Purpose: This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.Design/methodology/approach: This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.Findings: Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.Originality/value: Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.

In this article we study compatibility choices of two competing applications from an alternative perspective that considers demand sharing effect and reduced misfit effect to explain differences between the compatible and incompatible cases. We construct a game-theoretic model in which an application with two main businesses tries to share its exclusive demand of its unique business with its rival application on their competing business. We find that incentives to establish one-way compatibility—the application with exclusive demand grants access to its competitor—arise from the decrease of the reduced misfit cost while it may either arise or disappear from the decrease of the shared demand. In addition, consumer surplus increases in the reduced misfit cost and the shared demand. Moreover, we find the compatible case generates more social welfare than the incompatible case. And we find when the reduced misfit cost is beyond a threshold, the horizontal differentiation of the two applications is reduced to a lower level in the compatible case and the price competition is intensified in this situation. However, when the reduced misfit cost is less than a threshold, the price competition of the two applications can either be softened or intensified by compatibility, depending on the degree of shared demand.

Whereas the cooperation between a software application and a new marketplace could benefit both the two parties, the situation would be significantly perplexed by the competition in a market with two competing applications and a third-party (3P) marketplace. This article develops a game-theoretic model to examine the strategic interactions between two competing software applications and a 3P marketplace in the dimension of compatibility. For the additional segment of consumers, the competing applications decide whether to be compatible with a 3P marketplace. In the meantime, the 3P marketplace decides whether to open and open to which application(s) considering the commission fees from the applications compatible with it. We offer insights into how an application should cooperate with a 3P marketplace by considering compatible choice of its competitor and openness choice of the 3P marketplace. We show that the 3P marketplace will open to either of the two applications or just open to the high-quality application by weighing the awareness level of the consumers for both applications. Both applications will choose to be compatible with the 3P marketplace if its competitor is not available on the 3P marketplace. In addition, our results suggest that competition in the 3P marketplace may benefit the high quality firm under certain conditions, depending on the awareness level of consumers for both applications. However, the low-quality application will never be compatible with the 3P marketplace when the high-quality application is available on the 3P marketplace.

The pricing of ride-hailing platforms (e.g., Didi Rider and Uber) is heavily and simultaneously influenced by the cross-group network effect and congestion effect. To analyze the bilateral pricing of ride-hailing platforms under the influence of these two effects, in this paper we construct a game-theoretic model under four different scenarios and analyze the equilibrium outcomes. The results show that: (1) when both passengers and drivers are sensitive to hassle costs, if the cross-group network effect on the passenger side is higher than that on the driver side, then the platform’s pricing on both sides increases with the increase in the congestion effect, otherwise the prices on both sides of the platform decrease with the increase in the congestion effect; (2) when passengers are sensitive to hassle costs and drivers are sensitive to price, if the ratio for passengers’ and drivers’ different perceptions of price and hassle cost is greater than a certain threshold, then the platform’s pricing on the passenger side increases with the increase in the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect; (3) when passengers are sensitive to price and drivers are sensitive to hassle costs, if the ratio for passengers’ and drivers’ different perceptions of price and hassle costs is greater than a certain threshold, then the platform’s pricing on the passenger side decreases with the increase in the congestion effect and the platform’s pricing on the drivers’ side increases with the increase in the congestion effect, otherwise the platform’s pricing on the passenger side increases with the increase of the congestion effect and the platform’s pricing on the driver side decreases with the increase in the congestion effect; (4) when both passengers and drivers are price-sensitive, if the cross-group network effect on the passengers’ side is larger than that on the drivers’ side, then the platform should decrease its pricing on both sides with the increase in the congestion effect, otherwise, if the cross-group network effect on the passengers’ side is less than that on the drivers’ side, the platform should increase its pricing on both sides with the increase in the congestion effect; (5) the platform is able to generate the highest profit in each scenario, and the results of the profit comparison between the four scenarios depends on the cross-group network effects and the congestion effects on both the passengers’ and the drivers’ sides.