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Article
Publication date: 13 September 2024

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…

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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.

Details

Leadership & Organization Development Journal, vol. 46 no. 1
Type: Research Article
ISSN: 0143-7739

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Article
Publication date: 23 December 2024

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…

14

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

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