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The purpose of the study is to identify the factors affecting the entrepreneur's choice of the dairy value chain and evaluate the impact of the value chain on farm performance (profit).

Primary data were collected from dairy entrepreneurs in India, covering nine states. A multinomial treatment effect model (controlling for selection bias and endogeneity) was used to evaluate the impact of the choice of the value chain on entrepreneurs' profit.

Dairy entrepreneurs operating in any recognized value chain other than the value chain driven by the consumer household realize a comparatively lesser profit. Dairy farmers have established direct linkages with customers in urban areas – who could pay premium prices for safe and quality milk. Food safety compliance is positively associated with profit and entrepreneurs (who have undergone formal training in dairying) preferred partnerships with a formal value chain. The prospects of starting a dairy enterprise are slightly higher in villages compared to urban areas.

Dairy entrepreneurs can make a shift in accordance with the study's findings and boost their profitability. It aids in comprehending how trainees (who obtained advice and training for raising dairy animals from R&D organizations) and non-trainee dairy farmers make value chain selections, which ultimately affect profitability. However, purposive sampling and a small sample size limit the universal implications of the study.

Developing entrepreneurial behavior and startup culture is at the center of policymaking in India. The findings imply that the emerging value chain not only enhances the profit of dairy farmers by resolving consumer concerns about food safety and the quality of milk and milk products but also builds consumer trust.

This paper offers insight into how the benefits of dairy entrepreneurs vary with their participation in the different value chains. The impact of skill development/training programs on value chain selection and farm profitability has not yet been fully understood. Here is an attempt to fill this gap. This paper through light on how trained and educated dairy entrepreneurs are able to establish a territorial market by approaching premium customers – this is an addition to the existing literature.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

Automobiles have always been a major cause of air pollution. The vehicular emission contains several harmful pollutants that affect the surroundings and living beings. Mild CO exposure leads to headaches or unconsciousness. CO is also responsible for the global warming as well. CO is mainly generated in the IC engine due to incomplete combustion of the fuel. The purpose of this paper is to implement a CO absorption mechanism inside the exhaust pipe of vehicle.

In this research work the single cylinder four stroke spark-ignition engine is intended to run on petroleum and petroleum blended fuels. A new post-combustion emission control device called cobalt scrubber is employed to reduce the emission of CO during idling which absorb and later release it under transient conditions.

The emission of CO when the engine runs at petroleum is reduced from 6.24 to 3.02 per cent. Methanol 40 per cent+petrol 60 per cent gives a better emission result of 0.98 per cent of CO emission. So, the authors can implement the cobalt scrubber in automobiles to avoid CO emission during idling.

The scrubber clearly overcomes the drawbacks of the existing control techniques. The cobalt scrubber is a cheap, non-radioactive. It can be employed in any kind of vehicle irrespective of its engine.

The use of the scrubber design presented in this article, effectively reduces the emission of CO.

This study aimed to understand the agri-entrepreneurial traits of undergraduate university students using machine learning (ML) algorithms.

This study used a conceptual framework of individual-level determinants of entrepreneurship and ML. The Google Survey instrument was prepared on a 5-point scale and administered to 656 students in different sections of the same class during regular virtual classrooms in 2021. The datasets were analyzed and compared using ML.

Entrepreneurial traits existed among students before attending undergraduate entrepreneurship courses. Establishing strong partnerships (0.359), learning (0.347) and people-organizing ability (0.341) were promising correlated entrepreneurial traits. Female students exhibited fewer entrepreneurial traits than male students. The random forest model exhibited 60% accuracy in trait prediction against gradient boosting (58.4%), linear regression (56.8%), ridge (56.7%) and lasso regression (56.0%). Thus, the ML model appeared to be unsuitable to predict entrepreneurial traits. Quality data are important for accurate trait predictions.

Further studies can validate K-nearest neighbors (KNN) and support vector machine (SVM) models against random forest to support the statement that the ML model cannot be used for entrepreneurial trait prediction.

This research is unique because ML models, such as random forest, gradient boosting and lasso regression, are used for entrepreneurial trait prediction by agricultural domain students.

Despite the potential of social media in emerging markets (EMs), only a few studies published in high-quality information systems (IS) journals that have addressed issues related to social media in the context of EMs. The purpose of this paper is to analyse existing research related to social media published in high-quality IS journals for exploring initial research trends, emerging themes, limitations and future research directions in the context of EMs.

This study conducted a systematic review of 22 articles on social media, which were published in the “Senior Scholars Basket of IS Journals and Information Systems Frontiers” from 1997 to 2017. Manual literature search approach (i.e. screening through the table of contents of each journal) was employed to identify relevant articles. The content of relevant articles was systematically analysed and synthesised along with keyword analysis to understand research trends on social media related issues in the EMs context.

The study identified four major themes from existing research on the social media in the context of EMs, namely: social media frameworks; social media and consumers; social media and organisations; and social media and society with the majority of the studies focussing on consumers. A single subject was found as the major limitation with studies analysed focussing on single platform/country/domain hindering the generalisability whereas including a new exogenous variable to improve the validity of existing studies emerged as main future research direction.

This study conducted literature review on social media in EMs, which have not been undertaken yet. Moreover, it employed manual search (an effort and time-intensive approach) to overcome the shortcomings of keyword search to identify, locate, select and analyse the social media literature in the context of EMs.

Given the socialisation of men and women to their gender roles and expression of emotion, this study aims to investigate whether there are gender differences in the use of emotive language in electronic word-of-mouth (eWOM), specifically in online reviews. The authors propose that female reviewers will use strong emotive terms, such as love, more frequently in online reviews than do male reviewers. The authors further propose that the gender of the reviewer influences audience responses to the reviewer’s use of emotive terms in online reviews.

The authors conducted secondary data analysis of restaurant reviews (Study 1) to provide evidence on whether the gender of the reviewer affects the frequency of use of emotive terms in an online review. In addition, three separate experiments (Studies 2–4) were conducted to test the theoretical arguments.

The results of the secondary data analysis indicated that female online reviewers used the term “love” much more frequently in their reviews than male reviewers, whereas there was no usage difference for the term “like”. The experimental studies further showed that an emotive review by a male reviewer containing the word “love” resulted in a higher evaluation of the restaurant being reviewed than a non-emotive review containing the word “like”. This difference was stronger when the overall rating was less salient and for consumers who believe (vs do not believe) that men and women use emotional language differently.

First, the paper extends our understanding of gender differences in emotional expression within the domain of eWOM and online reviews as well as our understanding of consumer responses to these gender differences. Second, the authors identify a boundary condition for these gender effects, namely, the overall rating score. Third, the authors find that consumer beliefs regarding gender stereotypes in emotional expression provide an explanation for these effects.

The results of the research indicate that the electronic algorithms operating on review sites might be modified in terms of their criteria for selecting the reviews to display to consumers, as consumer decision-makers may find greater utility in reviews written by male reviewers that contain strongly positive emotive terms.

The research extends the knowledge on gender differences in emotional expression in online reviews by demonstrating the actual usage patterns and differing responses to the emotional expressions of each gender.

The purpose of this paper is to provide a structural overview of speech recognition system for developing Quranic verse recitation recognition with tajweed checking rules function. This function has been introduced, due to support the existing and manual method of talaqqi and musyafahah method in Quranic learning process, which described as face-to-face learning process between students and teachers. Here, the process of listening, correction and repetition of the correct Al-Quran recitation took place in real-time condition. However, this method is believed to become less effective and unattractive to be implemented, especially towards the young Muslim generation who are more attracted to the latest technology.

This paper focuses on the development of software prototype, mainly for developing an automated Tajweed checking rules engine, purposely for Quranic learning. It has been implemented and tested towards the j-QAF students at primary school in Malaysia.

The paper provides empirical insight about the viability and implementation of Mel-frequency cepstral coefficients (MFCC) algorithm of feature extraction technique and hidden Markov model (HMM) classification for recognition part, with the results of recognition rate reached to 91.95 percent (ayates) and 86.41 percent (phonemes), after been tested on sourate Al-Fatihah.

Based on the result, proved that the engine has a potential to be used as an educational tool, which helps the students read Al-Quran better, even without the presence of teachers (Mudarris)/parents to monitor them. Automated system with Tajweed checking rules capability functions could be another alternative due to support the existing method of manual skills of Quranic learning process, without denying the main role of teachers in teaching Al-Quran.

The purpose of this paper is to investigate how perceived usability and user characteristics influence the intention to use a crowdsourcing application for finding potentially trafficked children. As part of this effort, the authors also attempt to uncover the usability concerns surrounding the use of this application.

The authors first describe Zhongxun, which is the application used in the present paper. Next, they conducted a survey eliciting usability perceptions of Zhongxun. A total of 287 participants were recruited for the survey which used constructs adapted from the Computer System Usability Questionnaire as well as various demographic variables. Hierarchical regression analysis was used to ascertain factors influencing intention to use Zhongxun. Participants' qualitative feedback was also analyzed to derive themes pertaining to areas of improvement.

The results showed that system usefulness was the factor that most positively influenced intention to use Zhongxun, followed by information quality and interface quality. Interestingly, a higher level of education was negatively associated with intention to use the application. Qualitative feedback suggested various ways of improving Zhongxun's functionality. Participants recommended the incorporation of gamification mechanisms as a new feature of the application. Cultivating awareness of Zhongxun was also suggested as a means to attract new users.

The work can help inform the design of crowdsourcing applications for finding missing and potentially trafficked children, as well as similar systems. Implications include the need for simplicity of design, communication strategies to attract new and retain existing users, and instilling confidence in the quality of crowdsourced contributions.

Prior research in evaluating the usability of crowdsourcing applications has been performed but not in the context of finding missing and potentially trafficked children. The task of finding such children is markedly different from previous usage contexts and could impact perceptions of usability and usefulness. Hence, the present study attempts to plug this research gap.

This study aims to find how can fashion micro-influencers and their electronic word-of-mouth (eWOM) messages increase consumer engagement on social media, focusing on micro-influencers’ influence, typology, eWOM content and consumer engagement.

A total of 20,000 microblogs were collected from Irish fashion micro-influencers and analyzed through keyword classification and content analysis in NVivo. The determinants of eWOM persuasiveness for consumer engagement on social media were investigated based on Sussman and Siegal’s information adoption model.

The study finds that among the four types of micro-influencers, market mavens and their eWOM messages have the highest impact on consumer engagement on social media, and it presents a repetitive and persuasive eWOM model of market mavens to increase consumer participation. Also, the study discovers that micro-influencers’ occasion-related microblogs have an increasing impact on consumer interactions whereas microblogs with brands have a decreasing engagement with consumers on social media.

This study advances prior studies on the relationship between influencers’ eWOM messages and consumer participation on social media by the development of a persuasive eWOM model of micro-influencers to increase consumer engagement and fill in the lack of relevant literature. Also, findings provide actionable insights for marketing communication practitioners to persuade consumers to participate in eWOM communications and establish strong consumer-brand relationships on social media.