The Effectiveness of Specific Go-to-Market Strategies for Digital Innovation Adoption: An Abstract

Abstract

Increasingly, start-ups focus on developing and launching digital innovations (DIs). Whether consumers eventually decide to adopt a DI largely depends on their first impression of the DI’s trustworthiness. Nevertheless, research about consumers’ initial perceptions of DIs – especially initial trust perceptions – is scant. Based on signaling theory, this study proposes that start-ups can signal trustworthiness by designing specific aspects of their business models when launching their DIs. Our research based on several experiments investigates whether consumers integrate initial trust in their decision to adopt a start-up’s DI and how to overcome low initial trust perceptions. Specifically, we examine the influence of different digital business model aspects, i.e. benefit communication and revenue model, on initial trust perceptions. Overall, we demonstrate the importance of initial trust perceptions in DI adoption, and specify how start-ups can overcome low initial trust perceptions by designing specific aspects of digital business models as signals of trustworthiness. Our study contributes to the effectiveness of the design of specific digital business models. First, we add to research investigating the influence of message content on adoption intention. Here, we demonstrate that the communication of origin-specific benefits, such as being personal, leads to higher initial trust perceptions than focusing on digital benefits, such as being transparent. Second, we see a rise in the use of data-based revenue models in contrast to pay-per-use revenue models. Start-ups tend to implement data-based revenue models, which seem to come with the advantage of offering their DI for free, with the intention to increase consumer acceptance and adoption. Our study takes a first step in investigating the effectiveness of such data-based revenue models finding that in order to overcome low initial trust perceptions of DIs, start-ups should reevaluate whether to charge a data price or a monetary price. In fact, employing a pay-per-use revenue model seems to yield higher initial trust perceptions and adoption intentions than employing a data-based revenue model where consumers ‘pay’ with their data.