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Considering the transition of communicational channels from physical to digital spaces, this study aims to provide a theoretical foundation for understanding engagement in electronic word of mouth (eWoM) among managers and customers in the hospitality and tourism industry.

This study uses the four aggregate dimensions, namely, performance expectancy, efforts expectancy, social influence and facilitations condition. Further, this paper uses the 14 second-order themes of the Unified Theory of Acceptance and Use of Technology with a data set that represents elements that can trigger eWoM, both from managers’ and customers’ perspectives. The process of data structuration follows thematic analysis and axial coding techniques.

The results of this study show that performance expectancy, facilitation conditions, social influence and effort expectancy all trigger positive eWoM generation in the hospitality and tourism industry indicating customers’ and managers’ perspectives.

This novel study provides a theoretical foundation and novel propositions for future research work on the role of novel antecedents that can trigger eWoM in the hospitality and tourism industry. This paper also provides a benchmark for practitioners and policymakers in their strategic decisions-making towards improving business performance through positive eWoM.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

This research objective is to (1) examine the effect of organizational factors on quality performance, (2) examine the effect of quality performance on competitive advantage and (3) examine the mediating role of quality performance between organizational factors and competitive advantage.

The research sample includes 140 employees in the Windusari village-owned enterprise, in Magelang, Indonesia. Data are collected using 5-Likert scale questionnaires which follow Ferdousi et al. (2019). The dependent variable is a competitive advantage. The Independent variable is organizational factors which are top management support, employee empowerment, employee involvement, reward and recognition, training and customer focus. The mediating variable is quality performance. Data analysis uses path analysis provided by structural equation modeling.

Based on path analysis, organizational factors have a positive effect on quality performance, quality performance has a positive effect on competitive advantage and quality performance mediates the effect of organizational factors on competitive advantage. The results confirm the concept of quality management where continuous improvement of products and services can meet customer expectations and bring the organization to a better position in the industry to face other competitors.

This research extends the previous studies of the relationship between organizational factors and organizational outcomes by considering the effectiveness of the organizational process. This research also contributes to giving new evidence about the relationship between organizational factors, quality management and competitive advantage in the village-owned enterprise in Magelang, Indonesia. This research also contributes to updating the literature on the theory of quality management.

It is found that previous studies only focus on how digital transformation contributes to individual firms’ green innovation performance while ignoring the important role that it plays in the spillover and diffusion of green innovations among peer firms. Therefore, this study aims to investigate the influence of focal firms’ digital transformation on the spillover of green innovation among peer firms in heavily polluting industries mediated by environmental, social and governance (ESG) performance and agency conflict. Further, this study is also expected to explore the effects of digital transformation’s green innovation spillover.

This study chooses 6,438 A-share heavily polluting listed firms in the stock exchanges based in Shanghai and Shenzhen in China during 2010–2020 as samples and tests the hypothesis with ordinary least squares (OLS) regression. Results prove to be robust to a battery of robustness analyses the authors performed to take care of endogeneity.

The results show that the focal firm’s digital transformation may trigger their peer firms’ green innovation spillover and prompt them to engage in green innovation activities actively. The mechanism test shows that peer firms’ ESG performance and agency conflict mediate the influence path between digital transformation and peer firms’ green innovation spillover. Finally, among heavily polluting firms with high industry competition and large scale, digital transformation’s green innovation spillover effects are more significant in conventional energy-based source control, end-of-pipe treatment and substantive green innovation.

This study is possible to provide a potential driving mechanism of green innovation spillovers. The findings lay a sound foundation for future research, providing important theoretical support and practical insights for digital transformation to empower heavily polluting industries to achieve green transformation and low-carbon development.

The purpose of this paper is to analyse the future of the implementation of artificial intelligence (AI) technologies in services experience provided by cultural institutions (e.g. museums, exhibition halls and cultural centres) from experts’, cultural tourists’ and users’ point of view under the Industry 5.0 approach.

The research was conducted using a qualitative approach, which was based on the analysis of the contents obtained from two roundtable discussions with experts and cultural tourists and users. A thematic analysis using NVivo was done to the data obtained.

From a futuristic Industry 5.0 approach, AI is considered to be more than a tool – it as an integral part of the entire experience. AI aids in connecting cultural institutions with users and is beneficial since it allows the institutions to get to know the users better and provide a more integrated and immersive experience. Furthermore, AI is critical in establishing a community and nurturing it daily.

The most important contribution of this research is the theoretical model focused on the user experience and AI application in services experiences of museums and cultural institutions from an Industry 5.0 approach. This model includes the visitors’ and managers’ points of view through the following dimensions: the pre-experience, experience and post-experience. This model is focused on human–AI coworking (HAIC) in museums and cultural institutions.

External relational capital is the value created by an organization’s relationships with outside stakeholders, such as customers. This study introduces and examines the concept of sustainable enclothed cognition to support it, aligning rational reasons, personal values and emotions with sustainable clothing choices not only fosters envisioning sustainable learning from a user perspective but also holds the potential to help companies quickly adapt and find alternative solutions, thereby minimizing production impacts on the environment and promising the future for sustainable fashion in the industry.

This study aims to explore how sustainable enclothed cognition, combined with envisioning sustainable learning, can enhance external relational capital in the fashion industry by fostering deeper connections between fashion brands and environmentally conscious consumers. Data collection took place between May and September 2021. A survey of 211 young workers was conducted, and the data were analyzed using partial least squares-structural equation modeling (PLS-SEM).

The findings demonstrate that prioritizing sustainable enclothed cognition can satisfy consumer demands, strengthen customer relationships and enhance competitive positioning in the fashion industry. Furthermore, the study provides actionable strategies for implementing envisioning sustainable learning, highlighting its transformative role in turning consumer alignment into external relational capital. This insight inspires a new perspective on the potential of sustainable learning in the fashion industry.

This research offers a deeper understanding of how companies can strategically manage their external customer relationships by using sustainable enclothed cognition to drive eco-innovation and enhance relational capital in the sustainable fashion industry. Findings support that textile companies provide fresh insights into their innovative capacity by aligning consumer rational reasons, values and emotions with learning practices. The study also underscores the pivotal role of envisioning sustainable learning in embedding sustainability into the core of fashion industry practices, delivering both theoretical and practical guidance on achieving long-term business success through sustainability.

This study is of great significance in revealing the interactive and coordinated relationship between tourism development and the ecological environment, improving the level of environmental governance in the process of tourism economic development and realising the dual-carbon goal and sustainable development of the tourism industry.

This paper performs slack-based measure (SBM)-data envelopment analysis (DEA) analysis, benchmark regression and threshold effect measurement on a sample of 277 cities in China from 2011 to 2019, to estimate the impact of the digital economy on tourism eco-efficiency and to identify the moderating role of environmental regulation.

China’s tourism eco-efficiency shows a spatio-temporal characteristic of steady growth amid fluctuations. The digital economy has a positive facilitating effect on tourism eco-efficiency, which is non-linear with a single threshold effect (0.631), and when the level of the digital economy exceeds the threshold value, its facilitating effect increases from 0.696 to 0.927. Environmental regulation does not play the role of “the icing on the cake” during the digital economy’s impact on tourism eco-efficiency.

This study for the first time includes the digital economy, eco-efficiency and environmental regulation in the research perspective, and analysed the mechanism of action between the three, expanding the depth of research on the digital economy and environmental regulation in the field of tourism. Meanwhile, based on the development needs of policy specialisation and industrial refinement, this study has strong practical significance when conducted at the city level.

Given the importance of chatbots in customer service in tourism, this paper aims to understand the drivers that predispose regular consumers of restaurant recommendation chatbots to continue using them.

A total of 386 regular consumers of a chatbot via WhatsApp restaurant recommender responded to an online questionnaire (inspired by scales found in the literature on technology adoption). Structural equation modeling was used to test the hypotheses.

Significant predictors of intention to continue using these chatbots included “effort expectancy (EE),” “hedonic motivation (HM),” “price value (PV)” and “habit (HT).” Specifically, HT still has a long way to go in terms of its performance, and it will be possible to work on it. Furthermore, two variables, EE and HM, act as a bottleneck when it comes to explaining this recurrent usage intention. Factors such as “performance expectancy (PE),” “facilitating conditions (FC)” and “social influence (SI)” did not influence “behavioral intention (BI).” Likewise, the moderating variables, age and gender, are not significant. Finally, the predictive capability of the model is demonstrated. The study findings will enable the development of effective strategies to foster consumer loyalty to this new technology in the restaurant industry.

This study contributes, building on the suitability of the unified theory of acceptance and use of technology 2 model, to explain users’ intention to continue using chatbot tourism services in the context of an information search for an unplanned and varied purchase decision, namely, restaurant recommendation services. To the best of the authors’ knowledge, this is the first analysis of tourist’s intention to reuse a real and fully functional chatbot via mobile instant messaging.

鉴于聊天机器人在旅游客户服务中的重要性, 本研究旨在了解驱动消费者持续使用WhatsApp餐厅推荐聊天机器人的因素。

共收集386名WhatsApp餐厅推荐聊天机器人的常规用户在线问卷数据（问卷设计参考技术采纳相关文献中的量表）。研究采用结构方程模型（SEM）验证假设。

影响用户持续使用意图的显著预测因素包括“努力期望（EE）”、“享乐动机（HM）”、“价格价值（PV）”和“习惯（HT）”。其中, “习惯（HT）”表现仍有提升空间, 而“努力期望（EE）”和“享乐动机（HM）”是解释持续使用意图的瓶颈因素。此外, “绩效期望（PE）”、“促进条件（FC）”和“社会影响（SI）”对“行为意图（BI）”无显著影响。性别和年龄等调节变量同样不显著。研究结果验证了模型的预测能力, 能够为餐厅行业制定有效策略以增强消费者对这一新技术的忠诚度提供指导。

本研究基于UTAUT2模型, 首次分析了消费者在餐厅推荐服务中持续使用移动即时通讯（MIM）聊天机器人的意图, 为探索非计划性和多样化购买决策背景下的信息搜索服务提供了新见解。

The purpose of the study has been to fill the gap detected in the literature and to analyze whether the application of management of R&D in accordance with UNE 166002:2021 allows companies to obtain higher product innovation and better performance, specifically incremental and radical product innovations.

The population used in this study included Spanish manufacturing organizations that were active, had more than 50 employees according to the SABI. The information was collected through a structured questionnaire previously tested using a company specializing in the sector under the supervision of the authors. A total of 1,154 companies were randomly contacted in order to reach an acceptable number of 225 valid questionnaires. The data analysis has been carried out with structural equation methodology.

The results obtained with a sample of 225 companies show that the application of this standard for innovation management promotes the development of new products with incremental and radical changes, and improves business performance. It has also been found that incremental and radical product innovations mediate the relationship between this standard and performance.

Firstly, the survey is only addressed to the company's operations manager. Secondly, the sample used is cross-sectional, whereas innovation management implies a broad implementation process.

Managers must know that radical and incremental product innovation can improve the company's operational performance. And the most direct implication of this work is that, those companies that are committed to the development of innovations should seriously consider the application of the principles incorporated in Standard 166,002, as an instrument that improves the results of innovation in the organization. Since this SIMS promotes both types of innovations, it improves results directly and indirectly through these product innovations.

The existing literature indicates that no empirical study has focused on the benefits of this SIMSs for innovation and BP. This paper fills this gap detected in the literature and analyzes the results of the implementation of this standard on incremental and radical product innovations and business performance.

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study aims to establish a Greenness of Construction Technologies (GCT) evaluation model using the method of analytic network process (ANP).

Green construction is increasingly vital in promoting sustainability within the construction industry. The development and promotion of green construction technologies are central to this endeavor. However, existing evaluations mainly target building components, construction projects or certain construction processes. There is a notable absence of research into the greenness of construction technologies. Assessing the greenness of construction technologies is crucial for streamlining resource utilization and reducing waste. To address this gap, this study establishes a GCT evaluation model using the method of ANP.

Among the four formwork technologies of plastic, steel, plywood and wooden formwork, the plastic formwork exhibits the best performance in terms of environmental friendliness, economic and social effects, while facing issues like material wastage and low static strength. The results align with practical observations which validates the model’s applicability.

This research contributes to the field by introducing the concept of greenness into construction technology evaluation for the first time. The establishment of the GCT evaluation model promotes the adoption of green construction technologies and advances sustainable practices in the construction industry.