-19 lockdown in 2020 had on the income and food security of rural households in India and whether it differed across socioeconomic factors. Moreover, the study assesses the needs of rural households and determines whether they had received adequate support services during the lockdown.

The study adopted a repeated-measures quantitative survey design with 1,319 rural women from 16 states of India and conducted a telephonic survey.

The lockdown had directly impacted rural households' employment status and income but the impact differed depending on the type of employment. For example, working in a salaried job or on one's farm led to a lower likelihood of a complete decline in income in states other than Kerala as the lockdown continued. The study also revealed a change in the pattern of food consumption, with higher consumption of subsidized staple foods. It also became evident that the aid announced by the government reached the rural population with some delay.

A limitation of the study was that many respondents refused to participate in phase 2, which reduced the sample size when comparing the two phases because the women did not own mobile phones. Instead, they were using their husbands' phones.

The study's findings can help better understand the needs of rural populations during crises, such as the COVID-19 pandemic. This can help to plan better and build preventive actions for such populations once their needs are understood. In addition, this can aid disadvantaged people for a minimal level of preparedness and security during such a crisis in the future.

Offering unskilled people training in engineering and vocational skills helps to decrease unemployment rate. The purpose of this paper is to augment actual hands-on conventional vocational training methods with virtual haptic simulations as part of computer-based vocational education and training.

This paper discusses the design of a bi-manual virtual multi-modal training interface for learning basic skills in surface mount device hand soldering. This research aims to analyze human hand dexterity of novices and experts at micro level skill knowledge capture by simulating and tracking the users’ actions in the manual soldering process through a multi-modal user interface.

Haptic feedback can enhance the experience of a virtual training environment for the end user and can provide a supplementary modality for imparting tangible principles to increase effectiveness. This will improve the teaching and learning of engineering and vocational skills with touch-based haptics technology, targeted toward teachers and students of various disciplines in engineering. Compared with the traditional training methods for learning soldering skills, the proposed method shows more efficiency in faster skill acquisition and skill learning.

In this study, the authors proposed a novel bi-manual virtual training simulator model for teaching soldering skills for surface mount technology and inspection. This research aims to investigate the acquisition of soldering skills through virtual environment, with and without haptic feedback. This acts as a basic-level training simulator that provides introductory training in soldering skills and can help initially unskilled people find educational opportunities and job offers in the electronics industry.

The study aims to co-create a “priority action roadmap for women's economic empowerment” based on women's top priorities to charting recovery directions. Doing so contributes to the growing body of knowledge on COVID-19 literature in at least four areas: assessing COVID-19 impacts on women entrepreneurs; mapping these impacts with four interdependent women's entrepreneurial ecosystem components; innovating a co-creation methodology based on remote participatory research; and providing a replicable model to perform action-oriented research in the context of COVID-19 impacts.

A co-creation methodology is proposed, combining systems-thinking and remote participatory research to engage women entrepreneurs and institutional stakeholders to prioritize impact, response actions and recovery needs in the wake of COVID-19. A ranking exercise using the analytic hierarchy process was used to derive ranking and assess user inputs' consistency.

The study exemplifies the integration of participatory methods and mathematical tool to engage stakeholders in prioritizing recovery work. PARWEE action items ranked by entrepreneurs and vetted by institutional stakeholders cover: access to finances, capacity building, health care, public and private partnership, marketing opportunities and formation of active advocacy groups to voice out women entrepreneurs' needs to institutional stakeholders. Results show a slight difference in the ranking of priority actions between experience owners and fresh starters.

This study innovated a new co-creation methodology for remotely engaging stakeholders of the women's entrepreneurial ecosystem, which is grounded in evidence and provides a replicable model for performing action-oriented research.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Researchers and policymakers are figuring out the adaptation technologies to cope with the changing climate. Adaptation strategies for crop production followed by the farmers at selected study locations had ranged from 6-30 per cent only, and this was mainly due to lack of awareness about the actual cost associated with adaptation and non-adaptation of these strategies.

Hence, this study aims to address the cost of adaptation for rice using joint probability distribution of rainfall and crop prices.

Cost of adaptation varied from INR2,389 to 4,395/ha for System of Rice Intensification (SRI); INR646 to 1,121/ha for alternate wetting and drying (AWD) and INR8,144 to 8,677/ha for well irrigation (WI), whereas expected cost for not using these technologies has ranged from INR6,976 to 9,172/ha for SRI; INR4,123 7,764/ha for AWD and INR10,825 to 17,270/ha for WI. Hence, promotion of the adaptation technologies itself will minimize the income losses to the farmers.

Even though, there are many ways for farmers (other than technology), to adapt to climate change (such as out-migration to cities, selling farm assets, focus on children’s education, etc.), this report, given the framework of the major research study undertaken, addresses only farm-level adaptation of the technologies to enhance farm income.

Public–private partnership in providing the technologies at cheaper costs, capacity building in handling the technologies and creating awareness about the technologies to minimize the expected cost of adaptation are suggested to improve the adoption level.

The study is prompted by the fact that most SMEs may not have the wherewithal or the willingness to devote time and resources to strategy formulation but are focused to reconsider this position by the pressures of globalization.

Through a literature survey, a model suitable for SMEs is identified. An application of the selected model is presented. Applicability of strategic tools such as ERP in SMEs is examined. A case of a company, which has implemented ERP is analyzed.

SMEs need strategy but the strategy depends on the complexity of the process. The case study points to the need for top management support and commitment.

It is difficult to get financial data from individual SMEs as most SMEs are proprietorship concerns.

For countries, which are labor surplus, study on SMEs which are not capital intensive but create employment, has practical and social implication.

This paper attempts at filling research gaps on competitive strategies for SMEs.

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.

The purpose of this paper is to examine the status of technology adoption of Indian apparel manufacturing firms and the organizational factors that affect the level of technology adoption.

Fourteen technologies applicable to apparel manufacturing were examined. A survey with an online questionnaire to apparel manufacturers in India was conducted to collect the data.

The most frequently adopted technology was the internet. The least frequently adopted technologies found in this study were robot‐related. The level of a firm's technology adoption was found to be significantly related to firm size positively and its export orientation negatively. It was also moderately related to competitive advantage. The effects of top management commitment, cost of capital, and technical skills were not significant.

Generalizability of the results is cautioned because the data were from the firms in one industrial town. The results indicate that technology adoption is related to the recent, intensified trade competition. The relationships among export orientation, price competition, and technology adoption need to be studied further.

Given the importance of upgrading in today's competitive global trade environment, this study builds a knowledge base of the technology adoption in apparel manufacturing and influential factors in developing countries.

Virtual assembly process plays an important role in assembly design of complex product and is typically time- and resource-intensive. This paper aims to investigate a dynamic assembly simplification approach in order to demonstrate and interact with virtual assembly process of complex product in real time.

The proposed approach regards the virtual assembly process of complex product as an incremental growth process of dynamic assembly. During the growth process, the current-assembled-state assembly model is simplified with appearance preserved by detecting and removing its invisible features, and the to-be-assembled components are simplified with assembly features preserved using conjugated subgraphs matching method based on an improved subgraph isomorphism algorithm.

The dynamic assembly simplification approach is applied successfully to reduce the complexity of computer aided design models during the virtual assembly process and it is proved by several cases.

A new assembly features definition is proposed based on the notion of “conjugation” to assist the assembly features recognition, which is a main step of the dynamic assembly simplification and has been translated into conjugated subgraphs matching problem. And an improved subgraph isomorphism algorithm is presented to address this problem.

It is essential to track the development of resource and pollution intensive industries such as textile, leather, pharmaceutical, etc., under burgeoning pressure of environmental compliance. Therefore, the purpose of this paper is to analyze the progress of Indian leather industry in terms of individual factors and total factor productivity.

This study applies and examines the various concepts of productivity such as labor productivity, capital productivity, material productivity and energy productivity. Further, it assesses and compares the performance of Indian leather industry in Tamil Nadu (TN), West Bengal (WB) and Uttar Pradesh (UP) based on productivity analysis, spatial variations determinants in productivity and technology closeness ratio.

The findings suggest that as per the productivity analysis, WB leather clusters have performed remarkably better in terms of partial factor productivity and technical efficiency (TE), followed by TN and UP. This can be attributed to shifting of leather cluster of WB to a state-of art leather complex with many avenues for resource conservation. Further, the findings reveal that the firm size and partial factor productivities have significant positive correlation with TE which supports technological theory of the firm.

The results of this study can be useful for the policy makers associated with the Indian leather industry especially to design interventions to support capacity building at individual firm level as well as cluster level to enhance the efficiency and productivity of overall industry.

The findings also support the resource dependence theory of firm according to which the larger size firms should reflect on resource conservation practices, for instance the concept of prevention is better than cure based upon 3R (reduce, recycle and reuse) principles.

The paper gives an explanation of the productivity in the leather industry in terms of its factor productivity and TE.