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\"This book presents chapters discussing theoretical and empirical approaches on the importance of the renting industry and sharing economy to help in the recovery of tourism, hospitality and service industries in a post-COVID-19 world\"-- Provided by publisher.

The organizational problem firms face is the utilization of knowledge which is not, and cannot be, known by a single agent. Even more importantly, no single agent can fully specify in advance what kind of practical knowledge is going to be relevant, when and where. Firms, therefore, are distributed knowledge systems in a strong sense: they are decentered systems, lacking an overseeing `mind'. The knowledge they need to draw upon is inherently indeterminate and continually emerging, it is not self-contained Individuals' stock of knowledge consists of (a) role-related normative expectations; (b) dispositions, which have been formed in the course of past socializations; and (c) local knowledge of particular circumstances of time and place. A firm has greater-or-lesser control over normative expectations, but very limited control over the other two At any point in time, a firm's knowledge is the indeterminate outcome of individuals attempting to manage the inevitable tensions between normative expectations, dispositions, and local contexts

Netflix was once only an idea in the mind of Reed Hastings, a businessman who has done amazing things since starting the online movie and TV company. Discover how Reed was able to make Netflix a success around the world and find out what he has planned next to keep the company on top.

Revenue sharing contracts, in which retailers pay a royalty on sales to their suppliers, are now widely used in the video rental industry. We show that revenue sharing is valuable in vertically separated industries in which demand is either stochastic (unpredictable) or variable (e.g., systematically declining), downstream inventory is chosen before demand is realized and downstream firms engage in intrabrand competition. Unlike two-part tariffs, revenue sharing achieves the first best outcome by softening retail price competition without distorting retailers' inventory decisions. Our theories are also consistent with trends in prices and availability following retailers' adoption of revenue sharing contracts.

This title introduces readers to Reed Hastings, founder of Netflix,and a pioneer in the development of streaming entertainment.

The paper discusses the genesis of the 1985 paper \"Optimal Pricing and Return Policies for Perishable Commodities,\" as well as the critical ideas presented in that research. A brief review of the literature that cited and expanded the results of the paper is also presented.

Economic losses in the car rental industry due to customer breaches remain a critical issue. The rapid growth of the vehicle leasing market has given rise to a pressing concern for enterprises, namely the economic loss, vehicle idleness, and service quality degradation that are often associated with customer default. This study proposes an innovative vehicle rental early warning system that incorporates the improved DBSCAN clustering technique and the iTransformer model. The enhanced DBSCAN technique, which employs a snow ablation optimizer (SAO) algorithm, establishes an electronic barrier and integrates the iTransformer model for trajectory prediction. This enables the real-time monitoring of potential customer defaults and the reduction of economic losses that leasing companies may incur as a result of customer defaults. The system identifies and prevents default risks in a timely manner through a comprehensive analysis of vehicle driving data, thereby safeguarding the interests of corporate entities. The system employs vehicle driving data provided by a Chinese company to accurately identify the vehicle’s resident location and predict future trajectory, effectively preventing customer defaults. The experimental results demonstrate that the model is highly effective in predicting the vehicle’s resident location and future trajectory. The mean square error (MSE), mean absolute error (MAE), and location error reached 0.001, 0.003, and 0.08 kilometers, respectively, which substantiates the model’s efficiency and accuracy. This study has the additional benefit of providing effective warnings to customers of potential default behavior, thereby reducing the economic losses incurred by enterprises. Such an approach not only ensures financial security but also enhances operational efficiency within the industry. Furthermore, it offers robust support for the sustainable development of the car rental industry.

We analyze decisions for a car rental firm using a common pool of cars to serve both rental, and purchasing customers. The rental customers arrive successively, and rent out cars for random durations while effectuating random incremental mileages on them. This stochastic rental behavior makes the decision of when to sell a rental car quite a crucial one for the firm because it involves a certain amount of risk. On one hand, selling a car when its mileage is low proactively avoids a huge decline in the car’s residual market value (even though it could also cause the firm to forfeit income from future rental customers who intend to rent that car for long durations while driving it sparingly). On the other hand, delaying selling is equally risky because the sample of early rental customers whom that car serves may only successively keep the car for short durations while significantly adding to its mileage. Such opportunistic customers would therefore have the effect of drastically diminishing the car’s market value without providing sufficient rental income to compensate. We use optimal stopping theory to derive the firm’s optimal selling policy algorithm which unfortunately comes with a practical implementation challenge. To address this issue, we propose three heuristic selling policies, one of which is based on the restart-in formulation introduced by Katehakis and Veinott (Math Oper Res 12(2):262–268, 1987). Numerical experiments using real and artificial parameter settings (1) reveal conditions under which the proposed heuristic policies outperform the firm’s current (suboptimal) policy, and (2) demonstrate how all four suboptimal policies compare to the optimal policy.

In marketing durable goods, manufacturers use varying degrees of leasing and selling to consumers, e.g., cars, photo-copiers, personal computers, airplanes, etc. The question that this raises is whether the distinction between leases and sales is simply one of price, or whether the proportion of leases and sales effects a firm's ability to compete in the market. In this paper we use two approaches to argue that leasing and selling create strategic consequences that extend beyond prices. First, we develop a stylized theoretical model that shows that the optimal proportion of leases and sales depends on the competitiveness of the market and on the inherent reliability of the firm's product. And second, we find support for the implications of our theoretical model with data from the automobile industry. The U.S. automobile industry has seen a large increase in leasing over the last five years. However, the extent to which leasing has been embraced varies widely across manufacturers. For example, in 1993 the sport utility segment had the following lease percentages: Ford Explorer, 29%; Jeep Grand Cherokee, 24%; Toyota 4-Runner, 11%; and Chevrolet Blazer, 9%. In addition, manufacturers often vary lease percentages across models. For example, in 1993 Ford leased 22% of its Crown Victoria model, 35% of its Taurus model, and 42% of its Probe model. A popular argument for why we see these differences is that higher priced cars are leased more often because leasing makes them more \"affordable.\" However, this rationale is not compelling in the face of our data. For example, the Ford Probe was priced significantly lower than the Crown Victoria and yet it was leased almost twice as often. To develop a better understanding of why we observe differences in the proportion of leasing, we develop a two-period model of a duopoly in which each manufacturer chooses its optimal quantity and the fraction of units it wants to lease. We find that in equilibrium neither firm leases all its units—either they use a mix of leasing and selling or they use only selling. Our analysis suggests that the fraction of leased cars decreases as the manufacturers' products become more similar and the competition between them increases. The intuition for this result is that a higher fraction of leases puts the firm at a competitive disadvantage in the future. This occurs because, unlike firms that sell their product, firms that lease are at a price disadvantage. Another important finding in this paper is that the extent of leasing chosen by a manufacturer depends on the reliability of its product. In particular, all else being equal, the lower a product's reliability, the lower its proportion of leases. Within the context of the automobile industry, this suggests that more expensive cars may be leased more often because they are of higher quality and not necessarily because they are more expensive. Finally, we test the implications of our theoretical model with data from the U.S. automobile market. In particular, for 1993 model year cars, we develop a measure of reliability using data from Consumer Reports . In addition, we develop a measure of the extent of competition in each segment of the automobile market. We support our hypotheses by finding that the extent to which a car model is leased depends strongly on its predicted reliability and on the competitive intensity within the segment.

Popularity of one-way car rentals poses a challenge to rental car fleet management and brings to focus the importance of a strategic decision for rental car operators: whether to implement a single-fleet or a multifleet model. The single-fleet model allows movement of vehicles between regions, whereas the multifleet model does not. It is not obvious whether a single-fleet model is optimal due to its pooling effect, or a multifleet model due to shorter car relocation times. In practice, different rental car operators use different models. To answer this conundrum, we develop two simulation models and compare them in terms of fleet utilisation, branch service level, relocations, and operating profit. We have taken the New Zealand rental car industry as an example as the country consists of two well-defined regions: the North Island and the South Island. The results indicate that a multifleet model has a higher service level at key centres and higher utilisation. At the same time, the single-fleet model is relatively more profitable at the expense of a lower service level in key centres due to vehicles accumulating in the South Island due to a significant volume of one-way southbound travel. Overall, the implementation of either model should depend on the strategic goals of the rental car operator. Our work will be useful for practitioners considering whether or not to pool their fleet when allowing for one-way rentals with subsequent relocation.