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The presented research explored artificial intelligence (AI) application in the learning and development (L&D) function. Although a few studies reported AI and the people management processes, a systematic and structured study that evaluates the integration of AI with L&D focusing on scope, adoption and affecting factors is mainly absent. This study aims to explore L&D-related AI innovations, AI’s role in L&D processes, advantages of AI adoption and factors leading to effective AI-based learning following the analyse, design, develop, implement and evaluate approach.

The presented research has adopted a systematic literature review method to critically analyse, synthesise and map the extant research by identifying the broad themes involved. The review approach includes determining a time horizon, database selection, article selection and article classification. Databases from Emerald, Sage, Francis and Taylor, etc. were used, and the 81 research articles published between 1996 and 2022 were identified for analysis.

The result shows that AI innovations such as natural language processing, artificial neural networks, interactive voice response and text to speech, speech to text, technology-enhanced learning and robots can improve L&D process efficiency. One can achieve this by facilitating the articulation of learning module, identifying learners through face recognition and speech recognition systems, completing course work, etc. Further, the result also shows that AI can be adopted in evaluating learning aptitude, testing learners’ memory, tracking learning progress, measuring learning effectiveness, helping learners identify mistakes and suggesting corrections. Finally, L&D professionals can use AI to facilitate a quicker, more accurate and cheaper learning process, suitable for a large learning audience at a time, flexible, efficient, convenient and less expensive for learners.

In the absence of any systematic research on AI in L&D function, the result of this study may provide useful insights to researchers and practitioners.

In today’s technology-led volatile business environment all established businesses are trembling and retailing is no exception. Exploration of the entrepreneurial side of retailers will expose the essential attitudes to survive in the new world order. The present study is an endeavor towards this end.

In this descriptive research, a pre-tested entrepreneurial orientation questionnaire constructed by De Nobrega (2012) was adopted, and it was fine-tuned to suit for the retail environment. Initially, an exploratory study was organized, and it was followed by confirmatory factor analysis. Data collected were analyzed with SPSS 23.0, and the conceptual model was validated in AMOS 23.0.

The study evinces that the retailer’s entrepreneurial orientation is induced by five factors, namely, autonomy, risk-taking, innovation, competitive aggressiveness and pro-activeness. The study exposes the five dimensions and their ascendancy on business performance.

The study is operationalized in a small sample, confined to two types of trade, limited to small and medium retailers in Chennai and all the constructs are measured with the help of perceptual self-reporting scales.

The study highlights that the art of spearheading retail business performance lies in attitude orientation. This work will propel retailers and trade bodies to nurture the entrepreneurial orientation.

The study emphasizes that boosting entrepreneurial mindset of retailers will enable them to achieve business progress and protects the grass root sector of the society.

This work is the very first study to identify and evaluate the impact of five-dimensional entrepreneurial orientation construct on small and medium retailer’s business performance. The present study is a pioneering empirical contribution to the Indian context.

This research attempted to establish the underlying dimensions of supply chain management practices, blockchain technology and supply chain performance in the Indian dairy industry. Additionally, the study proposes a conceptual model that shows the mediating effects of blockchain technology in the relationship between supply chain management practices and supply chain performance.

Structural equation modelling (SEM) is incorporated to examine the proposed model using SPSS and AMOS version 24. The study population includes 119 registered Indian dairy processing units operating in Uttar Pradesh and New Delhi (source: Dairy – India). Individual registered dairy processing unit's top four executives, that is Head of the Dairy Processing Plant, Supply Chain head and Marketing Head, and IT head are chosen as the respondents of the study, which renders the sample size of 476. Judgmental sampling based on the organisation's market position and plant production capacity (i.e. one lakh litre per day) has been set as the benchmark for selecting the dairy processing units. The executives are selected as respondents as they are well-versed in the phenomenon of supply chain management practices, blockchain technology and supply chain performance compared to other staff working in the dairy industry. The data was collected from December 2021 to March 2022 through judgmental sampling. The target sample size was 476, but only 286 questionnaires were received in a completed state and were further used for analysis.

Manufacturing practices, information sharing, distribution management, inventory management and blockchain technology have a significant and positive impact on supply chain performance in the Indian dairy industry. Furthermore, the research demonstrates that blockchain technology partially mediates the relationship between supply chain management practices and supply chain performance in the context of the Indian dairy industry.

This research is focused on the Indian dairy industry operating in only two states, namely New Delhi and Uttar Pradesh. More research is needed to determine whether SCM practices and the prospects for blockchain technology among channel members are universally applicable to merchants in non-dairy products. Similar investigations should be carried out on dairy industry operating in various formats and in numerous geographic locations. Further, case studies can be conducted by future researchers to learn how supply chain management methods are deployed, what precisely these practices entail and what costs and time demands are required by these practices in context of small independent retailers across different germane expanse.

While the available literature on the research area is spread out, the influence of blockchain technology in the Indian dairy industry has not yet been sufficiently analysed. Therefore, the research article focused on exploring underlying dimensions of the constructs of supply chain management practices, blockchain technology adoption and supply chain performance in the context of the Indian dairy industry.

This paper aims to reflect upon the findings of a fast-track study carried out in April 2020, by the Brazilian Federation of Therapeutic Communities, focused on the impact of the first measures taken by the Brazilian therapeutic communities (TCs) in response to COVID-19.

An electronic survey was disseminated to TCs in the different regions of Brazil through online platforms. A total of 144 TCs responses were used in the final analysis. The survey collected the following information: suspected and confirmed cases of COVID-19 (only one case of COVID-19 was confirmed), changes in treatment protocols, the impact in admissions and daily activities and the safety measures adopted to stop or reduce the transmission between residents, families and staff.

The survey successfully collected general data regarding interruptions (82.6% of TCs interrupted admissions, 100% of TCs interrupted volunteer’s activities, 94% of TCs interrupted family visits and 93% of TCs interrupted external activities).

The caveat of this study is the fact that there were tight deadlines for the TCs to generate their responses and the limited availability of staff to answer long surveys. Because of this, the study could not explore other important qualitative data. The results were shared in Brazil and Latin America with the staff of TCs, the national Federations of Therapeutic Communities and government agencies linked with them, in all Latin America.

This research aims to contribute to the adoption of developed prophylaxis and prevention protocols in response to COVID-19.

Mega-infrastructure development plans pave the way for policies to upgrade the infrastructure, environmental management and different aspects of locals’ well-being. These developmental dynamics can positively affect rural tourism including heritage sites destinations. The quality of life of local people can be linked to this positive developmental change through long-term and sustainable economic revitalization projects. In terms of this process, developing large-scale infrastructure and incorporating tourism sustainability can improve the quality of life-related to different dimensions that are critical to the community's well-being. Therefore, researchers have attempted to address this issue.

Data were collected between September and October 2020. The study sample size was the residents of Zhabagly village, including Zhabagaly, Abaiyl and 115 Railway settlements. Moreover, the residents were older than 18 years. A systematic random sampling technique was utilised to reach the targeted sample size and the researchers received 243 responses from the locals. Structural equation modelling (SEM) was used for analysis.

The findings from the structural equation modelling suggest that sustainable tourism increases due to the positive effect of mega-infrastructure development and positively impacts the locals' quality of life. Notably, no direct effect of mega-infrastructure development on quality of life reveals the pivotal role of sustainable tourism. Therefore, during the COVID-19 period, the dimensions of sustainable tourism – economic, market, socio-cultural and environmental – played a role in securing the positive impact of mega-infrastructure development on the locals’ quality of life.

This research highlighted the fact that when infrastructure projects are implemented to their full potential, they will generate sustainable tourism activities, provide eco-adventure activities, relax, treat signatories and boost the economy of all stakeholders. The study used AMOS to test the hypotheses. Qualitative research methods, including interviews with citizens, government officials and tourism managers, require further study.

The infrastructural development on a mega-scale means building an upscaling tourism ecosystem. This ecosystem is marked by the availability of drinking water, waste and energy management facilities that support the elevation of living material, community, health, safety and emotional well-being. It reflects the policy-level implications for future Belt and Road initiatives (BRIs). The tourism industry's resilience during COVID-19 has practical lessons for other industries.

Large-scale infrastructure construction must create favourable conditions for the rapid development of tourism. The availability of clean water, waste and energy management facilities contributes to the food production, social cohesion, physical and mental health and general well-being of the ecosystem. This is one of the few studies that used sustainable tourism as a mediator between the impact of large infrastructure projects and their impact on the quality of life of locals during the COVID-19 pandemic. Aksu-Zhabagly, a World Heritage Site in Kazakhstan, was the site of this field study.