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Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

Service robots have increasingly been utilized in retail settings, yet empirical research on how frontline employees (FLEs) might deal with this new reality remains scarce. This mixed-methods study aims to examine how FLEs expect physical service robots to impact job characteristics and affect their job engagement and well-being.

First, explorative interviews (Study 1; N = 32) were conducted to investigate how FLEs currently experience job characteristics and how they believe robots might impact these job characteristics and job outcomes. Next, a survey (Study 2; N = 165) examined the relationship between job characteristics that retail FLEs expect to be impacted by robots and their own well-being and job engagement.

While the overall expectations for working with robots are mixed, retail FLEs expect that working with robots can alleviate certain job demands, but robots cannot help to replenish their job resources. On the contrary, most retail FLEs expect the pains and gains associated with robots in the workspace to cancel each other out, leaving their job engagement and well-being unaffected. However, of the FLEs that do anticipate that robots might have some impact on their well-being and job engagement, the majority expect negative effects.

This study is unique in addressing the trade-off between expected benefits and costs inherent to job demands-resources (JD-R) theory while incorporating a transformative service research (TSR) lens. By integrating different streams of research to study retail FLEs' expectations about working with robots and focusing on robots' impact on job engagement and well-being, this study offers new insights for theory and practice.

Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.

Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.

Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.

The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.

First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.

A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.

The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.

The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.

Modern subway transportation systems need to satisfy increasing safety demands to rapidly evacuate passengers under hazardous emergency circumstances, such as fires, accidents or terrorist attacks, to reduce passenger injuries or life losses. The emergency evacuation capacity (EEC) of a subway station needs to be revised timely, in case passenger demand increases or the evacuation route changes in the future. However, traditional ways of estimating EEC, e.g. fire drills are time- and resource-consuming and are difficult to revise from time to time. The purpose of this study is to establish an intuitive modelling approach to increase the EEC of subway stations in a stepwised manner.

This study develops an approach to combine agent-based evacuation modelling and building information modelling (BIM) technology to estimate the total evacuation time of a subway station.

Evacuation time can be saved (33% in the studied case) from iterative improvements including stopping escalators running against the evacuation flow and modifying the geometry around escalator exits. Such iterative improvements rely on integrating agent-based modelling and BIM.

The agent-based model can provide a more realistic simulation of intelligent individual movements under emergency circumstances and provides precise feedback on locations of evacuation bottlenecks. This study also examined the effectiveness of two rounds of stepwise improvements in terms of operation or design to increase the EEC of the station.

Based on the attention-based view (ABV), this study examines the mechanism of external pressure and internal managerial interpretation affecting the promotion of green entrepreneurial orientation (GEO) of agricultural enterprises.

Based on data collected from 208 agricultural enterprises in China, the conceptual model was tested by using hierarchical regression.

The results show that managerial interpretation can affect the promotion of GEO. Command and control regulation, market-based regulation and green market pressure are important external pressures that affect the promotion of GEO. In addition, managerial interpretation mediates the relationship between command and control regulation and GEO, market-based regulation and GEO, as well as green market pressure and GEO.

This study proposes a key path for promoting the adoption and implementation of GEO by agricultural enterprises. The research results provide experience for emerging and developing countries to promote the GEO of agricultural enterprises, which is helpful to alleviate the environmental problems caused by the development of agricultural enterprises.

For the first time, this study introduced the ABV into the research of GEO. The research results enrich the theoretical perspective of GEO and expand the research field of the ABV. In addition, this study fills the research gap that existing research has not paid enough attention to the internal driving factors of GEO and opens the black box between the external pressure and GEO.

Between 1840s and 1940s, 27 occidental concessions have been created in several cities in China which represented difficult signs and memories for Chinese. Nowadays, these territories are experiencing a joint phenomenon of heritagization and tourismification which makes them experimental theaters for modern urban life and identity. Taking the former concessions of Tianjin as place study, the purpose of this study is to analyze the role of the heritage and tourism in the former concessions in city branding and more specifically the actors, approaches and products of this phenomenon.

This research draws on the comparison and analysis of two place studies in China. The authors base their analysis on semi-structured interviews in Chinese with previously identified stakeholders. In all, 20 individuals, including developers, public authority representatives, business owners, academics and conservation association members, were interviewed. This research was completed, updated and triangulated by content analysis of Web-based materials; official documents such as urban plans, guidelines and urban and tourism strategies collected during the fieldwork, as well as non-intrusive spatial observations of the concession and its various developments.

The results of this study show that the heritage in the former concessions has become an attractive tool for the city branding through tourism development, often led by the public actors with the participation of private entrepreneurs.

This study looks at the hybrid dimensions of the former concessions in China. It provides a better understanding of the co-action of heritage and tourism in the processes of territorial rehabilitation, which contributes to both the practitioners and researchers in this domain.

The purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.

This paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.

The results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.

First, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

The purpose of this paper is to establish a more reasonable evaluation system and model for the development level of rural tourism, and provides a method for quantifying the development level of regional rural tourism.

This paper provides a method for evaluating the development level of rural tourism, constructs an evaluation index system according to the connotation of rural tourism, then calculates the index weight by entropy method, and makes a comprehensive evaluation by grey relational analysis. Taking the development of rural tourism in 11 cities in Jiangxi Province as the research object, the ranking results of 11 cities were obtained by using grey relational analysis.

The overall development level of rural tourism in Jiangxi Province is positive, but the spatial distribution is uneven, showing the characteristics of “low-level aggregation and high-level dispersion”. The barrier model diagnoses that the degree of financial input is the biggest constraint to the development level of rural tourism in Jiangxi Province.

This study puts forward an evaluation model based on entropy weight and grey relational analysis, which is an important supplement to the grey relational analysis method system and has a positive role in promoting the quantitative evaluation of regional rural tourism level.