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Open innovation (OI) is an effective way to achieve firms' sustainable development in emerging markets. This study aims to investigate the effects of business and philanthropic corporate social responsibility (CSR) on OI and the moderating role of firm proactiveness in such relationships. This study also examines the effects of OI on firms' financial and innovation performance.

This study uses multisource data from 688 firms in China, including data from surveys of top managers and objective data. The Tobit model, Poisson model, and ordinary least squares regression are adopted to test the hypotheses.

The results suggest that business CSR and philanthropic CSR both have positive effects on OI. Proactiveness weakens the positive effect of business CSR on OI, while strengthening the effect of philanthropic CSR on OI. The results also show that OI increases firm innovation and financial performance.

CSR enables firms to build wild, deep and trust-based relationships with external actors, which may benefit firms in open search of knowledge. However, it has not received adequate attention in the literature on OI. The findings contribute to the research on OI drivers from the perspective of social activities and enhance the understanding of how different types of CSR and firm proactiveness work together to influence OI.

Innovation has been identified as a critical element to achieve firms' growth. The purpose of this study is to investigate the impact of chief executive officer (CEO) passion on firm innovation, including exploratory and exploitative innovation and examine the moderating roles of market and technological turbulence.

This study adopts the methodology of survey and uses multisource and time-lagged data of 146 firms in China. Seemingly unrelated regression (SUR) is used to test the hypotheses of this study.

This study finds that CEO passion promotes exploratory and exploitative innovation. Results also indicate that market turbulence strengthens the effect of CEO passion on exploratory and exploitative innovation, whereas technological turbulence weakens such an effect.

CEO passion is an important, positive affect which inspires CEOs to work for firms, but it has not yet received enough attention in the innovation literature. This study contributes to examining the impact of CEO passion on firm innovation and contributes to the contingency under which CEO passion influences firm innovation. Furthermore, this research finds that the moderating effects of market and technological turbulence are different in the relationship between CEO passion and firm innovation.

Emotional intelligence (EI) is deemed important in developing interpersonal relationships. However, in the development of team-member exchange (TMX), the effect of EI on TMX and the team context have been largely ignored. For filling these gaps, this study explores the effect of employee EI on employee TMX and introduces EI-based leader-member exchange (LMX) differentiation as a team context to moderate the EI-TMX relationship.

Data were drawn from 51 teams (consisting of 293 followers and 51 team leaders) selected from 30 companies (across the industries of technology, real estate, commerce and manufacturing).

Results revealed that employee EI was positively related to employee TMX. EI acted as the basis of LMX differentiation (EI was positively related to LMX, EI variety was positively associated with LMX differentiation), and EI-based LMX differentiation acted as a favorable context for high-EI employees to develop high-quality TMX.

This study contributes to the understanding of EI’s significant and complex influence on interpersonal exchange relationships between leaders, followers and coworkers.

This paper investigates Chinese agricultural insurance agents willingness to offer (WTO) livestock insurance based on the variations of eight main attributes of livestock insurance.

This study implements discrete choice experiments (DCE) with actual insurance agents who design, sell and operate livestock insurance in China. The choice experiment of this study is based on the D-optimal approach, a six-block design, with 15 cards per block and two choices per card. The sample size was 211. Econometrics results are based on conditional and mixed logit models.

The authors find that the subsidy effect is enormous; a one level increase of subsidy leads to 3.166 times higher probability to offer. This subsidy effect is important as it confirms the endogenous structure between price and quantity in insurance offering, where subsidy does not only incentivize demand but also the supply. Another main factor of insurance investigated is the impact of different coverage types on agents' WTO. The authors find that agents prefer mortality insurance the most, followed by revenue insurance and profit insurance, while Index-Based Livestock Insurance (IBLI) is the least preferred to offer. Agents' knowledge about these newer types of insurance supports their WTO as well; thus, proper education is necessary to promote the more advanced types of livestock insurance.

A limitation is that in the presence of COVID 19, and administrative issues at the local level, the sample was not randomly drawn. Nonetheless, the authors believe that there is enough diversity across participants, insurers and provinces and have done sufficient robustness checks to support results and conclusions.

This study provides further validation for the DCE research method that could potentially be applied to different analyses: using choice experiments to study insurers and reveal their preferences, through combinations of various levels of core attributes for insurance products. The findings and contribution are critical to the reform and improvement of livestock insurance in China and for insurance markets more broadly. The authors find that insurers do not place equal weights or values on insurance product attributes and do not view types of insurance equally. In other words, while farmers may hold different preferences about the type of insurance they demand, the results suggest that insurers also hold preferences in the type of insurance they sell.

So far as the authors are aware, this is the first DCE designed around the supply of insurance products with the subjects being insurance agents, marketers and executives.

This paper aims to propose a novel real-time three-dimensional (3D) model-based work-piece tracking method with monocular camera for high-precision assembly. Tracking of 3D work-pieces with real-time speed is becoming more and more important for some industrial tasks, such as work-pieces grasping and assembly, especially in complex environment.

A three-step process method was provided, i.e. the offline static global library generation process, the online dynamic local library updating and selection process and the 3D work-piece localization process. In the offline static global library generation process, the computer-aided design models of the work-piece are used to generate a set of discrete two-dimensional (2D) hierarchical views matching libraries. In the online dynamic library updating and selection process, the previous 3D location information of the work-piece is used to predict the following location range, and a discrete matching library with a small number of 2D hierarchical views is selected from dynamic local library for localization. Then, the work-piece is localized with high-precision and real-time speed in the 3D work-piece localization process.

The method is suitable for the texture-less work-pieces in industrial applications.

The small range of the library enables a real-time matching. Experimental results demonstrate the high accuracy and high efficiency of the proposed method.

This study aims to address the challenge of automatic guided vehicle (AGV) scheduling for parcel storage and retrieval in an intelligent warehouse.

This study presents a scheduling solution that aims to minimize the maximum completion time for the AGV scheduling problem in an intelligent warehouse. First, a mixed-integer linear programming model is established, followed by the proposal of a novel genetic algorithm to solve the scheduling problem of multiple AGVs. The improved algorithm includes operations such as the initial population optimization of picking up goods based on the principle of the nearest distance, adaptive crossover operation evolving with iteration, mutation operation of equivalent exchange and an algorithm restart strategy to expand search ability and avoid falling into a local optimal solution. Moreover, the routing rules of AGV are described.

By conducting a series of comparative experiments based on the actual package flow situation of an intelligent warehouse, the results demonstrate that the proposed genetic algorithm in this study outperforms existing algorithms, and can produce better solutions for the AGV scheduling problem.

This paper optimizes the different iterative steps of the genetic algorithm and designs an improved genetic algorithm, which is more suitable for solving the AGV scheduling problem in the warehouse. In addition, a path collision avoidance strategy that matches the algorithm is proposed, making this research more applicable to real-world scheduling environments.

The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.

Aiming at the lack of explanatory power of traditional industrial organization theory in cross-border competition, by introducing the idea of ecological niche, the authors aim to explore the competitive situation of platform-based enterprises when they operate in multiple fields.

With the help of ecological niche theory, construct the niche width and niche overlap index of typical enterprises in the platform economy, and find out the advantages and the intensity of competition through comparative analysis.

In an environment of cross-border competition, large enterprises have significant competitive advantages, and the fierce competition is concentrated among medium-sized enterprises.

The conclusions of this paper not only provide new insights for explaining the phenomenon of cross-border competition in the platform economy, but also provide theoretical reference for the anti-trust enforcement practice in the platform economy.

This chapter presents a theoretical framework of the industrial relations (IR) system in China’s coal mining industry, combining the roles of management organizations, workers, and trade unions, as well as government agencies. It is one of the first empirical attempts to investigate the relationship between human resource (HR) practices, labor relations, and occupational safety in China’s coal mining industry over the past 60 years, based on the secondary data on coal mining accidents and case studies of two state-owned coal mines in a northern city in Anhui Province, China. The fluctuating occupational safety has been affected by government regulations over different time spans, marked by key political agendas, and by coal mining firms taking concrete measures to respond to these regulations, while exhibiting differing safety performance in state-owned versus township-and-village-owned mines. The field studies compared a safety-oriented to a cost-control-oriented HR and labor relations system, and their influences on safety performance. Coal mining firms and practitioners are advised to shift the traditional personnel management paradigm to a modern HR management system. In addition, although workers are often blamed directly for accidents, it is suggested that workers’ participation and voice in various processes of decision-making and policy implementation, and trade unions’ active involvement in protecting workers from occupational hazards, be encouraged.

The purpose of this paper is to explore what professional knowledge and competence (PKC) that knowledgeable others, namely, mathematics teaching research specialists (MTRS) in China, need to know, and how they may develop their PKC.

This study adopts mixed methods. A survey on PKC with 549 MTRS is utilized to examine MTRS’ perceived held and ideal PKC and perceived effective ways of developing their PKC. The responses to the open-ended questions on the survey were used to identify additional dimensions of PKC and ways of developing PKC. Multiple techniques of quantitative data analysis were employed to feature the characteristics of PKC and structure of the survey, and the relationship between background variables and perception of PKC. Collectively, this study paints a rich and comprehensive picture about Chinese knowledgeable others’ knowledge and competence, and its development.

The data analysis reveals that the participants appreciated the six-dimension model of MTRS’ PKC. They were confident with their PKC in general, but varied in different aspects. The factor analysis showed the six-dimension model could be further clustered into two components: knowledge about mathematics teaching and learning and competence in mentoring and educational leadership, and knowledge about content, assessing student learning, and use of technology. The participants perceived their learning through multiple ways including: learning through reading, attending specific training programs, attending and mentoring teaching research activities both school-based and across regions, observing and debriefing lessons, sharing within online learning communities. All these venues jointly contribute to developing MTRS’ PKC.

This is the first empirical study on MTRS’ PKC and its development in China based on such a large sample. The findings of this study not only contribute to an understanding of knowledgeable others in Chinese lesson study and providing suggestions for support of their development, but also provide implications for studies of practice-based mathematics teacher-educators globally.