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The emergence of the Software-as-a-service (SaaS) licensing model dramatically changes how enterprise software is released. Especially, it is favored by small and medium enterprises (SMEs) because of the cost-friendly feature. In contrast, many large enterprises (LEs) own relatively abundant budgets and prefer the on-premise software to fulfill demands through customization. Considering the differentiated cost-acceptance level among customers, this study aims to address the versioning problem of the enterprise software faced by software firms.

A two-point distribution model is formulated to calculate the maximal profits software firm earned from both LEs and SMEs under three strategies (On-premise, SaaS and Hybrid). Then through profit comparison, this paper obtains the optimal versioning strategy and corresponding feasible conditions. Finally, the optimal solutions are derived concerning social welfare.

A significant finding is that moving to SaaS becomes necessary for the software firms in product releases since the on-premise strategy will not be optimal. Based on this, this paper discovers that when LEs own a cost-acceptance level close to that of SMEs, the hybrid strategy is the only optimal choice. When LEs become less sensitive to costs, the hybrid strategy is suggested if the customization cost falls below the threshold. Otherwise, the SaaS strategy becomes the optimal option. The conclusions explain why some software vendors transit to “cloud companies” thoroughly and provide practical insights for software firms’ future decisions.

To the best of the authors’ knowledge, this paper is the first information economics study to consider consumer cost sensitivity in discussing enterprise software versioning. The differentiated cost-acceptance level is introduced to describe the customer utilities, and the results uncover the necessity of moving to SaaS under diversified customer composition. This work provides significant theoretical value and practical insights.

Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

As construction of commercial and recreational complex building projects (CRCBPs) is one of the most important issues in many developing countries and requires a very high cost of implementation, it is important to identify and prioritize the risks of such projects. Therefore, the purpose of this study is to identify and rank the risks of CRCBPs by studying the case of the “Hamedanian Memorial,” a CRCBP in Iran.

To pursue this aim, a descriptive-survey method was used. The statistical population of the study consists of 30 experienced experts (consultants, contractors and employers) of the “Hamedanian Memorial” project selected according to the Cochran formula and minimum population census. A questionnaire was used as the data collection tool, administered in all stages of risk identification and evaluation, and was devised by using library and field methods based on the literature and research background, as well as interviewing experts in the risk identification and evaluation stages. Kendall’s coefficient of agreement was used to validate the experts’ opinions in the risk identification stage. The ranking in qualitative evaluation was done based on the risk intensity and the cumulative risk index.

The results show that the risks are associated with exchange rate fluctuation, inflation fluctuation, access to skilled workers, contractors’ claims and foreign threats from international relations.

The results and findings of the present study can be of interest to the executives of large commercial, leisure, public and private projects in developing and developed countries; understanding risks can significantly improve the decision-making process of CRCBPs.

The purpose of this paper is to build a moderate mediation model to delineate the effects of leader humility on employee constructive voice behavior based on conservation of resources theory and crossover of resources model. Specifically, when a leader behaves with humility, the followers will be more likely to feel they receive psychological resources from their interactions with the leader (i.e. relational energy), and thus engage in more constructive voice behavior. In addition, this energizing effect only occurs when the leader is perceived as having higher apparent sincerity by their subordinates.

The research hypotheses of this study were empirically tested using multi-timepoint and multi-source (i.e. supervisors and subordinates) survey data in China. Study 1, based on data from 449 subordinates and 88 immediate supervisors, was conducted to test the proposed mediation effect. Study 2, based on data from 185 subordinates and 50 immediate supervisors, was conducted to replicate the findings of Study 1 and test the integrated model.

The results of Study 1 support the proposed mediation effect that leader humility positively predicts followers’ constructive voice behavior via boosting followers’ relational energy. The results of Study 2 replicate the findings of Study 1 and further indicate that leader humility is only positively related to perceived relational energy when a leader is perceived as having higher apparent sincerity by their subordinates.

This paper provides detailed instructions for business practitioners. First, given that employee constructive voice behavior is related to employee relational energy and is beneficial to organizations, leaders are encouraged to behave with humility when interacting with their subordinates. Second, from the perspective of human resource management, well-designed training programs can be used to help leaders to develop leader humility. Third, the findings of this paper call attention to a potential risk for humble leaders. Organizations should educate leaders on the potential negative consequences of false humility and encourage leader humility that will appear in leaders’ sincerity.

Overall, drawing on conservation of resources theory and crossover of resources model, this paper reveals that boosting relational energy is a mechanism via which humble leaders can shape employees’ voice behavior. Second, by examining the moderation effect of apparent sincerity of leaders from followers’ perspective, this paper suggests an actor–recipient perspective to identify the boundaries of the energizing mechanism. Third, the findings of this paper add to the knowledge on voice research by highlighting an additional source of energy for employee constructive voice behavior.

Prior research demonstrated that China's Outward FDI (OFDI) is aimed at sustaining long-term economic growth by promoting industrialization and technological upgrading in the country. However, empirical evidence on the effectiveness of this strategy remains scarce. This study intends to fill this gap by exploiting endogenous changes in industrial productivity stemming from OFDI to examine if China's new strategy to spur OFDI is economically beneficial for the industries involved.

The authors employed the two-step system-GMM and pooled mean group approaches on a panel dataset of 18 Chinese industries over the 2004–2017 period. The industrial sectors are further classified into the state dominated and non-state dominated ones to evaluate whether the productivity growth impact of OFDI varies by the level of ownership structure. Besides, the dataset is further decomposed into the ex ante and ex-post BRI era to test if this initiative has altered the underlying relationship.

The results provide robust evidence that China's OFDI through reverse spillover effects promotes productivity growth in the domestic industries, and such productivity gains are greater for the non-state dominated industries, and the OFDI in the BRI era. The findings suggest that OFDI can act as a catch-up strategy to release excess capacity and acquire technology and smart business practices.

This study is the first attempt to highlight the reverse productivity spillovers associated with OFDI at the industrial level. The study's findings guide the government officials and the practitioners of foreign investment to better understand the implications of their investment projects in terms of technology improvements and to optimize market opportunities.

This paper aims to build a moderate mediation model to delineate when and how employee with perceived overqualification will exert extra effort and therefore engage in more altruistic helping behavior.

The research hypotheses were empirically tested using multitime and multisource survey data. Given the nested nature of data (i.e. 52 immediate supervisors rated 143 subordinates), multilevel structural equation modeling analyses within Mplus were conducted to test the proposed model.

The results support the proposed moderated mediation effect and indicate that perceived overqualification is positively related to extra effort on a condition that there is either strong desire for higher workplace status or more developmental job opportunities. The extra effort will subsequently lead to more altruistic helping behavior.

Based on the findings of this paper, human resource managers should consider the job applicant’s desire for workplace status and the organizational context the employer can provide when hiring overqualified employees. Second, organizations should carefully conduct job design to improve overqualified employees’ on-the-job developmental experiences. Third, training programs should be conducted to help satisfy needs and improve workplace status of overqualified employees, so that they can exert extra job effort and engage in pro-organizational behaviors.

Drawing on motivation–opportunity–ability theory, this paper extends the limited understanding of important boundary conditions under which perceived overqualification can be beneficial. The findings add to the knowledge on extant literature by identifying altruistic helping behavior as a new outcome of perceived overqualification.

This paper aims to propose a precise turbulence model for vehicle aerodynamics, especially for vehicle window buffeting noise.

Aiming at the fact that commonly used turbulence models cannot precisely predict laminar-turbulent transition, a transition-code-based improvement is introduced. This improvement includes the introduction of total stress limitation (TSL) and separation-sensitive model. They are integrated into low Reynolds number (LRN) k-ε model to concern transport properties of total stress and precisely capture boundary layer separations. As a result, the ability of LRN k-ε model to predict the transition is improved. Combined with the constructing scheme of constrained large-eddy simulation (CLES) model, a modified LRN CLES model is achieved. Several typical flows and relevant experimental results are introduced to validate this model. Finally, the modified LRN CLES model is used to acquire detailed flow structures and noise signature of a simplified vehicle window. Then, experimental validations are conducted.

Current results indicate that the modified LRN CLES model is capable of achieving acceptable accuracy in prediction of various types of transition at various Reynolds numbers. And, the ability of this model to simulate the vehicle window buffeting noise is greater than commonly used models.

Based on the TSL idea and separation-sensitive model, a modified LRN CLES model concerning the laminar-turbulent transition for the vehicle window buffeting noise is first proposed.

Under emerging social media technology, mobile learners' behavior analysis and legality education have important practical significance. The research aims to detect the mobile learning (M-learning) learners' behavior in legality education under the background of the Internet era and improve the learning and teaching effect of online legality education and law popularization.

This paper proposes a model based on deep learning (DL) fuzzy clustering analysis (FCA), and bidirectional encoder and decoder (ENDEC) of converter model to detect the mobile learners' behaviors in online legality education under the current social media. Then, the effectiveness of the proposed model is tested. The proposed model expects to be applied to multimedia teaching and law popularization activities and provides some theoretical reference and practical value for improving the effectiveness of online teaching.

The experimental results show that in the learner behavior detection process of M-learning-oriented online legality education, the model's accuracy can reach 99.8%. The response time is shorter than other algorithms. Overall, the application effect of the proposed model and algorithm is good and can be applied in practice.

The research results may lack universality due to the selected research methods. Therefore, researchers are encouraged to test the proposed methods further. In the future, it is necessary to expand the type and scale of text data to improve the accuracy of data detection.

The research results provide a specific theoretical reference and practical significance for improving the learning effect of online M-learning-oriented legality education.

This paper meets the needs of mobile learner behavior analysis based on social media.

Most epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.

Two probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.

The managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.

Very few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.