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This paper aims to analyze high-quality papers on the research of electronic word-of-mouth (eWOM) for product and service quality improvement from 2009 to 2022, in order to fully understand their historical progress, current situation and future development trend.

This paper adopts the bibliometrics method to analyze the relevant literature, including publishing trend and citation status, regional and discipline area distribution, and influential publications. Secondly, the VOSviewer is used for literature co-citation analysis and keyword co-occurrence analysis to obtain the basic literature and research hotspots in this research field.

Firstly, the study finds that the number of publications basically shows an increasing trend, and those publications are mainly published in tourism journals. In addition, among these papers, China has the largest number of publications, followed by the USA and South Korea. Through co-citation analysis of literature and keyword co-occurrence analysis, 22 foundational papers and six main research topics are obtained in this paper. Finally, this paper elaborates on the development trend of the research topic and future research directions in detail.

This is the first paper that uses bibliometrics to analyze and review relevant researches on eWOM for product and service quality improvement, which is helpful for researchers to quickly understand its development status and trend. This review also provides some future research directions and provides a reference for further research.

This study aims to provide a method for classifying travellers’ requirements to help hoteliers understand travellers’ requirements and improve hotel services. Specifically, this study develops a strength-frequency Kano (SF-Kano) model to classify the requirements expressed by travellers in online reviews.

The strength and frequency of travellers’ requirements are determined through sentiment and statistical analyses of the 13,217 crawled online reviews. The proposed method considering the interaction between strength and frequency is proposed to classify the different travellers’ requirements.

This study identifies 13 travellers’ requirements by mining online reviews. According to the results of the improved Kano model, the six travellers’ requirements belong to one-dimensional requirements; two travellers’ requirements belong to must-be requirements; three travellers’ requirements belong to attractive requirements; two travellers’ requirements belong to indifferent requirements.

Results of this research can guide hoteliers to address hotel service improvement strategies according to the types of travellers’ requirements. This study can also expand the analysis scope of hotel online reviews and provide a reference for hoteliers to understand travellers’ requirements.

By mining online reviews, this study proposes an SF-Kano model to classify travellers’ requirements by considering both the strength and frequency of requirements. This study uses the optimisation model to determine the classification thresholds. This process maximises travellers’ satisfaction at the lowest cost. The classification results of travellers’ requirements can help hoteliers gain a deeper understanding of travellers’ requirements and prioritise service improvements.

The purpose of this study is to find the multi-factor influence law of stress, strain rate and sulfate-reducing bacteria (SRB) on X70 pipeline steel in a simulated solution of sea mud and the order of influence of the three factors on X70 steel to develop a scientific basis for pipeline corrosion protection.

This paper studied the effects of stress, strain rate and SRB on the X70 pipeline steel corrosion behavior in simulated sea mud solution through orthogonal testing, electrochemical experiments and morphological observations.

The results of this study showed that stress proved to be the most relevant element for corrosion behavior, followed by SRB and strain rate. At high stresses (301 MPa and 576 MPa), stress dominated the corrosion behavior of X70 pipeline steel. However, at low stress (82 MPa), SRB played the most important role.

Subsea pipelines are in a very complex environmental regime that includes stress, strain rates and SRB, which often cause pipeline pitting and perforation. However, most scholars have only looked into the influence of single factors on metal corrosion. So, the single-factor experimental results of previous studies could hardly be applied to actual working conditions. There is an urgent need to understand the multi-factor influence law of stress, strain and SRB acting together on the pipeline corrosion behavior, especially to determine the dominant factor.

This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition.

The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders.

The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models.

A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

Compensatory consumption focuses on the psychological value of products. Special agricultural products have symbolic and social functions that effectively meet psychological needs and stimulate compensatory consumption behavior. The social commerce context not only enriches consumer experience but also influences consumer purchase decisions. This study constructs a model based on the elaboration likelihood model (ELM) and the stimulus-organism-response (SOR) theory to explore the mechanism of compensatory consumption behavior of special agricultural products in a social commerce context.

This study uses a two-stage method of partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze 523 valid samples collected through random sampling. PLS-SEM was used to examine the relationships and effects between the variables; fsQCA was used to conduct a cohort analysis between the variables to further reveal the complexity and diversity of compensatory consumption behaviors.

PLS-SEM indicates that product attributes and social affordances influence consumers’ triggering of compensatory consumption behavior for control and belongingness needs. fsQCA shows that there are three different modes, and the satisfaction of belongingness or control needs is a necessary condition for triggering compensatory consumption behavior.

There is limited research on compensatory consumption behavior specifically focused on special agricultural products. This study explores the influencing factors and mechanisms of compensatory consumption behavior related to special agricultural products. The occurrence of compensatory consumption behavior is not only influenced by product attributes but also by the social commerce environment. In marketing strategies, it is important to not only consider product characteristics but also pay attention to consumers’ social and psychological needs.

The purpose of this study is to examine social media use and its impacts on first generation students by answering the two questions: how do FGS use social media on college campuses, compared to their peers? How does the use of social media affect their academic experiences?

This qualitative study adopted social capital theory as a sensitizing framework for understanding the social media (SM) use and the resources valued by first-generation students (FGS) and used a revealed causal mapping method to analyze the narratives of 96 informants to identify key constructs and linkages on SM use and perceived outcomes.

The revealed causal mapping (RCM) analysis revealed nine key constructs that shaped the SM use and academic experience of FGS and their peers. The linkages among the nine constructs: three types of social capital (bridging, family bonding and friend bonding), three types of SM use (social, cognitive and hedonic) and three outcomes (academic support, emotional support and distraction to work) were different between FGS and their peers. Among FGS, SM use and perceptions differed by gender.

Leveraging social media is critical for universities to enhance FGS persistence, yet knowledge remains limited. This study showed FGS differed from their counterparts in the SM use and perceptions. Among FGS, the SM use and perceptions differed by gender. The research contributions are: (1) SM technology can empower FGS by building social capital, impacting their academic experiences and psychological well-being and (2) the intersection of gender and student generation status is worth investigation. This paper enriches FGS research by proposing a model of SM use and social capital.

Women from many cultures have historically been closely tied to the land and the environment through their role as subsistence farmers. But as the more developed nations have shifted to commercial agriculture and improved technology, farming has become a male-dominated industry. China’s shift from traditional family-operated farms to government-controlled collectives required a system of incentives to encourage agricultural labor to remain and prevent mass exodus to the cities. Hukou was created in the 1950s as a system of governmental registration for restricting the internal migration of labor within China, identifying citizens’ residency by place of birth. Residents of rural or urban locations are classified agricultural or nonagricultural labor, respectively. But as China’s industrialization has grown and technology has reduced the need for human agricultural labor, the need and desire for urban employment has intensified. For women, relocating has changed marriage practices, influenced child rearing, and altered their right to land tenure in their home region. This paper examines the role of gender in the changing use of hukou in the development of China, focusing on the impact of women’s patterns of migration on land tenure. Although hukou policies are still changing and there is a lack of data on the most recent changes, initial studies show that there are few who wish to give up their rural hukou in order to obtain urban hukou. Changes over the past decade indicate that rural woman are not only taking on more of the agricultural workload as men are drawn to urban employment, but also that they are less likely to care about environmental degradation in China.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

Public–private partnerships (PPPs) are viewed as a reform tool for resolving inefficiency and absence of dynamism in water supply delivery in developing countries. However, the requirements for their successful implementation have received very little attention. This paper aims to describe a set of critical success factors (CSFs) that, when given special and continual attention, would ensure a successful project implementation and to provide a predictive tool to aid implementers to evaluate the likelihood of a successful PPP water supply project.

Fourteen perceived CSFs were initially derived from project cases and extant literature, and verified through a two-round Delphi survey. Factor analysis established five critical success factor groups (CSFGs) that were then used to develop a fuzzy synthetic evaluation tool for assessing the chance of a successful project.

The five key CSFGs are commitment of partners, strength of consortium, asset quality and social support, political environment, and national PPP unit. The model output showed that, overall, these factors have a “very high” positive impact on a successful implementation of a water supply project. Hence, there is an excellent correlation between achievement of the CSFGs and project success. Success indices of individual principal factors are also “very high”.

The study presents a tool to public clients and private audience, and it is hoped that the study will trigger policy development towards PPP practice in developing countries, because these findings have wider implications for legal and regulatory systems, public capacity, financing, public procurement and politics.