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Relationship management evolves with dynamic and complex environments of megaprojects. However, studies on the longitudinal measurement of relationship management performance for each stakeholder in dynamic and complex project environments are lacking. The purpose of this research is to propose an NK-network evolution model to evaluate stakeholder performance on relationship management in the development of megaprojects.

The model input includes the stakeholder-associated issues and stakeholders' relational strategies, the co-effects of which determine the internal effects of relationship management in megaprojects. The model processing simulates the stakeholder performance of relationship management under the dynamic and complex nature of megaprojects. The NK model shows the dynamic stakeholder interactions on relationship management, whereas the network model presents the complex stakeholder structures of the relationships between stakeholders and relevant issues. The model output is the evolution graph to reveal the weak stakeholder performance on relationship management in the timeline of the project duration.

The research finding reveals that all stakeholders experience the plunge of stakeholder performance of relationship management at the decision-making moment of the planning stage. Construction, environmental and pressure groups may experience the hardship of relationship management at the start of the construction stage. The government is likely to suffer difficulties in relationship management in the late construction stage. Local industry groups would face challenges in relationship management in the middle of the construction stage and handover stage.

The research provides a useful approach to measuring weak moments of relationship management for each stakeholder in various project phases, considering the dynamic and complex environments of megaprojects. The proposed model extends the current knowledge body on how to make project stakeholder analysis by modelling dynamic and complex environments of megaprojects, with bridging the knowledge domains of evolution modeling techniques and network methods.

Drawing on job demands and resources theory and the challenge-hindrance stressor framework, this study aims to investigate the effect of team knowledge complexity on team information sharing and information searching and examine whether team learning goal orientation mediates these effects.

The authors conducted two studies. Study 1 used a field survey study conducted among 374 employees positioned in 68 new product teams. Study 2 used a three-wave online survey study conducted among 208 leaders to investigate the teams they managed.

The findings of the two studies reveal that team knowledge complexity has a positive direct effect on team information sharing and information searching. Furthermore, team learning goal orientation mediates these two relationships.

The findings indicate that team knowledge complexity is generally beneficial for the team information process. Therefore, instead of fearing an increase in the knowledge complexity of the projects, organizations should dare to present challenge demands to team members to enhance their engagement in information processing. Organizations could also pay attention to team member selection during team composition processes. For example, selecting team members with a high level of learning goal orientation is helpful in facilitating team information processing.

Although previous studies have found that knowledge complexity is beneficial for team output, less is known about how knowledge complexity influences team processes. This study clarifies the relationships between team knowledge complexity, information sharing and information searching and examines team learning goal orientation as a vital mediator.

To investigate the impact of tax enforcement on (a) debt aggressiveness (DEA) and (b) dynamic adjustment of capital structure in Chinese listed firms.

The authors estimate the target capital structure by employing the different models. This study uses data of Chinese A-share listed firms from year 1998 to 2015.

The study suggests that the greater the intensity of tax enforcement, the more radical the listed companies' debt policy. The macroeconomic status and nature of property rights have significant moderating effect on the positive relationship between tax enforcement and DEA of listed companies. Further, tax enforcement has a significant impact on the dynamic adjustment of capital structure.

Research conclusions are conducive to tax administration departments to understand the economic consequences of tax enforcement and further promote tax administration efficiency. Additionally, listed companies can rationally adjust their capital structure to strengthen tax enforcement.

This research helps extend the influencing factors of corporate debt decision-making and capital structure dynamic adjustment to the level of tax enforcement and provide new evidence on the effects of tax enforcement on corporate capital structure.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

The purpose of this research is to examine the effect of temporal landmarks on positive illusions and the downstream implications of this effect on consumer preference for new products with functional risks.

Study 1 adopted a single factor (temporal landmarks: beginning vs ending) between-subjects design. Study 2 adopted a 2 (temporal landmarks: beginning vs. ending) × 2 (salience of the temporal landmark: salient vs not salient) between-subjects design. Study 3 used a single factor (temporal landmarks: beginning vs ending) between-subjects design.

Through three studies, we show that the ending temporal landmarks reduce positive illusions (Studies 1 and 2). The underlying process is enhanced perceptions of psychological resource depletion (Study 3). The authors further show that decreased positive illusions lead consumers to less prefer new products with functional risks (Study 3).

Existing studies on temporal landmarks have exclusively focused on the beginning landmarks and account for its effects from a motive perspective. In contrast, the authors take a look at the ending landmarks and identify perceptions of psychological resource depletion as the underlying process, which suggests a new angel understand how temporal landmarks influence individuals' cognitions and behavior.

Purpose: The public health burden caused by tobacco is heavy among first-generation Chinese immigrant men whose home country has significantly higher smoking rates than the United States. The current study is part of a larger effort to pilot an mHealth tobacco cessation intervention using MMS (graphic) mobile phone technologies to target East Asian immigrant populations. Grounded in the Extended Parallel Process Model (EPPM), our specific aims were to determine what message themes, level of graphic intensity, and types of efficacy information are most appropriate and useful for mHealth interventions targeting this population.

Methodology/Approach: A qualitative study utilizing a series of focus groups (k = 5) was conducted with male adult smokers who were born in China and currently reside in the United States. The primary aim of the focus groups was to solicit reactions to a series of preliminary messages developed by the research team. A secondary aim was to gauge receptivity to the use of MMS as a vehicle for smoking cessation intervention. Participants (n = 32) were recruited from local Chinese communities in a large Mid-Atlantic metropolitan area.

Findings: Opinions about different message strategies were mixed. However, participants tended to rate messages more positively when they focused on the impact of smoking on family and loved ones, particularly children. Messages with fear-arousing images were also perceived to be effective at low frequency of exposure, but there were concerns that they may backfire at high exposure. Awareness of and interest in Quitline were low, and concrete quitting tips were perceived as more effective. Participants reported a preference for receiving messages a few times a week, and an MMS message platform was generally preferred to WeChat (a Chinese social media platform).

Implications: Our results suggest that graphic MMS messaging holds promise as an effective intervention method for this population and that EPPM is an appropriate framework to develop, test, and analyze mHealth intervention messages. While messages that focused primarily on impact on children, health, and specific quitting tips were generally found to be more effective, a mix of different types of messages that address a wide range of issues may be most appropriate for this population.

Originality/Value: This study is the first to explore the utility of graphic text messaging as an intervention method to promote smoking cessation among male Chinese immigrants. Findings from the study provide important insights for future intervention work targeting this underserved population.

The purpose of this research is to investigate how generalisable the public service motivation (PSM) observed in Western society is to China and to examine the effects of public service motivation on job satisfaction.

Exploratory factor analysis and confirmatory factor analysis techniques are applied to survey data of 191 public servants in China to investigate the generalisability of Western PSM. Using hierarchical regression analysis, the paper examines the effects of the dimensions of PSM on job satisfaction.

The results show that the public service motivation observed in the West exists in China, but the generalisability of the construct is limited. Three of the four dimensions of public service motivation (attraction to public policy making, commitment to the public interest, and self‐sacrifice) exist in China, but the fourth dimension (compassion) is unconfirmed.

The paper is the first to examine the generalisability and instrumentality of PSM as observed in Western society to China. The results indicate that the public service motivation observed in the West also exists in China, but that the generalisability is limited. Public service motivation emerges from the results as a positively significant predictor of job satisfaction in the public sector of China. It enhances the applicability and meaningfulness of the concept of public service motivation across political and cultural environments.

The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds of “omics” data brings opportunities for developing new computational drug repositioning methods based on big data. The paper aims to discuss this issue.

Here, a new computational strategy is proposed for inferring drug–disease associations from rich biomedical resources toward drug repositioning. First, the network embedding (NE) algorithm is adopted to learn the latent feature representation of drugs from multiple biomedical resources. Furthermore, on the basis of the latent vectors of drugs from the NE module, a binary support vector machine classifier is trained to divide unknown drug–disease pairs into positive and negative instances. Finally, this model is validated on a well-established drug–disease association data set with tenfold cross-validation.

This model obtains the performance of an area under the receiver operating characteristic curve of 90.3 percent, which is comparable to those of similar systems. The authors also analyze the performance of the model and validate its effect on predicting the new indications of old drugs.

This study shows that the authors’ method is predictive, identifying novel drug–disease interactions for drug discovery. The new feature learning methods also positively contribute to the heterogeneous data integration.

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.