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Due to the growing complexity involved in leveraging the endless possibilities of ICT on all levels, the technical competence of faculties of higher education institutions (HEI) and effective methods for fostering e-readiness has become questionable.

This research has developed and validated an empirically supported e-readiness scale, which can be used by HEIs to assess faculty members’ preparedness toward online teaching. The measurement model and the structural model were developed as the results of exploratory factor analysis and confirmatory factor analysis (n = 245). The previously identified components and their indicators were validated using the structural models and the final scale was developed with five dimensions (“online technological readiness, pedagogical readiness, institutional readiness, learning and delivery readiness and content readiness”).

The faculties’ e-readiness assessment tool, as a useful tool, could aid institutions in identifying problems that affect the implementation of e-learning or digitalization in the institutions and developing strategies in response.

Like any research this research also has some limitations and can be considered as future research probability like the responses for this research were collected from HEI in India; however, a cross-cultural study can be conducted to understand the parameters across the globe. Although the psychometric qualities of the e-readiness scale are acceptable, additional research in various higher educational environments, both nationally and internationally, is required to further establish the scale’s relevance, validation and generalizability.

Although many scales have been developed to assess the readiness level in the education sector, a scale, that holistically measures, the readiness level of faculties from an overall perspective was required. This scale can be used to recognize the e-readiness level of teachers in HEIs. This scale can also help the institutions assess the readiness level of their faculty members and address any improvements required in their teaching and learning pedagogy, further acknowledging training needs.

Dynamic changes in the marketing scenario lead to the changes in consumer purchase preferences and it is often observed that consumers get more inclinations for the purchase of green products. This paper aims to focus on the influence of factors affecting the purchase of green products.

This study has used two different research phases. The first phase includes identification of factors from the extensive review of the literature followed by the second phase entailing the interpretive structural modeling (ISM).

The identification phase led to 20 factors after the literature review and in consultation with 3 academicians and 2 industry experts. In the second phase, ISM is applied to establish a hierarchical paradigm for the factors affecting the purchase of green products and to develop the contextual relationships among those factors.

This study can be used by researchers, academicians, marketing practitioners and environmentalists for filling the academic gap and to increase the usage of green products among consumers to a higher extent.

This study is based on the ISM providing significant insights related to factors affecting the purchase of green products. It provides valuable knowledge to marketing researchers and practitioners.

Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

Purpose: Significant structural changes are currently occurring in the Indian labour sector. Artificial intelligence (AI) and other emerging technologies are redefining the activities and skill requirements for various jobs in the healthcare sector. These adjustments have been accelerated by the economic crisis brought on by COVID-19, along with other considerations.

Need for the Study: Skills shortages, job transitions, and the deployment of AI at the company level are the three main challenges confronting the Indian labour market. This chapter aims to discuss policy alternatives to address a rising need for health workers and provide an overview of changes to the healthcare sector’s labour market.

Methodology: A review of the available literature was conducted to determine the causes of the widening skill gap despite a vibrant and prodigious young population. The background of the sustainable labour market is examined in this chapter, with a focus on workforce migration and mobility.

Findings: This chapter gives a comparative review of recent policy papers and evidence, as well as estimates of the health workforce and present Indian datasets. Furthermore, it highlights how important it is for all people concerned to invest in today’s workforce to close the skill gap and create better future opportunities.

Practical Implications: This chapter’s findings imply a severe shortage of human intellectual capital in India and a need to bridge this gap in the Indian labour market.