# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 4 Result Analysis
### Network Analysis  
#### 4.2.1 Co-Occurrence Network  
The keyword co-occurrence network in Figure 6 and Table 5 depicts the interconnected nature of various research themes in the evolution of e-payment technology. This section delves into each cluster and highlights their significance in the context of e-payment technology convergence. Adoption and Behavioral Intentions (Blue cluster):  
This cluster captures the essence of technological acceptance models (TAM), which are crucial for understanding how users accept and use e-payment systems. Terms like \"perceived usefulness,\" \"continuance intention,\" and \"adoption\" reflect the psychological and social factors that drive or hinder the adoption of e-payments. \"Mobile payment\" and \"e-payment\" highlight a shift towards mobile-driven transactions, a significant trend in e-payment evolution, accommodating the rise of smartphones. The inclusion of \"COVID-19\" signifies a pivotal moment in e-payment history, where the pandemic forced a swift move towards contactless and mobile payments, bypassing traditional cash transactions for health and safety reasons. E-Commerce and Market Dynamics (Red Cluster):  
The focus on \"e-commerce\" and \"online shopping\" indicates the environment in which e-payment technology has flourished. As consumers increasingly turned to online platforms for purchasing, e-payment systems had to evolve to meet these demands efficiently and conveniently. \"Consumer behavior\" reflects the central role that user preferences and habits play in shaping e-payment technologies. Understanding these behaviors is essential for designing user-friendly payment systems. \"Social commerce\" and \"social media\" point to the recent trend of purchases being made directly through social platforms, necessitating integrated e-payment solutions working seamlessly within these networks. Security, Trust, and Technology (Green Cluster):  
Security is paramount in the evolution of e-payment technologies. Terms like \"security,\" \"privacy,\" \"trust,\" \"blockchain,\" and \"cryptography\" underscore the ongoing arms race between payment security measures and the sophistication of cyber threats. \"Blockchain\" technology has been a game-changer in this evolution, offering decentralized and transparent transaction mechanisms that could potentially revolutionize how e-payments are conducted and recorded. \"Trust\" is both a result of and a prerequisite for  
Figure 6: Co-occurrence network. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Cluster Theme** & **Keyword** \\ \hline
**Blockchain and Security** & anonymity, authentication, blockchain, cryptography, privacy, security \\ \hline
**E-commerce and Consumer Behavior** & consumer behavior, consumer trust, customer satisfaction, data mining, e-commerce, electronic commerce, information technology, online shopping, recommender systems, service quality, social commerce, social media, supply chain management \\ \hline
**Peripheral Themes** & digital payment, financial inclusion, fintech \\ \hline
**Technology Adoption** & adoption, behavioral intention, China, COVID-19, e-payment, intention to use, mobile commerce, mobile payment, perceived usefulness, purchase intention, satisfaction, structural equation modeling, tam, technology acceptance, technology adoption, trust, UTAUT \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Co-occurrence network. effective security measures. As e-payment technologies become more secure, they earn greater trust from users, which, in turn, drives wider adoption. Data Analytics and Personalization (Orange Cluster):  
The emergence of \"data mining,\" \"machine learning,\" and \"recommender systems\" speaks to the increasingly personalized nature of e-commerce, where e-payment systems must integrate with back-end analytics to provide tailored purchasing experiences. This trend has significant implications for the evolution of e-payments.