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Artemisia stelleriana-mediated ZnO nanoparticles for textile dye treatment: a green and sustainable approach
Textile effluents being one of the major reasons for water pollution raises major concern for water bodies and the habitation surrounding them. The lack of biologically safer treatment solutions creates a major concern for the disposal of these effluents. The present study focuses on the degradation of textile dyes using leaf extract of Artemisia stelleriana-assisted nanoparticles of zinc oxide (ZnO-NPs). ZnO NPs synthesized were confirmed using spectroscopic, X-ray diffraction and microscopic analysis. The current research utilizes widely used major textile dyes, Reactive Yellow-145 (RY-145), Reactive Red-120 (RR-120), Reactive Blue-220 (RB-220) and Reactive Blue-222A (RB-222A), which are released accidentally or due to the non-availability of cost-effi-cient, dependable and environment-friendly degradation methods, making this work a much-needed one for preventing the discharge before treatment. The biosynthesized ZnO-NPs were top-notch catalysts for the reduction of these dyes, which is witnessed by a gradual decrease in absorbance maximum values. After 320 min, ZnO-NPs under UV light exposure showed 99, 95, 94 and 45% degradations of RY-145, RR-120, RB-220 and RB-222A dyes, respectively. The phytotoxicity study conducted at two trophic levels revealed that the A. stelleriana-mediated ZnO-NPs have great potential for the degradation of textile dyes, allowing them to be scaled up to large-scale treatments. 2023 The Authors. -
Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition
Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). All Rights Reserved. -
Artificial intelligence talent acquisition in HEIs recruitments
Purpose: The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA). Design/methodology/approach: A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management. Findings: The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA. Originality/value: AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition. 2024, Emerald Publishing Limited. -
Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development
This paper explores the potential impact of Generative Artificial Intelligence (Generative AI) on developing countries, considering both positive and negative effects across various domains of information, culture, and industry. Generative Artificial Intelligence refers to artificial intelligence (AI) systems that generate content, such as text, audio, or video, aiming to produce novel and creative outputs based on training data. Compared to conversational artificial intelligence, generative artificial intelligence systems have the unique capability of not only providing replies but also generating the content of those responses. Recent advancements in Artificial Intelligence during the Fourth Industrial Revolution, exemplified by tools like ChatGPT, have gained popularity and reshaped content production and creation. However, the benefits of generative artificial intelligence are not equally accessible to all, especially in developing countries, where limited access to cutting-edge technologies and inadequate infrastructure pose challenges. This paper seeks to understand the potential impact of generative AI technologies on developing countries, considering economic growth, access to technology, and the potential paradigm shift in education, healthcare, and the environment. The findings emphasize the importance of providing the necessary support and infrastructure to ensure that generative AI contributes to inclusive development rather than deepening existing inequalities. The study highlights the significance of integrating Generative AI into the context of the Fourth Industrial Revolution in developing countries, where technological change is a crucial determinant of progress and equitable growth. The Author(s) 2023. -
Artificial intelligence service agents: a silver lining in rural India
Purpose: The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services. Design/methodology/approach: Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact. Findings: The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population. Originality/value: Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims. 2023, Emerald Publishing Limited. -
Artificial Intelligence Technique Based Effective Disaster Recovery Framework to Provide Longer Time Connectivity in Mobile Ad-hoc Networks
Communication plays a vital role for effective management and for the execution of disaster response and emergency recovery efforts must be able to exchange information with each other from anywhere, at any time to successfully fulfill their missions. Therefore, it is important to configure emergency communications networks in disaster conditions using ad-hoc networks. This proposed framework collects the information and communication before or after a disaster. The aim of this research work is to propose a possible practical communication model by using ad-hoc network configuration technologies using Greedy Randomized Adaptive Search Procedure (GRASP) with the proposed algorithm. The development of this research work is to improve information exchange and facilitate coordination among emergency services and disaster field offices, state/level entities and private industry. This is accomplished by the integration of existing information systems, implementation of new efficient technologies and interconnection of established networks with artificial based techniques. IJCESEN. -
Artificial intelligence-based reverse logistics for improving circular economy performance: adeveloping country perspective
Purpose: Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance. Design/methodology/approach: In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis. Findings: Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation. Practical implications: The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals. Originality/value: Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs CE performance. 2024, Emerald Publishing Limited. -
Artificial Neural Network with Firefly Algorithm-Based Collaborative Spectrum Sensing in Cognitive Radio Networks
Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network. Copyright 2023 KSII. -
Artistic Representation of Gender Nonconforming Female Bodies in Social Media: A Study of Select Indian Graphic Artists on Instagram
The study critically examines gender nonconforming female identities via their sex-ualized representations through artistic imagination on Instagram. Instagram representation becomes a political act where this visual subversion allows the queer to reclaim their non-binary identity and thus articulate their choices through their body. The digital graphic art taken under study is select images from the Instagram pages of Indian artists artwhoring, aorists, and sayartic. The research study examines the question of an ideal hegemonic femininity perpetuated by the rhetoric of Indian heteronormative patriarchal assertions. It analyses select images that defy hegemonic femininity and gender binary by embodying an amalgamation of masculinity and femininity and lesbian desire which forms an act of subversion. The methodology of critical discourse analysis is employed to study Instagram art and the critical frameworks of the fantasy female body, and the notion of hetero-patriarchal femininity. In conclusion, the study discusses the treatment of female gender non-conforming bodies, their appearance, lesbian desire, and body image. Such transgressive depiction of bodies successfully situates the female body beyond the dichotomy of masculinity and femininity. 2023 The Author(s). All rights reserved. -
Artists' moving image: South Asian trajectories /
Moving Image Review & Art Journal (MIRAJ), Vol.7, Issue 2, pp.191-201, ISSN No: 2045-6298. -
AS-CL IDS: anomaly and signature-based CNN-LSTM intrusion detection system for Internet of Things
In recent years, the internet of things (IoT) has had a significant impact on our daily lives, offering various advantages for improving our quality of life. However, it is crucial to prioritize the security of IoT devices and the protection of user's personal data. Intrusion detection systems (IDS) play a critical role in maintaining data privacy and security. An IoT IDS continuously monitors network activity and identifies potential security risks or attacks targeting IoT devices. While traditional IDS solutions exist, intrusion detection heavily relies on artificial intelligence (AI). AI can greatly enhance the capabilities of IoT IDS through real-time monitoring, precise threat identification, and automatic response capabilities. It is essential to develop and utilize these technologies securely and responsibly to mitigate potential risks and safeguard user privacy. A hybrid IDS was proposed for anomaly-based and signature-based intrusions, leveraging convolutional neural network with long short-term memory (CNN-LSTM). The name of the proposed hybrid model is anomaly and signaturebased CNN-LSTM intrusion detection system (AS-CL IDS). The AS-CL IDS concentrated on two different IoT IDS detection strategies employing a combination of deep learning techniques. The model includes model training and testing as well as data preprocessing. The CIC-IDS 2018, IoT network intrusion dataset, MQTT-IoT-IDS2020, and BoTNeTIoTL01 datasets were used to train and test the AS-CL IDS. The overall performance of the proposed model was assessed using accepted assessment metrics. Despite reducing the number of characteristics, the model achieved 99.81% accuracy. Furthermore, a comparison was made between the proposed model and existing alternative models to demonstrate its productivity. As a result, the proposed model proves valuable for predicting IoT attacks. Looking ahead, the deployment strategy of the IoT IDS can anticipate the utilization of real-time datasets for future implementations. 2023 Jinsi Jose and Deepa V. Jose. -
Aspect Based Feature Extraction in Sentiment Analysis using Bi-GRU-LSTM Model
In Natural Language Processing (NLP), Sentiment Analysis (SA) is a fundamental process which predicts the sentiment expressed in sentences. In contrast to conventional sentiment analysis, Aspect-Based Sentiment Analysis (ABSA) employs a more nuanced approach to assess the sentiment of individual aspects or components within a document or sentence. Its objective is to identify the sentiment polarity, such as positive, neutral, or negative, associated with particular elements disclosed within a sentence. This research introduces a novel sentiment analysis technique that proves to be more efficient in sentiment analysis compared to current methods. The suggested sentiment analysis method undergoes three key phases: 1. Pre-processing 2. Extraction of aspect sentiment and 3. Sentiment analysis classification. The input text data undergoes pre-processing through the implementation of four typical text normalization techniques, which include stemming, stop word elimination, lemmatization, and tokenization. By employing these methods, the provided text data is prepared and fed into the aspect sentiment extraction phase. During the aspect sentiment extraction phase, features are obtained through a series of steps, including enhanced ATE (Aspect Term Extraction), assessment of word length, and determination of cosine similarity. By following these steps, the relevant features are extracted on the basis of aspects and sentiments involved in the text data. Further, a hybrid classification model is proposed to classify sentiments. In this work, two of the Deep Learning (DL) classifiers, Bi-directional Gated Recurrent Unit (Bi-GRU) and Long Short-Term memory (LSTM) are used in proposing a hybrid classification model which classifies the sentiments effectively and provides accurate final predicted results. Moreover, the performance of proposed sentiment analysis technique is analyzed experimentally to show its efficacy over other models. 2024 River Publishers. -
Aspect based sentiment analysis using a novel ensemble deep network
Aspect-based sentiment analysis (ABSA) is a fine-grained task in natural language processing, which aims to predict the sentiment polarity of several parts of a sentence or document. The essential aspect of sentiment polarity and global context have deep relationships that have not received enough attention. This research work design and develops a novel ensemble deep network (EDN) which comprises the various network and integrated to enhance the model performance. In the proposed work the words of the input sentence are converted into word vectors using the optimised bidirectional encoder representations from transformers (BERT) model and an optimised BERT-graph neural networks (GNN) model with convolutions is built that analyses the ABSA of the input sentence. The optimised GNN model with convolutions for context-based word representations is developed for the word-vector embedding. We propose a novel EDN for an ABSA model for optimised BERT over GNN with convolutions. The proposed ensemble deep network proposed system (EDN-PS) is evaluated with various existing techniques and results are plotted in terms of metrics for accuracy and F1-score, concluding that the proposed EDN-PS ensures better performance in comparison with the existing model. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Aspect based sentiment analysis using fine-tuned BERT model with deep context features
Sentiment analysis is the task of analysing, processing, inferencing and concluding the subjective texts along with sentiment. Considering the application of sentiment analysis, it is categorized into document-level, sentence-level and aspect level. In past, several researches have achieved solutions through the bidirectional encoder representations from transformers (BERT) model, however, the existing model does not understand the context of the aspect in deep, which leads to low metrics. This research work leads to the study of the aspect-based sentiment analysis presented by deep context bidirectional encoder representations from transformers (DC-BERT), main aim of the DC-BERT model is to improvise the context understating for aspects to enhance the metrics. DC-BERT model comprises fine-tuned BERT model along with a deep context features layer, which enables the model to understand the context of targeted aspects deeply. A customized feature layer is introduced to extract two distinctive features, later both features are integrated through the interaction layer. DC-BERT mode is evaluated considering the review dataset of laptops and restaurants from SemEval 2014 task 4, evaluation is carried out considering the different metrics. In comparison with the other model, DC-BERT achieves an accuracy of 84.48% and 92.86% for laptop and restaurant datasets respectively. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Assault on religion from modern behavioural and natural sciences
[No abstract available] -
Assembly of discrete and oligomeric structures of organotin double-decker silsesquioxanes: Inherent stability studies
Double-decker silsesquioxane (DDSQ), a type of incompletely condensed silsesquioxane, has been used as a molecular precursor for synthesizing new organotin discrete and oligomeric compounds. The equimolar reaction between DDSQ tetrasilanol (DDSQ-4OH) and Ph2SnCl2 in the presence of triethylamine leads to obtaining discrete [Ph4Sn2O4(DDSQ)(THF)2] (1). The change of sterically bulky aryl Ph2SnCl2 precursor to linear alkyl nBu2SnCl2 led to the isolation of oligomeric [nBu4Sn2O4(DDSQ)] (2). The structures of compounds 1 and 2 have been demonstrated using single-crystal X-ray diffraction measurements. Indeed, the formation of oligomeric organotin DDSQ compound (2) was determined using GPC and MALDI-TOF mass spectroscopy. In compound 1, the geometry of the tin atom is five-coordinated trigonal bipyramidal by two phenyl groups, two Si-O from DDSQ and one tetrahydrofuran. Compound 2 contains four coordinated two peripheral tin atoms and two five-coordinated central tin atoms, in which, the fifth coordinating oxo groups in the central tin atoms create the bridge between two different DDSQ units that leads to the formation of oligomeric structure. Density functional theory calculations on organotin DDSQs infer that the obtained lattice energy for compound 1 is far higher than for the case of compound 2, which indicates that the crystal of compound 1 is better stabilized in its crystal lattice with stronger close packing via intermolecular interactions between discrete molecules with coordinated THF compared to the crystal of compound 2. The greater stability arises mainly due to the sterically bulkier phenyl groups attached to the tin centers in compound 1, which provide accessibility for accommodating the THF molecule per tin via Sn-THF bonding, while contrarily the smaller n-butyl groups aid the polymerization of the four repeating units of [SnSi4O7] or two Sn2O4(DDSQ) through ?-oxo groups. Both compounds 1 and 2 were chosen to be promising precursors for the synthesis of ceramic tin silicates. The thermolysis of 2 at 1000 C afforded the mixture of crystalline SnSiO4 and SiO2 but the same mixture was only formed by thermolysis of 1 at relatively higher temperature (1500 C), which infers that compound 1 is more stable than compound 2 that is in good synergy with theoretical lattice energy. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
Assessing anticancer properties of PEGylated platinum nanoparticles on human breast cancer cell lines using in-vitro assays
This study describes the in-vitro cytotoxic effects of PEG-400 (Polyethylene glycol-400)-capped platinum nanoparticles (PEGylated Pt NPs) on both normal and cancer cell lines. Structural characterization was carried out using x-ray diffraction and Raman spectroscopy with an average crystallite size 5.7 nm, and morphological assessment using Scanning electron microscopy (SEM) revealed the presence of spherical platinum nanoparticles. The results of energy-dispersive x-ray spectroscopy (EDX) showed a higher percentage fraction of platinum content by weight, along with carbon and oxygen, which are expected from the capping agent, confirming the purity of the platinum sample. The dynamic light scattering experiment revealed an average hydrodynamic diameter of 353.6 nm for the PEGylated Pt NPs. The cytotoxicity profile of PEGylated Pt NPs was assessed on a normal cell line (L929) and a breast cancer cell line (MCF-7) using the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay. The results revealed an IC50 of 79.18 ?g ml?1 on the cancer cell line and non-toxic behaviour on the normal cell line. In the dual staining apoptosis assay, it was observed that the mortality of cells cultured in conjunction with platinum nanoparticles intensified and the proliferative activity of MCF-7 cells gradually diminished over time in correlation with the increasing concentration of the PEGylated Pt NPs sample. The in vitro DCFH-DA assay for oxidative stress assessment in nanoparticle-treated cells unveiled the mechanistic background of the anticancer activity of PEGylated platinum nanoparticles as ROS-assisted mitochondrial dysfunction. 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Assessing CRM practices in hotel industry: A look at the progress and prospects
Purpose: Customer Relationship Management (CRM) is a unique key element considered in the hotel diligence which is extremely thriving plus competitive market at present. The study purpose is to identify how CRM can be strategy tactics for solution as business attitudes towards the hotels. It generates nine different items with regards to CRM. Design/methodology/approach: The study progressed in four phases. The empirical data were collected. A sample of 100, four, five and five star luxury hotels was considered for the study. To explore CRM process in hotel industry, an analysis was carried out to identify different CRM levels (performance and importance). The study was tested and analyzed by adopting a scale developed by Reinartz, Krafft and Hoyer. Findings: The result largely supports the CRM process developed in hotels theoretically but does not seem to have the balance between the CRM levels evenly. Crucial discoveries of the learning are firstly, CRM remains characterized via nine different items in the hotel diligence connecting to Measure, Acquisition, Regain, Maintain, Retain, Cross up, Referrals, Termination Measure and Exit. The best predictor of CRM is the items on performance level referred as Measure followed by Regain and Acquisition and on the importance level, Referrals followed by Cross-up and Regain. Practical Implications: The result suggests that the hotels must understand today's customer's wants and needs. The hotels need to be more proactive in developing a balance between CRM levels for effective CRM in hotel diligence. Novelty/assessment: The learning highlights the importance of CRM and its relationship in service industries in the literature. The study focuses in particular on CRM in hotel industry and the linkage between the CRM levels (Performance and Importance). -
Assessing factors influencing intentions to use cryptocurrency payments in the hospitality sector
Purpose: The emergence of cryptocurrency has developed a new payment system that is changing how financial transactions happen in hospitality. Consumers/travelers have started experimenting with cryptocurrency payments in hotels and restaurants. However, extant research is lacking in understanding the consumer adoption intention of cryptocurrency payments. This study investigates the intention to use cryptocurrency payments in the hospitality industry. Design/methodology/approach: The conceptual model in this study is based on the Behavioral Reasoning Theory, and it explores the motivating and deterring factors influencing the adoption of cryptocurrency payments in the hospitality industry. A quantitative survey was conducted among 1,080 consumers to examine and confirm the model, with data being analyzed through the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Findings: The outcome of this work showed that the reasons for positively influence and reasons against negatively influence consumers attitudes and use intentions. Consumers values of openness to change positively influence the reasons for and do not influence the reasons against and attitude toward the use of cryptocurrency payments. Practical implications: This work contributes to practice by providing insights to customers (users/payee), hospitality managers (investors) and organizations/firms (receiving crypto payments) as well as to financial firms and the government. Originality/value: This research contributes to cryptocurrency payment adoption and behavioral finance literature. The research uniquely provides the adoption and inhibiting factors for cryptocurrency payment in an integrated framework in the hospitality sector. 2024, Emerald Publishing Limited. -
Assessing global perceptions of India: Policy implications drawn from foreign tourism narratives
This study scrutinizes Indias growing appeal as a tourist destination, accentuated by government initiatives and innovative tourism policies like the e-visa program, Incredible India Campaign 2.0 and digital advancements in the travel sector. With the diminishing impact of COVID-19, there is a noticeable surge in various forms of tourism inbound, outbound and domestic. The primary focus is to understand the driving factors behind the choice of India as a destination for inbound tourists. This research delves into these motivations, providing a global perspective on Indias attractiveness. A mixed-method approach was employed, utilizing convenience sampling for data collection. The quantitative analysis was based on a survey, informed by a literature review, comprising 390 respondents from 10 diverse Indian destinations. Additionally, 25 qualitative interviews were conducted, aiming to enrich and triangulate the quantitative findings. Exploratory factor analysis (EFA) revealed five predominant motivations among inbound tourists: culinary interests, spiritual pursuits, budget-consciousness, cultural curiosity and natural allure. These findings were substantiated through thematic analysis. The outcomes have significant practical ramifications for destination managers and tourism policy developers in India. By understanding these key motivators, they can devise targeted strategies for enhancing the appeal of India to these specific tourist segments. This study not only aids in refining tourism promotion efforts but also contributes to the academic discourse on tourist motivation offering a fresh international perspective on Indias image as a tourist destination. by the author, licensee University of Lodz Lodz University Press, Lodz, Poland.