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This paper aims to explore the effect of teacher–student collaboration on academic innovation in universities in different stages of collaboration.

Based on collaboration life cycle, this paper divided teacher–student collaboration into initial, growth and mature stages to explore how teacher–student collaboration affects academic innovation.

Collecting data from National Science Foundation of China, the empirical analysis found that collaboration increases the publication of local (Chinese) papers at all stages. However, teacher–student collaboration did not significantly improve the publication of international (English) papers in the initial stage. In the growth stage, teacher–student collaboration has a U-shaped effect on publishing English papers, while its relationship is positive in the mature stage.

The results offer suggestions for teachers and students to choose suitable partners and also provide some implications for improving academic innovation.

This paper constructed a model in which the effect of teacher–student collaboration on academic innovation in universities was established.

This study aims to explore the connection between travel content consumption in social networks and social comparison, envy as well as travel intention. It analyzes whether tourists’ travel intention is affected by travel content consumption in social networks, and more importantly, whether social comparison and envy play a mediating role in this process.

Data was collected through intercept in four popular tourist spots in Guangzhou and Zhuhai in South China. A self-administered questionnaire was used. A total of 400 participants were recruited, and 291 valid questionnaires were obtained. Bias-corrected nonparametric percentile bootstrap mediation variable test method was used to test hypotheses.

The study yielded three results. First, travel content consumption in the social networks positively influences travel intention. Second, travel content consumption in social networks indirectly affects travel intention through social comparison and envy. Third, the control variables, such as gender, age, education and income, mainly affect envy.

This study constructs a theoretical framework of stimulus–cognitive appraisal–emotion–behavioral responses. To the best of the authors’ knowledge, it is the first study to reveal that the internal psychological mechanism of travel content consumption affects travel intention. It also discloses that envy of seemingly negative emotions can encourage positive behaviors in certain situations.

The purpose of the paper was to design an anti-corrosion system that combined conductive coatings with cathodic protection for a 500-kV substation ground grid, and provide a basis for the anti-corrosion construction of the installation.

The study took the Shaoguan 500-kV substation grounding grid as the research object. The anti-corrosion performance of KV conductive coatings on grounding metal was researched. In parallel, the alkalinity of substation soil was evaluated according to the German DIN50929 Standard, and the combined protection system comprising conductive coatings and impressed current cathodic protection was designed.

KV conductive coatings, that have resistance to acids, alkalis and salts, can effectively slow down the corrosion rate of the grounding grid. The investigation also provided the outline design, installation, construction requirements and monitoring methods for the 500-kV substation grounding grid.

This report contains some guiding significance for anti-corrosion engineering of 500-kV substation grounding grids.

With the prevalence of the sharing economy phenomenon, there are an increasing number of hosts on Airbnb who manage more than one listing. Managing more listings likely makes hosts more seasoned in terms of serving guests, but it may undermine host quality due to hosts’ constrained capability. This paper aims to examine the effects of host quality attributes and the number of listings per host on the reservation performance of these listings.

Using a large-scale but granular data set of 5,805 active listings of 4,608 Airbnb hosts in Austin, Texas, this study estimates the effects of host attributes (host quality and listing quantity) on the performance of the hosts’ Airbnb listings through a blend of regression models.

This study evidences that host quality attributes significantly influence listing performance through cue-based trust. In addition, this study finds a “trade-off” between host quality and the quantity of their listings. As the number of listings managed by a host increases, the performance effects of host quality diminish.

The business implications of this study include the suggestion that sharing economy businesses such as Airbnb should sustain service quality through incentivizing hosts to improve host quality while balancing the quantity of listings managed.

This study contributes to the literature through its meaningful theoretical extension in the sharing economy context and unique data-driven insights enabled by an analytical approach. It addresses the critical but less researched topic of host quality and listing quantity and generates important practical business and policy implications.

With the appearance of additional review functionality on e-commerce platforms emotional changes in composite reviews have become more diverse. How consumers process the emotional changes in composite reviews is an important concern for companies. This study investigates the impact of explores how changes in the emotional valence and emotional intensity of composite reviews on consumers' information adoption.

Based on emotion as social information theory, this study constructs a double mediation model of how the change in emotional valence of composite reviews affects consumers' adoption intention and examines the moderating effect of the dynamic change of emotional intensity. One field and three online experiments were conducted to test the proposed hypotheses.

Consumers were more likely to adopt positive–negative composite reviews than negative–positive composite reviews. Compared to negative–positive composite reviews, positive–negative composite reviews led to higher perceived empathy and lower motivational suspicion, which, in turn, led to higher information adoption. Moreover, dynamic changes in emotional intensity played a moderating role in this effect. Interestingly, the amount of attribute difference changed the differences in perceived empathy and motivated consumer suspicion generated by the composite review when considering the reviewer’s attribute difference description.

The findings have important theoretical contributions that deepen business and consumer understanding of the impact of composite reviews and have practical implications for improving the management of composite reviews by businesses.

The purpose of the paper is to develop a hybrid method to prioritize risk factors (RFs) of sustainable supply chain (SSC) considering sustainable customer requirements (CRs) and uncertain evaluation.

In the proposed method, fuzzy Kano model (FKM) is applied to prioritize sustainable CRs considering customer satisfaction (CS) and objective weight of each CR, the interval-valued intuitionistic fuzzy (IVIF) set theory is integrated with quality function deployment (QFD) to translate the sustainable CRs into RFs of SSC under uncertain environment and the IVIF cross-entropy is used to conduct objective analysis to prioritize RFs. Finally, a case in air-conditioner-manufacturing company is presented to demonstrate the proposed method.

A case study of SSC risk management, the comparative analysis and associated discussions are conducted to illustrate the feasibility and effectiveness of the proposed method. The results obtained from the case study shows that RF5 (market share reduction) is the most important RF in the SSC. Compared with the existing methods, the proposed method can integrate sustainable CRs into SSC's RFs, handle uncertain information effectively and obtain objective importance of RFs.

Theoretically, the paper develops a customer-oriented model based on the FKM, QFD, IVIF sets and entropy theory to prioritize RFs of SSC under uncertain environment. The model enables to integrate sustainable CRs into RFs managements and is efficient to deal with the subjectivity and conduct objective analysis to prioritize RFs. In practice, the systematic and correct RFs' priorities analysis provides reliable decision support for the managers to take measures to avoid or mitigate the critical RFs.

This paper's purpose is to suggest that manufacturers strategically place soft orders for assembly materials with suppliers in Silk Road Economic Belt countries who probably doubt the realization of the soft orders placed.

First, a two-stage Stackelberg competition is constructed, taking into account the supplier's trust level in formulating the decision process in the assembly supply chain. The authors then provide a buyback contract to coordinate the supply chain, in which the manufacturer obtains enough supplies by sharing some of the perceived risks of not fully trusted suppliers. Furthermore, the authors conduct a numerical study to investigate the influence of trust under a decentralized case and a buyback contract.

The authors found that all supply chain partners in Silk Road Economic Belt countries experience potential losses due to not fully trusting certain conditions. The study also shows that, in Silk Road Economic Belt countries, operating under a buyback contract is better than being without one in terms of assembly supply chain performance.

On the one hand, the authors only consider the asymmetry of demand information without considering that of cost structure information. On the other hand, a natural extension of the paper is to integrate single-period transactions into the multi-period transaction problem setting. As all these issues require substantial effort, the authors reserve them for future exploration.

Doing business with not-fully-trustworthy partners in Silk Road Economic Belt countries is risky, and this study reveals how trust works in global cooperation and with strategic reactions in situations of partial trust.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques.

Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work.

The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose.

To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.

Drawing on Schumpeter’s theory of innovation and stereotype content model, this study aims to arrive at an integrated model that relates destination innovation type, destination innovativeness and revisit intention to uncover more about the drivers and outcomes of destination innovativeness from a consumer-centric perspective.

Three studies, including content analysis of news media, an onsite survey and an online survey in Chinese special featured towns, were conducted.

This study develops a consumer-centric destination innovation measure. The results reveal that input innovation and product innovation positively influence revisit intention through the serial mediation of destination innovativeness and perceived competence.

As the data was collected from tourists in China, any generalization of the results to other regions should be made with caution; accordingly, replication is needed to test the proposed model in different cultural contexts. Second, during the onsite data collection period, special featured town destinations were still recovering from the COVID-19 pandemic, which may have affected the perceptions of tourists. Third, the second round of data was collected using an online survey, which may have introduced bias due to a potential lack of representativeness. Fourth, some potential missing variables could also influence the links among innovation, destination innovativeness and revisit intention.

This study presents the first empirical test of the impact of innovation type and innovativeness on tourists’ response to tourism destinations. The results of this study could guide destinations to deliver more effective consumer-centric innovations to generate competitiveness.

本研究基于熊彼特的创新理论和刻板印象内容模型构建并且实证检验了目的地创新对游客重游意愿的中介影响机制, 旨在从消费者视角探究目的地创新性的驱动因素和影响结果。

本研究通过收集研究主题相关新闻稿件并进行内容分析获得高度情境化的测量问项, 并通过开展两轮问卷调查收集的数据对研究模型进行了检验。共回收有效问卷598份。

本研究开发了以消费者为中心的目的地创新量表。研究结果表明, 目的地投入创新和产品创新可能会影响游客对目的地创新性和感知能力的评价, 进而提升他们的重游意愿。并且, 研究发现目的地创新性和感知能力发挥了连续中介作用。

本研究从消费者感知角度出发, 揭示了目的地创新对游客重游意愿的影响作用及其内在机制, 为旅游目的地创新提供了启示。

Basándose en la teoría de la innovación de Schumpeter y en el modelo de contenido de los estereotipos, este estudio tiene como objetivo llegar a un modelo integrado que relacione el tipo de innovación del destino, capacidad de innovación del destino e intención de revisita del destino, para descubrir más acerca de los impulsores y resultados de la innovación desde una perspectiva centrada en el consumidor.

Se han llevado a cabo tres estudios entre los que se han incluido, el análisis del contenido de los medios de comunicación informativos, encuesta «in situ», además de una encuesta en línea en pueblos chinos que destacan por sus características especiales.

Esta investigación desarrolla una medición de la innovación del destino desde una perspectiva centrada en el consumidor. Los resultados revelan que la innovación en los recursos y la innovación del producto, influyen positivamente en la intención de revisitar a través de la mediación en serie de la innovación del destino y la competencia percibida.

Este trabajo presenta la primera prueba empírica del impacto del tipo de innovación, así como de innovaciones sobre la respuesta de los turistas a los destinos turísticos. Los resultados de este estudio podrían guiar a los gestores de los destinos en el ofrecimiento de innovaciones más efectivas centradas en el consumidor con el fin de generar competitividad.