Understanding Driver Continuance Behaviour in Twosided Ridesharing Platforms and its Consequences: Triadic-Interaction Perspective

Abstract

The emergence of two-sided sharing platforms has disrupted traditional markets, individuals, regulations, and social norms and beliefs. This disruption has also been expanded into user behaviour with this technology. Ridesharing platforms such as Uber and Lyft have been introduced and have achieved rapid growth and success in the taxi business; however, there are many campaigns conducted against ridesharing platforms that are organized by drivers. This might lead to a puzzling observation that while ridesharing drivers’ satisfaction is low, they still continue to use these apps. As these drivers’ apparent contradictory behaviour (i.e. continued use while unhappy) is inconsistent with predictions from the extant IT continuance model, this might be an obvious sign of needing to refine the technology continuance model to be consistent with the current evolution of the technologies and within the context of ridesharing platforms. Moreover, although ridesharing platforms have provided important benefits to individuals and society, those advances can also have negative consequences. Academic literature has shown a significant interest in investigating the consequences of two-sided sharing platforms. While significant debate has surrounded the consequences of sharing economy platforms, both positive and negative implications, limited empirical research has been devoted to investigating the consequences of such platforms on provider peers. Therefore, this study, by focusing on the driver perspective within the context of ridesharing platforms in Saudi Arabia, aims to understand driver behaviour by suggesting that the driver experience can be considered as a triadic-dimension experience, including driver-app interaction (i.e. online side) and physical driver-passenger interaction (i.e. offline or hidden side). Building on this suggestion, the study aims to refine the continuance use model to include the influencing factors of both sides of the driver experience in order to investigate the driver continuance intention to use ridesharing platforms. In addition, as the potential forward user perspective could play an important role in explaining puzzling observations, the study seeks to incorporate a forward-looking factor with the continuous model in order to improve the prediction ability and consequently try to explain the puzzling observation in ridesharing platforms. Moreover, to address the call for research on the consequences of sharing platforms, the current research plans to extend the research model to investigate the outcomes of ridesharing platforms on driver family/work balance and their well-being. To achieve our aims, a sequential explanatory mixed-methods design including a quantitative phase followed by a qualitative phase was used. In the first quantitative study, a theoretical framework was developed based on the expectation confirmation model (ECM), collaboration technology model (CTM), and the guidelines for context-specific theorizing in IS research to investigate the determinants of driver continuance behaviour and its impact on driver family/work balance and well-being. By conducting an online survey of 420 ridesharing drivers, the developed model was validated through PLS-SEM analysis providing support that several factors of both sides of the driver experience have an influence on the behavioural beliefs and attitudes (performance and effort expectancies and satisfaction), which subsequently determine their behavioural intention towards continuance usage of ridesharing platforms. More specifically, financial benefits and perceived flexibility as online-usagerelated factors, and social capital and sustainability as offline-related factors, have a positive and significant influence on performance and effort expectancies and subsequently on satisfaction and continuance intention. In addition, perceived fairness and perceived monetary value during offline use of ridesharing platforms have a positive and significant influence on satisfaction and, subsequently, continuance intention. Interestingly, consideration of future consequences has a negative influence (rather than positive) on continuance intention. In addition, unexpectedly, the influence of effort expectancy on continuance intention is moderated by experience such that the influence is strongest for drivers with more experience. In terms of ridesharing platform consequences, the results have revealed that using ridesharing platforms unexpectedly has a negative and significant influence on the driver’s family/work balance and well-being. However, since some quantitative results contradicted expectations, in-depth interviews were conducted to enlighten the survey results. Thus, in the second qualitative study, the template analysis of the qualitative gathered data, derived from 12 semi-structured interviews with ridesharing drivers, validated the findings of the quantitative study and provided deeper insights revealing a set of contextual and explanatory factors that explained unexpected quantitative results. The results reveal that the key factors explaining the significant relationship between effort expectancy and continuance intention among drivers with more experience were uncertainty, lack of platform support, constant difficulties faced in the offline interaction with passengers, and drivers’ tendency for proactive behaviour to find solutions for potential issues. The results also indicate that temporary use intention and anticipated long-use negative consequences were important factors that explain the negative relationship between consideration of future expectations and continuance intention. Regarding the negative outcomes of ridesharing platforms, stimuli of work overload, the unavailability of sufficient time for full-time employed users, unavailability of sufficient income for unemployed users, and habitual use were discovered as important factors that explained the work-family conflict in the ridesharing context, while labour exploitation and physical and mental exhaustion were the key contextual factors that explained the poor driver well- being. Overall, the current research enhances extant literature by providing a theoretical development to IT usage and establishing a contextspecific theory for ridesharing platforms by adding, validating and testing new conceptual constructs for drivers’ perceptions of continuance intention in ridesharing platforms. It also contributes to the literature on a forward-looking perspective on IT continuance by empirically examining and exploring a future-oriented factor that can influence IT continuance intention. From a practical perspective, this study benefits all digital sharing platform stakeholders interested in restoring, maintaining or engendering worker experience and well-being in two-sided sharing economy platforms.