When carrots and sticks meet: A mixed-methods study on internet taxi drivers’ job engagement in the IT-driven sharing economy

2023 | Information & Management | Citations: 0

Authors: Hou, Tingting; Cheng, Xusen; Luo, Xin (Robert)

Abstract: Information technology (IT)-enabled taxi services facilitate people’s lives. How ... Expand

Abstract: Information technology (IT)-enabled taxi services facilitate people’s lives. However, little is known about how Internet taxi employees are motivated and how motivation relates to their job engagement. Using a mixedmethods design, this research explores intrinsic motivators and the effects of external regulations on intrinsic motivation and job engagement. The qualitative study identified three context-specific intrinsic motivators: stress reduction, self-efficacy, and job autonomy. A follow-up quantitative research revealed the significance of external regulations in determining intrinsic motivation and job engagement. The study contributes to knowl­ edge by emphasizing the role of intrinsic motivators and the significance of both monetary incentives and punishment. Collapse

Topics: internet technology intrinsic motivation self efficacy sharing platform missing data

Methods: qualitative interview mixed method survey partial least squares regression PLS tool

Theories: self determination theory

Value of Information Sharing via Ride-Hailing Apps: An Empirical Analysis

2023 | Information Systems Research | Citations: 0

Authors: Rhee, Kyung Sun (Melissa); Zheng, Jinyang; Wang, Youwei; Tan, Yong

Abstract: A ride-hailing platform is an app-based, two-sided platform that matches riders ... Expand

Abstract: A ride-hailing platform is an app-based, two-sided platform that matches riders with vehicles via information technology (IT). In 2015, the Shanghai government introduced a policy to restrict taxi drivers’ access to and acceptance of ride requests via ride-hailing apps during certain hours. We conceptualize this policy shock as the restricting of information sharing enabled by IT and collect comprehensive data on various uses of transportation to gauge the economic benefits of this information sharing for existing capacity and its subsequent externalities on other transportation. Through a time series analysis, we identify significant decreases in the ridership of an affected taxi fleet during times of enforcement but significant increases at some times of nonenforcement postlaunch. Furthermore, the traffic on public transportation, via the city’s transportation cards, and the congestion on the surface streets and expressways significantly increase after launching the policy during both enforcement and most nonenforcement times. These suggest that information sharing via ride-hailing apps can improve the utilization of existing taxi capacity, which further alleviates traffic during alternative times and the burden placed on alternative transportation modes. Interestingly, our mechanism analysis shows decreased profitability after the restriction, which supports the notion that information sharing via ride-hailing apps reduces drivers’ search cost and thus enables them to match not only with more orders but also with those of higher marginal profit. This study contributes to the literature on ride-hailing platforms’ impact and the economic value of information sharing and IT by dissecting the compound ride-hailing’s impact to extract the value of information sharing enabled by IT and to reveal the underlying mechanism. Practically, we evaluate the policy studied, make postrevision suggestions for general contexts, and provide managerial insights on precise policymaking that best extracts the economic value of information sharing in ride-hailing and general forms of online two-sided platforms. History: D. J. Wu, Senior Editor; Yili (Kevin) Hong, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71729001, 71972047, 71831005, and 91746302]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1181. Collapse

Topics: information exchange ride sharing platform mobile application Uber Lyft

Methods: longitudinal research time series analysis robustness check natural experiment Poisson regression

Ridesharing and Digital Resilience for Urban Anomalies: Evidence from the New York City Taxi Market

2023 | Information Systems Research | Citations: 0

Authors: Zhang, Yingjie; Li, Beibei; Qian, Sean

Abstract: Urban anomalies bring uncertainties to society, urban transportation systems, an ... Expand

Abstract: Urban anomalies bring uncertainties to society, urban transportation systems, and businesses. Some urban anomalies, such as no-notice and/or unpredictable terrorist attacks or other urban strikes, if not handled in timely ways may result in loss of life or property and pose tremendous risks to public safety overall. Previous studies have focused on developing emergency-management technology but without in-depth analysis exploring how technology-mediated digital systems perform in reality. Besides, the recent literature has demonstrated significant interest in analyzing and comparing the traditional on-demand service (i.e., taxies) and ridesharing platforms (e.g., Uber). The majority of prior studies have focused on their complementary roles in determining environmental conditions. Little is known, however, regarding how and why the two types of platforms perform in contexts of uncertainty (e.g., under emergency situations). This paper aims to bridge this literature gap. Specifically, we consider different types of unexpected urban anomalies (including terrorist attacks, car crashes, and subway shutdowns) and collect large-scale trip data on taxi and ridesharing services. Empirically, we employ a difference-in-differences econometric model to compare the platform-level performances (measured by the number of fulfilled trips) of a traditional taxi system and a ridesharing platform after urban anomaly shocks. We observe that the ridesharing platform significantly outperforms the traditional taxi platform in coping with the uncertainties brought about by unexpected anomalies. We conduct a set of robustness checks to verify our findings and propose multiple possibilities to explain them. We conclude, conservatively, that the technological effect and the technology-enabled supply elasticity of the digital platforms are the main factors determining the differences between the platforms during an urban anomaly. This work offers important insights into the design of platform strategies, especially for the stimulation of the labor supply and incentivization of the adoption and use of technology in urban transportation systems in response to anomalous urban upheavals. History: Wonseok Oh, Senior Editor; Tianshu Sun, Associate Editor. Funding: Y. Zhang acknowledges the funding support from the National Nature Science Foundation of China [Grant 72272003]. S. Qian acknowledges the support from the National Science Foundation Award [CMMI-1751448]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1212. Collapse

Topics: ride sharing platform Uber terrorist incident Lyft emergency management

Methods: robustness check experimental group econometric modeling longitudinal research mixed method

Surge Pricing and Short-Term Wage Elasticity of Labor Supply in Real-Time Ridesharing Markets

2022 | Management Information Systems Quarterly | Citations: 0

Authors: He, Shu; Qiu, Liangfei; Cheng, Xusen

Abstract: The prominence of real-time ridesharing services, such as Uber and Lyft, has dra ... Expand

Abstract: The prominence of real-time ridesharing services, such as Uber and Lyft, has dramatically changed the landscape of traditional industries. This study provides a comprehensive analysis of the short-term wage elasticity of labor supply in real-time ridesharing markets using data from a major ridesharing platform in China. By exploiting an exogenous shock from uneven driving restrictions as an instrumental variable, we find a negative labor supply elasticity for ridesharing drivers, suggesting that drivers tend to drive less during days with a higher average hourly wage. Specifically, a percent increase in hourly wage will lead to a 0.931 percent decrease in daily working hours. This surprising finding is consistent with the behavioral income-targeting model based on the theory of reference-dependent preferences: Drivers have heuristic daily targets for total earnings and are more motivated to supply labor when they are below their income target than when they are above it. Therefore, they work less on days when earnings per hour are high and quit the market once their income target is reached. In addition, we find that taxi drivers are more rational and have positive labor supply elasticity, which implies that drivers are more rational when they have repeated opportunities for learning. Estimating labor supply elasticity is critical to understanding the economic efficiency of various surge pricing algorithms and driver subsidization programs for ridesharing platforms and policymakers. Our research suggests that a uniform price surging or driver subsidization approach for all ridesharing drivers may not incentivize the labor supply of drivers effectively. Collapse

Topics: ride sharing platform platform worker mobile application mobile system Uber

Methods: robustness check design artifact autocorrelation analysis k-means clustering linear regression analysis

Digital attrition: The negative implications of the sharing economy for the digital options of incumbent firms

2022 | Information Systems Journal | Citations: 1

Authors: Meng, Tian; Ng, Evelyn; Tan, Barney

Abstract: One of the key competitive advantages of sharing economy platforms stems from th ... Expand

Abstract: One of the key competitive advantages of sharing economy platforms stems from their superior IT capabilities, which enable operations that are more efficient and/or effective than the incumbent firms. However, given that many of the incumbents tend to be established market leaders or resource-rich multi-nationals, their general inability to acquire the appropriate digital options (ie, the IT capabilities that enable the launch of, and response to, competitive actions) to meet the challenge of the sharing economy platforms is puzzling. This study explores this phenomenon by posing the research question: How do the “forces at work” associated with the sharing economy paradigm impact the digital options of incumbent firms? Based on the case of Qiangsheng Taxi and how its IT capabilities have been affected by the emergence of ridesharing platforms, the evidence uncovered suggests that the sharing economy can influence the digital options of incumbent firms through a process of digital attrition, which may be induced by a blend of unfavorable contextual influences (ie, an unbalanced regulatory regime, evolving market preferences and the resourcing advantages of sharing economy platforms). A framework is inductively derived from the data collected that depicts digital attrition as a process of three phases: (a) deinstitutionalization, (b) technological incapacitation, and (c) competitive erosion. In doing so, our study suggests that digital attrition may culminate in IT-induced competitive disadvantages for incumbent firms, which in turn, could exacerbate the unfavorable contextual influences to complete a vicious cycle that reinforces the negative influence of the sharing economy even further. Collapse

Topics: sharing platform information technology capability ride sharing platform mobile application IT investment

Methods: qualitative interview business process modeling qualitative coding case study grounded theory

Non-Adoption of Dynamic Pricing in Traditional Taxi Dispatch Organizations

2022 | HICSS | Citations: 0

Authors: Tervo, Erkki; Väyrynen, Karin

Abstract: Dynamic pricing (DP) was introduced into the taxi industry through ride-sharing ... Expand

Abstract: Dynamic pricing (DP) was introduced into the taxi industry through ride-sharing platforms such as Uber. Prior research has identified benefits of DP for ridesharing organizations, drivers and consumers. A legal reform in Finland in July 2018 made DP of taxi rides legally possible. However, even though traditional Finnish dispatch organizations have adopted different technology (e.g., ride-hailing apps), they have not adopted DP. To find out the reasons for the non-adoption, we conducted a qualitative case study among Finnish dispatch organizations. Utilizing IT artifact as an analytical lens, we identified ten aspects related to the technology, the structure and the context within which DP is embedded that help explain why it has not been adopted. We propose that DP in the taxi industry should be seen more broadly than just as “Uber-type” realtime DP, as it has been viewed in previous literature. Our findings have implications for research and practice. Collapse

Topics: price management technology adoption Uber task structure mobile application

Methods: qualitative interview design artifact case study qualitative content analysis group interview

The Empowering and Competition Effects of the Platform-Based Sharing Economy on the Supply and Demand Sides of the Labor Market

2021 | Journal of Management Information Systems | Citations: 0

Authors: Li, Ziru; Hong, Yili; Zhang, Zhongju

Abstract: The sharing economy has fundamentally changed the way many individuals work. In ... Expand

Abstract: The sharing economy has fundamentally changed the way many individuals work. In this paper, we study the impact of the entry of a major ridesharing platform into U.S. Metropolitan Statistical Areas (MSAs), on the supply and demand sides of the labor market. Leveraging the difference-in-differences (DID) research design and a data set combining multiple U.S. Census archival sources, we exploit the variation in labor market metrics before and after Uber's entry into the MSAs. Our empirical findings reveal that the introduction of the ridesharing platform has an empowering effect on workers (the supply side of the labor market) and a competition effect on traditional jobs (the demand side of the labor market). Specifically, Uber's entry into the MSAs increases labor force participation, decreases the unemployment rate of residents living below the poverty level, and improves the employment and financial status of low-income workers. In addition, Uber's entry reduces the employment number and increases wages of conventional low-skill and/or low-wage jobs. This paper provides empirical evidence of the impact of a digital sharing economy platform on the labor market and suggests that policymakers and platform operators should account for this broader impact when they devise policies and make strategic decisions. Collapse

Topics: Uber sharing platform ride sharing platform Google+ Lyft

Methods: survey econometric modeling robustness check longitudinal research descriptive statistic

The Impact of the Sharing Economy on Household Bankruptcy

2021 | Management Information Systems Quarterly | Citations: 6

Authors: Nian, Tingting; Zhu, Yuyuan (Anthony); Gurbaxani, Viijay

Abstract: Powered by digital technologies, many peer-to-peer platforms, or what is called ... Expand

Abstract: Powered by digital technologies, many peer-to-peer platforms, or what is called the sharing economy, have emerged in the past decade. Although the impact of the sharing economy has received considerable attention over the past few years, extant research has not fully documented the impact of the sharing economy on consumers, workers, industry, or society as a whole. In this study, we exploit the geographical and temporal variation in Uber's entry to examine its impact on the personal bankruptcy rate as well as on other consumer credit default rates. We empirically document the changes in personal bankruptcy filings after Uber's entry, and show that personal bankruptcy filings under Chapter 7 experience a drop of 0.047 per 1,000 people after Uber enters a county, which translates to a 3.26% reduction in quarterly bankruptcy filings. Uber's entry also leads to a reduction in Chapter 13 personal bankruptcy filings, but to a smaller degree (0.018 cases per 1,000 people per quarter). We check the validity of our estimates using business bankruptcy filings, which we find are uncorrelated with Uber's entry. Collapse

Topics: Uber sharing platform Google+ Lyft digital divide

Methods: experimental group propensity score matching difference in differences longitudinal research survey

The Deterrent Effect of Ride-Sharing on Sexual Assault and Investigation of Situational Contingencies

2021 | Information Systems Research | Citations: 0

Authors: Park, Jiyong; Pang, Min-Seok; Kim, Junetae; Lee, Byungtae

Abstract: Sexual assault is one of the most repellant and costly crimes, which inflicts ir ... Expand

Abstract: Sexual assault is one of the most repellant and costly crimes, which inflicts irrecoverable harms on victims and society. This study examines the effect of information technology (IT)-enabled ride-sharing platforms on sexual assaults. Drawing upon routine activity theory from the criminology literature, we posit that ride-sharing can reduce a passenger’s risk of being a suitable target of sexual assault by providing a more reliable and timely transportation option for traveling to a safer place. By exploiting the nationwide quasi-experimental setting of Uber’s city-by-city rollouts in the United States during 2005–2017, we demonstrate that Uber’s entry into a city is negatively associated with the number of rape incidents. To zoom into the effects of ride-sharing at a more granular level, we employ precinct-hour–level data on Uber pickups and rape occurrences in New York City in 2015 and conduct spatiotemporal analyses. Our results from the spatiotemporal analyses corroborate those of the quasi-experiment and further reveal situational contingencies in the deterrent effect of ride-sharing. Specifically, ride-sharing contributes to a more significant reduction in the likelihood of rape occurrences in neighborhoods with limited transportation accessibility, and ride-sharing is more effective in deterring sexual crime in riskier circumstances, such as around alcohol-serving places on weekend nights or when the probability of crime occurrences increases. This study sheds new light on the potential of IT-enabled platforms to improve social well-being beyond their economic contributions and offers a new theoretical insight on the distinct role of digital platforms in public safety. Collapse

Topics: ride sharing platform criminality Uber sharing platform digital platform

Methods: quasi experiment robustness check experimental group longitudinal research propensity score method

Theories: activity theory

COVID-19, Urban Transportation, and Air Pollution

2021 | International Conference on Information Systems | Citations: 0

Authors: Wang, Juan; Yu, Yifan; Xue, Wendao; Tan, Yong

Abstract: Urban air pollution induced by heavy transportation is among the most serious p ... Expand

Abstract: Urban air pollution induced by heavy transportation is among the most serious problems of the modern society. However, transportation decisions are endogenous to air quality, which makes it challenging to quantify the causal effect of transportation on air pollution. We aim to overcome this empirical difficulty by leveraging the spread of COVID-19 as instrumental variables and by combining a double/debiased machine learning approach with fixed effects ridge regression model. Collaborating with a famous Internet company which provides search engine and mobile mapping services, we collect a rich dataset including the number of COVID-19 cases, COVID-19-related search queries, public and private transportation, etc. We find that the effects of bus, rail, and private transportation would be significantly underestimated and those of taxi transportation would be overestimated without using instrumental variables. We also find the effects of transportation on different air pollutants are heterogeneous. Theoretical and managerial implications are provided. Collapse

Topics: pandemic search engine covid 19 website internet technology

Methods: ridge regression instrumental variables estimation longitudinal research machine learning computational algorithm

Theories: theory of economic growth