The IT‐driven ridesharing economy at the base of the pyramid: Unravelling the impact of uncertainty reduction on drivers' engagement in ridesharing

Author:

Cheng Xusen1ORCID,Zhang Xiaoping1ORCID,Luo Xin (Robert)2ORCID

Affiliation:

1. School of Information Renmin University of China Beijing China

2. Anderson School of Management The University of New Mexico Albuquerque New Mexico USA

Abstract

AbstractDespite the potential benefits for drivers at the base of the pyramid (BoP) of IT‐driven ridesharing businesses, drivers are susceptible to uncertainty owing to their low education and incomes, potentially compelling them to engage less in ridesharing over time. Although this important sociotechnical phenomenon has garnered increasing attention, IS studies on this topic are scarce. Drawing on uncertainty reduction theory, we explored mechanisms for reducing BoP drivers' uncertainty and building their trust to enable them to engage in ridesharing. We identified three means of uncertainty reduction at three levels: trust in passengers, trust in ridesharing platforms, and the effectiveness of ridesharing institutional mechanisms. Using a sequential, triangulated mixed‐methods approach, we conducted 30 semi‐structured interviews to explore trust building with drivers and used data provided by 303 ridesharing drivers to quantitatively test the proposed research model. We found that perceived empathy and information congruity increased BoP drivers' trust in passengers, whereas effective escrow services, perceived justice, matching accuracy, and a high level of privacy control increased BoP drivers' perceived trust in ridesharing platforms, motivating them to engage more frequently with ridesharing platforms. Furthermore, the results indicated that increasing trust in both passengers and ridesharing platforms and improving the effectiveness of ridesharing institutional mechanisms can enhance BoP drivers' engagement in ridesharing. We conducted 30 follow‐up interviews with BoP drivers to corroborate the quantitative results and draw robust inferences.

Funder

National Natural Science Foundation of China

Publisher

Wiley

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