Schlüsselwort: risk-reducing signals

Service providers, such as hotels, airlines, or car rental companies, face great challenges when marketing their services. With regard to service characteristics such as intangibility or perishability, customers face higher risk when purchasing services than they do with tangible products. The lack of physical evidence makes it difficult for customers to evaluate services before purchase, and thus assess the quality and the value of a service. Furthermore, since services cannot be stored, service providers use multiple pricing and distribution strategies to sell a given inventory before it perishes. The consequence is that the customer often finds different rates for the same service across different distribution channels. Hence, the customer might be uncertain about the service provider's offered price compared to the market. Consequently, information asymmetry occurs because the service providers possess information on service quality and price level that the customer does not have. To overcome this information asymmetry, service providers can send signals such as brand names and best-rate-guarantee. One of the central questions that service providers face when sending signals is whether their signals successfully reduce customers' perceived uncertainty. And, how can service providers measure the effectiveness of risk-reducing signals? To answer the question of how service providers can measure the effectiveness of a brand signal, the study proposes two monetary approaches to determine brand-specific earnings based on the assessment of price premiums in the service industry by comparing distribution channel prices. The first approach is based on the hedonic price function. The second approach is a non-parametric matching estimation. Presenting an innovative solution for generating data to assess price premiums, the empirical study applies the proposed approaches to major hotel brands comparing branded with non-branded services. To answer the question of whether best-rate guarantees for services can elicit positive customer response to service providers, e.g. increase the booking intention or reduce the price image, three experiments were conducted. The experiments take into account moderating characteristics such as type of distribution channel, service provider's reputation and market price dispersion. For example, the book answers the question of whether a best-rate guarantee is more effective in the direct or indirect online distribution channel. The empirical findings should help service providers to manage risk-reducing signals in the context of online distribution.