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This paper aims to examine the effects of task and relationship conflicts on team creativity, and the moderating role of shared leadership in inter-organizational teams. An inter-organizational team normally comprises employees from collaborated organizations brought together to conduct an initiative, such as product development. Practitioners and researchers have witnessed the prevalence of conflict in inter-organizational teams. Despite significant scholarly investigation into the importance of conflict in creativity, a deep theoretical understanding of conflict framework remains elusive.

A questionnaire survey was conducted in China to collect data. Consequently, 54 teams, which comprised 54 team managers and 276 team members, were deemed useful for the study.

By testing our hypotheses on 54 inter-organizational teams, we found that relationship conflict has a negative relationship with team creativity, whereas task conflict has an inverted U-shaped (curvilinear) relationship with team creativity. Furthermore, when shared leadership is stronger, the negative relationship with team creativity is weaker for relationship conflict, whereas the inverted U-shaped relationship with team creativity is stronger for task conflict.

The main limitation is cross-sectional, which cannot establish causality in relationships. Despite this potential weakness, the present research provides insights into conflict, leadership and inter-organizational collaboration literature.

The findings of this study offer some guidance on how managers can intervene in the conflict situations of inter-organizational teams.

Managers are struggling to identify ways to effectively manage team conflict when a team of diverse individuals across organizational boundaries are brought together to solve a problem. The findings of this study offer some guidance on how managers can intervene in the conflict situations of inter-organizational teams.

This paper provides understandings about how relationship and task conflicts affect team creativity in inter-organizational teams.

This paper aims to examine the moderating effects of job alternatives and policy support on the relationship between job satisfaction and turnover intention.

A questionnaire survey was conducted in China. The study sample consisted of employees from organizations of different sizes, ownerships and industry types. Finally, 462 valid questionnaires were obtained.

Cognitive job satisfaction has a stronger negative effect on turnover than affective job satisfaction, and both effects depend on the factors related to ease of movement. Cognitive job satisfaction is more effective when job alternative is low and policy support is high, whereas affective job satisfaction leads to lesser turnover when job alternative is high and policy support is low.

First, the demography of the respondents may have limited the generalizability of our findings. Second, this study has the limitation common to all cross-sectional studies. Third, this study focuses on turnover intention of employees rather than actual turnover rates. Finally, although the authors have identified specific factors related to ease of movement as the moderators by drawing upon the organizational equilibrium theory and current HRM literature, there may be other moderators that can affect the relationship between job satisfaction and turnover.

HRM managers should apply organizational HRM to the local institutional environment, especially to the human resource policies of local governments, which vary significantly across regions in China.

HRM managers should be very cautious to approach career development task in China, especially when they have an attitude of whatever works in mature economies will surely work in organizations in Chinese society.

The findings extend previous career development literature that assumes unconditional effects of job satisfaction on turnover intention. With the objective of exploring the effects of conditional factors, the current study explores the special role of job alternatives and policy support in the job satisfaction – turnover relationship in the context of China. Additionally, the findings provide support for the application of organizational equilibrium theory in the context of China.

Robot vision is a fundamental device for human–robot interaction and robot complex tasks. In this paper, the authors aim to use Kinect and propose a feature graph fusion (FGF) for robot recognition.

The feature fusion utilizes red green blue (RGB) and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and word embedding method to enhance the recognition results.

The authors also collect DUT RGB-Depth (RGB-D) face data set and a benchmark data set to evaluate the effectiveness and efficiency of this method. The experimental results illustrate that FGF is robust and effective to face and object data sets in robot applications.

The authors first utilize Jaccard similarity to construct a graph of RGB and depth images, which indicates the similarity of pair-wise images. Then, fusion feature of RGB and depth images can be computed by the Extended Jaccard Graph using word embedding method. The FGF can get better performance and efficiency in RGB-D sensor for robots.

To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.

Stock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and viable.

This study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+Attention.

Experimental results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature models.

The study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

The purpose of this paper is to promote the efficiency of RGB-depth (RGB-D)-based object recognition in robot vision and find discriminative binary representations for RGB-D based objects.

To promote the efficiency of RGB-D-based object recognition in robot vision, this paper applies hashing methods to RGB-D-based object recognition by utilizing the approximate nearest neighbors (ANN) to vote for the final result. To improve the object recognition accuracy in robot vision, an “Encoding+Selection” binary representation generation pattern is proposed. “Encoding+Selection” pattern can generate more discriminative binary representations for RGB-D-based objects. Moreover, label information is utilized to enhance the discrimination of each bit, which guarantees that the most discriminative bits can be selected.

The experiment results validate that the ANN-based voting recognition method is more efficient and effective compared to traditional recognition method in RGB-D-based object recognition for robot vision. Moreover, the effectiveness of the proposed bit selection method is also validated to be effective.

Hashing learning is applied to RGB-D-based object recognition, which significantly promotes the recognition efficiency for robot vision while maintaining high recognition accuracy. Besides, the “Encoding+Selection” pattern is utilized in the process of binary encoding, which effectively enhances the discrimination of binary representations for objects.