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1 – 10 of 13
Article
Publication date: 10 March 2023

Jiandong Lu, Xiaolei Wang, Liguo Fei, Guo Chen and Yuqiang Feng

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational…

Abstract

Purpose

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

Design/methodology/approach

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

Findings

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

Originality/value

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 26 March 2024

Feng Wang, Rong Fu, Fu Yang and Ren Yingwei

Although the targets of envy have received increasing attention in management research, how envied employees respond to envy remains ambiguous and merits further investigation…

Abstract

Purpose

Although the targets of envy have received increasing attention in management research, how envied employees respond to envy remains ambiguous and merits further investigation. Drawing upon regulatory focus theory, this paper aims to reconcile these inconsistent findings by developing and testing a model that elucidates how different types of being envied (i.e. benignly or maliciously) can elicit either favorable or unfavorable motivational and behavioral reactions.

Design/methodology/approach

An experience sampling study was conducted on 131 employees across 10 consecutive workdays in China. Focusing on within-person effects, multilevel mediation models using multilevel structural equation modeling were applied.

Findings

Results indicated that on days when employees are benignly envied, they engage in more organizational citizenship behavior (OCB) due to increased daily promotion focus. On the contrary, on days when employees are maliciously envied, they participate in more counterproductive work behavior (CWB) due to decreased daily promotion focus.

Practical implications

Organizations and managers should take a more holistic view of workplace envy when considering that envied employees may use OCB to deal with benign envy. Conversely, considering that CWB may emerge from employees who are maliciously envied, it is crucial for managers to be vigilant in discouraging and addressing malicious envy in the workplace.

Originality/value

This paper takes an initial foray into incorporating the concepts of benign envy and malicious envy into the literature on being envied and provides a novel perspective to explain why being envied can lead to both functional and dysfunctional responses.

Details

Nankai Business Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-8749

Keywords

Article
Publication date: 21 November 2023

Hua Pan and Rong Liu

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the…

Abstract

Purpose

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

Design/methodology/approach

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Findings

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

Originality/value

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

Highlights

  1. The stability of electricity consumption is important to the stable operation of the grid.

  2. An improved FP-growth algorithm is employed to explore the influencing factors.

  3. The improved algorithm enables the mining of rules containing specific attribute labels.

  4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

Details

Management of Environmental Quality: An International Journal, vol. 35 no. 3
Type: Research Article
ISSN: 1477-7835

Keywords

Article
Publication date: 17 April 2023

Xiangou Zhang, Yuexing Wang, Xiangyu Sun, Zejia Deng, Yingdong Pu, Ping Zhang, Zhiyong Huang and Quanfeng Zhou

Au stud bump bonding technology is an effective means to realize heterogeneous integration of commercial chips in the 2.5D electronic packaging. The purpose of this paper is to…

Abstract

Purpose

Au stud bump bonding technology is an effective means to realize heterogeneous integration of commercial chips in the 2.5D electronic packaging. The purpose of this paper is to study the long-term reliability of the Au stud bump treated by four different high temperature storage times (200°C for 0, 100, 200 and 300 h).

Design/methodology/approach

The bonding strength and the fracture behavior are investigated by chip shear test. The experiment is further studied by microstructural characterization approaches such as scanning electron microscope, energy dispersive spectrometer and so on.

Findings

It is recognized that there were mainly three typical fracture models during the chip shear test among all the Au stud bump samples treated by high temperature storage. For solder bump before aging, the fracture occurred at the interface between the Cu pad and the Au stud bump. As the aging time increased, the fracture mainly occurred inside the Au stud bump at 200°C for 100 and 200 h. When aging time increased to 300 h, it is found that the fracture transferred to the interface between the Au stud bump and the Al Pad.

Originality/value

In addition, the bonding strength also changed with the high temperature storage time increasing. The bonding strength does not change linearly with the high temperature storage time increasing but decreases first and then increases. The investigation shows that the formation of the intermetallic compounds because of the reaction between the Au and Al atoms plays a key role on the bonding strength and fracture behavior variation.

Details

Microelectronics International, vol. 41 no. 2
Type: Research Article
ISSN: 1356-5362

Keywords

Abstract

Details

The Integrated Application of Effective Approaches in Supply Chain Networks
Type: Book
ISBN: 978-1-83549-631-2

Content available
Book part
Publication date: 8 April 2024

Amaresh Panda and Sanjay Mohapatra

Abstract

Details

The Online Healthcare Community
Type: Book
ISBN: 978-1-83549-141-6

Article
Publication date: 22 March 2024

Rongxin Chen and Tianxing Zhang

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation…

Abstract

Purpose

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

Design/methodology/approach

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

Findings

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

Originality/value

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

Details

Chinese Management Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-614X

Keywords

Article
Publication date: 4 December 2023

Yang Liu, Xin Xu, Shiqing Lv, Xuewei Zhao, Yuxiong Xue, Shuye Zhang, Xingji Li and Chaoyang Xing

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the…

44

Abstract

Purpose

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

Design/methodology/approach

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Findings

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

Originality/value

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

Details

Soldering & Surface Mount Technology, vol. 36 no. 2
Type: Research Article
ISSN: 0954-0911

Keywords

Open Access
Article
Publication date: 19 March 2024

Jiayuan Zhao, Hong Huo, Sheng Wei, Chunjia Han, Mu Yang, Brij B. Gupta and Varsha Arya

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood…

Abstract

Purpose

The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.

Design/methodology/approach

This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.

Findings

The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.

Originality/value

This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.

Details

British Food Journal, vol. 126 no. 13
Type: Research Article
ISSN: 0007-070X

Keywords

Article
Publication date: 17 July 2023

Anaile Rabelo, Marcos W. Rodrigues, Cristiane Nobre, Seiji Isotani and Luis Zárate

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

Abstract

Purpose

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

Design/methodology/approach

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

Findings

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

Originality/value

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

Details

Information Discovery and Delivery, vol. 52 no. 2
Type: Research Article
ISSN: 2398-6247

Keywords

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