Ahmed Mohammed Sayed Mostafa, Suhaer Yunus, Wee Chan Au and Ziming Cai
Not much is known about the conditions under which the negative relationship between co-worker undermining and employee outcomes may wax or wane. This study seeks to address this…
Abstract
Purpose
Not much is known about the conditions under which the negative relationship between co-worker undermining and employee outcomes may wax or wane. This study seeks to address this issue by analysing the role of leadership in mitigating the negative impact of co-worker undermining on employee outcomes. Drawing on expectancy violation theory (EVT), the study proposes that servant leadership will alleviate the association between co-worker undermining, emotional exhaustion and consequently organisational commitment.
Design/methodology/approach
Two-wave time-lagged data were collected from a sample of 345 nurses working under 33 supervisors in a large public hospital in Malaysia. To account for the nested nature of the data, generalised multilevel structural equation modeling (GSEM) in STATA was used to test the hypotheses.
Findings
After controlling for transformational leadership, co-worker undermining was indirectly related to organisational commitment via emotional exhaustion, and this indirect relationship was weaker when servant leadership was high.
Practical implications
Organisations need to invest in interventions that help reduce co-worker undermining and put emphasis on promoting servant leadership.
Originality/value
The study extends the literature by introducing EVT as a new theoretical lens to analyse the consequences of co-worker undermining on employee outcomes. The study also addresses calls for research on the role of leadership in ameliorating the negative consequences of co-worker undermining.
Details
Keywords
Yu Liu and Ziming Zeng
Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features…
Abstract
Purpose
Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features for multimodal information effectively. To address these limitations, this study proposes a novel multimodal sarcasm detection model based on the directed graph isomorphism network with sentiment enhancement and multimodal fusion (DGIN-SE-MF).
Design/methodology/approach
The approach extracts image and text features through vision transformer and BERT, respectively. To deeply integrate the extracted features, the author develops a text-guided multi-head attention fusion mechanism module. Subsequently, a directed graph is constructed through SE and the multimodal factorized bilinear pooling method to integrate image features into the graph. The DGIN then fuses the image and text features, using a weighted attention mechanism to generate the final representation.
Findings
The model is validated on three datasets: English, Chinese and an Indonesian–English dataset. The results demonstrate that the proposed model consistently outperforms other baseline models, particularly on the Chinese and English sarcasm datasets, achieving F1 scores of 88.75 % and 83.10 %, respectively.
Originality/value
The proposed model addresses the inadequacies of previous methods by effectively integrating emotional cues and image features into sarcasm detection. To the best of the authors’ knowledge, this is the first work to leverage a DGIN-SE-MF for this task, leading to significant improvements in detection performance across different languages.
Details
Keywords
Since smartphones became ubiquitous, online grocery and food purchases through take-away delivery platforms have steadily increased in China. Nevertheless, whether the development…
Abstract
Purpose
Since smartphones became ubiquitous, online grocery and food purchases through take-away delivery platforms have steadily increased in China. Nevertheless, whether the development of take-away delivery can ameliorate urban–rural wage inequality still requires further analysis. The purpose of this paper is to clarify whether this positive effect exists.
Design/methodology/approach
This paper makes estimations based on city and individual levels combining the Chinese Household Income Project (CHIP) 2008, CHIP 2013, CHIP2018 survey data and the take-away delivery site data. At the city level, the Oaxaca-Blinder (O-B) decomposition method is employed to construct wage inequality index of urban and rural labors. At the individual level, this paper analyzes urban–rural wage differentials with high or low formal education level.
Findings
The rapid establishment of take-away delivery sites has resulted in an increase of 52.425 yuan on average in the annual wage of rural labors with low formal education level. When the cumulative number of sites increases by 1 unit, the annual wage inequality index decreases by 0.007 on average. Labors with the characteristics of rural household registration and low education can enjoy more dividends. Through inter-/within-industry decomposition, this paper elaborates formal education, age and cross-industry transfer as the main factors for the improvement of urban–rural wage inequality. Narrowing effect of wage differences between different groups in multiple sample slices also contributes to the mechanism analyses.
Originality/value
To the best of the author’s knowledge, this paper is the first to analyze the impact of take-away delivery development on the urban–rural wage inequality from the perspective of the establishment of take-away delivery sites. This empirical study will enrich the existing theoretical perspectives on urban–rural divide under the emergence of new forms of employment. The results indicate that new forms of employment represented by take-away delivery can not only promote economic growth, but also eliminate urban–rural inequality.
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Keywords
Qingqing Li, Ziming Zeng, Shouqiang Sun and Tingting Li
Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the…
Abstract
Purpose
Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.
Design/methodology/approach
The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.
Findings
Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.
Originality/value
This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.
Details
Keywords
Juan Wu, Ziming Kou and Gongjun Cui
The purpose of this paper is to prepare carbon fiber-reinforced polyimide matrix composites and to investigate the single role of carbon fiber in polyimide composites on…
Abstract
Purpose
The purpose of this paper is to prepare carbon fiber-reinforced polyimide matrix composites and to investigate the single role of carbon fiber in polyimide composites on tribological performance under distilled water condition.
Design/methodology/approach
Three carbon fiber-reinforced polyimide matrix composites were fabricated by using a hot press molding technique. The tribological behaviors of carbon fiber-reinforced polyimide matrix composites sliding against steel ball were evaluated with a ball-on-disk tribotester under distilled water condition. Meanwhile, the effect of different length of carbon fiber on the wear resistance of polyimide matrix composites was investigated during the sliding process.
Findings
The friction coefficients and specific wear rates of polyimide composites containing 100 μm carbon fibers were lower than those of other specimens. The wear mechanism of carbon fiber-reinforced composites was delamination under distilled water condition. The interfacial combination between the carbon fiber and matrix became worse with the increase of length of carbon fiber.
Originality/value
This paper reported the effect of the different length of carbon fiber on polyimide matrix composites to prepare mechanical parts in mining industrial fields.
Details
Keywords
Ziming Zhou, Fengnian Zhao and David Hung
Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine…
Abstract
Purpose
Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.
Design/methodology/approach
To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.
Findings
The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.
Originality/value
The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.
Details
Keywords
Junyi Chen, Buqing Cao, Zhenlian Peng, Ziming Xie, Shanpeng Liu and Qian Peng
With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application…
Abstract
Purpose
With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.
Design/methodology/approach
In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.
Findings
Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.
Originality/value
In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.
Details
Keywords
Ziming Zeng, Yu Shi, Lavinia Florentina Pieptea and Junhua Ding
Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects…
Abstract
Purpose
Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.
Design/methodology/approach
First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system
Findings
Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.
Originality/value
This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.
Details
Keywords
Zhirui Wang, Yezhuo Li, Bo Su, Lei Jiang, Ziming Zhao and Yan-An Yao
The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable…
Abstract
Purpose
The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable working space and eliminates the engineering difficulties caused by the multilevel extension compared with liner actuators. Furthermore, the rolling locomotion is improved to reduce displacement error based on dynamics analysis.
Design/methodology/approach
The main body of deforming mechanism with a tetrahedral exterior shape is composed of four vertexes and six RRR chains. The mobile robot can achieve the rolling locomotion and reach any position on the ground by orderly driving the rotation actuators. The global kinematics of the mobile modes are analyzed. Dynamics analysis of the robot falling process is carried out during the rolling locomotion, and the rolling locomotion is improved by reducing the collision impulse along with the moving direction.
Findings
Based on global kinematics analysis of TMRR, the robot can realize the continuous mobility based on rolling gait planning. The main cause of robot displacement error and the corresponding improvement locomotion are gained through dynamic analysis. The results of the theoretical analysis are verified by experiments on a physical prototype.
Originality/value
The work introduced in this paper is a novel exploration of applying the mechanism with only revolute joints to the field of tetrahedral rolling robots. It is also an attempt to use the improved rolling locomotion making this kind of mobile robot more practical. Meanwhile, the reasonable engineering structure of the robot provides feasibility for load carrying.
Details
Keywords
Rua-Huan Tsaih, James Quo-Ping Lin and Yu-Chien Chang
Service innovation, ICT-enabled services, museum, cultural and creative industries.
Abstract
Subject area
Service innovation, ICT-enabled services, museum, cultural and creative industries.
Study level/applicability
Graduate-level courses of “Innovation Management,” “Service Innovation,” or “Cultural and Creative Industries”.
Case overview
In 2006, the National Palace Museum (NPM) in Taipei, Taiwan, announced its new vision “Reviving the Charm of an Ancient Collection and Creating New values for Generations to Come”. In recent years, the NPM has been shifting its operational focus from being object-oriented to being public-centered, and the museum has held not only the physical forms of artifacts and documents but also their digital images and metadata. These changes would inject new life into historical artifacts. In addition, archives as its collections would be given a refreshingly new image to the public and become connected with people's daily lives. Among these endeavors for displaying historical artifacts online and prevailing Chinese culture in the modern age, the key issues are related to digital technology applications and service innovations. The service innovations would be further divided into information and communication technologies (ICT)-enabled ones and non-ICT-enabled ones. These shifts clearly claim that adopting digital technologies and innovative services can bring positive impacts to the museum. The NPM administrative team wants to keep infusing life into ancient artifacts and texts, sustaining curiosities of the public for Chinese culture and history, and invoking their interests to visit the NPM in person. However, to develop for the future while reviewing the past, the NPM administrative team has to meditate on the next steps in terms of implementation of service innovations.
Expected learning outcomes
Students will learn motivations of digital establishment and service innovations from the organization perspective and the necessities of technological implementation. Students will understand the difference in innovations between ICT-enabled services and non-ICT-enabled services. Students would be able to understand the process of developing a new service. Students will be aware of challenges the organization would face in developing a new service.
Supplementary materials
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