Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

In order to enhance the effectiveness of educational learning support services, this paper designs a quality evaluation model for open education learning support services in the context of digital transformation. Firstly, a quality model for educational learning support services is constructed. Secondly, the evaluation dimensions are analysed and an indicator system is designed using the service quality (SERVQUAL) model. Then, the reliability of the indicators is tested using Cronbach's alpha analysis method. Finally, the weights are calculated using the average assignment method, and the factor weights are estimated using the Advanced Mortar System (AMOS) software to obtain service scores, achieving the quality evaluation of learning support services. The results show that college students have high expectations and perceived values for the quality of learning support services, but their satisfaction is relatively low due to unsatisfactory service quality in the actual learning process.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

This study examines startup AI adoption and its effects on business-to-business collaboration, competitiveness, and organisational success, as well as AI adoption best practices and issues. The quantitative study uses a cross-sectional survey of 350 Indian startups by industry and factor and regression analyses to explain AI activities. The analysis found that AI is pervasive, boosting competitive advantage through cost reductions and innovation. AI startups that reduce costs, scale, and improve operations are most likely to succeed. AI's ability to improve customer experience and boost business growth boosts growth and innovation. A strategy plan can overcome high deployment costs, untrained talent, and integration complexity using cost efficiency, talent recruitment, and a good integration process. The report examines how firms may utilise AI to reduce costs, improve process efficiency, and boost customer service to grow. The study's cross-sectional design and lack of longitudinal data to track AI's dynamic effects and changes hinder it.

Educational apps involve creating and using digital programs designed for educational purposes, ranging from interactive learning platforms to tools that promote academic engagement. This study examines the interrelations among customer satisfaction, trust, and switching costs in educational mobile applications. It employs structural equation modeling (SEM) to analyse data from a sample of 102 users acquired by convenience sampling. Data were gathered using a questionnaire that evaluated customer satisfaction, trust, loyalty, and switching costs. SEM results indicate that customer satisfaction substantially influences customer loyalty, with customer trust functioning as a mediating variable and switching costs acting as a moderating component. These results not only add to the existing body of knowledge, but they also have major intellectual and practical implications. The study provides practitioners and society with insights into effective ways to increase consumer loyalty in the competitive landscape of educational technology.

In the evolving landscape of education, personalised learning (PL) has gained prominence as a methodology that adapts educational practices to fulfil unique needs and the integration of technology customises it. Therefore, this research aims to gain insight into integrating technology to enhance learning outcomes by meditating on metacognition awareness (MA) between technology assistance learning platforms (TALP) and PL. This study employed the method of cross-sectional analysis among the graduates and used a simple random technique. The sample size of the research is 272, which is administered through a questionnaire from undergraduate and postgraduate students across select Arts & Science Colleges in Chennai City. The SMART PLS 4.1.0.6 software is used for the structural equation modelling (SEM) analysis. The study's findings demonstrate that TALP significantly improves PL directly and that MA partially mediates the association between TALP and PL.

With the continuous development of teaching Chinese as a foreign language, more teaching methods are combined with network teaching.However, it is difficult for network teaching methods to find ways that are suitable for different learners from various teaching resources.Therefore, to help learners obtain appropriate teaching methods from the network teaching platform, the research establishes a network teaching recommendation model for Chinese as a foreign language based on the user's interest similarity.Three experimental schemes are designed to verify the effect of the proposed model.The experimental results show that the mean absolute error (MAE) scores of the model in the three schemes are 0.67, 0.7095, and 0.7428, respectively; the RMSE scores are 0.88, 0.9346, and 0.9695, respectively.Thus, the proposed collaborative filtering recommendation algorithm based on user interest similarity migration has good recommendation performance.