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This study attempts to elucidate the role of key influencers impacting the student decision-making process of enrollment for higher education in India from the lenses of Stephen Covey's theory on circles of life.

A sample of 556 students of Delhi NCR, India was selected based on a multi-staged sampling method. PLS-SEM was subsequently applied for statistical data analysis.

This quantitative finding voiced the relationships among the constructs in the proposed theoretical framework, i.e. Stephen Covey's circle of life theory. Additionally, it tinted the crucial role of “College attributes” in reconnoitering the relationship dynamics between key influencers (Circle of Influence and Circle of Concern) and student college choice (Circle of Control).

The present study incorporates only the first-year student population of undergraduate management courses in private universities from Delhi NCR, India limiting the generalization of findings substantially.

The study garners the attention of education policymakers on the cognizance of the role played by parents and cohorts in driving the student's decision-making process of college choice under the circle of influence.

This study is pioneering research disseminating a comprehensive outlook of the circle of life theory of great Stephen Covey engrained upon a compendious conceptual model which enlightens the landscape of the decision-making process of student on enrollment under the influence of key influencers.

This paper explores the potential that block teaching offers to enhance employability in the context of large-scale classes. It suggests that block teaching, with its condensed structure, necessitates curriculum innovation, fosters participatory learning and peer-to-peer networking, and has been shown to increase student focus and enhance engagement and attainment, especially amongst diverse learners. As these are the same challenges that large-scale teaching faces, it is proposed that intensive modes of delivery could be scaled up in a way that may help to mitigate such problems as cohorts in business schools continue to increase in size.

The paper is based on secondary research and provides an overview of literature that looks at block teaching, followed by that which explores the challenges of large-scale teaching contexts. It compares and contrasts the gaps in both to suggest a way that they could be combined.

The paper provides key insights into changes in the contemporary landscape of teaching within UK business schools, which have seen increasingly large cohorts and draws out the key strengths of intensive modes of delivery, which include helping students to time manage effectively, encouraging curriculum innovation and the creation of participatory learning opportunities as well as providing closer personal relationships between students and staff. Outlining some of the well-documented issues that can arise when teaching larger cohorts, the paper suggests that scaling up blocked delivery may offer a new way help to overcome them.

Because of the chosen research approach, the research results are subject to generalisation. Therefore, researchers are encouraged to test the proposed propositions in large-scale teaching scenarios.

This paper includes implications for the development of innovative modes of teaching in the context of large cohorts, an experience that is increasingly common amongst British business schools and beyond.

This paper brings together two bodies of literature for the first time – that of intensive modes of teaching and that focuses on large-scale teaching contexts – for the first time to show how the former may help to overcome some of the key issues arising in the latter.

The purpose of this paper is to understand how those involved in executive pay determination in large publicly quoted UK businesses see the role of diversity within remuneration committees (Remcos) as enabling the input of different perspectives, which can enhance their decision-making and potentially improve pay outcomes.

Qualitative, semi-structured interviews were undertaken with 18 high-profile major-enterprise decision-makers and their advisers, i.e. non-executive directors (NEDs) serving Remcos, institutional investors, executive pay consultants and internal human resources (HR) reward specialists, together with data from three focus groups with 10 further reward management practitioners.

Remco members recognise the benefits of social category/demographic diversity but say the likelihood of increasing this is low, given talent pipeline issues. The widening of value diversity is considered problematic for Remcos’ functioning. Informational diversity is used as a proxy for social category/demographic diversity to improve Remcos’ decision-making on executive pay. While the inclusion of members from wider social networks is recognised as potentially bringing a different informational perspective, the social character of Remcos, reflecting their elite nature and experience of wealth, appears ingrained.

Our original contribution is to extend the application of upper echelons theory in the context of Remco decision-making to explain why members do not welcome widening informational diversity by appointing people from different social networks who lack value similarity. Instead, by drawing views from employees, HR acts as a proxy for social network informational diversity. The elite, upper-echelons nature of Remco appointments remains unchanged and team functioning is not disrupted.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

Image Processing is an emerging field that is used to extract information from images. In recent years, this field has received immense attention from researchers, especially in the research domains of object detection, Biomedical Imaging and Semantic segmentation. In this study, a bibliometric analysis of publications related to image processing in the Science Expanded Index Extended (SCI-Expanded) has been performed. Several parameters have been analyzed such as annual scientific production, citations per article, most cited documents, top 20 articles, most relevant authors, authors evaluation using y-index, top and most relevant sources (journals) and hot topics.

The Bibliographic data has been extracted from the Web of Science which is well known and the world's top database of bibliographic citations of multidisciplinary areas that covers the various journals of computer science, engineering, medical and social sciences.

The research work in image processing is meager in the past decade, however, from 2014 to 2019, it increases dramatically. Recently, the IEEE Access journal is the most relevant source with an average of 115 publications per year. The USA is most productive and its publications are highly cited while China comes in second place. Image Segmentation, Feature Extraction and Medical Image Processing are hot topics in recent years. The National Natural Science Foundation of China provides 8% of all funds for Image Processing. As Image Processing is now becoming one of the most critical fields, the research productivity has enhanced during the past five years and more work is done while the era of 2005–2013 was the area with the least amount of work in this area.

This research is novel in this regard that no previous research focuses on Bibliometric Analysis in the Image Processing domain, which is one of the hot research areas in computer science and engineering.

This paper aims to evaluate the impact of various preview modes on tourist attitudes and intentions to visit a destination based on consumers’ level of involvement in travel decision-making.

The study was conducted as a between-subjects one-factor [preview mode: static images vs 360-degree tour vs virtual reality (VR) mode] in a laboratory experiment setup to examine how consumers with different levels of involvement in travel decision-making respond to destination marketing toward three different preview modes.

The findings indicated that VR preview mode highly influences tourist attitudes and visit intentions toward a destination compared to static images and 360-degree tours. This effect is more significant among participants with higher levels of customer involvement. Finally, the results from the study offer empirical evidence of the effectiveness of VR in shaping user behavior compared to traditional preview modes.

The limitations are using a non-probability sampling method, a small sample size and affordable mobile-compatible VR headsets.

This study offers empirical evidence on the effectiveness of VR in shaping tourist behavior compared to traditional preview modes. It helps destination marketers develop appropriate strategies for promoting tourist destinations.

The novelty of this paper lies in understanding the effectiveness of VR in shaping tourist behavior with different levels of customer involvement in travel decision-making.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

Consumers' perceptions of product intelligence affect their willingness to accept smart offerings. This paper explores how people perceive various smart products based on their smartness profiles, composed of five distinct smartness facets. Additionally, the study investigates how these perceptions of product intelligence impact consumers' evaluation of factors that either promote or impede the adoption of smart products. These factors are examined as potential mediators in the adoption process. This paper aims to determine if the value-based adoption model can be applied to a broad range of smart service systems.

Consumers assessed one of 28 smart products in a scenario-based quantitative study. Multilevel structural equation modeling (SEM) is used to test the conceptual model, taking the nested data structure into account.

The findings show that product smartness essentially enhances usage intention via adoption drivers (enjoyment and usefulness) and reduces usage intention via adoption barriers (intrusiveness). In particular, the ability to interact in a humanlike manner increases the benefits consumers perceive, which in turn increases consumer acceptance. Only the smartness characteristic of awareness impairs usage intention, mediated by the perceived benefits of enjoyment and usefulness.

In contrast to previous research, which usually focuses on single smart products, this work examines a variety of different products, which allows for better transferability of the results to other smart offerings. Furthermore, prior research has mainly focused on single facets of product smartness or researched smartness on an aggregated level. By considering the consumer perception of each smartness facet, the authors gain deeper insights into the perceptual differences regarding product smartness and how this affects technology adoption via conflicting key acceptance drivers and barriers.

Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.