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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.

Due to rapid digitalization, the emergence of the “phygital” environment, which blends physical and digital experiences, creates unique challenges for researchers. This paper aims to introduce an interpretivist methodological framework designed to understand consumer behavior in phygital environments. The framework enables an in-depth exploration of the contextual factors, subjective experiences, personal emotions and social networks that influence consumer behavior in this space.

The framework was developed after a thorough literature review of the phygital environment and interpretivist research landscape. Consistent with the phygital transformation theory, this approach allows researchers to go beyond the limitations of purely quantitative methods, gaining a deeper understanding of consumer behavior in phygital environments. The framework is organized into four meticulously designed pillars, each focusing on specific aspects of research and using distinct data collection and analysis approaches.

The systematic framework facilitates exploration of various dimensions of consumer experiences in phygital settings through qualitative research techniques. Uncovering the richness of contextual factors, subjective meanings, consumer experiences and social interactions within the phygital environment yields meaningful insights into consumer decision-making and preferences. These insights help marketers craft better phygital marketing strategies.

This interpretivist framework presents a unique approach for researchers hoping to investigate consumer behavior in phygital environments. It offers deep insights and understanding of this largely unexplored space, contributing to the evolving body of knowledge in phygital studies.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.