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The purpose of this paper is to describe a framework for the implementation of clinical governance (CG) within a mental health and addictions service at all functional levels within the system (consumer, clinician, team, service and unit level). It aims to include and enlarge on the functional subdivisions of CG (as practised in the National Health Service (NHS)) by identifying 11 component domains of interest.

The paper briefly reviews the varying interpretations of the concept of CG in the literature and associated difficulties in its implementation.

Several authors have pointed out the difficulties in the implementation of CG at the operational level. In particular, CG is often seen as top‐down, “managerial” in its focus rather than providing clinicians at the coal‐face with a device for quality assurance and improvement.

The framework asserts that the 11 component domains are relevant at all levels within a healthcare delivery system; in fact, conversations already occur around these domains at all levels with variable frequency, with a focus that is relevant to that level, determined by the needs at each level.

The paper describes a practical framework for implementation of CG within a mental health and addictions service that addresses some of the criticism levelled against the concept of CG in the literature. This conceptualisation provides a seamless merging of the so‐called managerial and clinical imperatives around clinical governance.

This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study.

This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies’ success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing.

Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment.

This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers’ understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes.

The purpose of this study is to present Industry 4.0 technologies for advancing the circular economy (CE) adaption in manufacturing industry’s supply chain (SC) network. To pursue the same, Industry 4.0 technological aspects were recognized as solution measures to overcome the challenges for CE implementation in SC.

A new hierarchical framework containing 13 leading CE challenges and eight promising Industry 4.0 technological aspects had been proposed, representing their mutual relationship. The proposed framework was analysed using a hybrid approach of analytic hierarchy process (AHP) and combinative distance-based assessment (CODAS) under interval-valued intuitionistic fuzzy (IVIF) environment. The IVIF-AHP was used to acquire the priority weights of the CE challenges, whereas the IVIF-CODAS was used to attain the preference order of the proposed technological aspects.

The key findings of the present work indicate that “Information disruptions among the SC members due to multiple channels” and “Manpower inability to handle the toxic materials” are the two most critical challenges hindering the adoption of CE practices in SC. Along with, the results also demonstrate that to overcome these challenges, “Smarter equipment to empower flexibility and mass customization” and “Big data driven decision-making system” are the two most significant Industry 4.0 technological solutions, adoption of which might encourage the organizations to align their operations with CE philosophies.

The sample size of the experts engaged in work was limited; however, big data studies could be conducted in future to capture more insights of the stated topic. In addition to this, to understand the implication of CE on Industry 4.0-based manufacturing, a separate study can be synthesised in future.

The proposed work facilitates a new framework consolidating various perspectives associated with CE implementation into a manufacturing industry considering the scenario of Indian rubber industry. This study enables the decision-makers to recognize the challenging factors for CE implementation into their organizations and up-taking the proposed Industry 4.0 practices as technological measures for improving the organization overall performance.

The purpose of this paper is to help supply chain (SC) decision makers successfully penetrate through SC collaboration and strengthen their SC in the global market by understanding collaborative activities, and understand how these activities are related to each other in the SC.

This paper develops a set of collaborative activities from literature, and the developed model is helpful for SC decision makers to monitor their SC activities and take corrective actions to improve collaboration in their SC by using interpretive structural modeling (ISM) and MICMAC analysis.

This study reveals that collaborative activities increase the value of whole SC. The various activities are modeled on the basis of “an activity influencing other activities” and “an activity influenced by other activities,” which is useful for SC managers to take a decision.

The current study is literature based; therefore, there would be need of more explanation of the activities which lead to understand and implement SC collaboration in case of service and manufacturing industry.

The model of this study is helpful for decision makers to implement supply chain collaboration (SCC) and to understand various SCC activities on the basis of their driving and dependence power.

This research provided insight into skills needed for SC decision makers to implement collaboration in the SC using ISM. The results of the study could be adopted to monitor the existing SCC program or design new collaboration program to meet the global market requirements. To the best of knowledge, there is no reference that discusses SC collaborative activities on the basis of their driving and dependence powers.

This paper is a Conference Report (ICDT-2019) organized by Rajiv Gandhi National University of Law, Patiala, Punjab, India

The whole conference is articulated in the form of a Conference Report.

The conference provided an opportunity for participants to share ideas, reach some consensus, agree on differences of opinion and create a future action agenda. All the participants from all different backgrounds were able to bring new things to light and deliberate on contemporary issues related to knowledge trends and digital strategies that will lead to smart future.

The International Conference on Digital Transformation-2019 on the theme of “A Cognitive learning towards Artificial Intelligence” was convened by Rajiv Gandhi National University of Law, Patiala, Punjab, India during September 6-8, 2019 in Patiala, Punjab, India.

India has been withstanding increasing pressure of enrolment in the higher education system, resulting in the creation of new universities in consonance with the recommendations of the Knowledge Commission (2007). Barring a few institutions of paramount excellence, the mushrooming universities fail to conform to equitability of quality and standards, that is teaching-learning-dissemination and research, except for accommodating higher gross enrolment ratio. It has resulted in an asymmetric and sporadic development of human resources, leaving a large basket of learners out of the pursuit for aspiring higher academic, research and professional enrichment. The country needs to develop an innovative common minimum curriculum and evaluation framework, keeping in view the trinity of diversity, equity and inclusion (DEI) across the Indian higher education system to deliver human resources with equitable knowledge, skill and intellectual acumen.

The paper has been developed using secondary information.

The manuscript has developed an innovative teaching-learning framework that would ensure every Indian HEI to follow a common minimum curriculum and partial common national evaluation system so that the learners across the country would enjoy the essence of equivalence.

This research has designed a comprehensive model to integrate the spirit of the “DEI” value proposition in developing curriculum and gearing common evaluation. This would enable the country to reinforce the spirit of social equity and the capacity to utilise resources with equitability and perpetuity.

A government website is considered as an electronic government service delivery system (eGSDS). The authors look at this eGSDS from its process point of view. This study aims to expand the existing knowledge of e-government adoption, and to identify the significance of eGSDS process to user satisfaction.

This study uses a quantitative technique using data collected from 197 respondents. Structure equation modeling has been used to test the model and the proposed hypotheses.

The findings indicate that as the eGSDS process improves, a user’s perception of the government website’s ease of use increases, leading to increased perceived usefulness, which increases user satisfaction. The findings also indicate that eGSDS process has a significant impact on perceived usefulness and user satisfaction. This study provides evidence that the technological capabilities embedded in the government website processes are an important factor in determining e-government service quality and ultimately e-government user satisfaction.

The sample used was sufficient for this study and allowed reasonable conclusions to be drawn, but cannot be considered representative of all e-government users in India. Further research may help to validate the findings and generalize the results to a wider population. This study extends the current literature that looks at e-government adoption. Academicians and information systems researchers may use these findings for further research.

The research provides evidence that the technological capabilities embedded in the eGSDS process are critical in adopting e-government services. Government or concerned agencies may consider eGSDS process attributes (e.g. navigation, searching and transacting) while designing e-government service to give improved experience to its user.

E-government websites have become an important point of contact with citizens for many public services. However, more study is needed to understand how Web-based technological capabilities of e-government services affect user satisfaction. This study is an attempt to explore the impact of the eGSDS process on e-government user satisfaction.

The purpose of this study is to explore the factors that enable citizens to adopt e-government services in India.

The study uses a qualitative approach by conducting semi-structured interviews.

The study reveals novel e-government adoption factors, namely, auxiliary facilities, corruption avoidance, transparency and fairness in process, customer support, connectedness and forced adoption, previously unexplored in e-government adoption literature. In addition, the results highlight 17 e-government adoption factors that strengthen the findings from previous literature.

This study was qualitative in nature, and rather than generalization, the focus was explicitly on obtaining an in-depth understanding. The sample used was sufficient for the purpose of this study and allowed reasonable conclusions to be drawn; however, it cannot be considered representative of a vast country like India. Academicians and information systems researchers can use these findings for further research.

The findings of this study provide useful insights into the decision-making process of e-government services users in India and similar emerging economies. These findings can be important for government officials tasked with providing e-government services.

Previous studies in the context of e-government adoption, so far, have tried to integrate adoption factors from previous technology adoption models. Hence, these studies have not been able to capture the complete essence of e-government characteristics. In addition, there are limited studies in e-government adoption in the Indian context.

This study aims to identify how ICT appeared as an emergent business strategy and to investigate the impact of ICT adoption factors on the perceived benefits of micro, small and medium enterprises (MSMEs).

A total of 393 responses from Indian small and mid-size enterprises (SMEs) were collected for the final analysis. The study presents the partial least-squares structural equation modeling with the Chi-square test and descriptive analysis as a methodology based on numerous independent variables and one dependent variable.

The findings indicate that ICT adoption during and following the COVID-19 pandemic is constant in nature of the enterprise. Moreover, the results indicate that different adoption of ICT factors influence on perceived benefits of organizational performance of Indian MSMEs that lent good support except for the regulatory framework.

The implications of the current research help Indian MSMEs to take investment decisions in various technologies that help the organization. Furthermore, managers and practitioners help the organization in deciding which technology adoption factors are more critical to the betterment of the organization.

The study found certain ICT adoption factors that have a significant role in organizational performance in Indian MSMEs. Moreover, during COVID-19, investigate ICTs' role as a business strategy.

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.