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The study focuses on influential factors of collaboration on government data security by the Chinese government.

The article explores the case of e-government in the Chinese centralized unitary state system context, using a structured–pragmatic–situational (SPS) approach and the boundary theory as an analytical lens.

The findings indicate that e-government operates in highly interconnected environments where the safe flow of government data requires collaborative and cross-boundary strategies. Any organization is a potential “weakest link”. In addition, collaboration is fragmented by ambiguous accountability and organizational inertia across government departments, resources differences and limited visibility and measurability of security efforts across government levels and conflicts and uncertainties in principal–agent relationships. The solutions for those obstacles are also discussed from the multi-function, multi-level and multi-actor dimensions, respectively. A multi-dimensional overarching security model for the flow of government data is proposed.

The study advances the technology-oriented micro-analysis of previous studies on government data security to cross-organizational revealing at the macrolevel by connecting streams of research in information systems and public administration. These findings will contribute to making the safe flow of government data more resilient in the transformation of e-government.

This study aims to debunk the myth that knowledge sharing will harm one’s competiveness, and argues, from the learning theory perspective, that sharing knowledge benefits both knowledge sharers and knowledge recipients.

Survey data were collected from 233 respondents across a variety of industries in Taiwan and the data were analyzed using Partial Least Squares analysis.

The results showed that both knowledge sharers and knowledge recipients benefitted from knowledge sharing, developing deeper and wider knowledge, leading to better performance, and that the benefits were greater for the knowledge sharers.

It is suggested that managers advocate knowledge sharing as a learning activity for both knowledge sharers and knowledge recipients. Regular knowledge sharing activities are advised, with knowledge sharers engaged fully in active learning, and knowledge recipients encouraged to take a more active role, by not only listening but also starting to share themselves.

This research adds to the knowledge management literature by investigating the results of knowledge sharing for both the sharer and recipient through a learning theory lens. It challenges the conventional view that employees will be worse off if they share knowledge, and debunks it with theoretical argument and empirical evidence. It offers insights into the impact on knowledge breadth and depth at the individual level, which was previously only studied at the organizational level.

This paper aims to develop an optimal buyback promotion strategy for enterprises, including multibuyback strategy and self-buyback strategy, taking both the consumer's multichannel psychological acquisition attributes and remaining market into account.

Based on the game theory and Hotelling model, the authors formulate a new model to study the equilibrium of different buyback models, given the utility maximization of the consumers, the profit maximization and the constraint on nondecreasing market share of the enterprises, and the authors conduct comparative analysis.

Intuitively, enterprises buying back products of other brands would appeal to some consumers. However, the authors find that after implementing the multibuyback scheme, enterprises may not be able to seize competitors' markets or even lose their original customer base in the context considered in this article counterintuitive. In addition, the size of remaining market share and the consumer's multichannel psychological acquisition affect the choice of buyback promotion strategies. Moreover, after implementing multibuyback scheme, customers with old products subsidize those who receive additional discounts. Finally, the authors point out that the buyback strategy choices of companies with different goal-oriented are diverse.

This study has a very solid realistic background and provides guidance for enterprises to implement buyback promotion strategies. In addition, the authors unearth new influencing factors to provide a reasonable explanation for different buyback strategies in reality.

To the best of the authors’ knowledge, this study is one of the first to explore the multibuyback promotion strategy as a new buyback method, where the two influencing factors the authors have not been proposed so far.

This study aims to investigate the influence of digital transformation on the overall financial performance of firms, with a specific focus on Chinese-listed companies from 2010 to 2021. It seeks to understand the impacts on various accounting and financial indicators in emerging economies such as China.

This study employs a text-mining approach to construct a digital transformation index based on the data sample of 11,814 firm-year observations from China’s A-share listed companies. This index serves as a proxy to measure the extent of digital transformation and its impact on financial performance and health.

The findings indicate that digital transformation significantly enhances overall financial performance and health, as evidenced by increased profitability, reduced operational costs, and lowered financial risks. The study reveals a time-lagged effect, where the benefits of digital transformation become more apparent after about one year. Further analysis shows that the value of digital transformation is more evident in a firm’s asset items. This raises the possibility of recognising the by-product, such as data resources, in the digital transformation process.

This research offers a unique contribution by linking digital transformation to financial performance using a large dataset from China's A-share listed firms. Doing so enhances our understanding of the tangible effects of digital transformation on corporate performance. Furthermore, this research provides valuable insights for the advancement of future accounting practices and the development of standards.

The impact of rolling on the performance of micro arc oxidation (MAO) coatings on ZM5 alloy has been underreported. The purpose of this study is to explore the correlation between rolling and the failure mechanism of MAO coatings in greater depth.

The influence of rolling on the corrosion and wear properties of MAO coating was investigated by phase structure, bond strength test (initial bond strength and wet adhesion), electrochemical impedance spectroscopy and wear test. The change of the surface electrochemical properties was studied by first principles analysis.

The results showed that the MAO coating on rolled alloy had better corrosion and wear resistance compared to cast alloy, although the structure and component content of two kinds of MAO coating are nearly identical. The difference in interface bonding between MAO coating and Mg substrate is the primary factor contributing to the disparity in performance between the two types of samples. Finally, the impact of the rolling process on MAO coating properties is explained through first-principle calculation.

A comprehensive explanation of the impact of the rolling process on MAO coating properties will provide substantial support for enhancing the application of Mg alloy anticorrosion.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

The purpose of this paper is to explore and empirically test fairness in predicting online customer satisfaction in the internet banking context. The paper also aims to further explore the mechanism through which fairness influences customer satisfaction online, i.e. identifying the mediators.

A survey is used to validate a research model based on equity theory and relationship marketing theory that incorporates fairness, trust, perceived value and customer satisfaction. A total of 331 surveys were analyzed using Partial Least Square.

In internet banking, fairness that includes distributive fairness, procedural fairness and informational fairness is positively related to customer satisfaction. Trust is identified as the key mediator of fairness to customer satisfaction.

In an online context, where human interactions are replaced by graphic user interfaces, fairness still plays an important role in customer satisfaction. Further comparison of online and offline context could be conducted.

For online service managers, it is crucial to incorporate the fairness perspective into operations, which involves topics like segmented marketing efforts, consistency in procedures, and providing ample information to customers.

This research is one of the first attempts to examine fairness in internet banking, as well as to delineate the mediating process of fairness to satisfaction online.

In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]).

First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error.

The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality.

An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.