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In the last 10 years, India has amended its laws dealing with sexual offences against women with the changes ranging from increasing terms of imprisonment for the offence of rape to state-funded compensation schemes for women and child victims. In this regard, challenges persist for the agencies of the criminal justice system in India especially the courts to realise the vision of restorative justice as these forums have to navigate the relevant statutory provisions and binding precedents. This chapter seeks to analyse the challenges faced by courts in proper reintegration of victims and offenders of sexual offences, the institutional responses of the courts and suggests reforms to the criminal justice system in India in consonance with the principles of restorative justice acknowledged in the restorative justice movement in the international discourse.

The legal status of citizenship is constitutive in that it determines the boundaries of formal membership of a nation-state and, by implication, the lines of exclusion. The ways in which Australian law has defined membership over time – from subject status to citizen – provide a case study of the factors at play in understanding citizenship within a constitutional setting. We see that the constitution of citizenship may be a complex and unsettled evolutionary process.

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.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

One area in which strategy and organizational ecology converge is organizational change. This essay weaves together salient themes in my (and my co-authors’) various writings on organizational change, and is anchored in the research literature of the last twenty years. Among other ideas developed here, I point out that there is now a convergence of agendas in strategy and ecology, with an important role being played by intraorganizational ecology. I develop the distinction between strong and weak selection approaches to organizational ecology. While the strong selection view does not find empirical support, there is stronger support for the weak selection view. I lay out some key features of an emerging evolutionary synthesis for the study of strategy and organization, and develop an evolutionary approach to organizational change.

This chapter aims to analyse the evolution of research in the Indian tourism and hospitality domain from 1976 to 2021. By employing the bibliometric and systematic literature review, the chapter highlights the prominent authors, institutions, methods, emerging and explored research themes in the tourism and hospitality field. Accordingly, the authors collected 458 articles from core tourism and hospitality related journals using PRISMA and evaluated them using VOSViewer. The findings revealed an emic perspective of research contribution in Indian tourism and hospitality. While some topics such as religious tourism, sustainability, cultural and heritage tourism products have attained more attention, topics such as tourism entrepreneurship, technology and human resource development failed to seek attention in the eyes of journals and researchers. Although the literature has expanded significantly, there is a need to build global academic networks to examine the state of Indian tourism and hospitality. The chapters suggest that future research should critically evaluate the schemes and initiatives undertaken by the government to promote Indian tourism, expand research to western and eastern parts of the country, and follow the mixed-method research to contribute holistically to the topic.

This chapter discusses the application of community informatics (CI) principles in the rural Southern and Central Appalachian (SCA) region to further the teaching of information and communication technologies (ICT) literacy concepts in courses that formed part of two externally funded grants, “Information Technology Rural Librarian Master’s Scholarship Program Part I” (ITRL) and “Part II” (ITRL2), awarded by the Institute of Museum and Library Services’ (IMLS) Laura Bush 21st Century Librarian Program to the School of Information Sciences (SIS) at the University of Tennessee (UT).

The chapter documents ICT use in ITRL and ITRL2 to extend librarian technology literacy training, allowing these public information providers to become change agents in the twenty-first century. It discusses aspects of CI that influenced these two projects and shaped the training of future rural library leaders embedded in traditionally underrepresented areas to further social justice and progressive changes in the region’s rural communities.

The chapter demonstrates the role that CI principles played in the context of ITRL and ITRL2 from project inception to the graduation of the rural librarians with examples of tangible IT services/products that the students developed in their courses that were directly applicable and tailored to their SCA contexts.

ITRL and ITRL2 provided a unique opportunity to apply a CI approach to train information librarians as agents of change in the SCA regions to further economic and cultural development via technology and management competencies. These change agents will continue to play a significant role in community building and community development efforts in the future.

Hypothesis tests for dominance in income distributions has received considerable attention in recent literature. See, for example, Barrett and Donald (2003a, b), Davidson and Duclos (2000) and references therein. Such tests are useful for assessing progress towards eliminating poverty and for evaluating the effectiveness of various policy initiatives directed towards welfare improvement. To date the focus in the literature has been on sampling theory tests. Such tests can be set up in various ways, with dominance as the null or alternative hypothesis, and with dominance in either direction (X dominates Y or Y dominates X). The result of a test is expressed as rejection of, or failure to reject, a null hypothesis. In this paper, we develop and apply Bayesian methods of inference to problems of Lorenz and stochastic dominance. The result from a comparison of two income distributions is reported in terms of the posterior probabilities for each of the three possible outcomes: (a) X dominates Y, (b) Y dominates X, and (c) neither X nor Y is dominant. Reporting results about uncertain outcomes in terms of probabilities has the advantage of being more informative than a simple reject/do-not-reject outcome. Whether a probability is sufficiently high or low for a policy maker to take a particular action is then a decision for that policy maker.

The methodology is applied to data for Canada from the Family Expenditure Survey for the years 1978 and 1986. We assess the likelihood of dominance from one time period to the next. Two alternative assumptions are made about the income distributions – Dagum and Singh-Maddala – and in each case the posterior probability of dominance is given by the proportion of times a relevant parameter inequality is satisfied by the posterior observations generated by Markov chain Monte Carlo.