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This study integrates the theory of planned behaviour (TPB) and value-belief-norm (VBN) theory to investigate tourists' intention and behaviour to visit green hotels in Malaysia.

A total of 160 valid questionnaire responses were collected via an online survey. The partial least square–structural equation modelling (PLS-SEM) technique was utilised to assess the study framework and the hypothesised relationship.

The study's results confirmed that tourists' intention to stay at a green hotel is directly influenced by their subjective norms and perceived behavioural control. Besides, the study confirms the insignificant relationship between green trust, personal norms and tourists' stay intention. On the other hand, perceived morals, responsibility, willingness to pay more and perceived consumer effectiveness were significant in explaining the customer's subjective norms, personal norms and perceived behaviour control.

The hotel industry may benefit from this empirical outcome to devise effective marketing strategies for retaining their customers, particularly in rejuvenating the impact of the COVID-19 pandemic on the industry.

This study provides valuable practical implications for green hotel operators to develop effective strategies to attract tourists to green hotel visits.

This study is the first to integrate the extended TPB and VBN theory to understand tourist intention to visit a green hotel. Notably, the extended TPB and VBN theory was practical and helpful in predicting tourist intention to visit a green hotel.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Research on employee non-/compliance to information security policies suffers from inconsistent results and there is an ongoing discussion about the dominating survey research methodology and its potential effect on these results. This study aims to add to this discussion by investigating discrepancies between what the authors claim to measure (theoretical properties of variables) and what they actually measure (respondents’ interpretations of the operationalized variables). This study asks: How well do respondents’ interpretations of variables correspond to their theoretical definitions? What are the characteristics of any discrepancies between variable definitions and respondent interpretations?

This study is based on in-depth interviews with 17 respondents from the Swedish public sector to understand how they interpret questionnaire measurement items operationalizing the variables Perceived Severity from Protection Motivation Theory and Attitude from Theory of Planned Behavior.

The authors found that respondents’ interpretations in many cases differ substantially from the theoretical definitions. Overall, the authors found four principal ways in which respondents interpreted measurement items – referred to as property contextualization, extension, alteration and oscillation – each implying more or less (dis)alignment with the intended theoretical properties of the two variables examined.

The qualitative method used proved vital to better understand respondents’ interpretations which, in turn, is key for improving self-reporting measurement instruments. To the best of the authors’ knowledge, this study is a first step toward understanding how precise and uniform definitions of variables’ theoretical properties can be operationalized into effective measurement items.

Wine consumer behavior has long been a topic of discussion among scholars and industry professionals aiming to understand the underlying predictors of key behavioral outcomes. To help explain wine consumer behavior, concepts such as involvement, expertise, loyalty, satisfaction and perceived risk are often examined. The overarching objective of this study is to determine the relationship between these predictors and their impact on wine purchase intention utilizing a meta-analytical structural equation modeling (MASEM) technique.

As MASEM provides substantive evidence regarding the relationships between theoretical constructs through the combination of multiple studies, the researchers’ aim is to make definitive statements about the predictors of purchase intention.

Findings revealed several relationships that support previous research but also identified relationships that contradict previous literature. This study contributes valuable insights into consumer behavior that wine brands can utilize to improve their marketing efforts.

Wine marketers with a greater understanding of the stronger predictors of purchase intention should be able to create marketing plans that drive wine sales.

Despite the abundance of research that has utilized these theoretical constructs to demonstrate their propensity for determining behavioral outcomes such as purchase intention, no previous attempts have synthesized this body of literature through the use of meta-analysis.

Drawing on social exchange theory, this study clarifies the roles of authenticity, interactivity and involvement in predicting customer engagement (CE) and, ultimately, customer loyalty towards an online peer-to-peer accommodation platform. In addition, the study explores the effect of interactivity in increasing authenticity.

Data were collected through an online questionnaire of a sample of Italian tourists who had previously booked a service on Airbnb. The analyses were conducted by adopting partial least squares structural equation modelling.

The model has high power in predicting customer loyalty to an online peer-to-peer accommodation platform. Specifically, involvement is the primary predictor of CE and customer loyalty. Authenticity and interactivity also have a significant and positive effect both on CE and customer loyalty. In addition, CE partially mediates the relationship between authenticity, interactivity and involvement and customer loyalty. Finally, interactivity has a significant positive effect on authenticity.

The results encourage hospitality service providers to invest in the creation (and co-creation) of authentic experiences to increase CE and customer loyalty. Hospitality managers can also enhance CE by increasing involvement and interaction with customers through various touchpoints (online and offline) in different moments of the customer journey.

This study proposes an original model to predict customer loyalty to peer-to-peer hospitality platforms. The findings shed new light on the drivers of CE and provide empirical support for the mediating effect of CE. The study also contributes to the literature on authenticity by demonstrating the positive effect of interactivity on authenticity.

This study aims to explore how the absorptive capacity of emerging market multinationals (EMNEs) facilitates increased acquirer performance in industry exploration and technology exploration cross-border acquisitions (CBAs).

The research context for this study is Brazilian EMNEs and their CBAs. The final database contains 101 CBAs.

The authors find that industry exploration strategies negatively affect financial performance, but technology exploration strategies have a positive effect. The acquirer’s absorptive capacity can exacerbate the negative effects, except in instances of technology exploration strategies, where there is a demonstrable benefit from the acquirer’s absorptive capacity.

The study contributes first by providing a more nuanced understanding of the effects of absorptive capacity on postacquisition performance, depending on the type of knowledge explored. Second, by drawing on EMNE learning perspectives, the authors demonstrate the versatility of absorptive capacity in emerging markets.

Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city.

This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study.

76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou.

To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

This study aims to examine how external and internal conditions drive the impact of circular economy mechanism by decomposing into three policy networks in terms of reduce, reuse and recycle, to better understand the contingency model of climate change and effect of firm size on subsequent performance.

Drawing on circular economy network and resource-based view (RBV)-network-resilience strategy framework, a pooled longitudinal cross-sectional data model is developed using a sample of 4,050 Taiwanese manufacturing multinational corporations (MNCs) making foreign direct investment between 2013 and 2018. Structural equation modeling analysis is used to comprehensively examine and investigate each circular economy policy network in the context of climate change and firm size. Post hoc multigroup analysis (MGA) is also conducted.

MGA shows that the reduce policy network is positively and negatively related to manufacturing know-how and production size, respectively. The impact of reuse policy network can enhance the competence of large firms. The recycle policy network is more prominent in terms of competence enhancement of climate change.

MNCs are seeking to build circular economy policy networks to a greater extent, given climate change pressure and guidelines.

This study adds to the circular economy and RBV-network-related literature on climate change and interactions to enhance performance, echoing the recent call on the sustainability of the circular economy of MNCs.

Green consumption is fundamental to sustainable development, as it involves adopting practices and technologies that reduce the environmental impact of human activities. This study aims to analyze the influence of consumers’ green orientation on their environmental concerns and green purchase decisions. Furthermore, the study investigates the mediating role of consumers’ environmental concerns in the relationship between pro-sustainable orientation and green purchase decisions.

This study uses a quantitative methodology, applying the partial least squares method to a sample of 927 Portuguese consumers of green products. The sample was collected through an online survey.

Perceived benefits and perceived quality of products play a positive and significant role in influencing green behavior, especially when consumers are endowed with greater environmental concerns. In addition, consumers’ awareness of the prices of green products and their expectations regarding the future benefits of sustainable consumption positively impact green consumption behavior, further intensifying their environmental concerns.

According to the present findings, companies should adopt a holistic and integrated approach to promote green consumption. This means creating premium eco-friendly products, communicating their benefits, addressing the cost factor, emphasizing the future impact of eco-friendly options and raising consumers’ environmental awareness.

It is critical that environmental education is a priority in schools and that there are political incentives for green behaviors. In addition, media campaigns can be an important tool to raise awareness in society.

The results of this study provide important insights for companies on consumer engagement in the circular economy. Deepening knowledge of the antecedents of consumers’ environmental concerns contributes to a deeper understanding of green purchasing decision behavior, allowing companies to support new business strategies.