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The optimal payment in the whole operation and maintenance period of water environment treatment PPP projects has become the main approach to realize sustainable development of projects. This study is aimed at constructing an effective payment model for the whole life period of projects to achieve win-win among all stakeholders, so as to provide a theoretical reference and managerial implications for the public sector in the whole operation and maintenance period.

In the whole operation and maintenance period of water environment treatment PPP projects, this article investigates how the public sector optimizes the payment in the whole operation and maintenance period of projects. Firstly, the projects' whole operation and maintenance period is divided into several stages according to the performance appraisal period. And then, the multi-stage dynamic programming model is constructed to design the payment construct model for the public sector in each performance appraisal stage. The payment from the public sector is the decision variable, and the deduction from the private sector is a random variable.

The optimal payment model showed that the relatively less objective weight of public sector leaded to its relatively more total payment and vice versa. Therefore, the sustainable development of the projects can only be ensured when the objective weights both of them should be balanced. Additionally, the deduction from the performance appraisal of private sector plays an important role in the model construction. The larger deduction the private sector undertakes, the smaller profits private sector has. Since the deduction at each stage is a random variable, the deduction varies with the different probability distributions obeyed by the practical deduction in each stage.

The findings from this study have provided theoretical and application references, and some managerial implications are also given. First, the improvement of the pricing system of public sector should be accelerated. Second, the reasonable profit of the private sector must be guaranteed. While pursuing the maximization of social benefits, the public sector should make full use of the price sharing mechanism in the market and supervise the real income situation of the private sector. Third is increasing the public to participate in pricing. Additionally, it is a limitation that the deduction is assumed to conform to a uniform distribution in this study. Other probability distributions on deduction can be essentially further sought, so as to be more line with the actual situation of the projects.

The optimal payment in whole operation and maintenance period of the projects has become an important issue, which is a key to project success. This study constructs a multi-stage dynamic programming model to optimize payment in the whole period of projects. Additionally, this study adds its value through deeply developing the new theories of optimal payment to more suitable for the practical problems, so that to optimize the design of payment mechanism. Meanwhile, a valuable reference for public and private sectors is provided to ensure the sustainable development of the projects.

Using the industrial data between 2000 and 2016, this study analysed the process of coupling and coordinated development of technological innovation and standardisation. Accordingly, the study considered the high-tech industry (five sub-sectors) in China as the research object.

Based on the summary of innovation and standardisation literature review, this study constructed a theoretical model of the influence of technological innovation and standardisation on industrial development from the perspective of the coupling system. Furthermore, the study employed multivariate linear regression analysis to explore coupling coordination relationships.

The study results revealed that high coupling coordination between technological innovation and standardisation is highly conducive to industrial development. Moreover, requirements for standardisation levels differ owing to different stages and characteristics in each segmented industry.

This study primarily contributes to the literature by using a bibliometrics tool to summarise related literature on innovation and standardisation and provides a new perspective of reviewing, and it also offers new evidence on the coupling coordination relationship between innovation and standardisation in the high-tech industry.

Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper.

This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models.

The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent.

This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.

Developments regarding the use of artificial intelligence (AI) in transportation systems, one of the important stakeholders of tourism, are remarkable. However, no review thus far has provided a comprehensive overview of research on AI in transportation systems.

To fill this gap, this study uses the VOSviewer software to present a bibliometric review of the current scientific literature in the field of AI-related tourism research. The theme of AI in transportation systems was explored in the Web of Science database.

The original search yielded 642 documents, which were then filtered by parameters. For publications related to AI in transportation systems, the most cited documents, leading authors, productive countries, co-occurrence analysis of keywords and bibliographic matching of documents were examined. This report shows that there has been a recent increase in research on AI in transport systems. However, there is only one study on tourism. The country that contributed the most is China with 298 studies. The most used keyword in the documents was intelligent transportation system.

The bibliometric analysis of the existing work provided a valuable and seminal reference for researchers and practitioners in AI-related in transportation system.

Background: This paper examines the impact of coronavirus COVID-19 outbreak on employees’ mental health, specifically psychological distress and depression. It aims at identifying the main stressors during and post COVID-19, examining the main moderating factors which may mitigate or aggravate the impact of COVID-19 on employees’ mental health and finally to suggest recommendations from a human resource management perspective to mitigate COVID-19’s impact on employees’ mental health.

Methods: This paper is a literature review. The search for articles was made in Google scholar, Web of Science and Semantic scholar. We used a combination of terms related to coronavirus OR COVID-19, workplace and mental health. Due to the paucity of studies on the COVID-19 impact on employees’ mental health, we had to draw on studies on recent epidemics.

Results: The identified literature reports a negative impact of COVID-19 on individual’s mental health. Stressors include perception of safety, threat and risk of contagion, infobesity versus the unknown, quarantine and confinement, stigma and social exclusion as well as financial loss and job insecurity. Furthermore, three dimensions of moderating factors have been identified: organizational, institutional and individual factors. In addition, a list of recommendations has been presented to mitigate the impact of COVID-19 on the employee’s mental health, during and after the outbreak, from a human resource management perspective.

Conclusions: Coronavirus is new and is in a rapid progress while writing this paper. Most of current research are biomedical focusing on individuals’ physical health. In this context, mental health issues seem overlooked. This paper helps to broaden the scope of research on workplace mental health, by examining the impact of a complex new pandemic: COVID-19 on employees’ mental health, from social sciences perceptive, mobilizing psychology and human resource management.

The Denial of Service (DoS) attack is a category of intrusion that devours various services and resources of the organization by the dispersal of unusable traffic, so that reliable users are not capable of getting benefit from the services. In general, the DoS attackers preserve their independence by collaborating several victim machines and following authentic network traffic, which makes it more complex to detect the attack. Thus, these issues and demerits faced by existing DoS attack recognition schemes in cloud are specified as a major challenge to inventing a new attack recognition method.

This paper aims to detect DoS attack detection scheme, termed as sine cosine anti coronavirus optimization (SCACVO)-driven deep maxout network (DMN). The recorded log file is considered in this method for the attack detection process. Significant features are chosen based on Pearson correlation in the feature selection phase. The over sampling scheme is applied in the data augmentation phase, and then the attack detection is done using DMN. The DMN is trained by the SCACVO algorithm, which is formed by combining sine cosine optimization and anti-corona virus optimization techniques.

The SCACVO-based DMN offers maximum testing accuracy, true positive rate and true negative rate of 0.9412, 0.9541 and 0.9178, respectively.

The DoS attack detection using the proposed model is accurate and improves the effectiveness of the detection.

A critical step in influencer marketing is influencer outreach, where a brand reaches out to an influencer and forms a partnership. Yet little is known about how factors related to this process might influence the outcomes of sponsored posts. To address this gap, the authors investigated whether, how and when the order of influencers' product use and brand outreach (i.e. use/outreach order) affects post persuasiveness.

The authors conducted three experimental studies. Studies 1 and 2 examined the effect of disclosure type (use-first, outreach-later vs. outreach-first, use-later vs. no disclosure) on consumers' responses to the post. Study 3 investigated the moderating effects of compensation disclosure type.

The results revealed that when the influencer used the product before (vs. after) being contacted by the brand, consumers had more favorable attitudes about the product and greater purchase intention upon reading the sponsored posts; perceived information diagnosticity mediated this effect. However, this tendency was mitigated if the influencer disclosed the specific monetary payment from the brand.

This research advances understanding of sponsorship disclosure and provides a way to manage its impact on message persuasiveness.

This study aims to integrate reviewers’ and readers’ discrepant perspectives on extremely negative reviews. Specifically, this study examines the relationship between negative emotion intensity levels and reviews helpfulness on two platforms: integrated websites and social networking sites (SNS) to emphasize the role of platform types on customers’ purchase decisions.

This research adopts a mixed-method approach of business intelligence approach and quasi-experimental design. Study 1 performed text mining and Welch’s t-test to compare reviewers’ negative emotion intensity levels on two platforms. Study 2 adopted a 2*2 factorial quasi-experimental design to examine how intense negative emotions impact the perceived reviews helpfulness on two platforms. A 3*2 factorial design in Study 3 also tested social tie strength’s moderating effect between the intensity of negative emotions and review helpfulness.

The current study reveals that integrated website reviewers tend to express more extreme negative emotions than SNS reviewers. SNS and integrated website readers deem reviews that embed severe negative emotions as less helpful. The moderating role of social tie strength between extremely negative emotions on review helpfulness was insignificant in the study.

This study enriches the online review literature by comparing writers’ and readers’ perspectives on online reviews with extremely negative emotions across two online platform types: integrated websites and SNS. From the writers’ perspective, this study highlights anonymity and the presence of an audience as essential factors that reviewers consider in selecting an online review platform to express themselves. This research also sheds light on how readers’ perspectives on extremely negative reviews conflict with the presumptions of writers of extremely negative reviews on integrated websites by demonstrating that content embedding extremely negative emotions is less helpful regardless of platform type.

This research provides online negative review management strategies to platform and hotel managers. The findings suggest hotel and review platform managers should consider adopting review alignment or monitoring systems based on negative emotions intensity levels since readers on both platforms perceive reviews embedding extremely negative emotions as less helpful. Additionally, hotel managers can progress promotions to guests who share online reviews on SNS since SNS reviewers are more likely to attenuate their extremely negative emotions when writing reviews.

This research innovatively provides a comprehensive overview of negative reviews’ production and consumption process from reviewers’ and readers’ perspectives. This research also provides practitioners insight into the nature of two different platform types and the management of negative reviews on these platforms.

The green innovation behavior of construction enterprises is the key to reducing the construction industry's carbon emissions and realizing the green transformation of the construction industry. The purpose of this study is to reveal the evolutionary mechanism of green innovation behavior in construction enterprises.

This study is based on resource-based theory, Porter's hypothesis and signaling theory. First, a measurement model of the green innovation behavior of construction enterprises was constructed from three aspects: environmental regulation, enterprise resources and public opinion through hierarchical analysis. Then, the state values of the measurement model of green innovation behavior of construction enterprises were calculated through the time series data from 2011–2018. Finally, the Markov chain model was used to predict the evolutionary trend of green innovation behavior of construction enterprises, and the accuracy of the prediction effect of the Markov chain model was verified using the time series data of 2019.

The Markov chain model of green innovation behavior of construction enterprises constructed in this study has high accuracy. This model finds that the transition of the growth state of green innovation behavior in China's construction industry is fluid and predicts the evolution trend of the innovation behavior of construction enterprises. In the future, the green innovation behavior of construction enterprises has a probability of 70.17% to be in a continuous growth state and 40.27% to be in a rapid growth state.

Based on the Markov chain model of green innovation behavior of construction enterprises, this study finds that the transition of the growth state of green innovation behavior of construction enterprises in China has the characteristics of liquidity. In addition, it reveals the development process of the green innovation behavior of construction enterprises from 2011–2018 and predicts the evolution trend of the green innovation behavior of construction enterprises.

This study explores the influence of heritage brand extension on consumer purchase intention and analyses the effects of pop culture involvement. The extension of heritage brands is becoming increasingly difficult because such an extension needs to be consistent with the unique characteristics of brands and resonate with consumers. However, few scholars discuss the influence of consumers' level of pop culture involvement on brand extension and purchasing behaviour.

Taking time-honoured brands as an example, this study established a conceptual model based on a comprehensive review of the literature, and then tested the model using a sample of 255 respondents who were familiar with one of the selected Chinese time-honoured brands. Structural equation modelling was used to analyse the relationships amongst brand extension fit, pop culture involvement, perceived value and purchase intention.

Time-honoured brand extension fit has a positive impact on consumer purchase intention, and this path is significantly influenced by the mediation mechanisms of perceived value. Situational pop culture involvement can significantly strengthen the relationship between time-honoured brand extension fit and perceived value, whereas enduring pop culture involvement does not.

The results clarify and expand on the different roles of cultural involvement in time-honoured brands and broaden research on the influence of cultural involvement in this regard. This study has significant theoretical value for the inheritance and revival of heritage brands and provides a reference for the practice of time-honoured brands.