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This paper aims to evaluate whether promoter holding influences a bank’s liquidity in India’s leading emerging market. Furthermore, it also evaluates the moderating role of risk-weighted assets (RWA) on the relationship between promoter holding and liquidity.

The data consists of 24 banks for the period of 12 years from 2010 to 2021. Static panel data is used to analyze the relationship between the liquidity coverage ratio (LCR) as the dependent variable, the promoter used as an explanatory variable and RWA used as a moderating variable in this study.

This study concludes that an increase in promoter holding helps to improve the liquidity of Indian banks. Moreover, it also shows that using RWA as a moderating term enhances the relationship between promoter holdings and Indian banks’ liquidity.

This study evaluated the impact of promoter ownership solely on the LCR, a statistic used to measure the short-term liquidity of banks in the Indian setting. Additional corporate governance factors, such as the makeup of the board of directors, relevant ownership concentration factors and external factors with the potential to affect the liquidity position of banks, could potentially be the subject of future investigations.

This paper has both managerial and policy-level implications. It shows that it is advantageous for banks’ ownership composition to include more enormous promoter holdings to enhance banks’ liquidity. Policymakers can, thus, formulate policies to encourage banks to have more extensive promoter holdings.

The impact of promoter ownership on bank liquidity has not been evaluated in earlier research projects. Furthermore, the use of RWA as a moderating variable to determine this link has not been fully investigated, particularly in the context of a developing country like India.

The study aims to explore and validate the revised psychological contract scale in this new normal era.

To serve the purpose, four studies were conducted. Study 1 was conducted for item generation through the extant literature review and phenomenological study. Study 2 highlighted the expert review. Study 3 explained the confirmatory factor analysis. At the end of study 3, the new psychological contract content had 14 items along with 15 traditional psychological contract content items. The nomological study validated the scale with the help of antecedent, i.e. supervisor's support, and outcomes, i.e. well-being and innovative behavior.

The revised psychological contract was bifurcated into two categories: new and traditional. Further, the revised psychological contract scale was having two dimensions: content and breach/fulfillment. The new content was the outcome of changes in perceived obligations due to pandemic. The nomological study found that supervisor support had a positive impact on the content of the psychological contract and fulfillment/breach of the psychological contract. Further, it was found that the new content of psychological contract was impacting more on well-being and innovative behavior than the traditional psychological contract.

In the new normal era, the working style and patterns have changed. Thus, it was important to capture changes in perceived obligations and employees' perception regarding to which extent their organizations were able to meet these altered perceived obligations. The study has direct implications for the practitioners as the revised psychological contract scale enlisted the perceived obligations of the employee and the extent to which these obligations were fulfilled by the employer. The study is also helpful in developing new normal HR policies and practices in the organization.

The study is original as it creates a new scale to measure the content of psychological contract and fulfillment/breach of psychological contract during new normal.

Cyberloafing is an organization-directed counterproductive work behavior (CWB). One stream of literature deems cyberloafing to be bad for organizations and their employees, while another suggests cyberloafing is a coping response to stressful work events. Our work contributes to the latter stream of literature. The key objective of our study is to examine whether cyberloafing could be a means to cope with a stressful work event-abusive supervision, and if yes, what mediating and boundary conditions are involved. For this investigation, the authors leveraged the Stressor-Emotion-CWB theory which posits that individuals engage in CWB to cope with the negative affect generated by the stressors and that this relationship is moderated at the first stage by personality traits.

Using a multi-wave survey design, the authors collected data from 357 employees working in an Indian IT firm. Results revealed support for three out of the four hypotheses.

Based on the Stressor-Emotion-CWB theory, the authors found that work-related negative affect fully mediated the positive relationship between abusive supervision and cyberloafing, and work locus of control (WLOC) moderated the positive relationship between abusive supervision and work-related negative affect. The authors did not find any evidence of a direct relationship between abusive supervision and cyberloafing. Also, the positive indirect relationship between abusive supervision and cyberloafing through work-related negative affect was moderated at the first stage by the WLOC such that the indirect effect was stronger (weaker) at high (low) levels of WLOC.

This work demonstrates that cyberloafing could be a way for employees to cope with their abusive supervisors.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

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.

Neuromarketing is a marketing communication field that applies neuroscience and physiological research tools to study consumer behavior toward stimuli, viz., ads and brands. This study aims to assess research trends in the neuromarketing field on the most influential journals, authorships, countries, citations and co-occurrences. The Scopus database is used to analyze identified articles from 2013 to 2022 and for the eligible research articles, a “systematic methodological review” (SMR) on consumer behavior through neuromarketing approach was done. “Visualization of Science (VOS)” viewer and “Biblioshiny” by R-studio software have been used for mapping the keyword analysis, co-citation analysis and author occurrence analysis. It was further found that of the top 10 academic institutions, the list is dominated by the six Asian institutions. It was further witnessed that journal “Physiology and Behavior” is trending as the most dedicated and emerging journals on neuromarketing and consumer behavior. Asian nations such as Bangladesh, China, India, Indonesia, etc., are turning out to be an emerging collaborators and publishers in this niche area of research, thereby giving tough competition to most developed countries. The findings of the thematic mapping show that neuromarketing is itself a very novel and newest area of study and topics such as “human marketing,” “neuromarketing,” “consumer behavior” and “electroencephalography” are new dimensions that can be looked upon in future.

One of the most marginalized communities in India is the Lesbian, Gay, Bisexual and Transgender (LGBT) community which commonly experiences discrimination. Many studies have countered that the LGBT community faces high discrimination in the banking and financing industry. As a result, this study concentrates on this marginalized community and its acceptance and continuation habit regarding mobile wallets. Consequently, this study has considered continuance intentions as a response to confirm the progress of the mobile-wallet industry. Also, this study tried to study the relationship between behavioral intention (BI) and continuous intention (CI) which is seriously lacks in the library of literature. The research operationalized the stimulus–organism–response (SOR) framework for the conceptual model and surveyed 100 self-proclaimed members of the LGBT community in India. The analysis has been done using the partial least structure (PLS). The findings demonstrate that variables like perceived trust (PT) directly influence the BI. On the other hand, variables like perceived ease of use (PEoU), social influence (SI), and satisfaction (S) doesn’t influence BI of the LGBT Community. The main outcome was a favorable association between BI and CI. It will help the stakeholders to understand how important this new market avenue is and how it can be explored. To ensure safe and secure transactions, a group think tank composed of important parties (financial institutions, mobile-wallet providers, the government, security specialists, etc.) should make recommendations. Mobile-wallet providers will attain benefit from this study’s understanding of user categories and ability to tailor their service offers as per the community.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.

This study aims to discover how domain familiarity and language affect the cognitive load and the strategies applied for the evaluation of search engine results pages (SERP).

This study used an experimental research design. The pattern of the experiment was based upon repeated measures design. Each student was given four SERPs varying in two dimensions: language and content. The criteria of students to decide on the three best links within the SERP, the reasoning behind their selection, and their perceived cognitive load of the given task were the repeated measures collected from each participant.

The evaluation criteria changed according to the language and task type. The cognitive load was reported higher when the content was presented in English or when the content was academic. Regarding the search strategies, a majority of students trusted familiar sources or relied on keywords they found in the short description of the links. A qualitative analysis showed that students can be grouped into different types according to the reasons they stated for their choices. Source seeker, keyword seeker and specific information seeker were the most common types observed.

This study has an international scope with regard to data collection. Moreover, the tasks and findings contribute to the literature on information literacy.