International Journal of Knowledge-Based Development (5 papers in press)

Regular Issues

• AI-Powered Startups: Leveraging Artificial Intelligence for Competitive Advantage  
  by Rakesh Kumar Gupta, Bhopendra Singh, Sonia Singh, S. Suman Rajest, Pankaj Gupta 
  Abstract: 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.
  Keywords: Entrepreneurship; Artificial Intelligence; Technological Integration; Data-driven decision making; Competitive Advantage.
  DOI: 10.1504/IJKBD.2025.10074432
   
  
• Using Deep Learning for User Activity Monitoring: Knowledge Level Evaluation on E-Learning Platform  
  by Sasikala P, K. Santosh, Amit Agnihotri, S. Prema, R. Manikandan 
  Abstract: The area of e-learning has recently gained a lot of attention as a consequence of the advancement of technologies This is so that individuals from all across the world may now access and learn fresh knowledge Additionally, E-learning is widely preferred by universities of higher education throughout the globe This is done to help higher education institutions accomplish goals related to the proportion of people who have access to educational possibilities However, the success of users and educators in e-learning settings is sometimes a source of concern The evaluation of flow state in e-learning systems is a difficult issue since it depends on the capacity to retrieve the metrics that more accurately represent user behaviour and efforts The goal of this research is to create a futuristic, independent, and smart e-Learning platform in which case users' behaviour analysis and deep learning serve as an automated knowledge level assessor 450 university
  Keywords: E-learning platform; User activity; Deep learning; Convolutional auto encoder; Heatmaps; Automatic evaluator,.
  DOI: 10.1504/IJKBD.2025.10074742
   
  
• The Impact of Big Data Acceptance and Analysis on Company Performance  
  by Elham Nikmanesh, Ata Harandi, Payvand Khamseh, Shib Sankar Sana 
  Abstract: This study investigates the strategic impact of big data adoption and analytics on organizational performance, with a specific focus on the telecommunications sector in a developing economy. Addressing a key gap in the extant literature, it proposes an integrated framework assessing how big data technologies influence financial and operational outcomes, as well as exploratory and exploitative innovation capabilities. A quantitative research design was employed, using structured questionnaires distributed among senior and mid-level managers of telecommunications firms actively engaged in analytics. Structural equation modeling (SEM) was used to empirically validate the conceptual model. Results indicate that big data analytics significantly enhance decision-making and forecasting, which subsequently improve financial and operational performance. Big data adoption fosters exploratory and exploitative innovation, explaining the variance in organizational performance. Innovation and decision-making emerge as critical mediators in translating data capabilities into competitive advantage. Practical implications emphasize continued investment in analytics infrastructure and human capital development.
  Keywords: Big Data; Decision Making; Forecasting; Financial Performance; Operational Performance; Overall Performance; Innovation.
  DOI: 10.1504/IJKBD.2025.10074884
   
  
• Factors Influencing the Teaching Quality of Online Open Courses in Colleges and Universities based on Social Network Analysis  
  by Ying Zhou, Lei Zhang 
  Abstract: To Enhance the standard of educational delivery in open online educational offerings at higher education institutions social network analysis is introduced to carry out an Investigation into the factors that impact educational instruction quality of open online educational offerings at higher education institutions. Firstly, the elements that influence the teaching standard of college open digital coursework are analysed, the relevant hypotheses are put forward, the conceptual model of the factors influencing educational excellence in college accessible online learning opportunities is constructed, and then the questionnaire is designed to demonstrate the hypotheses. The empirical results show that the rapport between educators and learners, network cohesive subgroups and network density have a significant impact on the teaching standard of college open online educational offerings at institutions of higher learning, namely colleges and universities can improve the teaching quality of online open courses by optimizing the network structure, expanding the network scale, and promoting the interaction and communication among users
  Keywords: Social network analysis; Higher education; Online open courses; Teaching quality; Influence factor.
  DOI: 10.1504/IJKBD.2025.10075038
   
  
• To Share or Not to Share: The Role of Motivation in Fostering Knowledge Sharing Behaviours on Enterprise Social Networks  
  by Urfa Fazil, Nasir Mehmood 
  Abstract: Sharing knowledge has become essential for organisational success in the knowledge-driven economy of today. Organisations are using enterprise social networks (ESNs) more and more to encourage knowledge exchange among their staff members. The incentive of individuals to share their knowledge is just one of several elements that affect knowledge sharing behaviour (KSB) on ESN. In order to promote KSB on ESN, this study investigates the function of motivation. The study discovers that motivation considerably moderates the association between KSB and ESNs using survey data from 405 operational personnel working in the telecom industry. In order to match motivation and KSB on ESNs, organisations might use customised incentive schemes. Furthermore, theoretical foundation of the study is Social Capital theory, in order to effectively accept cultural variations, multinational organisations may need to consider a variety of motivational approaches from different countries. The research adds to the expanding body of studies that emphasises the significance of motivation in predicting knowledge-sharing behaviour and linkages within ESNs.
  Keywords: Knowledge Sharing Behaviour; Enterprise Social Network; Motivation.
  DOI: 10.1504/IJKBD.2025.10075045