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As a pillar of integrated digital delivery (IDD), building information modeling demonstrates the tremendous potential to enhance productivity for the architectural, engineering and construction (AEC) industry worldwide. However, the implementation of digital solutions presents numerous challenges related to its adoption and implementation. Distinguishing a comprehensive set of critical factors can facilitate the construction professionals to execute their strategies in a properly planned manner, thus augmenting the possibilities of successfully implementing BIM in their organization. This study aims to identify critical success factors (CSFs) for BIM adoption and implementation in Singapore.

This study adopted structured empirical questionnaire survey. Relevant data were collected from the various stakeholders in Singapore AEC industry through an online survey questionnaire. Furthermore, data analysis was done using SPSS Statistics software in order to identify the key factors (KFs) based on which the CSFs were derived for BIM adoption and implementation during the construction phase.

From a set of 45 influencing factors, 35 KFs were derived after performing ranking analysis, from which a set of 26 CSFs were finally obtained based on the factor analysis methodology.

This study has identified the CSFs of BIM adoption in Singapore, as well as in the builders' perspective on how to enhance the digitalization in construction projects.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

This paper attempts to analysis the credit risk at the angle of industrial and macroeconomic factor using grey incidence analysis method.

Credit asset quality problem is one of the obstacles limiting the further development of commercial banks; the research on credit risk becomes an important part of the implementation of a commercial bank's risk management. Different industries may have different effects on the credit risk of commercial bank. This paper proposes finding out the different incidences between industries and credit risk, as well as macroeconomics. Incidence identification method is established to investigate whether the industry and macroeconomic factor could affect an impaired loan ratio of a bank using the grey incidence analysis method.

The results indicate that the impaired loan ratio differs with diverse industry's influence and the macroeconomics also affect it. From the angle of the industry, the result can also determine the risk deviation scope in the grey risk control process which offers new content and ideas within the grey risk control.

Under the guidance of the principle of “differential treatment, differential control”, this research will help to strengthen the implementation of differentiated credit policy, focus on guiding and promoting the optimization of credit structure, so as to maintain a reasonable size of credit facilities and build a steady currency credit system.

The paper succeeds in finding the top five influent industries compared with others by using one of the newest developed theories: grey systems theory.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

The well-operating condition of journal bearing is the assurance to keep superior performance of water-lubricated twin-screw compressor. To design the journal bearing more reasonably for this type of compressor, this paper aims to study the effects of rotating speed and design parameters on bearing characteristics, considering surface roughness and bending deformation of the shaft at the same time.

The average Reynolds equation considering the effect of surface roughness is adopted and solved by finite difference method and successive over-relaxation method to calculate pressure distribution with real bearing shapes and boundary conditions. The bending deformation of the shaft is calculated using simply supported beam model of variable cross-section.

The dynamic lubrication characteristics of four water-lubricated journal bearings in twin-screw air compressor are calculated and analyzed. In addition, the static characteristics of journal bearing including friction coefficient, film thickness ratio distribution and water film pressure distribution are calculated numerically with different rotating speed and design parameters. Moreover, some design principles of water-lubricated bearing for twin-screw compressor are put forward.

The lubrication characteristics of the water-lubricated journal bearing in twin-screw air compressor are calculated considering surface roughness and bending deformation of the shaft at the same time. The paper’s results may provide design guidelines for journal bearing in this kind of twin-screw compressor.

The purpose of this study is to address a significant gap in the understanding of entrepreneurship at the microfoundation level. It focuses on how individual entrepreneurs, specifically Hongbang entrepreneurs in China from 1896 to 1949, shape and transform their contexts. The aim is to provide a deeper understanding of the mechanisms that facilitate entrepreneurial success.

The study adopts a microhistorical approach, investigating the case of Hongbang entrepreneurs in China during 1896-1949. It involves an in-depth examination of historical records to explore the strategic interactions between these entrepreneurs and core stakeholders such as consumers, financial intermediaries, government regulators, and human resources. The research methodology emphasizes a process-oriented view, examining the evolution of personalized networks into extensive connections.

The research reveals that Hongbang entrepreneurs successfully reshaped their unfavorable embedded contexts by strategically collaborating with key stakeholders. They influenced consumer tastes, allied with financial intermediaries, negotiated with governments on regulation policies, and developed human resource stocks. The transformation was facilitated by the evolution of their networks from personalized to extensive connections. These findings highlight the localized strategies such as cronyism in resource acquisition within China’s private property development industry.

This study contributes to the field by offering insights into entrepreneurial contextualization and networking. It sheds light on the complex interplay between entrepreneurs and their contexts, providing a nuanced understanding of localized strategies in the Chinese context. The findings add value to the discourse on entrepreneurship by elucidating the strategic and processual acts through which entrepreneurs engage with stakeholders and reshape their environments.

Due to the limitation of acknowledgment, the complexity of software system and the interference of noises, this paper aims to solve the traditional problem: traditional software cost estimation methods face the challenge of poor and uncertain inputs.

Under such circumstances, different cost estimation methods vary greatly on estimation accuracy and effectiveness. Therefore, it is crucial to perform evaluation and selection on estimation methods against a poor information database. This paper presents a grey rough set model by introducing grey system theory into rough set based analysis, aiming for a better choice of software cost estimation method on accuracy and effectiveness.

The results are very encouraging in the sense of comparison among four machine learning techniques and thus indicate it an effective approach to evaluate software cost estimation method where insufficient information is provided.

Based on the grey rough set model, the decision targets can be classified approximately. Furthermore, the grey of information and the limitation of cognition can be overcome during the use of the grey rough interval correlation cluster method.

This paper proposed the grey rough set model combining grey system theory with rough set for software cost estimation method evaluation and selection.

Coronavirus disease 2019 (COVID-19) has resulted in traveling restrictions and decreased the demand among travelers since the end of 2019. Policies and people’s behaviors are gradually changing with the increase in the number of new diagnoses of COVID-19. Tourism increases tourists’ risk of contracting COVID-19 and will undoubtedly be affected by this disease. Based on this assumption and social cognitive theory, this paper aims to explore the impact of COVID-19 on tourism in terms of people’s willingness to travel.

Based on social cognitive theory, this study has designed the model and variables. The data of the paper came from a survey conducted in 29 provinces of China and a total of 618 responses were used for data analysis after deleting invalid questionnaires. Drawing on the structural equation modeling method, this paper processes the data collected from questionnaires to test the hypotheses and come to the results.

The results indicate that the negative impact of COVID-19 will affect the severity perceptions, personal negative effects and positive effects of tourism during the outbreak, which will ultimately influence people’s willingness to travel. During the outbreak of COVID-19, impacted by environmental factors and personal factors, Chinese people’s willingness to travel has been significantly reduced.

This study mainly focuses on people’s willingness to travel and the demand side of tourism to analyze the effects of COVID-19 on tourism, which neglects the supply side of tourism for analysis. Subsequent research should take account of the supply side of tourism and expanding the sample size worldwide to determine the impact of COVID-19 on the tourism industry from a broader perspective.

This study quantitatively analyzes how COVID-19 influenced the tourism industry and precisely determines the possible mechanism of the effect of COVID-19 on tourism by using social cognitive theory to build a model. The insights from the work help to understand how COVID-19 affects people’s willingness to travel during the pandemic and how to address this issue.

新型冠状病毒（COVID-19）自2019年底以来开始被发现, 随后逐渐蔓延至全球, 给人们的生活带来不小的影响。该病毒的发展使得人们的出行受到了较大的限制, 因而减少了人们对于旅游的需求。随着新冠疫情确诊人数的增加, 政府政策和人们的行为也处在变化之中。基于这一假设和社会认知理论, 本文旨在从人们的旅游意愿角度探究新冠疫情对旅游业的影响。

本文所设计的变量和模型是在社会认知理论的基础之上提出的。本研究的数据来源于面向中国29个省市自治区的问卷调查。在剔除无效问卷后, 总共搜集618份有效问卷进行数据分析。本研究采用了结构方程模型, 对假设进行检验、并得出最终结论。

研究结果表明, 新冠疫情带来的负面影响会增加人们对疫情严重性的认知程度、疫情期间旅游给人带来的负面影响增大、正面影响减少, 从而最终影响人们的旅游意愿。在新冠疫情之下, 受到环境、个人因素的影响, 人们的旅游意愿显著降低。

本研究的数据均来源于中国, 所以将本文的结论应用于其他地区时, 由于不同地区的自然和社会环境差异, 结论可能会产生差异。本研究主要聚焦于人们旅游意愿的变化, 即旅游业的需求端, 来分析新冠疫情对旅游业的影响, 忽视了旅游业的供给端在新冠疫情期间对旅游业的影响分析。后续的研究可以考虑从旅游业供给端的变化进行分析、以及将样本数量扩大到全球范围, 从而可以从更广阔的视角来探究新冠疫情对旅游业的影响。

本研究定量分析了新冠疫情如何影响旅游业, 并利用社会认知理论建立模型来精确判断这一影响的可能机理。本文的发现有助于了解新冠疫情如何影响人们在疫情行期间旅行的意愿, 以及制定相关的应对措施。

El coronavirus (COVID-19) se descubrió desde finales de 2019 y luego se extendió gradualmente a todo el mundo, lo que tuvo un gran impacto en la vida de las personas. La propaganda del coronavirus ha provocado muchas restricciones en los viajes de las personas, por lo tanto, reducía la demanda de viaje de las personas. Con el aumento en el número de casos confirmados de COVID-19, las políticas gubernamentales y el comportamiento de las personas también se están cambiando. Basado en esta hipótesis y teoría cognitiva social, este artículo tiene como objetivo explorar el impacto del COVID-19 en el turismo desde la perspectiva de la disposición de las personas a viajar.

Las variables y modelos diseñados en este artículo se proponen sobre la base de la teoría cognitiva social. Los datos para este estudio provienen de las encuestas por cuestionario de 29 provincias, municipios y regiones autónomas de China. Después de excluir los cuestionarios no válidos, se recopilaron un total de 618 cuestionarios válidos para el análisis de datos. En este estudio, se utiliza el modelo de ecuación estructural y la prueba de la hipótesis, al final llegar a la conclusión.

El resultado de la investigación muestra que el impacto negativo del COVID-19 aumenta la conciencia de las personas sobre la gravedad de la epidemia, y el impacto negativo del turismo durante la epidemia se aumenta y el impacto positivo se disminuye, lo que al fianl afecta la disposición de las personas a viajar. Bajo el COVID-19, afectada por factores ambientales y personales, la disposición de las personas a viajar se ha reducido significativamente.

Los datos de este estudio son todos de China, por lo que cuando las conclusiones de este artículo se aplican a otras regiones, las conclusiones pueden diferir debido a las diferencias en los entornos naturales y sociales de diferentes regiones. Esta investigación se centra principalmente en el cambio del deseo de viajar de las personas, es decir, el lado de la demanda del turismo, para analizar el impacto del COVID-19, por eso ignora el análisis del impacto de la oferta turística en el turismo durante el COVID-19. Los estudios posteriores se pueden considerar desde el aspecto de analizar los cambios en el lado de la oferta del turismo y expandir el número de muestras a una escala global, por lo tanto, se puede explorar el impacto del COVID-19 desde una perspectiva más amplia.

El encuentro de este artículo ayuda a comprender cómo el COVID-19 afecta la disposición de las personas a viajar durante la epidemia y a formular medidas de respuesta relevantes.