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The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

This paper aims to provide a critical reflection on diversity and inclusion research from the hospitality and tourism literature.

Through conducting a critical reflection, this paper used a thematic analysis focused on integrating the scholarly literature that has developed separately: one focusing on the human resources perspective and another concentrating on customer behavior. This critical reflection bridges the gap between these two perspectives.

The authors develop and offer a research agenda for future research drawing from three areas ripe for future research: human resources management, diversity resistance and marketing. They focus on theory-driven research that has practical applications to make hospitality and tourism more inclusive for both the workforce and consumers.

Meaningful research must be translated into practice, and by addressing these research gaps, organizations can gain insights into diverse worker and customer experiences and create more effective diversity initiatives.

The current literature often lacks an integrated approach that bridges the gap between the two reviewed perspectives: the human resources management and marketing perspectives. A holistic understanding of diversity and inclusion is vital, as it recognizes the interconnectedness between employees and customers within the context of the hospitality and tourism sector is important for several reasons.

This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot anthropomorphism and consumer usage intention and explores the mediating effect of perceived risk.

To examine the developed model, two complementary studies are designed. In Study 1, multi-time data of 511 participants show that service robot anthropomorphism inverts U-shaped (curvilinear) relationship on consumer usage intention and perceived risk mediates this curvilinear relationship. In Study 2, multi-source data of 460 volunteers are used to confirm the findings of Study 1 and examine that consumer empathy moderates the complex nonlinear effect of service robot anthropomorphism on perceived risk, and the indirect curvilinear effect of service robot anthropomorphism on consumer usage intention through perceived risk.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

There are numerous studies on satisfaction, but not enough on dissatisfaction when its consequences can be harmful. This study aims to examine different unsatisfactory situations during customer–supplier relationships in industrial markets combining the appraisal theory with the critical incident technique to identify potential problems and strategies to minimize their effect.

This research follows an exploratory qualitative approach based on 18 in-depth interviews with managers from business-to-business firms. The information obtained was object of a textual and conceptual analysis using the analytical software ATLAS TI 9.0.

The results show that negative cognitions have greater influence than negative emotions, and those dissatisfied customers may respond by expressing complaints, ending transactional relationships, reporting the other party legally, asking for explanations or continuing commercial relationships, even though they are dissatisfied. This will depend on the severity of the critical incident and the negative cognitions and emotions perceived. Proactivity and understanding of this situation will allow for understanding what specific actions to take to resolve conflicts and mitigate the negative effects among the parties.

This paper focuses on dissatisfaction, instead of satisfaction, in industrial markets through the appraisal theory. Furthermore, it applies the critical incident technique to understand the cognitions and emotions related with dissatisfaction in the commercial relationships. Finally, it provides ideas on what are the main source of dissatisfaction and how to manage them to anticipate and better manage those incidents.

The purpose of this paper is to assess the validity and utility of two theoretical approaches to understanding the relationship between strategic orientations, innovation and firm performance and to examine the role of innovation in the relationship while avoiding circular arguments. The universalistic approach suggests that strategic orientations have independent and parallel effects on firms’ performance, and that innovation does not influence this relationship. The holistic approach proposes that strategic orientations in a complementary and interrelated view have both direct and indirect effects on firms’ performance through innovation.

A meta-analytic path analysis applying two-stage structural equation modeling (TSSEM) was conducted on data from 132 primary studies and 33,063 observations.

The holistic approach was demonstrated to be superior due to its more explanatory power in linking more complex relationships through simultaneous direct and indirect effects and its capacity for including the interrelatedness and complementarity of strategic orientations. It was found that innovation has a full mediating role in the relationship between entrepreneurial orientation (EO) and firm performance, and a partial mediating role in the relationship between market orientation (MO) and learning orientation (LO) and firm performance.

The study used observed variables instead of latent variables for meta-analytic path analysis, which may reduce some sources of endogeneity. However, causal inference is not possible due to the nature of meta-analysis. The scope of the final sample was limited by some studies not reporting the estimates of correlations between constructs.

Managers can improve an organization's chances of success in the marketplace by adopting a holistic view of strategic orientations focusing on customer satisfaction, learning from the external environment and pursuing new market opportunities. Furthermore, an organization can gain a competitive advantage through innovation by creating products and services that are different from what is currently available in the market. To be successful, an organization must not only create innovative products and services but also market them effectively to consumers.

This study is the first to meta-analytically assess the explanatory value of two theorized models linking strategic orientations, innovation and firm performance. It also clarifies the role of innovation in the relationship between strategic orientations and firm performance.

Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.