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This paper looks at mainstream lesbian, gay, bisexual, transgender, queer, intersex and asexual (LGBTQIA+) support services in Aotearoa New Zealand, which predominantly center white queer voices and services and fail to account for the intersectional identities of young ethnic queers.

This exploratory, qualitative study investigated the social and professional support experienced and responded to by 43 young ethnic queers living in Aotearoa New Zealand, who were between 18 and 35 years of age. Participants identified as queer, non-binary, gay, pansexual, demisexual, gender fluid, non-binary and trans among others and held ethnic heritage from Africa, Asia, the Middle East and the Americas. Persadie and Narain's Mash Up methodological approach (2022) was used to analyze the data. Mash Up allowed us to understand the intersectional spaces of queer ethnic lives in white-dominated spaces, the ways in which young ethnic queers resisted the marginalization of their racialized being and took agency to counter actions and decisions that negated their presence and intersectional identities.

The findings from the study showed that young ethnic queers responded to the lack of adequate support services by establishing their own voluntary organizations and support networks. The study revealed that ethnic queer young people were critical of the white-dominated LGBTQIA+ support organizations; they created their own transformative spaces where they found “family” and community where they could be open about their queerness without the fear of rejection and stigma, while still advocating for equitable resources and an intersectional approach in queer mainstream services.

This paper provides valuable information on the lack of support for queer ethnic young people in Aotearoa New Zealand. The absence of information on the needs of this group poses a challenge to government departments, which rely on data to inform policy and allocate resources. The limited research and knowledge of this community make them less visible and, consequently, less likely to be given resources. It also means that harmful practices and behaviors toward queer ethnic young people by families and communities are more likely to go unnoticed and unaddressed. The paper also shows that the agency of young ethnic queers to create their own transformative spaces and to challenge the white-centric spaces, which have failed to consider their intersectional identities, has been instrumental to their well-being.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

It is generally believed that business spending on capital expenditure tends to decrease as interest rates rise, and vice versa, this is not always the case. The previous literature produces inconclusive results vis-à-vis the interest rate and investment nexus. This study analyzes the responsiveness of investment to changes in high and low levels of interest rates in India through a quantile-based, non-parametric method utilizing annual data from 1980 to 2022.

This study uses Quantile-on-quantile (QQ) technique proposed by Sim and Zhou (2015) to examine the impact of interest rate quantiles on quantiles of investment. In addition, long-term association and the direction of causality are estimated through the Cho et al. (2015) test of quantile cointegration and the Jeong et al. (2012) Granger causality in quantile (GCQ) test, respectively.

The empirical evidence validates that the linkage between investments and interest rate is not consistently negative and varies from quantile to quantile. The study finds a negative impact at median quantiles and a positive impact at extreme higher quantiles. Conversely, the impact at lower quantiles is negligible, which is also observed from quantile cointegration, indicating the presence of a statistically significant association above the median quantiles. Additionally, the study finds one interesting finding that there exists unidirectional causality from investment to interest rates in India rather than other way around.

The study provides significant implications for policymakers as it suggests that during extreme economic conditions, the effectiveness of traditional monetary policy tools to boost capital formation is restricted. Policymakers may consider alternative measures to stimulate investment during these time periods. The study additionally posits that the neoclassical theory of investment may not be readily applicable in emerging economies in its unaltered state, mostly due to the lack of well-developed financial markets.

There is a limited literature available on non-linear linkage between interest rates and investment. The present study adds to the existing knowledge by investigating how investment responds differently to fluctuations in interest rates, while incorporating the complete distribution of both the variables.

The genesis of “shareholder activism” in the USA may be traced back to several decades, but it only evolved in India at the start of the 21st century. This paper aims to explore the concept of “shareholder activism” in the Indian context, in light of the New Companies Act, 2013. The act is envisioned as a precursor to invoking the intention of shareholders to confront managers. Further, it aims to look at the possibilities of using tools of shareholder activism to make companies aware of their concerns.

Authors explore the concept of shareholder activism with the help of textual analysis, using R. Then, the authors study the mediating effects of “shareholder’s intention towards activism” between the “regulatory mechanisms” and “the usage of various tools of activism”, using the partial least square approach.

Regulatory mechanisms, such as the Companies Act, 2013, enhance the shareholders’ power to sensitise companies towards various corporate governance issues. It also increases their intention towards shareholder activism, eventually leading to favourable opinion on using various tools of “activism” in their investee companies.

This study is a unique attempt to assess the minority shareholders’ potential to become active in their investee companies induced by changes in the rules and regulations of a country.

Shareholder activism in India has not been thoroughly explored thus far. This paper specifically studies the opinions of retail investors, who possibly could increase companies’ accountability towards their minority shareholders, especially in light of the New Companies Act, 2013.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

This bibliometric study aims to analyze publication trends, active countries, collaborations, influential citations and thematic evolution in learning analytics (LA) research focused on higher education (HE) during and after the COVID-19 lockdown period.

From the Scopus database, this bibliometric analysis extracts and evaluates 609 scholarly articles on LA in HE starting in 2019. The multidimensional process identifies the scope impacts, advancing the understanding of LA in HE. An analysis of co-citation data uncovers the key influences that have shaped the literature. This study uses the stimulus-organism-response (SOR) theory to suggest future research directions and organizational adaptations to new LA technologies and learner responses to LA-enabled personalized interventions.

Learning analytics are becoming important in the HE environment during and after the COVID-19 lockout. Institutions have used LA to collect socio-technical data from digital platforms, giving them important insights into learning processes and systems. The data gathered through LA has assisted in identifying areas for development, opening the path for improved student success and academic performance evaluation and helping students transition to the workforce.

The study’s concentration on the post-COVID-19 timeframe may lead to paying attention to potential pandemic developments. Nonetheless, the findings provide a thorough picture of LA’s contributions to HE and valuable ideas for future study initiatives. Future research with the SOR framework suggests areas for additional study to maximize LA’s potential in diverse HE situations.

This study adds to the growing corpus of knowledge on learning analytics in HE, especially in light of the COVID-19 lockdown and its aftermath. By using bibliometric analysis, the study provides a complete and evidence-based understanding of how LA has been used to address challenges related to HE. This study uses bibliometric analysis and SOR theory to appraise and map HE learning analytics research. The selected study themes can help scholars, educators and institutions shape their future efforts to improve teaching, learning and support mechanisms through learning analytics.

Population ageing is a demographic transition and burdens every country’s health sector. A lack of social security combined with low government health-sector spending has made health-care utilisation difficult among elderly in many developing countries like India. This makes population ageing a global phenomenon for policymakers, researchers and stakeholders to discuss and debate in 21st century. The purpose of this paper is to examine the behavioural determinants of health-care utilisation among elderly population in Punjab.

This study examines health-care utilisation among the elderly population in Punjab. A structured questionnaire was used to collect the data in the state of Punjab, with a multi-stage stratified random sample of 385 respondents. Logit regression was used to analyse the association between several behavioural factors like age, gender, place of residence, source of livelihood, education and frequency of visits with health-care utilisation among elderly aged 60 and above.

This study revealed that gender, age, place of residence, source of livelihood, education and frequency of visits were significant determinants of health-care utilisation among elderly population.

This study directs the need to improve health-care utilisation in a country, reducing inequalities that exist among the elderly population with respect to socio-economic and demographic differences.

Health-care delivery organizations (hospitals) constitute a complex adaptive system; hence, a contingency perspective is imperative to guide the design of customized approaches to quality management in different health-care settings. Accordingly, this paper aims to propose a contingency framework to advance the understanding of the relationship between situational factors and effectiveness of quality approaches in health-care organizations (HCOs), such as hospitals in India.

Related literature was reviewed to identify existing research and theories related to quality and quality approaches, situational factors of the HCOs (hospitals) and some existing logical evidence on public and private hospitals in India. Then a contingencies framework for quality and quality approaches was conceptualized.

This paper proposes contingent determinants arise out of conceptualization of the HCOs (hospitals) from different system perspective such as rational system, natural system, open system and integrative system; uncertainty because of physicians’ behaviour, nurses’ approach and a dual line of authority; and the task environment such as patients, competition and economic pressure. These determinants represent situational constructs to the quality enhancement of any attempt at quality approaches. While these determinants have an influence on the quality and quality approaches of the HCOs (hospital), it is imperative to build any quality improvement strategy to work effectively, i.e., quality approach is dependent on determinants of the contingencies of the hospital’s environment, be it external or internal. Propositions for future research are also incorporated.

This paper proposes a conceptual model as well as research propositions that need to be validated and confirmed empirically. It advances the research and theory related to quality and quality approaches in a health-care setting. It can enable policymakers, hospital managers to analyze and gauge the appropriateness of quality approaches in a given context before implementing them and could help to improve the introverted quality approaches and quality dimensions currently followed in HCOs (hospitals).

Contingency framework is a new approach for research on the effectiveness of quality approaches in hospitals. The fundamental idea behind this framework is that effectiveness of quality approaches can be understood best by examining its contingent determinants. Thus, it has the capacity to contribute to the efforts of government and policymakers to make the quality of care affordable to all in India. Essentially, we examine the contexts and variables that determine the effectiveness of quality approaches.

This paper aims to evaluate the progress made in achieving sustainable development goal-2 (SDG 2) in India, with a focus on ending hunger, ensuring food security, improving nutrition and promoting sustainable agriculture. The assessment uses data from SDG Index reports, which offer a comprehensive overview of the advancements made by 28 states and 8 union territories (UTs) in India.

The evaluation is based on information derived from three editions of the SDG Index reports, initially published in 2018 and subsequently in 2019 and 2020. These reports provide a detailed analysis of the status and achievements of different states and UTs in relation to SDG 2. The categorization of states and UTs into aspirant, performer, front runner and achiever categories serves as a crucial framework for assessing the progress.

Despite concerted efforts by India, the majority of states and UTs are positioned in the aspirant and performer categories, suggesting that significant challenges persist in achieving SDG 2 targets. The results emphasize the necessity for stronger measures to elevate states and UTs to the categories of front-runners and achievers. The persistent challenges of malnutrition, hunger and their economic ramifications require immediate and strategic interventions to address these pressing concerns.

This paper contributes to the existing literature by providing a comprehensive analysis of the progress towards SDG 2 in India, using the insights from the SDG Index reports. The categorization framework used in this assessment offers a nuanced understanding of the challenges faced by different regions, highlighting the original contribution of this study. The findings underscore the urgency of targeted efforts to address malnutrition, hunger and related issues, emphasizing the importance of sustained commitment to achieving SDG 2 for the overall well-being of vulnerable populations.

Deep learning techniques are unavoidable in a variety of domains such as health care, computer vision, cyber-security and so on. These algorithms demand high data transfers but require bottlenecks in achieving the high speed and low latency synchronization while being implemented in the real hardware architectures. Though direct memory access controller (DMAC) has gained a brighter light of research for achieving bulk data transfers, existing direct memory access (DMA) systems continue to face the challenges of achieving high-speed communication. The purpose of this study is to develop an adaptive-configured DMA architecture for bulk data transfer with high throughput and less time-delayed computation.

The proposed methodology consists of a heterogeneous computing system integrated with specialized hardware and software. For the hardware, the authors propose an field programmable gate array (FPGA)-based DMAC, which transfers the data to the graphics processing unit (GPU) using PCI-Express. The workload characterization technique is designed using Python software and is implementable for the advanced risk machine Cortex architecture with a suitable communication interface. This module offloads the input streams of data to the FPGA and initiates the FPGA for the control flow of data to the GPU that can achieve efficient processing.

This paper presents an evaluation of a configurable workload-based DMA controller for collecting the data from the input devices and concurrently applying it to the GPU architecture, bypassing the hardware and software extraneous copies and bottlenecks via PCI Express. It also investigates the usage of adaptive DMA memory buffer allocation and workload characterization techniques. The proposed DMA architecture is compared with the other existing DMA architectures in which the performance of the proposed DMAC outperforms traditional DMA by achieving 96% throughput and 50% less latency synchronization.

The proposed gated recurrent unit has produced 95.6% accuracy in characterization of the workloads into heavy, medium and normal. The proposed model has outperformed the other algorithms and proves its strength for workload characterization.