Jiaojiao Qu, Mingwei Liu, Shuming Zhao, Yixuan Zhao and Xia Cao
The function of cognitive diversity has not yet been studied to a sufficient degree. To address this gap, the current study aims to answer the questions of how and when team…
Abstract
Purpose
The function of cognitive diversity has not yet been studied to a sufficient degree. To address this gap, the current study aims to answer the questions of how and when team cognitive diversity fosters individual creativity by integrating the intellectual capital view and the inclusion literature.
Design/methodology/approach
With a paired and time-lagged sample consisting of 368 members and 46 leaders from Chinese high-tech organizations, a multilevel moderated mediation model was developed to test the hypothesized relationships using structural equation modeling.
Findings
Team cognitive diversity is positively related to individual creativity via team intellectual capital, but this positive indirect effect is obtained only when the inclusive team climate is high.
Research limitations/implications
Team intellectual capital serves as an alternative mechanism for translating team cognitive diversity into favorable outcomes, and an inclusive team climate plays a pivotal role in harvesting the benefits of team cognitive diversity. Future research could extend our study by adopting a multiwave longitudinal or experimental design, examining the possibility of curvilinearity, considering the changes in patterns over time, and conducting cross-cultural studies.
Practical implications
Managers should take the initiative to assemble a team featuring cognitive diversity when facing creative tasks, and should proactively cultivate an inclusive culture when leading such a team.
Originality/value
This study is among the first to consider the mediating role of team intellectual capital in the cross-level effect of team cognitive diversity on individual creativity and to examine the boundary role of an inclusive team climate with respect to this indirect effect.
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Tingxi Wang, Boming Yu, Mingwei Liu and Yue Zhou
The primary purpose of this study is to investigate the relationship between leader bottom-line mentality (BLM) and employee innovative behavior, which may be interpreted by…
Abstract
Purpose
The primary purpose of this study is to investigate the relationship between leader bottom-line mentality (BLM) and employee innovative behavior, which may be interpreted by employees’ perceived creativity expectations and moderated by employee time orientation.
Design/methodology/approach
A multi-wave and multi-source questionnaire survey with 259 paired Chinese employee–leader dyads provided data to test the theoretical model. Hypotheses were tested with Statistical Package for the Social Sciences (SPSS).
Findings
Consistent with hypotheses, leader BLM reduces employees’ perceived creativity expectations and thus inhibits employees’ innovative behavior, and this effect is stronger for employees with short-term orientation.
Practical implications
Our findings highlight the negative influences of leader BLM on innovative behavior and the buffering role of employees’ long-term orientation. Organizations may incorporate BLM in leadership promotion and evaluation and provide corresponding training for leaders to overcome BLM. In addition, long-term orientation can be a valuable indicator in employee recruitment and selection.
Originality/value
This study contributes to a new theoretical perspective of the Pygmalion effects for understanding leader BLM’s influence on employee innovative behavior.
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Jiaqi Liu, Jialong Jiang, Mingwei Lin, Hong Chen and Zeshui Xu
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are…
Abstract
Purpose
When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.
Design/methodology/approach
First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.
Findings
The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.
Originality/value
We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.
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Weixing Wang, Yixia Chen and Mingwei Lin
Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after…
Abstract
Purpose
Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.
Design/methodology/approach
To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.
Findings
To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.
Originality/value
This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.
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Junior Polo Salinas, Jairo Jhonatan Marquina Araujo and Marco Antonio Cotrina Teatino
This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering…
Abstract
Purpose
This study aims to provide a comprehensive review of the existing literature on uncertainty in underground mining operations, using a bibliometric and systematic analysis covering the period from 1975 to 2024.
Design/methodology/approach
To achieve this, the following questions were addressed using a mixed-method approach involving bibliometrics, text mining and content analysis: How has the field of uncertainty research in underground mining operations evolved? What are the most prominent research topics and trends in uncertainty in underground mining operations? and What are the possible directions for future research on uncertainty in underground mining operations?
Findings
As a result, bibliometric networks of 327 journal articles from the Scopus database were created and examined, the main research topics were underground mining management; rock mechanics; operational optimization; and stochastic systems. Finally, the inclusive investigation of uncertainty in underground mining operations and its prominent patterns can serve as a basis for real-time direction for new research and as a tool to improve underground mining activities by implementing advanced technology for innovative practices and optimizing operational efficiency. This is fundamental to identify unknown variables that impair the planning, operation, safety and economic viability of underground mines.
Originality/value
This research is 100% original because there is no review research on the uncertainty present in underground mining operations.