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Virtual goods consumption has risen dramatically in recent years. Recognizing the benefits of virtual goods in generating revenue for online game companies, marketers strive to understand the motives behind virtual goods purchases. We investigated the direct and indirect effects of functional, emotional, and social values through player satisfaction on purchase intention toward virtual goods among online players.

In total, we surveyed 332 online game players utilizing a structured questionnaire. We employed a multi-analytic approach combining partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) to examine the proposed relationships.

The findings show that all dimensions of value and player satisfaction significantly affect the intention to acquire virtual goods. However, social value does not exert a significant effect on player satisfaction. Moreover, we confirmed that player satisfaction mediates the relationships between functional value, emotional value, and purchase intention. Furthermore, NCA results indicated that all predictors in the model are necessary conditions of purchase intention for virtual goods.

These findings contribute to an enhanced understanding of purchase intentions among online game players from a symmetric (PLS-SEM) and asymmetric (NCA) perspective by proposing a multi-analytic approach.

India started economic reforms at a rapid pace to catch up the world economy by following the services-led-growth model during the post-liberalisation period. Over the years, the growing unemployment rate posits a re-look into the dynamics of growth model for wider work force participation. In this backdrop, the paper aims to examine the dynamics of structural changes in employment pattern in view of economic growth led by services-led growth model in India.

The study employs a non-linear autoregressive model (NARDL) to examine the effect of the growth rates in three broad economic sectors namely agriculture and allied, services and industry on work force participation representing the employment opportunities in India.

The results highlight that the rapid expansion of the service sector has not occurred with enough employment opportunities by the same rate. By contrast, the growth in the industrial sector significantly creates employment opportunities in the short and long run. These results support the industry led growth model over the services for sustainable and inclusive economic growth in the country.

The study relies on combined labour force participation rates rather than gender-specific rates. Further, the regulatory, working conditions and economic incentives may affect the gender-specific engagement of the labour force in three broad sectors.

The results offer important insight into changing patterns in employment with policy lessons. A wider workforce force participation calls for expansion of manufacturing activities through pro-industry programmes.

The study makes pioneer efforts to examine the dynamics of labour force participation with respect to the growth of three broad economic sectors of the Indian economy. The results provide new insights with policy implications for the changing employment pattern and policy response.

The aim of this study is to examine the behavioral intention (BI) to adopt the Coinbase Wallet by Spanish users.

A survey was administered to individuals residing in Spain between March and April 2021. There were 301 questionnaires analyzed. This research applies a new predictive model based on technology acceptance model (TAM) 2, the unified theory of acceptance and use of technology (UTAUT) model, the theory of perceived risk and the commitment trust theory. A mixed partial least squares structural equation modeling (PLS-SEM)/fuzzy-set qualitative comparative analysis (fsQCA) methodology was employed for the modeling and data analysis.

The results showed that all the variables proposed have a direct and positive influence on the intention to use a Coinbase Wallet. The findings present clear directions for traders, investors and academics focused on improving their understanding of the characteristics of these markets.

First, this study addresses important concerns relating to the adoption of crypto-wallets during the global pandemic. Second, this research contributes to the existing literature by adding electronic word of mouth (e-WOM), trust, web quality and perceived risk as new drivers of the intention to use the Coinbase Wallet, providing unique and innovative insights. Finally, the study offers a solid methodological contribution by integrating linear (PLS) and nonlinear (fsQCA) techniques, showing that both methodologies provide a better understanding of the problem and a more detailed awareness of the patterns of antecedent factors.

This paper comprehensively addresses the influence of chopped strand mat glass fiber-reinforced polymer (GFRP) patch configurations such as geometry, dimensions, position and the number of layers of patches, whether a single or double patch is used and how well debonding the area under the patch improves the strength of the cracked aluminum plates with different crack lengths.

Single-edge cracked aluminum specimens of 150 mm in length and 50 mm in width were tested using the tensile test. The cracked aluminum specimens were then repaired using GFRP patches with various configurations. A three-dimensional (3D) finite element method (FEM) was adopted to simulate the repaired cracked aluminum plates using composite patches to obtain the stress intensity factor (SIF). The numerical modeling and validation of ABAQUS software and the contour integral method for SIF calculations provide a valuable tool for further investigation and design optimization.

The width of the GFRP patches affected the efficiency of the rehabilitated cracked aluminum plate. Increasing patch width WP from 5 mm to 15 mm increases the peak load by 9.7 and 17.5%, respectively, if compared with the specimen without the patch. The efficiency of the GFRP patch in reducing the SIF increased as the number of layers increased, i.e. the maximum load was enhanced by 5%.

This study assessed repairing metallic structures using the chopped strand mat GFRP. Furthermore, it demonstrated the superiority of rectangular patches over semicircular ones, along with the benefit of using double patches for out-of-plane bending prevention and it emphasizes the detrimental effect of defects in the bonding area between the patch and the cracked component. This underlines the importance of proper surface preparation and bonding techniques for successful repair.

Over the last decade, investments in green energy companies have witnessed noticeable growth rates. However, the glacial pace of the world economic restoration due to COVID-19 pandemic placed a high degree of uncertainty over this market. Therefore, this study investigates the short- and long-term relationships between COVID-19 new cases and WilderHill New Energy Global Innovation Index (NEX) using daily data over the period from January 23, 2020 to February 1, 2023.

The authors utilize an autoregressive distributed lag bounds testing estimation technique.

The results show a significant positive impact of COVID-19 new cases on the returns of NEX index in the short run, whereas it has a significant negative impact in the long run. It is also found that the S&P Global Clean Energy Index has a significant positive impact on the returns of NEX index. Although oil has an influential effect on stock returns, the results show insignificant impact.

Governments have the chance to flip this trend by including investment in green energy in their economic growth stimulation policies. Governments should highlight the fundamental advantages of investing in this type of energy such as creating job vacancies while reducing emissions and promoting innovation.

First, as far as the authors are aware, the authors are the first to examine the effect of oil prices on clean energy stocks during COVID-19. Second, the authors contribute to studies on the relationship between oil prices and renewable energy. Third, the authors add to the emerging strand of literature on the impact of COVID-19 on various sectors of the economy. Fourth, the findings of the paper can add to the growing literature on sustainable development goals, in specific the papers related to energy sustainability.

This study aims to provide insight into the determinants of wellness tourism satisfaction, thereby taking a nonlinear approach regarding the relationships between attribute-level performance of wellness facility attributes, on the one hand, and wellness destination attributes, on the other hand, and global wellness tourist satisfaction. In addition, scores of impact range are calculated to reveal the potentially most determinant wellness facility and destination attributes.

This study uses data from a survey-based study conducted among 1,331 wellness tourists who have engaged in wellness tourism activities at one of 28 hotels with wellness offerings and 10 spas in Croatia. Impact-asymmetry analysis and impact-range analysis are used to quantify the potential of individual wellness attributes to generate satisfaction and dissatisfaction among wellness tourists and to perform a classification of wellness attributes according to the three-factor theory of customer satisfaction.

Operators of wellness tourism facilities, as well as managers of wellness destinations, must not make any compromises in quality levels because most wellness attributes have significantly higher potential to frustrate than please tourists. Basic factors such as cleanliness, punctuality or safety turned out to have the strongest influence on global satisfaction levels. Moreover, in line with previous research, wellness tourists have large expectations from destinations to have a preserved and beautiful nature, which is by far the most influential destination attribute. In addition to a safe environment and high-quality accommodation, wellness tourists further prefer rich cultural offerings.

To the best of the authors' knowledge, this is the first study to apply a nonlinear analysis approach to the quality–satisfaction relationship in a wellness tourism setting. Moreover, to the knowledge of the authors, this is the only study that used separate attribute models for wellness facilities, on the one hand, and wellness destinations, on the other hand, based on a nation-wide sample that covers multiple cases (i.e. multiple facilities and destinations).

本研究旨在深入了解养生旅游满意度的决定因素, 从而采用非线性方法来研究（i）养生设施属性和 （ii）养生目的地属性对国际养生游客满意度的关系。此外, 本文还计算了影响范围的分数, 以揭示潜在的最具决定性的养生设施和目的地属性。

本研究使用了基于对 1,331 名养生游客进行调查问卷的数据, 这些游客曾在克罗地亚 28 的酒店以及10个水疗中心进行了养生旅游活动。本文采用影响不对称分析（IAA）和影响范围分析（IRA）来量化个体养生属性在健康游客中产生满意度和不满意的潜力, 并根据顾客三因素满意度理论对健康属性进行分类。

养生旅游设施的运营商以及养生目的地的管理者不能在质量水平上做出任何妥协, 因为大多数养生属性很可能使游客感到沮丧, 而不是取悦游客。事实证明, 清洁、准时及安全等基本因素对全球满意度影响最大。此外, 根据之前的研究, 健康游客对目的地抱有很大的期望, 希望拥有保存完好且美丽的自然风光, 这是最具影响力的目的地属性。除了安全的环境和高品质的住宿外, 养生游客更看重丰富的文化产品。

这是第一项将非线性分析方法应用于养生旅游环境中的质量与满意度关系的研究。此外, 据作者所知, 这是唯一一项基于涵盖多个案例（即多个设施及目的地）的国家样本, 一方面对养生设施使用单独的属性模型, 另一方面对养生目的地使用单独的属性模型的研究。

Este estudio tiene como objetivo proporcionar información sobre los determinantes de la satisfacción del turismo de bienestar, adoptando así un enfoque no lineal con respecto a las relaciones entre el rendimiento a nivel de atributos de (i) atributos de instalaciones de bienestar, por un lado, y (ii) atributos de destino de bienestar, por otro lado, y la satisfacción del turista de bienestar global. Además, se calculan puntajes de rango de impacto para revelar las instalaciones de bienestar y los atributos de destino potencialmente más determinantes.

este estudio utiliza datos de un estudio basado en encuestas realizado entre 1,331 turistas de bienestar que participaron en actividades de turismo de bienestar en uno de los 28 hoteles con ofertas de bienestar y diez spas en Croacia. El análisis de asimetría de impacto (IAA) y el análisis de rango de impacto (IRA) se utilizan para cuantificar el potencial de los atributos de bienestar individuales para generar satisfacción e insatisfacción entre los turistas de bienestar y para realizar una clasificación de los atributos de bienestar de acuerdo con la teoría de los tres factores del cliente. satisfacción.

Los operadores de instalaciones de turismo de bienestar, así como los administradores de destinos de bienestar, no deben comprometer los niveles de calidad porque la mayoría de los atributos de bienestar tienen un potencial significativamente mayor para frustrar que para complacer a los turistas. Los factores básicos, como la limpieza, la puntualidad o la seguridad, resultaron ser los que más influyeron en los niveles de satisfacción global. En consecuencia, estos atributos no deben verse como fuentes potenciales de satisfacción y deleite del cliente, sino que deben otorgarse altos niveles de desempeño para evitar una fuerte insatisfacción. Además, en línea con investigaciones anteriores, los turistas de bienestar tienen grandes expectativas de que los destinos tengan una naturaleza preservada y hermosa, que es, con mucho, el atributo de destino más influyente. Además de un entorno seguro y un alojamiento de alta calidad, los turistas de bienestar prefieren una rica oferta cultural. Aplicando la teoría de los tres factores, una visión más matizada de la formación de la satisfacción del turista de bienestar mostró que estos atributos del destino tienen un potencial mucho mayor para crear una fuerte insatisfacción que satisfacción.

Este es el primer estudio que aplica un enfoque de análisis no lineal a la relación calidad-satisfacción en un entorno de turismo de bienestar. Además, según el conocimiento de los autores, este es el único estudio que utilizó modelos de atributos separados para instalaciones de bienestar, por un lado, y destinos de bienestar, por el otro, en base a una muestra nacional que cubre múltiples casos (es decir, múltiples instalaciones y destinos).

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.