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Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

With the presence of pricing errors, the authors consider statistical inference on the variance risk premium (VRP) and the associated implied variance, constructed from the option prices and the historic returns.

The authors propose a nonparametric kernel smoothing approach that removes the adverse effects of pricing errors and leads to consistent estimation for both the implied variance and the VRP. The asymptotic distributions of the proposed VRP estimator are developed under three asymptotic regimes regarding the relative sample sizes between the option data and historic return data.

This study reveals that existing methods for estimating the implied variance are adversely affected by pricing errors in the option prices, which causes the estimators for VRP statistically inconsistent. By analyzing the S&P 500 option and return data, it demonstrates that, compared with other implied variance and VRP estimators, the proposed implied variance and VRP estimators are more significant variables in explaining variations in the excess S&P 500 returns, and the proposed VRP estimates have the smallest out-of-sample forecasting root mean squared error.

This study contributes to the estimation of the implied variance and the VRP and helps in the predictions of future realized variance and equity premium.

This study is the first to propose consistent estimations for the implied variance and the VRP with the presence of option pricing errors.

The purpose of this study is to develop a theoretical model for examining how innovation speed mediates the relationship between knowledge management (KM) and performance and empirically tests the proposed model using data collected in the USA and China over three years.

To avoid common method bias and increase ability to draw causal effects of KM on performance, data were collected over three years. KM data were collected by survey; innovation speed data were collected in the following year; and sales growth and gross margin data were collected over the next three years. After merging the three data sets, the final empirical data used for this study contained data from 354 USA and 647 Chinese firms. Multiple regression analyses were used to test the research hypotheses. Sobel mediation tests were performed to test the mediating effects of innovation speed on the relationship between KM and performance.

Innovation speed has a U-shaped relationship with performance in both US and Chinese firms. Knowledge generation has an inverted U-shaped relationship with innovation speed in both US and Chinese firms. Knowledge dissemination increases innovation speed in US firms but not in Chinese firms. While knowledge application increases innovation speed in the US firms, it decreases innovation speed in Chinese firms.

This study is among the first to propose and empirically test the KM-innovation speed-performance relationship. This paper advances the KM literature by demonstrating that there is an inverted U-shaped relationship between knowledge generation and innovation speed and that there is a U-shaped relationship between innovation speed and performance. In addition, this study contributed to the cross-national study of KM.

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.

Drawing on a sensemaking perspective, this study aims to theoretically and empirically investigate the effects of participative corporate political activity (PCPA) on radical innovation and how regulatory uncertainty and technological uncertainty affect firms’ choice of PCPA as well as its effectiveness on radical innovation.

Hierarchical regression analyses were conducted to test the research model based on survey data collected from 227 Chinese manufacturing firms.

The results indicate that PCPA has a significantly positive effect on radical innovation. Both regulatory and technological uncertainty are positively related to PCPA. In addition, regulatory uncertainty strengthens the positive relationship between PCPA and radical innovation, whereas technological uncertainty weakens this relationship.

This study reveals that firm managers should be mindful that PCPA is beneficial to firms’ radical innovation activities in China. Additionally, although regulatory uncertainty and technological uncertainty can drive firms to engage in PCPA to cope with the ambiguity they experienced, managers should also be alert to the complicated role of environment forces in enlarging or discounting the positive effect of PCPA on radical innovation.

The findings offer fresh insights into the use of PCPA to manage the uncertain external environment when pursuing radical innovation activities in China.

The purpose of this paper is to unravel the specific service quality dimensions that significantly influence startup survival, providing actionable insights for service managers and entrepreneurs.

Empirical data were collected from 372 service startups over a period of seven years. The Cox proportional hazard model was used to analyze the data.

The results indicate that the tangible dimension of service quality emerges as the most critical determinant of startup survival. Additionally, the reliability and responsiveness of the service also significantly affect startup survival. Furthermore, the assurance and empathy dimensions have a positive, albeit modest, influence on the survival prospects of service startups.

This study contributes to the service literature by investigating the relative importance of each dimension of service quality in relation to the survival of service startups.

The empirical findings empower service startups to make informed decisions, allocate resources judiciously and prioritize aspects of service quality that have a significant impact on their survival and success.

The social implications indicate the significance of service quality dimensions not only for the success of service startups but also for the overall well-being of customers, local economies and the competitive landscape of the service sector.

This study contributes to service science by uniquely highlighting the critical role of tangibles in startup survival, challenging conventional beliefs about the primacy of service reliability.

In light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.

Multivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.

According to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.

This study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

Although the literature provides insights into the role of experiential learning based on prototypes in identification of latent customer need, it offers different views on the role of product prototypes in improving the efficacy of learning customer need, and also neglects the role of vicarious learning in prototype-based experiential learning. In a data-rich environment, market big data create new opportunities to learn from vicarious, digitalized experiences that are not observable with prototype-based learning. Therefore, the purpose of this study is to compare the effects of product prototype strategies – basic prototype strategy and enhanced prototype strategy – on identification of latent customer needs, and determine how each prototype strategy interacts with vicarious learning based on market big data to identify latent customer needs.

We collected data from 299 Chinese manufacturing firms via on-site surveys to explore our research question. All of our hypotheses were supported by the regression results.

This study finds that both the enhanced and basic prototype strategies (experiential learning from direct market experience based on prototyping) have positive effects on latent need identification, but the effect of enhanced prototypes is stronger. Furthermore, the enhanced and basic prototype strategies have different interaction effects with market big data (vicarious learning from indirect market experiences) on latent need identification.

This research extends the literature on prototype-based learning for latent need identification. It also contributes to the experiential prototype-based learning literature by exploring the role of vicarious learning based on market big data.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

This paper aims to explore what factors influence the possibility of internal and external risk propagation in R&D networks and investigate how government intervention moderates the associations between the influencing factors and risk propagation.

The authors divided government intervention into directive and facilitative intervention and adopted an empirical research approach in this study. They collected 228 questionnaires from managers and R&D personnel participating in R&D projects in Shanghai and Jiangsu province through e-mail and in person. The data were used to carry out multiple regression analysis to test hypotheses.

The results show that the probability and consequence of risks positively affect the possibility of internal and external risk propagation; risk perception and transformation ability negatively influence the possibility of internal and external risk propagation; both directive and facilitative intervention weaken the relationship between the probability of risks and internal risk propagation when they are high than low the association between transformation ability and internal risk propagation is weaker when directive intervention is high than low, whereas facilitative intervention presents the insignificant moderation effect on the relationships between risk perception ability and internal and external risk propagation.

This study provides a distinctive theoretical perspective for risk conduction theory, government intervention theory and risk management. It also offered managers and the government a clear understanding of how to reduce or avoid risk propagation by leveraging directive and facilitative government intervention.