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This study aims to explore the underlying mechanism of human–pet interaction in pet tourism affecting tourism experiences and daily lives of tourists. The research investigates the moderating role of pet attachment in this mechanism as well.

Based on the social exchange theory and value co-creation theory, this research develops and empirically tests a theoretical framework of human–pet interaction. Data were collected through an online survey of US tourists who have pet travel experiences. The data were analyzed by partial least squares structural equation modeling.

The results show that emotional value partially mediated the relationship between human–pet interaction and travel intention/quality of life, while social value partially mediated the relationship between human–pet interaction and quality of life. The findings of multi-group analysis suggest that the travel experience of tourists with low (vs high) levels of pet attachment is strengthened by human–pet interaction, leading to favorable outcomes.

This study enriches the empirical evidence on pet tourism experience. This study extends the existing literature by demonstrating the heterogeneity of the relationship between human–pet interaction, co-creation value, quality of life and travel intention of tourists with different pet attachment levels.

本研究旨在探索宠物旅游中人与宠物互动影响游客旅游体验和日常生活的潜在机制。本研究还考察了宠物依恋在这一机制中的调节作用。

基于社会交换理论和价值共创理论, 本研究开发并实证检验了人-宠物互动的理论框架。通过对有宠物旅游经历的美国游客进行在线调查收集数据。采用偏最小二乘法-结构方程模型(PLS-SEM)对数据进行分析。

情感价值部分中介了人宠互动与旅游意向/生活质量的关系, 社交价值部分中介了人宠互动与生活质量的关系。多群组分析的结果表明, 低(vs .高)宠物依恋水平的游客的旅游体验因人-宠物互动而得到加强, 从而导致良好的结果。

本研究丰富了宠物旅游体验的实证证据。本研究通过论证具有不同宠物依恋水平游客的人-宠互动、共创价值、生活质量和旅游意向之间关系的异质性, 拓展了现有文献。

El objetivo de este estudio es explorar el mecanismo subyacente de la interacción humano-mascota en el turismo con mascotas que afecta a las experiencias turísticas y a la vida cotidiana de los turistas. El estudio también examina el papel moderador del apego a las mascotas en este mecanismo.

Basada en la teoría del intercambio social y la teoría de la cocreación de valor, esta Investigación desarrolla y prueba empíricamente un marco teórico de la interacción entre humanos y mascotas. Los datos se recopilaron a través de una encuesta en línea a turistas estadounidenses que han tenido experiencias de viaje con mascotas. Los datos fueron analizados mediante un modelo de ecuaciones estructurales basado en mínimos cuadrados parciales (PLS-SEM).

Los resultados muestran que el valor emocional medió parcialmente la relación entre la interacción humano-mascota y la intención de viajar/calidad de vida, mientras que el valor social medió parcialmente la relación entre la interacción humano-mascota y la calidad de vida. Los resultados del análisis multigrupo sugieren que la experiencia de viaje de los turistas con niveles bajos (vs altos) de apego a las mascotas se ve reforzada por la interacción humano-mascota, lo que conduce a resultados favorables.

Este estudio enriquece la evidencia empírica sobre la experiencia del turismo con mascotas. Este estudio amplía la literatura existente al demostrar la heterogeneidad de la relación entre la interacción humano-mascota, el valor de cocreación, la calidad de vida y la intención de viaje de turistas con diferentes niveles de apego a las mascotas.

Based on the key dimensions of the Metaverse environment (immersiveness, fidelity and sociability), this paper aims to develop the concept of sensory word-of-mouth (WOM) in Metaverse – the metaWOM. It attempts to upgrade the Reviewchain model and suggests the utilization of non-transferable tokens (NTTs) in curbing the explosion of fake WOM.

Following Macinnis’ (2011) approach to conceptual contributions, the authors browsed the currently available literature on WOM, Metaverse and NTT to portray the emergence of metaWOM.

By relying on Metaverse’s three building blocks, the authors map out the persuasiveness of metaWOM in the Metaverse-like environment. By incorporating NTT in the Reviewchain model, the authors upgraded it to provide a transparent, safe and trusted review ecosystem. An array of emerging research directions and research questions is presented.

This paper comprehensively analyzes the implications of a Metaverse-like environment on WOM and debates on technologies that can enhance the metaWOM persuasiveness. The proposed model in this paper can assist various stakeholders in understanding the complex nature of virtual information-seeking and giving.

This is the original attempt to delineate the sensory aspect of WOM in the Metaverse based on three crucial aspects of the Metaverse environment: immersiveness, fidelity and sociability. This paper extends the discussion on the issue of fake reviews and offers viable suggestions to curb the ever-growing number of fraudulent WOM.

This study investigates how personality traits influence individuals’ intention to become community group buying (CGB) leaders.

Data include 517 valid questionnaires that are employed to examine the research model and test the hypotheses using partial least squares structural equation modeling.

This study reveals that among the Big Five personality traits, extroversion and neuroticism have more impact on the perceived ease of use and usefulness of social media, and individuals with high levels of these traits are more likely to become CGB leaders. Perceived ease of use only mediates the relationship between agreeableness and CGB leader intention, whereas perceived usefulness mediates the relationships between conscientiousness and CGB leader intention and neuroticism and CGB leader intention.

This study can serve as a catalyst for advancing the exploration of how personality traits and social media affect the intention of being CGB leaders. In addition, the study investigates the mediating effect of social media technology acceptance obtaining valuable insights into how social media affects individuals’ intention to become CGB leaders, expanding the research in this field.

• (1)

  Individuals with extroversion, neuroticism, and conscientiousness personality traits exhibit higher perceived ease of use and usefulness of social media.

• (2)

  Unlike previous research suggested, neurotic individuals appear to be attracted to becoming community group buying (CGB) leaders.

• (3)

  Individuals with high agreeableness are encouraged by ease in pursuing CGB leadership.

• (4)

  Perceived usefulness mediates the relationship between conscientiousness and CGB leadership intention and neuroticism and CGB leader intention.

Individuals with extroversion, neuroticism, and conscientiousness personality traits exhibit higher perceived ease of use and usefulness of social media.

Unlike previous research suggested, neurotic individuals appear to be attracted to becoming community group buying (CGB) leaders.

Individuals with high agreeableness are encouraged by ease in pursuing CGB leadership.

Perceived usefulness mediates the relationship between conscientiousness and CGB leadership intention and neuroticism and CGB leader intention.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

The present study integrates inclusive leadership and protection motivation theory to propose a new model predicting employees' intention to work from home during an emergency situation such as the COVID-19 pandemic.

A questionnaire was developed to collect data from 939 Taiwanese and Vietnamese office employees using a non-probability convenience sampling method. A total of 887 valid questionnaires were used for further analysis. The data were analysed following a two-stage structural equation modelling using SPSS 22 and AMOS 20 software. The validity and reliability of the instrument were tested and ensured.

The results revealed that inclusive leadership and factors related to protection motivation theory– including perceived severity and perceived vulnerability – have positive direct and indirect effects on employees' work-from-home intentions through the mediating role of employees' work-from-home-related attitudes. Protection motivation theory factors were found to have a stronger effect on employees' work-from-home intention than inclusive leadership. Differences in the relationship between perceived vulnerability, perceived severity and employees' intentions towards working from home were also discovered among participants from the two studied countries.

The integration of inclusive leadership and protection motivation theory brings into light what will drive employees' intention to work from home during an emergency situation. The present study has several theoretical and practical implications for scholars, governments, managers and policymakers that can help them improve management policies for working from home in the future.

Based on integrating inclusive leadership and protection motivation theory to explore employees' intention to work from home during an emergency situation, the present study demonstrated that inclusive leadership and protection motivation theory should be considered for studies on working from home in a pandemic setting.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The purpose of this study is to examine the impact of technological diversification (TD) on enterprise innovation performance, meanwhile focusing on the moderating effects of various organizational slack (i.e. absorbed and unabsorbed slack) and ownership types (i.e. state-owned or privately-owned) in the context of Chinese listed firms.

This study formulates five hypotheses based on organization and agency theories. Our empirical analysis employs a fixed-effect regression estimator with a unique panel dataset of Chinese-listed manufacturing firms and 13,566 firm-year observations over 9 years from 2012 to 2020.

Our findings show that an inverted U-shaped relationship exists between TD and innovation performance, varying with different types of organizational slack and ownership. In state-owned enterprises (SOEs), unabsorbed slack negatively moderates the inverted U-shaped relationship; however, in privately-owned enterprises (POEs), this relationship is positively moderated. Although absorbed slack has negative moderating effects in both SOEs and POEs, its impact is only significant for POEs.

Our results imply that organizational slack has a contrasting impact on the relationship between TD and innovation performance when the type of ownership varies. Therefore, the managers that intend to achieve optimal innovation performance through TD should understand how organizational slack can be leveraged.

This study contributes to the existing literature by applying the relationship between TD and innovative performance to the transition economy, as well as examining the double-edged sword impact of state ownership on firm innovation performance.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

This paper aims to examine whether managerial sentiment, extracted from annual reports, is associated with corporate risk-taking in the context of Chinese companies. This study expands the vein of literature on overconfidence theory.

By leveraging textual analysis on Chinese listed companies’ annual reports, the authors construct firm-level managerial sentiment during 2007 and 2021 to examine how managerial sentiment influences corporate risk-taking after control for firm characteristics. Corporate risk-taking is denoted by corporate investment engagements: capital expenditures and net fixed asset investment.

Results show that incentives for corporate risk-taking are likely to increase with the positive managerial sentiment and decrease with the negative sentiment in companies’ annual reports. Positive managerial sentiment is associated with over-/under-investment and low/high investment efficiency. Further additional tests show that the managerial sentiment effect only holds during low economic uncertain years and samples of private-owned firms. Furthermore, the robust tests indicate that there is no endogenous issue between managerial sentiment and corporate risk-taking.

Annual report textual-based managerial sentiment may not perfectly reflect managers’ lower frequency sentiment (e.g. weekly, monthly and quarterly sentiment). Future studies could attempt to capture managers’ on-time sentiment by using media sources and corporate disclosures.

To the best of the authors’ knowledge, this paper is the first research to provide insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach of measuring managerial sentiment might be a solution to monitoring managerial class.

This paper contributes to the literature on accounting and finance studies, adding another piece of empirical evidence on content analysis by examining a unique language and institutional context (i.e. China). Besides, the paper notes that in line with the English version disclosure, based on Chinese semantic words, managerial sentiment in the Chinese-speaking world has magnitude on corporate decisions. The research provides insights into supervising managers’ corporate decisions by observing their textual information usage in corporate disclosure. Moreover, the approach to measuring managerial sentiment may be a practical solution to monitoring managerial class.