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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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

This study contributes to a new theoretical perspective of the Pygmalion effects for understanding leader BLM’s influence on employee innovative behavior.

This paper examines the relationship between human resource practices in 173 hospitals in the United Kingdom and four organizational outcome categories – clinical, financial, employee attitudes and perceptions, and patient attitudes and perceptions. The overarching proposition set forth and examined in this paper is that human resource management (HRM) practices and delivery of care practices have varied effects on each of these outcomes. More specifically, the authors set forth the proposition that specific practices will have positive effects on one outcome category while simultaneously having a negative effect on other performance outcomes, broadly defined.

The paper introduces a broader stakeholder framework for assessing the HR–performance relationship in the healthcare setting. This multi-dimensional framework incorporates the effects of human resource practices on customers (patients), management, and frontline staff and can also be applied to other sectors such as manufacturing. This approach acknowledges the potential for incompatibilities between stakeholder performance objectives. In the healthcare industry specifically, our framework broadens the notion of performance.

Overall, our results provide support for the proposition that different stakeholders will be affected differently by the use of managerial practices. We believe that the findings reported in this paper highlight the importance of examining multiple stakeholder outcomes associated with managerial practices and the need to identify the inherent trade-offs associated with their adoption.

Volume 17 of Advances in Industrial and Labor Relations (AILR) contains seven diverse, provocative and perhaps in some cases controversial papers. Preliminary versions of several of these papers were presented at Advances in Industrial Relations/Labor and Employment Relations Association ‘Best Papers’ sessions held at the 2008 and 2009 meetings of the Labor and Employment Relations Association.

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.

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.

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.

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.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

The purpose of this study is to explore a sustainable hierarchical framework for the business environment in smart cities. However, this hierarchical framework must consider unnecessary attributes and interrelationships between criteria to capture the difference between smart cities and traditional cities.

Hence, the fuzzy set theory is used for screening unnecessary attributes, the decision-making and trial evaluation laboratory (DEMATEL) is applied to manage the complex interrelationships among the aspects and attributes and interpretive structural modeling (ISM) is used to divide the hierarchy and construct a hierarchical theoretical framework. Ultimately, this research is applied to develop a sustainable hierarchical framework of the business environment in smart cities.

The results show that traditional social problems are still at the core of business environment development in smart cities, new smart opportunities may be discovered, but they are still limited by traditional social factors, the economy is still the main aspect of the business environment and there are still obstacles to solving social problems with smart technologies.

This theoretical hierarchical framework aims to guide smart cities toward sustainability. This study also proposes creating a predictable business environment by improving administrative efficiency, transparency, social mobility and infrastructure services and cultivating new business opportunities with intelligent technology.