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Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply chain (SC). There is a need to trace and track the retail products before it reaches the customers to check the quality of the products so that expired products can be recycled and reused, which in turn will help gain customers' trust. This research aims to investigate retail employees' behavioural intention to adopt blockchain in the retail SC.

To examine the behavioural intention of employees in the retail SC, the research uses three theories – the technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour. The technology acceptance model measures the employee's acceptance of blockchain in the retail SC. The unified theory of acceptance is used in this research to measure how blockchain adoption will improve the performance of the employees. The theory of planned behaviour is used in this research to measure whether the employees intend to adopt blockchain. A survey was carried out in the retail stores of India. Exploratory factor analysis and structural equation modelling were used for data analysis.

This study found that the employees of the retail stores have a positive intention and attitude to adopt blockchain technology. Further, it was found that perceived behavioural control and effort expectancy was not promoting blockchain adoption in the retail sector.

This study will help the retail stores' employees understand the blockchain in their operations and will motivate the top management of the retail companies to adopt this technology. The study is limited to the retail SC in India only.

This study uses three theories technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour, which were not used in earlier studies of blockchain adoption in the retail SC.

The present study aimed to evaluate the psychometric properties of the short form of personal optimism and self-efficacy optimism-extended (POSO-E) among Indian teachers.

Two studies were conducted to adjudge the reliability and validity of the scale. In the first study, the sample of 510 respondents was randomly divided into subsamples. The first subsample was subjected to the Exploratory Factor Analysis which yielded a two-factor solution explaining 71.02% of the variance. This model was subjected to the Confirmatory Factor Analysis using a second subsample. Acceptable model fit indices suggested factorial validity of the two-dimensional POSO-E among Indian teachers. In the second study, acceptable Cronbach's alpha and composite reliability estimates (greater than 0.70) indicated the scale's reliability. Also, as expected, personal optimism, self-efficacy optimism and overall optimism reported a positive correlation with spiritual well-being and a negative association with distress. It confirmed the criterion validity of the POSO-E among Indian teachers.

The results showed appreciable psychometric properties of the POSO-E in the context of Indian teachers. The study offered a valid and reliable scale to measure teachers' optimism levels. It is poised to generate renewed interest among scholars to emphasize teachers' positive and optimist thinking. The findings also reported a positive association between teachers' optimism and spiritual well-being. It suggests that spiritual practices and interventions could be used to develop an optimistic academic workforce.

The study is one of the pioneer studies that evaluated the reliability and validity of the POSO-E among Indian teachers.

The purpose of this paper is to study the problems faced by small‐scale entrepreneurs in Nellore District of Andhra Pradesh, India and also to study the opinions of entrepreneurs regarding what are the different factors which are helpful for success of entrepreneurship.

The present paper is conducted by choosing a sample size of 30 per cent (196 units) randomly from the total population of 653 units. The data are collected through a structured questionnaire, informal interview and analyzed by using mean, ANOVA and Z‐test.

It is found that high price of raw materials, lack of marketing information and marketing of products are major problems faced by the entrepreneurs, followed by competition from small industries and absenteeism of labour. The majority (about 90.3 per cent) of the respondents did not want to make any complaint to government agencies.

The findings help to know the problems faced by small‐scale entrepreneurs in a developing country such as India and also help the policy makers to solve these problems.

Extraction of leaf region from the plant leaf images is a prerequisite process for species recognition, disease detection and classification and so on, which are required for crop management. Several approaches were developed to implement the process of leaf region segmentation from the background. However, most of the methods were applied to the images taken under laboratory setups or plain background, but the application of leaf segmentation methods is vital to be used on real-time cultivation field images that contain complex backgrounds. So far, the efficient method that automatically segments leaf region from the complex background exclusively for black gram plant leaf images has not been developed.

Extracting leaf regions from the complex background is cumbersome, and the proposed PLRSNet (Plant Leaf Region Segmentation Net) is one of the solutions to this problem. In this paper, a customized deep network is designed and applied to extract leaf regions from the images taken from cultivation fields.

The proposed PLRSNet compared with the state-of-the-art methods and the experimental results evident that proposed PLRSNet yields 96.9% of Similarity Index/Dice, 94.2% of Jaccard/IoU, 98.55% of Correct Detection Ratio, Total Segmentation Error of 0.059 and Average Surface Distance of 3.037, representing a significant improvement over existing methods particularly taking into account of cultivation field images.

In this work, a customized deep learning network is designed for segmenting plant leaf region under complex background and named it as a PLRSNet.