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The purpose of this paper is to introduce an augmented high-order capital asset pricing model (AH-CAPM) as a new risk-based model to price stocks.

The AH-CAPM is defined as a linear model with high-order marginal moments and co-moments from the joint distributions of the sorted stock portfolio returns and the market return.

The performance of the AH-CAPM is tested in the Chinese and US stock markets. Empirical results show that the high-order marginal moments and co-moments from the joint distributions in AH-CAPM contain the risk and return information implied by the Fama–French factors, indicating it as a better risk measurement. Moreover, the AH-CAPM performs better than the Fama–French three-factor model and the Carhart four-factor model in both the Chinese and US stock markets.

Overall, this study introduces a new asset pricing model with better measurements to incorporate risk information in the stock market.

This paper presents a new method to automate robot motion planning in automotive manufacturing environments. A general framework is developed for CAD‐guided robot motion planning. The problem is formulated as a constraint‐satisfying problem of tool configurations or, robot hand poses. Two types of robot motion are considered: discrete motion, or point to point motion, and continuous motion. Triangular facets are used to approximate the part surfaces. A pre‐partition process decomposes the complex part surfaces into several simple, easy‐to‐solve patches. For each patch, robot hand poses are determined to satisfy certain task constraints. In this paper, the approach is applied to two applications: vision sensor planning and spray painting gun path planning. It is our belief that more robot planning applications in manufacturing can benefit from this method.

This paper aims to use this method to explore a new approach and possible technical optimal design for lubricant formulation.

The component of the developed oil was determined based on the physical and chemical properties of the base oil and the tribological properties. The analytic hierarchy process (ANP) method and SuperDecisions software were used for hydraulic oil modeling and calculation while taking performance index, work circumstance and economy into consideration.

The hydraulic oil formulation can be optimized using the ANP method, where the technical performance, economy and working circumstances of the hydraulic oils were taken into consideration in the evaluation system. The experiment analyzed and scored and screened the hydraulic oil formula in an objective and comprehensive manner.

The experiments showed that the newly developed hydraulic oils could meet the performance requirements for new energy vehicles equipped with hybrid hydraulic engines.

Automatic trajectory generation for spray painting is highly desirable for today’s automotive manufacturing. Generating paint gun trajectories for free‐form surfaces to satisfy paint thickness requirements is still highly challenging due to the complex geometry of free‐form surfaces. In this paper, a CAD‐guided paint gun trajectory generation system for free‐form surfaces has been developed. The system utilizes the CAD information of a free‐form surface to be painted and a paint gun model to generate a paint gun trajectory to satisfy the paint thickness requirements. A paint thickness verification method is also provided to verify the generated trajectories. The simulation results have shown that the trajectory generation system achieves satisfactory performance. This trajectory generation system can also be applied to generate trajectories for many other CAD‐guided robot trajectory planning applications.

Automated chopper gun trajectory planning (CGTP) for spray forming is highly desirable for today's automotive manufacturing. Generating chopper gun trajectories for free‐form surfaces to satisfy material distribution requirements is still highly challenging due to the complexity of the problems. In this paper, a user‐friendly software for automated CGTP has been developed. The CGTP software can take different formats of the CAD models of parts. A chopper gun trajectory is generated based on the CAD model of a part, chopper gun model, and constraints. A part is partitioned into patches to satisfy the given constraints. A trajectory integration algorithm is developed to integrate the trajectories of the patches to form a trajectory for the part. The CGTP software has been tested by Ford Motor Company and achieved satisfactory results.

Given migrant workers’ critical role in the Chinese economy, the increasing number of migrant workers who leave their organizations and return to their hometown has caused severe socioeconomic issues in China. The purpose of this paper is to contribute to migrant worker literature by revealing the micro-mechanism underlying migrant workers’ return-to-hometown intention and turnover.

Data were collected from a convenience sample from seven Chinese companies that employed migrant workers (n=147). The authors used path analysis to test the hypotheses.

Migrant workers’ family encouragement of returning to hometown was positively related to their return-to-hometown intention, which subsequently predicted their turnover decision in six months. Further, migrant workers’ perceived career sacrifice associated with returning to hometown weakened the effect of family encouragement to return.

For organizations that need to retain migrant workers, the findings indicate that it is particularly important to take migrant workers’ family needs and their career-related concerns into account. For migrant workers, the study highlights the importance of assessing gains and losses in the process of making turnover-related decisions.

This study contributes to migrant worker literature by investigating psychological processes underlying migrant workers return-to-hometown intention and the subsequent turnover from a micro perspective.

Sizing functions are crucial inputs for unstructured mesh generation since they determine the element distributions of resulting meshes to a large extent. Meanwhile, automating the procedure of creating a sizing function is a prerequisite to set up a fully automatic mesh generation pipeline. In this paper, an automatic algorithm is proposed to create a high-quality sizing function for an unstructured surface and volume mesh generation by using a triangular mesh as the background mesh.

A practically efficient and effective solution is developed by using local operators carefully to re-mesh the tessellation of the input Computer Aided Design (CAD) models. A nonlinear programming (NLP) problem has been formulated to limit the gradient of the sizing function, while in this study, the object function of this NLP is replaced by an analytical equation that predicts the number of elements. For the query of the sizing value, an improved algorithm is developed by using the axis-aligned bounding box (AABB) tree structure.

The local operations of re-meshing could effectively and efficiently resolve the banding issue caused by using the default tessellation of the model to define a sizing function. Experiments show that the solution of the revised NLP, in most cases, could provide a better solution at the lower cost of computational time. With the help of the AABB tree, the sizing function defined at a surface background mesh can be also used as the input of volume mesh generation.

Theoretical analysis reveals that the construction of the initial sizing function could be reduced to the solution of an optimization problem. The definitions of the banding elements and surface proximity are also given. Under the guidance of this theoretical analysis, re-meshing and ray-casting technologies are well-designed to initial the sizing function. Smoothing with the revised NLP and querying by the AABB tree, the paper provides an automatic method to get a high-quality sizing function for both surface and volume mesh generation.

Extant research has paid considerable attention to the effects of enterprise social media (ESM) on employees' work attitudes and outcomes, yet the authors know little about the influence of job demands arising from the implementation of ESM. Drawing on resource allocation theory, the purpose of this study is to unravel how ESM-related job demands influence employee outcomes.

This study conducts a two-wave time-lagged survey of 223 employees from 53 teams in 14 financial service firms in China to test the conceptual model.

The findings of this paper indicate that ESM-related job demands have indirect effects on employee outcomes (i.e. job satisfaction and work–family conflict), and emotional exhaustion plays an intermediary role in these relationships. Specifically, ESM-related job demands have a U-shaped effect on emotional exhaustion.

This study combines job demands with ESM research and clarifies the mechanism behind how ESM-related job demands at different intensity affect employee outcomes from a new perspective. Moreover, this study’s findings suggest several beneficial courses of action for managers to take advantage of ESM.

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.

This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation.

The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory.

The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data.

This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives.

This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights.

Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.