Search results

The purpose of this study is to examine how the anticipated positive evaluation of a tourist’s social media posts by significant others, known as social return (SR), impacts the memorable tourism experience (MTE) and how this evaluation influences the revisit intention and recommend intention (operationalized dimensions of behavioral intention-BI).

The relationship among SR, MTE and BI was measured using established scales that were assessed for reliability and validity. Structural equation modeling was applied to the data collected from 316 respondents who had visited a heritage site.

The findings indicate that SR significantly impacts MTE and BI and MTE partially mediates the relationship between SR and BI. However, the impact of SR on revisit intention is weak despite being statistically significant.

This paper seeks to extend the SR concept introduced in tourism and hospitality literature in 2018. This study validated the scale in a new context while retaining the inviolability of the scale by including a world heritage site. This study used an extended version of the MTE scale and an adapted version of the BI scale. The use of these three scales together is an attempt to examine the symbolic nature of social media posts that can generate perceptions regarding the memorability of the tourist’s visit.

SR is a relatively new construct and has been very sparsely studied with no known study linking SR, MTE and BI.

The rise in the use of Internet technologies and social media has shifted the marketing practices from offline to online. This study aims to determine the pros and cons of social media marketing in the beauty and wellness industry.

In-depth interviews were conducted with the owners and marketing executives of beauty and wellness centers to understand the use of popular social media platforms in this industry and their pros and cons.

The researchers identified eight merits and seven demerits of social media in the beauty and wellness industry. Every respondent is happy and satisfied with social media use, particularly Instagram and Facebook. Irrespective of the demerits, they have shown the intention to increase its usage in the future. The merits override demerits; thus, social media is a blessing for this industry from the owners' perspective.

The research is exploratory and is confined to just one industry. Research implication is that the visual nature of social media makes it a powerful tool for the promotion of the beauty and wellness industry.

The study's findings will be beneficial for small-scale businesses as it will push them to take advantage of this low-cost marketing tool.

Social media marketing is helpful for communication and marketing purposes for society.

The beauty and wellness industry remained unfocused by researchers because it is highly unorganized, fragmented and not regulated, yet has huge growth potential. This research will provide a closer look at this industry as well as social media marketing.

Growing concern over sustainability adoption has presented an array of challenges to businesses. While vital to an economy's success, banking is not immune to societal, environmental and economic consequences of business practices. The study has examined the sustainable performance of banking institutions on the suggested multidimensional framework comprising economic, environmental, social, governance and financial dimensions and 52 sustainability indicators. The study benchmarks the significant performance indicators of leading banks indispensable to sustainable banking performance. The findings attempt to address research questions concerning the extent of sustainable banking performance, ranking the sustainability dimensions and indicators and standardizing sustainability adoption metrics.

To determine the responsiveness of the banking industry to sustainability dimensions, content analysis was conducted using NVivo software for the year 2021–2022. Furthermore, a hybrid multicriteria decision-making (MCDM) approach is used by integrating entropy, the technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija KOmpromisno Resenje (VIKOR) to provide relative weights to performance indicators and prioritize banks based on their sustainable performance. Sensitivity analysis is used to ensure the robustness of results.

In the context of the Indian banking industry, the pattern of sustainability reporting is inconsistent and concentrated on addressing environmental and social concerns. The results of the entropy methodology prioritized “Environmental” sustainability over other selected dimensions while “Financial” dimension has been assigned the least priority in the ranking order. The significant sustainable performance indicators delineated in this study should be used as standards to ensure the accountability and credibility of the sustainable banking industry. Additionally, the research findings will provide valuable inputs to policymakers and regulators to assure better contribution of the banking sector in meeting sustainability goals.

Considering the paucity of studies on sustainable banking performance, this study makes two significant contributions to the literature. First, the suggested multidimensional disclosure model integrating financial and nonfinancial indicators would facilitate banking institutions in addressing the five aspects of sustainability. As one of the first studies in the context of the Indian banking industry, the findings would pave the way for better diffusion of sustainability practices. Second, the inclusion of MCDM techniques prioritizes the significance of sustainability indicators and benchmarks the performance of leading banks to achieve better profits and more substantial growth.

Advancement in technology has increased the use of social media among Internet users. People are on social media all day and brands cannot miss this opportunity to turn these users into potential customers. The purpose of this research paper is to identify the prominent social media platforms in Indian beauty and wellness industry and to establish dimensions of social media activities.

This is a qualitative study in which in-depth interviews were conducted with owners and managers of beauty and wellness centres, who use social media for promotion of their business.

This study establishes 5M's of social media activities (Motives, Media, Management, Merits and Metrics) that play a major role in social media promotion. It also identifies that Facebook and Instagram are prominent social media platforms for this industry, followed by Snapchat, YouTube and LinkedIn.

Being a qualitative study, it is exploratory in nature and confined to beauty and wellness centres only, thus, findings cannot be generalised. Research implication is that social media still preserves its popularity over traditional media for marketing purposes.

The results of the study are useful for business owners, promoters and marketers, who are struggling to effectively use this low-cost marketing tool.

The result of the study are useful in providing awareness and the importance of social media in promoting benefits to the society.

Social media is extensively used for promotion of beauty services but there are very few theoretical studies on the same. This paper provides rich understanding about how to use social media effectively using 5M's.

This paper aims to propose a new sleep signal controlled footless domino circuit for reducing the subthreshold and gate oxide leakage currents.

In the proposed circuit, a P channel MOSFET (PMOS) sleep switch transistor is inserted between the power supply and the output node. The sleep transistor, the source of the pull-down network, and the source of the N channel MOSFET (NMOS) transistor of the output inverter are controlled by this additional sleep signal to place the footless domino circuit in a low leakage state.

The authors simulate the proposed circuit by using HSPICE in 45-nm CMOS technology for OR and AND logic gates such as OR2, OR4, OR8, AND2 and AND4 at 25°C and 110°C. The proposed circuit reduces leakage power consumption as compared to the existing circuits.

The proposed circuit significantly reduces the total leakage power consumption up to 99.41 and 99.51 per cent as compared to the standard dual-threshold voltage footless domino circuits at 25°C and 110°C, respectively, and up to 93.79 and 97.98 per cent as compared to the sleep control techniques at 25°C and 110°C, respectively. Similarly, the proposed circuit reduces the active power consumption up to 26.76 and 86.25 per cent as compared to the standard dual-threshold voltage and sleep control techniques footless domino circuits at 25°C and 110°C, respectively.

This study aims to examine the effect of motivating language on employee performance and assesses the mediating roles of organisational citizenship behaviour and employee engagement between motivating language and employee performance in the Indian health-care sector, which is a highly demanding work environment, wherein employee burnout is high.

The study was in the context of COVID-19 pandemic set for health-care workers in India. To collect data and test the proposed research model, 328 questionnaires were respondent by multi-level health-care professionals from private and government hospitals in North India.

The findings suggest that leader’s motivating language is crucial for health-care leaders, inducing employee’s performance in context to patientcare, safety and satisfaction. Underpinning theory of leader member exchange substantiates that the role of leader is pivotal in daily interaction with the stakeholders. Self-determination theory of motivation is determined by psychological needs satisfaction inducing employee engagement and organisational citizenship behaviour, amplified through the leader’s motivating language, resulting into improved patientcare and patient safety. The findings state that leader’s motivating language impacts the high culture context like health-care professionals, as observed in the Indian health-care sector during COVID-19. The findings are indicative of developing non-cognitive personality traits for managerial skills.

The study substantiates the pivotal role of the leader’s communication with stakeholders such as patients/attendants and health-care staff. The findings, which are an indicator of patientcare, as an outcome of patient compliance, will be indicative of developing the non-cognitive skills in the personality traits of managerial skills, inducing patients’ trust in their health-care providers, using motivating language. Therefore, the health-care professionals must be trained in the application of motivating language with stakeholders, namely, patients/attendants and staff.

The findings state that leader’s motivating language impacts on employees of high culture context like health-care professionals, as observed in the Indian health-care sector during the recent global medical emergency of COVID-19, whereas the earlier studies posited leader’s motivating language to be effective on employees with low-cultural context. The role of leader is pivotal in daily interaction with the stakeholders, namely, patients/attendants and health-care staff.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The aim of this paper is to explore the brain–computer interface (BCI) as a methodology for generating awareness and increasing reliable use cases of the same so that an individual's quality of life can be enhanced via neuroscience and neural networks, and risk evaluation of certain experiments of BCI can be conducted in a proactive manner.

This paper puts forward an efficient approach for an existing BCI device, which can enhance the performance of an electroencephalography (EEG) signal classifier in a composite multiclass problem and investigates the effects of sampling rate on feature extraction and multiple channels on the accuracy of a complex multiclass EEG signal. A one-dimensional convolutional neural network architecture is used to further classify and improve the quality of the EEG signals, and other algorithms are applied to test their variability. The paper further also dwells upon the combination of internet of things multimedia technology to be integrated with a customized design BCI network based on a conventionally used system known as the message query telemetry transport.

At the end of our implementation stage, 98% accuracy was achieved in a binary classification problem of classifying digit and non-digit stimuli, and 36% accuracy was observed in the classification of signals resulting from stimuli of digits 0 to 9.

BCI, also known as the neural-control interface, is a device that helps a user reliably interact with a computer using only his/her brain activity, which is measured usually via EEG. An EEG machine is a quality device used for observing the neural activity and electric signals generated in certain parts of the human brain, which in turn can help us in studying the different core components of the human brain and how it functions to improve the quality of human life in general.

The manufacturing industry is presently experiencing technological disruption on a global scale. Consequently, to tackle such disruption, firms are identifying a volatile, uncertain, complex and ambiguous (VUCA) scenario and seeking ways to counter it. Accordingly, this paper aims to investigate the employee performance through assessing organizational citizenship behaviour (OCB) among the shop floor employees of the fast-moving consumer goods (FMCG) industry where a high-performance work system (HPWS) has been implemented.

A descriptive research design was used in the study, and 395 shop floor employees working in leading multinational firms, with a minimum global turnover of US$1bn, were interviewed. These manufacturing firms were located in three industrial clusters in the northern part of India.

The results indicate that HPWS influences OCB. Most of the dimensions of HPWS and OCB were found to be positively associated. The findings also disprove the labour process theory in the context of the study.

The findings report a broad view of the relationship between HPWS and OCB in the Indian manufacturing context. The study offers the practical insights that HPWS is a universally accepted framework and that organizations should focus on the effective implementation of HPWS in a VUCA scenario, which is in line with past studies. The study also provides future directions for research.

This paper has established the relationship between HPWS and OCB in the manufacturing sector, especially for shop floor employees.

The purpose of this study to brought new dimensions by inserting market conditions and investor sentiments as independent variable measure their impact on government policy formulation and sustainable development. This research also measures the moderating effective stakeholder engagement. Previous research has focused on demystifying the relationship between green bonds and sustainable development.

The analysis part of the research is initiated by factor analysis on the sample size of 100. After the construction of appropriate statements matching the research objective, it was circulated to the respondents of northern region of India. The sampling technique was random in nature through which data analysis on 700 respondents was done. For meeting research objectives present research applies PLS algorithm on the conceptual model framed through review of literature.

Out of all independent variables green bond issuance is having statistically significant impact on government policy formulation and investors’ sentiment is having statistically significant impact on sustainable development. Rest all other pairs are statistically insignificant. For an investor, it is necessary to understand that how its sentiments impacts government policy formulation and the health of ecology.

The research produced results with management implications for practitioners and policy makers that are very significant to the fields of sustainability, green finance and environmental policy. Green bonds also influence government policy, illustrating how green financing may revolutionize environmental laws and regulations.

The social implications of this revelation are considerable. The research enhances knowledge about sustainable development by emphasizing the importance of green bonds in supporting environmentally friendly activities. It allows for transparent reporting, increasing social accountability and reputation while attracting environmentally conscious consumers and fostering community trust. According to the survey, investor sentiment and their enthusiasm for eco-friendly investments may push more money to efforts that are good for society and the environment. This study enhances consciousness about sustainable finance, which has the potential to inspire beneficial social shifts towards a more environmentally and socially equitable future.

These social ramifications manifest themselves in various socioeconomic and environmental issues of the society in addition to credit and public policy. Second, it is evident that green bond emissions are influencing government policy, demonstrating the power of financial instruments to encourage environmentally beneficial social outcomes by providing officials with an incentive to modify environmental regulations.