When delivering digitalization of educational services, a specific administration plan should be established, designed for maximum access, quick recovery from failure, and lowering the length of time it is unavailable, all while guaranteeing good security performance. It should include responsibilities such as providing dedicated servers for digitizing educational services, increasing security by using encryption and security-compliant network protocols, and database administration. To speed up the digitization of educational services, it is recommended that all databases be placed on a single server that will serve as the publisher, and that separate databases be distributed to three different servers that will act as the subscribers.

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Database Administration Practical Aspects in Providing Digitalization of Educational Services

Algorithmic management, as a digital management tool in the gig economy, has become a hotspot of concern at the intersection of theory and practice. However, there is a lack of research on the mechanisms and boundary conditions through which algorithmic management affects gig workers' job crafting. Based on the social information processing theory, this study constructed a dual-mediation model of how algorithmic management influences gig workers' job crafting through gameful experience and perceived job autonomy. Data from 687 valid samples were collected through a two-stage survey and statistically analyzed using structural equation modeling (SEM). The results demonstrate that algorithmic management increases gig workers' promotion-focused job crafting behaviors (increasing job resources and challenging job demands) by stimulating their gameful experiences and increases gig workers' prevention-focused job crafting behaviors (decreasing hindering job demands) by inhibiting their perceived job autonomy. In addition, the higher-order personality trait core self-evaluation moderates the relationship between algorithmic management and gameful experience and perceived job autonomy, as well as the indirect effects of algorithmic management on job crafting through gameful experiences and perceived job autonomy. This study advances empirical research on algorithmic management in the field of the gig economy and human resource management practices. Crucially, it provides practical insights for optimizing algorithmic systems in platform companies, potentially enhancing their efficiency and economic benefits. 

How Algorithmic Management Influences Gig Workers’ Job Crafting

Purpose Against the background of the digital economy, odd-job platforms rely on artificial intelligence algorithms to efficiently allocate tasks and monitor platform workers’ performance, putting these workers under enormous pressure. This paper explores the relationship between work overload and turnover intention of platform workers on odd-job platforms and the factors that lead to platform workers’ turnover. Design/methodology/approach Based on the job demands–resources model (JD-R), we construct a theoretical model to explain the relationship between work overload and turnover intention of platform workers. We test job burnout as a mediator variable and perceived algorithmic fairness and job autonomy as moderating variables. We conducted a study at food delivery platforms and ride-hailing platforms in China. Findings The empirical results show that: (1) work overload increases the turnover intention of platform workers by increasing job burnout and (2) perceived algorithmic fairness and job autonomy moderate the positive relationship between work overload and job burnout. Originality/value We provide a theoretical basis to explain the influence of work overload on turnover intention of odd-job platform workers and provide practical recommendations for management of platform workers. 

Does work overload of odd-job platform workers lead to turnover intention? An empirical study on platform workers

As the latest version of HTTP, HTTP/3 reduces the loading time of web pages and improves the user experience by replacing TCP and TLS with QUIC. Previous studies have already demonstrated the importance of prioritization for optimizing the performance of HTTP. By default, HTTP/3 schedules all the requests in a round-robin (RR) way on the server. However, in practice, in addition to the different characteristics of web resources, the differences and dynamic changes of network conditions and user equipment (e.g., mobile and PC) will also have a great impact on prioritization. Without considering these factors, existing prioritization schemes deployed on both clients and servers can not always ensure optimal performance for HTTP/3. In light of the above issues, in this paper, we proposed Dynamic Resources Prioritization via Reinforcement Learning (DRP-RL) to provide fine-grained resource prioritization for HTTP/3 by considering the effects of both network and user equipment. Reinforcement learning (RL) is adopted, in which the RL agent can leverage network, user and web page information to learn the best prioritization of resources across different user groups. The HTTP/3 server is instructed to send the resources in a particular order for different clients dynamically to improve performance. DRP-RL has been implemented based on quic-go, and extensive evaluations indicate that DRP-RL minimizes 3.1%∼21.0% of First Content Paint (FCP) and saves 5.3%∼23.7% of Page Load Time (PLT) across various web pages when compared with RR. 

Fine-grained HTTP/3 prioritization via reinforcement learning

University websites are gaining importance rapidly, making it crucial to ensure its well-designed, user-friendly, and perform optimally. However, there is a lack of research on the web performance of Malaysian university websites, making it challenging for universities to determine the necessary steps to enhance their websites' performance. The research aims to evaluate and enhance the web performance of Malaysian university websites. This study utilized an experimental approach by employing performance metrics from Google Lighthouse version 10.0.1 to measure the web performance of these websites and examine website design and content to identify factors that contributed to poor performance and user experience. The strategies were developed to improve website performance and enhance user experience through Web Content Accessibility Guidelines (WCAG) and Web Performance Index (WPI). This research will contribute to the existing knowledge on website performance and provide valuable insights and recommendations for enhancing the online presence and reputation of Malaysian universities.

Assessing Web Performance of Malaysian University Website

The dependencies between the resources on web page slow down the page load process, resulting in degradation of user experience and provider revenue. Recent solutions reprioritize requests at client side to fetch resources according to the page's dependency structure. However, since resources on modern web pages are often served by the direct‐connect and multiple third‐party domains, optimizing web performance only at the client side is not able to minimize the long waiting time for the resources across multiple domains. To address this inefficiency problem, we present fast page load (FPL), a scheme of restructuring the interaction between multiple domains to accelerate web page loads. The key of our solution is that the credible servers in third‐party domains proactively push resources to client with the aid of server in the direct‐connect domain. Therefore, FPL eliminates the waiting time of resource requesting and TCP handshaking between the client and servers in third‐party domains. Furthermore, we propose FPL+ to schedule resources based on the dependency and size of objects to improve user perception and experience in terms of time‐to‐first‐paint. Experiment results show that our approach FPL and FPL+ effectively reduces the median page load time by up to 44% across popular websites.

Achieving fast page load for websites across multiple domains

With the rapid development of artificial intelligence technology, algorithmic management is increasingly prevalent in enterprises. Despite the considerable scholarly attention given to the impact of algorithmic management, a research gap remains regarding its influence on employee creativity. To address this gap, the authors developed a theoretical model using ability-motivation-opportunity (AMO) theory. This model aims to investigate the direct impacts of algorithmic management (opportunity) on employee creativity (performance) while also considering the mediating roles played by knowledge combination capability (ability) and achievement goal (motivation). Using a sample of 327 paired leader-employee data from an information technology service company, the findings reveal that algorithmic management has a negative effect on employee creativity. Furthermore, the results demonstrate that algorithmic management negatively influences employee creativity through its impact on knowledge combination capability and achievement goal.

Why Does Algorithmic Management Undermine Employee Creativity?

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Achieving fast page load for websites across multiple domains

Why Does Algorithmic Management Undermine Employee Creativity?