This dissertation consists of three essays on the empirics of firms' strategies in new industries. In the first chapter, I study the spatial mismatch between consumers and bikes in the dockless bike-sharing industry and an externality exacerbating the problem: when a consumer uses a bike for a low and inflexible price, she both displaces another consumer's usage for a potential higher-value trip, and may ride the bike to unpopular destinations. With a trip-level dataset of a bike-sharing company in Beijing, China, I develop a spatial structural model to estimate the demand for bikes with search frictions and local matchings. Compared to the scenario in which consumers always get bikes immediately, I find that local spatial mismatch between consumers and bikes reduces the total usage by 29.95%, or a net loss of 332,979 trips. Counterfactual analyses show that (1) doubling the number of bikes increases the trip volume by 28.46% while halving the number of bikes decreases the trip volume by 46.40%; (2) price-discriminating against short trips by 2% increases the total trip time by 0.22%; and (3) changing the frequency of bike reshuffling does not have a significant impact on the total usage of bikes. In the second chapter, I study how efficient capital markets are in supplying funds to new firms by looking at how a platform start-up, ofo, made its investment decisions in response to capital infusions. I fit the business performance of ofo, a bike-sharing platform start-up, in China and show how its financial conditions affected investment decisions. I analyze the effects of funding rounds from venture capitalists on the investment and business of the company with an event study framework. My estimates find that the firm increased its users and bikes by about 40% two weeks before receiving funds, suggesting that it spent much more on bike fleet and promotional offers in expectation of capital infusions. I also show that such boosts in business performance were short-lived: the number of trips and users often returned to normal levels two weeks after the funding day. My findings suggest that the capital market is inefficient in supplying funds to start-up companies. In the third chapter, I study the shakeout in the U.S. automobile industry with data retrieved from old annals of the automobile industry. I simulate a research productivity model and see if I could successfully trigger a shakeout. I find that only the cost reduction from technology advancements is not enough to trigger an industry shakeout and propose that more extreme settings are needed for further studies.