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This study aims to introduce and evaluate a model of host community support for sustainable tourism development (SSTD) based on the influential factors contributing to community support in a biosphere reserve.

Partial least squares structural equation modeling using SmartPLS 3 software is applied to analyze data collected from 473 residents of the Miankaleh Biosphere Reserve, Iran.

The findings reveal that the locals’ community support is affected by their level of environmental awareness, opportunity and attitudes toward SSTD. However, the results do not reveal an influence of environmental knowledge and community attachment on SSTD. The findings enrich the existing literature on community attitude predictors by showing that locals’ SSTD level is not consistently based on common predictors. Such support strongly depends on host communities’ attitudes toward supporting tourism, which is definitely not the same among residents. It is imperative to know whether people’s attitudes arise from a desire to protect the area or for reasons of self-interest.

The findings provide further support for the tenets of the segmentation approach and challenge existing knowledge on host communities’ attitudes about factors influencing residents toward SSTD. The findings have several practical implications regarding community participation for regional and national authorities and destination policymakers.

采用SmartPLS3软件建立偏最小二乘结构方程模型, 对从伊朗米安卡莱生物圈保护区的473名居民获得的数据进行分析。

本研究旨在介绍和评估基于影响促进生物圈保护内社区支持的可持续旅游发展(SSTD)的东道社区支持模型。

结果表明, 本地社区的环境意识水平、机会和对SSTD的态度影响着社区支持。然而, 研究结果并没有揭示环境知识和社区依恋对SSTD的影响。

这一发现丰富了现有的关于社区态度预测因子的文献, 表明当地人的SSTD并不一致基于共同的预测因子。这种支持很大程度上取决于东道社区对支持旅游的态度, 这在居民中肯定是不一样的。有必要了解人们的态度是出于保护该地区的愿望还是出于自身利益的原因。此外, 研究结果为细分方法的原则提供了进一步的支持, 并挑战了关于东道社区对影响居民SSTD态度的现有认知。

Se aplica un modelo de ecuaciones estructurales por mínimos cuadrados parciales con el programa SmartPLS 3 para analizar los datos recogidos de 473 residentes de la reserva de la biosfera de Miankaleh (Irán).

Esta investigación pretende introducir y evaluar un modelo de apoyo de la comunidad anfitriona al desarrollo del turismo sostenible (SSTD) basado en los factores que contribuyen al apoyo de la comunidad en una reserva de la biosfera.

Los resultados revelan que el apoyo de la comunidad local se ve afectado por su nivel de concienciación medioambiental, sus oportunidades y sus actitudes hacia el desarrollo del turismo sostenible. Sin embargo, los resultados no muestran una influencia del conocimiento medioambiental y el apego de la comunidad sobre el desarrollo turístico sostenible.

Los resultados enriquecen la literatura existente sobre predictores de la actitud de la comunidad al mostrar que el nivel de desarrollo turístico sostenible de los autóctonos no se basa únicamente en factores tradicionales. Dicho apoyo depende en gran medida de las actitudes de las comunidades anfitrionas hacia el apoyo al turismo y éste no es el mismo entre los residentes. Es necesario conocer si la actitud de los individuos surge de un deseo de proteger la zona o por razones de interés propio. Además, los resultados corroboran los principios del enfoque de segmentación y retan los conocimientos existentes sobre las actitudes de las comunidades anfitrionas acerca de los factores de los residentes sobre el desarrollo turístico sostenible.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

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.

Implementing the 17 Sustainable Development Goals (SDGs) unarguably demands huge financial investments. However, the United Nations has acknowledged the huge financial gap militating against the implementation of the SDGs worldwide, leading experts to question the possibility of complete implementation of the goals by their terminal dateline of 2030. While the bulk of the finance currently outlaid on the SDGs comes from traditional sources including foreign direct investments (FDIs), there is the need to focus more attention on developing and exploiting impact investments that are more suitable for financing development programmes and projects. In this chapter, the SDG implementation profiles of the 12 Arab West Asia countries concerning the five most targeted SDGs were evaluated and sustainable finance issues were discussed. Secondary data were retrieved from World Bank's DataBank. The data were descriptively analyzed. Based on the profiles generated, debt relief is put forward as a possible impact investment mechanism suitable for funding the SDGs. Specifically, this chapter recommends that outright cancellation of debts based on the debt-for-SGD swap could serve as some of the impact investments needed to boost the global drive for a developed, peaceful, and just world.

The problem of environmental pollution is becoming increasingly severe, and international consensus confirms the need for eco-friendly consumption. Worldwide, the eco-friendly food market is booming, so understanding consumers’ motivations to purchase these foods is crucial. This paper aimed to construct a model explaining consumers’ intentions to purchase eco-friendly food by combining stimuli-organism-response (SOR) and signalling theories and exploring the mechanisms by which macro- and micro-signals impact perceptions of value and consumers’ subsequent willingness to purchase eco-friendly food.

An online questionnaire was distributed through the Qualtrics platform, and the completed questionnaires were collected in March and April 2023. The study used partial least squares structural equation modelling (PLS-SEM) to analyse the 331 valid responses received.

The results indicated that trustworthy eco-labels for high-quality and health-promoting products, as conveyed in macro signals, significantly enhanced consumers’ perceptions of functional value. The peer effect and a moderate level of food anthropomorphism conveyed in micro-signals substantially improved their perceptions of social value, whilst the perceived value of products significantly and positively influenced their purchase intentions.

This study explains consumers’ motivations to purchase eco-friendly products. This provides an explanation for the effect of macro- and micro-signals on value perceptions. By integrating the different dimensions of these signals to create a unified research perspective, the paper provides an integrated model, thereby filling a research gap concerning the influence of two-dimensional signals on purchase intention. By supporting eco-friendly food use, the paper contributes to environmental protection and sustainable development.

In this study, we drew on the theories of social exchange and social learning and hypothesized that the online social capital (SC) and offline SC of social networking affect the online self-disclosure (OSD) of individuals through social self-efficacy (SSE).

After retrieving 514 valid questionnaires, we used structural equation modeling to analyze the data.

The results indicated that the users’ SSE affected their OSD, and that both online and offline bridging and bonding SC increased their SSE. However, online bonding SC directly affected their OSD, whereas online bridging SC did not considerably affect their OSD. Given these findings, we presented both theoretical and practical implications to elucidate SSE and OSD behavior from the perspective of online and offline bridging and bonding SC.

In this study, we drew on theories of social exchange and social learning to examine the effects of online and offline bridging and bonding SC on users’ SSE and OSD on SNSs. Given the importance of SC and SSE in social relationships and the effects of OSD on SNSs, our goal was to provide SNS marketers with a thorough understanding of how to facilitate SSE and OSD from the perspective of online and offline bridging and bonding SC.

The purpose of this study is to reveal the effect of nano-Al₂O₃ particle addition on the nucleation/growth kinetics, microhardness, wear resistance and corrosion resistance of Co–P–xAl₂O₃ nanocomposite plating.

The kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating prepared by electroplating were investigated by electrochemical measurements, scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, Vickers microhardness measurement, SRV5 friction and wear tester and atomic force microscopy.

A 12 g/L nano-Al₂O₃ addition in the plating solution can transform the nucleation/growth kinetics of the plating from the 3D progressive model to the 3D instantaneous model. The microhardness of the plating increased with the increase of nano-Al₂O₃ content in plating. The wear resistance of the plating did not adhere strictly to Archard’s law. An even and denser corrosion product film was generated due to the finer grains, with a high corrosion resistance.

The effect of different nano-Al₂O₃ addition on the nucleation/growth kinetics and properties of Co–P–xAl₂O₃ nanocomposite plating was investigated, and an anticorrosion mechanism of Co–P–xAl₂O₃ nanocomposite plating was proposed.

The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

This study aims to test the mean and volatility spill over from the environmental, social, and governance (ESG) market to the stock markets of G7 countries. The study used ARMA-GARCH model to predict the results. The findings of the study reveal that as the spill over exists in the markets, however the mean volatility does not exist showing efficiency of the market as significant results depict that past prices cannot predict the future prices. It provides new insights for the international portfolio investors and policymakers by shedding light on how cross-markets correlate in two different markets.