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The aim of this study was to investigate the themes related to the achievement of Sustainable Development Goal (SDG) 12 in relation to tourism, and specifically to explore how the emotional psyche affects tourists’ environmentally responsible behaviors.

Based on the value-belief-norm theory, a research framework was developed to examine the serial mediation effects of environmental emotions in predicting tourists’ environmentally responsible behaviors. A total of 741 responses was collected from an online survey. Data were analyzed by the partial least squares structural equation modeling.

Environmental concern does not directly predict tourists’ environmentally responsible behaviors. Instead, environmental awe and environmental worry serially mediate the relationship between environmental concern and tourists’ environmentally responsible behaviors.

This study extends the value-belief-norm theory by integrating environmental emotions and empirically tests the effect of multiple psyches on responsible consumption, contributing to the achievement of SDG 12 in UN Agenda 2030.

本研究的目的是探究与旅游相关的可持续发展目标12的实现, 特别是探讨环境情感如何影响旅游者的环境责任行为。

基于价值信念-规范理论, 构建了环境情感预测旅游者环境责任行为的链式中介模型。研究共收集741份有效样本, 并采用偏最小二乘结构方程模型进行分析。

环境关心并不能直接预测旅游者的环境责任行为。但是, 环境敬畏和环境忧虑在环境关心与环境责任行为之间起链式中介作用。

本研究将环境情感扩展到价值信念规范理论中, 并实证检验了环境敬畏和环境忧虑两种环境情感对旅游者的负责任消费行为的影响, 呼应了联合国2030年议程中的可持续发展目标12。

el objetivo de este estudio fue investigar los temas relacionados con el logro del Objetivo de Desarrollo Sostenible 12 en relación con el turismo, y específicamente explorar cómo la psique emocional afecta los comportamientos ambientalmente responsables de los turistas.

Basado en la teoría del valor-creencia-norma, se desarrolló un marco de investigación para examinar los efectos de mediación en serie de las emociones ambientales en la predicción de los comportamientos ambientalmente responsables de los turistas. Se recopiló un total de 741 respuestas de una encuesta en línea. Los datos se analizaron mediante el modelo de ecuaciones estructurales de mínimos cuadrados parciales.

la preocupación ambiental no predice directamente los comportamientos ambientalmente responsables de los turistas. En cambio, el temor ambiental y la preocupación ambiental median en serie la relación entre la preocupación ambiental y los comportamientos ambientalmente responsables de los turistas.

este estudio amplía la teoría del valor-creencia-norma al integrar las emociones ambientales y prueba empíricamente el efecto de múltiples psiques en el consumo responsable, contribuyendo al logro del ODS 12 en la Agenda 2030 de la ONU.

Drawing upon Hofstede's cultural dimensions theory, a comprehensive model exploring the relationships among four distinct culture values and their influence on employees' turnover intention through the mediating roles of organizational commitment and organizational citizenship behavior was developed and tested.

This study covers 585 migrant workers from nine countries who work in the food and beverage industry in Guangzhou, China. Structural equation modeling (SEM) was used to analyze the data collected from these migrant workers.

The results reveal that (1) among the four culture values, only uncertainty avoidance, collectivism orientation and masculinity orientation significantly affect organizational commitment and organizational citizenship behavior, (2) organizational commitment is significantly positively related to organizational citizenship behavior and (3) both organizational commitment and organizational citizenship behavior have a negative direct effect on turnover intention, while organizational citizenship behavior has a stronger impact on migrants' turnover intention than organizational commitment.

The issue of cultural diversity, particularly among relevant migrant workers in the hospitality industry in developing countries, such as China, needs more attention than ever.

This study aims to explore the antecedents of perceived value and the moderating effect of trust and the relationship between these antecedents and perceived value in the context of the service sector.

The multivariate statistical analysis technique of structural equation modeling was used to test the proposed theoretical model.

The results indicate that self-efficacy, motivation, social influence, facilitating conditions and emotions have a significant and direct relationship with customers’ perceived value and that trust can enhance the effect of these antecedents on perceived value. These findings have several significant implications for service robot implementation within the service sector.

With the advancement in artificial intelligence and sensor technology, various industries have launched the practice of deploying intelligent robots to build competitive advantages. The use of intelligent robots to assist with the customer service process and improve consumers’ experience within the service sector is becoming more commonplace.

本论文旨在研究服务业中决定感知价值以及哪些中介变量调节感知价值决定因子和感知价值之间的关系。

本论文采用多变量SEM法分析提出的理论模型。

研究结果表明, 自我效能、动机、社会影响、辅助条件、以及情感对消费者感知价值有直接且显著的影响, 此外, 信任对于其影响因子对感知价值的关系有增强作用。本论文研究结果对服务型机器人在服务业中的应用有着很大的启示。

随着人工智能和感应科技的发展, 很多产业都开始应用智能机器人来增强竞争力。使用智能机器人来辅助消费者服务过程以及提高服务体验感受越来越成为普遍。

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

The purpose of this paper is to explore how consumers’ trust is enhanced by e-commerce-based agribusiness companies. It also aims to shed light on the role of social commerce in improving consumers’ trust.

To achieve the research purpose, an in-depth multiple case study is performed. In this study, three cases in short food supply chain (SFSC) in China are selected, and they are all e-commerce agribusiness companies. They adopted common ways to build up, maintain and reinforce consumers’ trust.

It is revealed that the companies innovatively adopted social commerce, both online and offline, to overcome the trust problems usually faced by e-commerce companies. It is also shown that offline contact with potential consumers is an important first step for agribusiness e-commerce entrepreneurs to build up trust with consumers.

By adopting a multiple case study method, the research has limited generalizability to other types of SFSCs. Since the findings are from Chinese agribusiness e-commerce companies, the generalization to other sectors must be done with caution.

Some managerial implications are given as follows: first, offline contact with consumers could be realized through different channels. Taking advantage of existing social network or trying to find consumers in urban communities might be effective ways. Second, trust building with consumers is not an easy task, managers need to emphasize trust building, trust maintaining, as well as trust reinforcing with consumers. In agri-food sector, managers might need to specifically address the importance of food safety and quality so as to not lose consumer trust in one night.

The study has mainly two contributions: first, it has managerial implications for agribusiness e-commerce entrepreneurs, addressing the important role of social presence in building up consumer trust. Second, it contributes to social presence and social relations literature by providing new empirical evidence from e-commerce in agri-food sector and in developing countries.

The purpose of this paper is to present a multidimensional evaluation of brand equity performance incorporating dimensions adopted from the balance scorecard (BSC) approach to business performance.

In this study, text mining is used for automatic extraction of valuable information from textual data such as the financial reports of firms. Instead of expert opinions, linguistic scales built upon outcomes of text mining are used as inputs for decision-making. The proposed model combines fuzzy DEMATEL (FDEMATEL), fuzzy ANP (FANP), fuzzy TOPSIS (FTOPSIS) and fuzzy VIKOR (FVIKOR) methods for weighting criteria and ranking alternatives.

Using data from five privatized firms in Turkey, the study’s findings demonstrate that the customer is the most important dimension of brand equity performance evaluation. Cash flow and brand loyalty are identified as the most important criteria in the measurement of brand equity performance.

Findings highlight the importance of firms taking action to increase consumer perceptions, attitudes and behaviors in the privatization processes. For this purpose, privatized firms need to understand the expectations of customers to increase customer satisfaction and loyalty and therefore improve brand equity.

The paper contributes to literature in several important ways. First, by adopting the BSC approach, it proposes a holistic and a multidimensional model for measuring brand equity performance. Second, the study offers a novel methodology using a hybrid multi-criteria decision-making model designed for the fuzzy environment. Third, the study uses the knowledge extraction tool of text mining in the fuzzy decision-making process. Finally, the study evaluates the brand equity performance of privatized firms in an emerging country context.