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X-ray pulsar navigation (XPNAV) is an autonomous celestial navigation technology for deep space missions. The error in the pulse time of arrival used in pulsar navigation is large for various practical reasons and thus greatly reduces the navigation accuracy of spacecraft near the Earth and in deep space. This paper aims to propose a novel method based on ranging information that improves the performance of XPNAV.

This method replaces one pulsar observation with a satellite observation. The ranging information is the difference between the absolute distance of the satellite relative to the spacecraft and the estimated distance of the satellite relative to the spacecraft. The proposed method improves the accuracy of XPNAV by combining the ranging information with the observation data of two pulsars.

The simulation results show that the proposed method greatly improves the XPNAV accuracy by 70% compared with the conventional navigation method that combines the observations of three pulsars. This research also shows that a larger angle between the orbital plane of the satellite and that of the spacecraft provides higher navigation accuracy. In addition, a greater orbital altitude difference implies higher navigation accuracy. The position error and ranging error of the satellite have approximately linear relationships with the navigation accuracy.

The novelty of this study is that the satellite ranging information is integrated into the pulsar navigation by using mathematical geometry.

Considering strategic information sharing (SIS), this paper aims to develop a better understanding of how relation-specific investments (RSIs) influence cooperative innovation performance (CIP) in downstream channel relationships. Also examined was the moderating effect of relational trust in that the indigenous practice of guanxi is especially critical in China.

Data were collected through a questionnaire in Chinese high-tech industries, with a valid response from 310 companies. A hierarchical regression analysis was used to test the conceptual model and hypotheses, combining mediation and moderation analysis.

Results show that the influences of specific investments vary according to the specificity dimensions examined. Specifically, human RSI influences CIP and SIS most significantly, and the impact of procedural RSI is, relatively, the weakest. Relational trust’s moderating role is confirmed, and SIS plays a partially mediating role in enhancing vertical cooperative innovation.

Managers should know clearly different roles of RSIs in inter-firm cooperative innovation and prioritize human RSI and brand RSI when investing into channels. More importantly, the findings reveal that strategy-level information sharing should be valued more. It is also recommended that relational ties are vital, especially in Chinese business context.

To the best of the authors’ knowledge, this paper is among the first few to investigate how the effects of disaggregated RSIs in inter-firm cooperative innovation vary and the importance of SIS in vertical relationships. The results provide insightful guidance for researchers and managers in how to better manage RSIs to improve CIP.

Drawing on the wisdom of ancient Chinese philosopher Xunzi, this paper aims to present a novel mechanism for governing opportunism, referred to as “cultivational governance.” By examining the role of artificial intelligence (AI) resources possessed by e-commerce platforms, the authors explore how these resources contribute to mitigating seller opportunism. The central hypothesis of this study posits that two distinct types of AI resources, namely, AI technology resources and AI human resources, serve as crucial factors in curbing seller opportunism. Furthermore, the authors propose that platform digital empowerment and value cocreation act as mediating variables linking AI resources to opportunism.

Based on the resource-based view and resource orchestration theory, the authors developed a framework and tested it using survey data from sellers. This framework encompasses five key variables: e-commerce platform’s AI technology resources, AI human resources, platform digital empowerment, value cocreation and seller opportunism. Regression analysis was used for data analysis.

The empirical results validate the effectiveness of cultivational governance mechanisms, as both AI resources effectively suppress seller opportunism through digital empowerment and value cocreation. Specifically, e-commerce platforms’ AI technology resources significantly promote value cocreation and platform digital empowerment, while AI human resources primarily contribute to platform digital empowerment. Although platform digital empowerment encourages value cocreation, its direct impact on reducing seller opportunism was not supported. Notably, value cocreation negatively affects seller opportunism.

The present research mainly contributes to the marketing channel governance literature by introducing a new approach to inhibit opportunism, namely, the cultivational governance mechanism.

Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both technological and human – possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual).

Based on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform’s AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique.

The empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively.

This study contributes to the existing literature by exploring how e-commerce platforms’ AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.

In this paper, extending the research on the positive effects of marketing channel conflicts, this paper aims to examine how functional and dysfunctional conflicts influence channel innovation capability by triggering channel cohesion and investigate the moderating role of a distributor’s network structure.

Based on social network theory and Coser’s conflict theory, this paper develops a framework, tested using Chinese manufacturers’ data, which incorporated six key variables, namely, functional conflict, dysfunctional conflict, channel cohesion, channel innovation capability, network density and network centrality.

The empirical results revealed that functional conflict can arouse channel cohesion and that distributor network density and centrality positively moderates this relationship; dysfunctional conflict negatively affects channel cohesion, but distributor network density negatively moderates this relationship; channel cohesion had a mediating effect on the relationship between channel conflict and channel innovation capability.

This paper contributes to the research on channel conflict by incorporating the entire channel system’s innovation capability as a positive consequence of channel conflict and expands the channel conflict literature that adopts a network structure perspective.

Consumption volatility is a key source of economic growth volatility; thus, it is an important factor in designing macroeconomic policy. The purpose of this paper is to investigate the factors that determine household consumption volatility, using urban household survey (UHS) data over the period 2002–2009 in 18 provinces in China.

Both a traditional variance decomposition method and an advanced variance decomposition method are used.

The traditional variance decomposition method suggests that heterogeneity of consumption goods is the key to analyze consumption volatility in China. Consumption of transportation makes the highest aggregate contribution and per-unit volatility in consumption volatility, whereas consumption of food makes the second highest aggregate contribution and the lowest per-unit volatility. Further investigation with the advanced variance decomposition method, which allows the authors to capture intertemporal dynamics and cross-household differences simultaneously, finds that the main factor determining the consumption volatility in China is intertemporal dynamics, rather than cross-household differences.

Future research could fruitfully explore four issues. First, consumption upgrading has increased the volatility of China’s household consumption. How much will this affect economic growth in China under its “new normal” conditions, and how should the Chinese government respond? Second, differences between UHS data and aggregate data in the calculations of consumption risk sharing need to be investigated. Third, it is important to investigate the channels through which the Chinese government can enhance its ability to spread consumption risks and thus reduce consumer consumption volatility. Finally, further study could extend the current 18 provinces to a nation-wide sample and update the data beyond 2009 to estimate the impact of the global financial crisis.

The results suggest that when policy makers design macroeconomic policies to smooth consumption volatility, they should consider heterogeneity in household consumption goods, regional disparity and intertemporal dynamics simultaneously. Well-managed volatility of Chinese household consumption can contribute to a stable economic growth in China and the world.

Well-managed volatility of Chinese household consumption can contribute to a stable economic growth in China and the world.

This paper fills this gap by using China’s UHS data to assess consumption volatility from the perspectives of heterogeneity in household consumption goods, cross-household differences and intertemporal dynamics. We make three contributions to the literature. The first contribution of this paper consists of demonstrating the contributions of heterogeneity in household consumption goods to consumption volatility. The second contribution consists of using the advanced variance decomposition method proposed by Crucini and Telmer (2012). This decomposition methodology allows the authors to examine whether household consumption volatility is due to cross-household differences or intertemporal dynamics. The third contribution is that this paper takes Chinese residents’ consumption fluctuations as the starting point to analyze the impact of consumption fluctuations on the future trend of China’s economy.

Learning as the way in which labor acquire new knowledge and skills has important strategic implications for the competitive advantage of an organization. The purpose of this paper is to present an agent-based modeling (ABM) approach to investigate the learning behavior of workers. The effect of interactions among different workers as well as the factors affecting the workers’ learning behavior is assessed using the proposed ABM approach.

For this purpose, the processes through which the competency value of worker is changed are understood and the workers’ learning behavior is modeled, taking account of various influencing factors such as knowledge flow, social ability to teach and forgetting factor.

The proposed model is implemented on a real steel structure project to evaluate its applicability and performance. The variation in the competency value of different workers involved in the project is simulated over time taking account of all the influencing factors using the proposed ABM approach.

In order to assess the effect of interactions among welders as well as the welders’ characteristics on their learning behavior, the competence value of different welders is evaluated.

This research presents an ABM approach to investigate the workers’ learning behavior. To evaluate the performance of the proposed ABM approach, it was implemented on a real steel structure project. The learning behavior of different welders (agents) was simulated taking account of their interactions as well as the factors affecting the welders’ learning behavior. The project involved the welding of a 240-ton steel structure. The initial project duration was estimated as 100 days. In this project, it has been planned to execute the welding process using three different welders namely welder A, B and C.