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This paper aims to comment upon the governance systems of state‐owned enterprises in Shanghai and to consider the adequacy or otherwise of those governance structures.

The paper presents a study of existing governance arrangements and structures and considers areas for possible reform.

A sound corporate governance system is of great significance not only to the healthy development of enterprises but also for the enterprises to survive when they face the economic crisis. With the continuous spread of the world economic crisis, how to improve the corporate governance of state‐owned enterprises in Shanghai so that they can pass the financial crisis period safely has become one urgent and critical issue.

The study presents detail and reform areas for further reflection.

Enterprise resource planning (ERP) consultants have the expertise required to understand the specific contextual needs of an ERP client, implement tailored business processes that meet those needs, and ensure that no potential benefit offered by the ERP remains unexplored by the client. However, conflicts between ERP clients and consultants are a significant source of non-benefit realisation, making managing client–consultant agency crucial to ERP post-implementation benefits realisation. This paper aims to elucidate how managing client–consultant agency affects the benefits derived from ERP systems.

This paper uses microfinance institutions in 15 sub-Saharan African countries to explore different paths through which managing client–consultant agency leads to benefit realisation in ERP projects. It uses partial least squares structural equation modelling to analyse data from 127 managers and explains the results using insights from agency theory and the information system (IS) success model.

This paper reveals three routes through which contractual agreements and conflict resolution strategies lead to benefits realisation in ERP projects.

This is the first study that attempts to provide quantitative evidence of how managing the complex relationship between ERP project stakeholders affects ERP project success. It also contributes a novel theoretical model for ERP benefits realisation to complement existing research on ERP agency issues, critical success factors, and benefits realisation.

Glass is a promising interposer substrate for 2.5 D integration; yet detailed analysis of the interfacial reliability of through-glass vias (TGVs) has been lacking. The purpose of this paper is to investigate the design and material factors responsible for the interfacial delamination in TGVs and identify methods to improve reliability.

The interfacial reliability of TGVs is studied both analytically and numerically. An analytical solution is presented to show the dependence of the energy release rate (ERR) for interfacial delamination on the via design and the thermal mismatch strain. Then, finite element analysis (FEA) is used to investigate the influence of detailed design and material factors, including the pitch distance, via aspect ratio, via geometry and the glass and via materials, on the susceptibility to interfacial delamination.

ERR for interfacial delamination is directly proportional to the via diameter and the thermal mismatch strain. Thinner wafers with smaller aspect ratios show larger ERRs. Changing the via geometry from a fully filled via to an annular via leads to lower ERR. FEA results also show that certain material combinations have lower thermal mismatch strains, thus less prone to delamination.

The results and approach presented in this paper can guide the design and development of more reliable 2.5 D glass interposers.

This paper represents the first attempt to comprehensively evaluate the impact of design and material selection on the interfacial reliability of TGVs.

The purpose of this paper is to propose a approach for data visualization and industrial process monitoring.

A deep enhanced t-distributed stochastic neighbor embedding (DESNE) neural network is proposed for data visualization and process monitoring. The DESNE is composed of two deep neural networks: stacked variant auto-encoder (SVAE) and a deep label-guided t-stochastic neighbor embedding (DLSNE) neural network. In the DESNE network, SVAE extracts informative features of the raw data set, and then DLSNE projects the extracted features to a two dimensional graph.

The proposed DESNE is verified on the Tennessee Eastman process and a real data set of blade icing of wind turbines. The results indicate that DESNE outperforms some visualization methods in process monitoring.

This paper has significant originality. A stacked variant auto-encoder is proposed for feature extraction. The stacked variant auto-encoder can improve the separation among classes. A deep label-guided t-SNE is proposed for visualization. A novel visualization-based process monitoring method is proposed.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

This paper summarizes the severity of global warming, collaboration and endeavor within international government and the trend of international development for “energy-saving and emission reduction.” The Chinese government is enduring high pressure under the environment of “global warming” and “energy-saving and emission reduction” and it has made a policy for “energy-saving and emission reduction.” Based on this, we analyzed the possibility and feasibility for our logistics to “energy-saving and emission reduction,” then propose some solutions for our logistics industry to development and “energy-saving and emission reduction.”

The purpose of this paper is to integrate social capital theory and motivation theory to identify the factors that affect the intention of users to share mobile coupons (m-coupons) via social network sites (SNS). Social capital includes social ties, trust, and perceived similarity, whereas motivation comprises sense of self-worth and socializing.

A research model that integrates three social capital factors, two motivations, and m-coupon sharing is developed. Quantitative data from 297 users who had coupon usage experience are collected via offline and online survey. Partial least squares is used to conduct data analysis and test hypotheses.

Social ties, trust, and perceived similarity are positively related to m-coupon sharing intention and positively affect sense of self-worth and socializing, which have significant positive effects on m-coupon sharing intention and mediate the relationships between social capital factors and sharing intention.

This study highlights the integrated effects of social capital and motivations on m-coupon sharing intention in SNS. While social capital factors (i.e. social ties, trust, and perceived similarity) and motivations (i.e. sense of self-worth and socializing) positively affect m-coupon sharing, motivations are more directly associated with m-coupon sharing than social capital factors.

The purpose of this paper is to investigate the influence of two intrinsic (i.e. sense of self-worth and socializing) and two extrinsic motivations (i.e. economic reward and reciprocity) on mobile coupon (m-coupon) sharing by users in social network sites (SNSs). Moreover, this study examines how coupon proneness moderates the relationship between motivations and m-coupon sharing in SNSs.

A research model that integrates four motivations, coupon proneness, and m-coupon sharing is developed. Quantitative data from 247 users are collected via online and offline survey. Partial least squares technique is employed to evaluate the measurement model, and hypotheses are tested through hierarchical regression analysis.

Sense of self-worth, socializing, economic reward and reciprocity have positive effects on m-coupon sharing in SNSs. Furthermore, coupon proneness positively moderates the relationship of socializing and reciprocity with m-coupon sharing, whereas the moderating effects of coupon proneness on the relationship of sense of self-worth and economic reward with m-coupon sharing are insignificant.

The findings highlight the integrated effects of coupon proneness and motivations on m-coupon sharing in SNS. The impact of socializing and reciprocity on m-coupon sharing is higher for users with higher coupon proneness. However, the effect of sense of self-worth and economic reward on m-coupon sharing is the same regardless of coupon proneness of users. Therefore, although users with different motivations should be identified, SNSs and merchants should develop different incentive mechanisms to promote m-coupon sharing among various users.

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

The purpose of this paper is to examine how consumers’ value evaluation and personality factors influence consumers’ intention to adopt mobile coupon (M-coupon) applications in China. The moderating effect of gender on the relationships between personality factors and consumers’ adoption intention is also tested.

This study conducted a survey to collect data from M-coupon application users. In total, 271 valid responses were analyzed using structural equation modeling (SEM) technology.

The results indicate that perceived value, personal innovativeness, and coupon proneness positively affect consumers’ acceptance of M-coupon applications. Personal innovativeness has more positive impact on behavioral intention for males than for females. However, the differential effects of coupon proneness on behavioral intention are not significant between males and females. In addition, the findings show that perceived convenience, perceived enjoyment, and perceived money savings positively influence perceived value, whereas perceived fees and perceived privacy risk negatively influence it.

This study helps M-coupon application providers to identify who is positive toward their services and how to improve consumers’ perceived value of the services, eventually expanding their user base.

Prior studies mainly focussed on the usage behavior of M-coupons and overlooked the important role of M-coupon applications in promoting M-coupon use. This research fills this gap. The research findings offer insights into the factors influencing consumers’ behavioral intention to adopt M-coupon applications. Besides, the results of gender’s moderating effect advance the understanding of the differences in males’ coupon usage intentions between the contexts of M-coupon applications and paper-based coupon services, which enrich couponing research.