The enticing characteristics of decentralization, transparency, security, traceability, and anonymity signify a novel technology paradigm, drawing a significant number of followers to engage in cryptocurrency trading and holding. This interest has extended to both individual and institutional investors, prompting research into the mechanisms underpinning the pricing of blockchain-based cryptocurrencies. According to the latest NBC News poll, 21% of American adults have used, traded, or invested in cryptocurrency as of March 31, 2022 (Franck, 2022). As of March 31, 2024, the total quantity of Bitcoin holdings by institutional investors reaches 2.5 million (Bitcoin Treasuries, 2024), accounting for 12.71% of the circulating supply (19.67 million). In the literature, many possible factors affecting cryptocurrency valuation have been extensively investigated, such as social media sentiments (Suardi et al., 2022), shocks, technology, and economic factors (Li and Wang, 2017). While many studies concentrate on the impacts of certain drivers on crypto markets within specific timeframes, there remains a limited exploration in the literature regarding the evolution of influential factors or the dynamic nature of key drivers over time. Moreover, the multitude of potential drivers complicates investors' ability to discern clues before trading cryptocurrencies. Consequently, they often mimic the trading behavior of prominent influencers, such as publicly listed firms and/or crypto whales. Motivated by the volatile nature of Bitcoin prices, the absence of reliable and consistent open information sources for investors to rely on when making trading decisions, and the scarce exploration of Bitcoin's influential drivers' dynamics in existing literature, this study aims to: (1) provide recommendations on pivotal factors affecting Bitcoin prices, drawing from accessible channels, to empower investors in making well-informed trading choices; and (2) examine the herding behavior observed among individual traders within the Bitcoin market. More specifically, we present a comprehensive model that encompasses the majority of drivers examined in existing literature, spanning a broader time frame from July 2010 to September 2023. This model aims to monitor the dynamics of both the direction and magnitude of the relationship between Bitcoin prices and potential drivers.