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This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

This study aims to analyze the moderating roles of familiarity, generation and gender on the impacts of coronavirus fear-uncertainty on the destination image and visiting intentions post-COVID-19.

This paper seeks to provide evidence for a research conclusion by conducting a survey of 431 potential travelers of various nationalities who visited Egypt. The data were examined using structural equation modeling with a multigroup analysis and PROCESS MACRO.

The findings indicated that the links between coronavirus fear-uncertainty, the destination image and intention to visit were significantly different from one gender and generation group to another. Moreover, it showed that destination familiarity moderates the negative effects of coronavirus fear-uncertainty on the destination image and intention to visit.

To the best of the authors’ knowledge, this paper is the first to explore the moderating role of destination familiarity, generation and gender in the effects post-COVID-19 of coronavirus fear-uncertainty on the destination image and travelers’ intention to visit using generational cohort theory and gender schema theory.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Virtual reality (VR) offers unprecedented immersion and interactivity in education, and working and learning from home have become the norm during the COVID-19 pandemic. This study empirically investigated the factors affecting the use of a VR online learning system (VROLS).

To explore factors affecting users’ continuance behavioral intentions toward using VROLSs, a research framework was formed comprising factors that constitute benefits (i.e. pull factors) and costs (i.e. push factors); these factors included perceived value, flow and social influence. The data for this study were collected via online survey questionnaires. A total of 307 valid responses were used to examine the hypotheses in the research model, employing structural equation modeling (SEM) techniques.

Perceived value, flow experience and the number of peers using VR primarily affect the decision to adopt a VROLS. The pull factors of spatial presence, entertainment and service compatibility, along with the push factors of complexity and visual fatigue, affect perceived value. Therefore, we conclude that perceived value is a primary factor positively influencing both flow experience and the decision to adopt the service.

This study contributes to a theoretical understanding of factors that explain users’ intention to use VROLSs.

Career burnout is prevalent in the PR industry, precisely when demand for professionals is increasing. While career burnout has been included in studies and theorising on professionalism and feminisation, issues with turnover and burnout remain.

Using a grounded theory approach, this qualitative study draws upon the lived experiences of 30 current and former female Australian PR professionals to gain an understanding of how they perceive signs of career burnout and the factors that contribute to it.

Career burnout is an occupational syndrome whereby someone gradually morphs from being highly motivated in their role to emotionally exhausted, cynical and/or experiencing feelings of failure. It is a protracted response to chronic workplace demands and stressors, and includes three dimensions: emotional exhaustion, depersonalisation and reduced personal accomplishment. It is specifically a workplace phenomenon, distinguished from anxiety and depression, which can emerge in any context.

A key contributor to career burnout were PR-specific workplace stressors that were perceived to stem from a lack of respect for, or understanding of, PR as a profession. The stressors included the need to‘prove the spend’of PR, unreasonable deadlines, clients disregarding advice or counsel, as well as broader societal perceptions of PR as ‘spin doctors’. This often led to the PR practitioner undertaking work that went against their own advice or resulted in unsuccessful organisational outcomes they felt could have been avoided had their advice been listened to and valued. The workplace factors contributing to burnout overlap in complex ways and the study supports the idea that burnout is a product of situational contexts, despite being acutely felt at the individual level.

Based on stimulus-organism-response theory, this study aims to develop and tests a model of memorable heritage tourism experience (MHTE). The model proposes that experiencescape, experience co-creation, education and photography are important antecedents of MHTE, which is then a driver of place attachment.

Data for this study were collected using a Web-based questionnaire of people aged 18 years and over who had a heritage tourism experience during the previous three months (February–April 2023). The survey was distributed in May 2023 using Amazon Mechanical Turk (MTurk). A survey link was posted on MTurk, which remained active for the first week of May 2023. Out of the 283 responses received, 272 were valid responses from individuals who met the participation criteria.

Experiencescape, experience co-creation, education and photography were found to be positive drivers of the MHTE, with a positive relationship between MHTE and place attachment.

Many studies linked to memorable tourism experience (MTE) mainly replicate Kim, Ritchie, & McCormick’s (2012) MTE scale, regardless of the specific study context. This study offers an alternative framework through which alternative antecedents and outcomes of tourists’ MTE can be identified.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

Mianzi can powerfully influence tourists’ behavior. Compared with product brands, destination brands are more multidimensional, consisting of intangible and tangible elements. Therefore, this paper aims to explore the relationships among the Chinese culture-related variable mianzi, destination product quality, destination service quality, destination brand value, destination brand resonance, destination brand self-congruity and destination overall brand equity.

A questionnaire survey was conducted in 2021, yielding 475 valid responses from tourists who had traveled to Hainan, China. Data was analyzed using structural equation modeling.

The results showed that mianzi plays a significant role in shopping destination brand equity, and the mianzi effect shapes tourists’ positive perceptions of destination product and service quality. Meanwhile, destination product quality and service quality enhance shopping destinations’ overall brand equity through destination brand value, brand resonance and brand self-congruity.

This study, focusing on shopping destinations, presents a novel view of brand equity. The research also uncovers influencing factors (e.g. product quality and service quality) that further enrich the destination brand equity model. Finally, findings offer valuable insights for academic research and the practical development of shopping destinations.

面子能有力地影响游客的行为。与产品品牌相比, 目的地品牌更加多维, 由无形要素和有形要素构成。因此, 本文旨在探讨与中国文化相关的变量面子、目的地产品质量、目的地服务质量、目的地品牌价值、目的地品牌共鸣、目的地品牌自我一致和目的地整体品牌资产之间的关系。

2021年对去过中国海南旅游的游客进行问卷调查, 最终获取了 475 份有效问卷。

果表明, 结果表明面子在购物目的地品牌资产中发挥着重要作用, 面子效应塑造了游客对目的地产品和服务质量的积极认知。同时, 目的地产品质量和服务质量通过目的地品牌价值、品牌共鸣和品牌自我一致提升了购物目的地的整体品牌资产。

本研究以购物目的地为重点, 提出了一种新颖的品牌资产观点。研究还发现了一些影响因素（如产品质量和服务质量）, 进一步丰富了目的地品牌资产模型。最后, 研究结果为学术研究和购物目的地的实际发展提供了宝贵的见解。

Mianzi puede influir poderosamente en el comportamiento de los turistas. En comparación con las marcas de producto, las marcas de destino son más multidimensionales y constan de elementos intangibles y tangibles. Por lo tanto, este artículo pretende explorar las relaciones entre la variable mianzi relacionada con la cultura china, la calidad del producto del destino, la calidad del servicio del destino, el valor de la marca del destino, la resonancia de la marca del destino, la autocongruencia de la marca del destino y el valor general de la marca del destino.

En 2021 se llevó a cabo una encuesta por cuestionario, que arrojó 475 respuestas válidas de turistas que habían viajado a Hainan, China. Los datos se analizaron mediante un modelo de ecuaciones estructurales.

Los resultados mostraron que el mianzi desempeña un papel significativo en el valor de marca de los destinos de compras, y que el efecto mianzi determina las percepciones positivas de los turistas sobre la calidad de los productos y servicios del destino. Por su parte, la calidad de los productos y servicios del destino mejora el valor de marca global de los destinos de compras a través del valor de marca del destino, la resonancia de la marca y la autocongruencia de la marca.

Este estudio, centrado en los destinos de compras, presenta una visión novedosa del valor de marca. La investigación también descubre factores influyentes (por ejemplo, la calidad del producto y la calidad del servicio) que enriquecen aún más el modelo de valor de marca del destino. Por último, los resultados ofrecen valiosas perspectivas para la investigación académica y el desarrollo práctico de los destinos de compras.

This paper aims to examine the role of social media communication, tourist satisfaction and destination brand equity components in enhancing destination brand equity based on the Stimulus – Organism – Response (S-O-R) theory.

The conceptual model and research hypotheses were assessed using covariance-based structural equation modeling (SEM). An online survey was used to collect data from 369 domestic tourists who had traveled to Danang and knew about content related to Danang generated by either DMOs or other users on social media.

Except for the effect of DMO-generated social media communication on tourist satisfaction and the impact of destination brand awareness on destination brand loyalty, the findings confirmed the sequential causal relationships between research concepts based on the S-O-R model.

Future research should explore the proposed model based on comparisons of different nationalities to better understand the impact of cultural factors.

DMOs should associate social media with their marketing strategies to enhance destination brand equity, using cutting-edge technologies to create content and update information in a significant way to make communications by DMOs more effective. The findings especially suggest that UGC plays a vital role in improving brand equity dimensions, so DMOs could exploit UGC to engage existing customers and build relationships with potential customers. This research provides guidance for DMOs to improve their brand equity based on social media.

This study has contributed to the destination marketing literature by applying the S-O-R theory to propose a pathway for effectively increasing destination brand equity and highlight the importance of social media communication as a driver to achieve a hierarchical relationship between destination brand equity components and tourist satisfaction from stimulus to organism (e.g. cognition to affect).

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.