Organizational scholars have long posited that business firms are, in essence, learning entities. This is because firms must learn to adapt and survive in the domains in which they operate. In familiar and stable environments, firms leverage their previous experiences to improve their routines and operational efficiencies. Familiarity with and stability of the external environment allow firms to incrementally build on their existing know-how. However, learning from prior experiences is not usually sufficient when the external environment is dynamic and existing routines are subject to rapid obsolescence. Prior experience is also of limited use when the firm extends the scope of its operations into unfamiliar domains where it lacks the know-how necessary for success. So, how do firms learn when they enter a new domain of operations? How does their need to learn and adapt to the new domain interact with the need to continue learning in the primary domain? Furthermore, what challenges do firms face when both the primary and new domains are subject to rapid change while being vastly different? I examine these and related questions in this dissertation. Drawing upon the existing organizational learning theories, I develop a theoretical model to outline the mechanisms that allow focal firms to succeed in the new domain while continuing to perform to expectations in their primary domain. In the main, I argue that when entering a new domain, firms rely significantly on vicarious learning from specialist organizations that are already steeped in the complexities of that domain. Such reliance on external parties allows the focal firms to continue to develop in their primary domain while also becoming familiar with the imperatives of the new domain and, over time, to develop new processes and routines necessary for success. After reviewing the relevant literature and developing a theoretical model, I empirically examine my theory with a sample of technology firms that entered the capital markets through an Initial Public Offering. These firms face challenges due to their limited experience in the capital markets. Moreover, they must continually improve their operations in their primary product-market domain to remain competitive in a rapidly changing technological landscape. As such, my central thesis is that the survival and performance of technology firms post-IPO depend on their ability to learn and adapt to the changing dynamics of the capital markets and the operational environment. To test my hypotheses, I use a longitudinal sample of high-technology firms. The sample comprises Initial Public Offerings (IPOs) from 1990 through 2022. I have created my final dataset by combining SDC-Platinum, Compustat, and CRSP data. I use a discrete-hazard model and regression analyses.