Impact of COVID-19 outbreak and vaccination on ride-sharing services: a social media analysis

The global COVID-19 pandemic produced several changes in nearly every aspect of our lives. Ride-sharing platforms such as Uber and Lyft must adapt their strategies and aims to stay afloat. The analysis in this study is based on 216,120 tweets in the U.S. between January 1, 2019, and December 30, 2021, about Uber. It includes four separate analyses: Popularity and Usage Analytics, Sentimental Analytics, Voice Analytics, and Topic Mining Analytics. The result shows that usage and popularity of Uber on Twitter negatively affect Covid and death cases. In contrast, vaccination helps mitigate the shock of Covid. Additionally, ' ‘Uber's policy and business model was beneficial in improving its positive image during the pandemic;On the early breakout of Covid in the U.S. Uber had a jump on the positive sentiment, mainly because Uber provided safer service than public transportation. [ FROM AUTHOR] Copyright of Transportation Letters is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Platform-centric vs. multi-party service failure: an examination of consumers' negative word of mouth about sharing economy platforms

PurposeThis paper aims to identify platform-centric versus multiparty service failure on sharing economy platforms via topic modeling analysis of consumers' negative online reviews. The authors also sought to understand consumers' reactions to these experiences by detecting negative discrete emotions. The authors then contrasted consumers' responses to platform-centric and multiparty service failure through the theoretical lens of failure controllability.Design/methodology/approachThe authors used a large-scale data set containing more than 81,000 negative app reviews on eight representative hospitality and tourism sharing economy platforms. Topic modeling coupled with emotion detection algorithms revealed 11 themes reflecting diverse forms of platform-centric versus multiparty service failure and their associations with negative discrete emotions based on regression analysis.FindingsThe 11 themes reflecting diverse forms of platform-centric versus multiparty service failure were as follows: app glitch, customer service, locating and pooling, account issues, transaction, offer redemption, interface challenges, intermediary inaction, service lateness and cancellation, incorrect order and fee structure. The analysis suggests that platform-centric service failure is more likely than multiparty service failure to elicit negative discrete emotions.Originality/valueThe research enriches the understanding of platform-related service failure beyond dyadic service interaction. In particular, the authors bring to light two forms of platform-related service failure that warrant scholarly attention: platform-centric versus multiparty service failure. By uncovering the distinct negative emotional associations of platform-centric versus multiparty service failure, the research adds novel empirical evidence to the service failure literature and the relevant attribution theory. Findings offer long-term implications for the sustainable development of sharing economies and platform businesses in contemporary hospitality.

Effects of customers' perceived risks of sharing economy on self-protective behavior toward COVID-19

PurposeThis study aims to investigate how customers' perceived risks of sharing economy (SE) affect their self-protective behaviors when using SE, leading to their future behavioral intention. Additionally, this study looks into whether there are any differences between accommodation-sharing and ride-sharing customers in the aforementioned relationships.Design/methodology/approachAn online survey targeting two groups of SE customers (i.e. accommodation sharing and ride sharing) was used. Using partial least squares structural equation modeling, the mechanism of how SE customers' perceived risks of SE affect their self-protective behaviors, which in turn influence their future behavior intention. A multigroup analysis was performed to assess the difference between the two groups of SE customers. Finally, a multivariate analysis of variance (MANOVA) was conducted to see the potential differences between the five classifications of self-protective behaviors in their perceived risks.FindingsSE customers' psychological risks positively affected their hygiene protective behaviors and social protective behaviors, influencing their behavior intention and relative intention (compared with traditional services). Social risk had a negative impact on SE customers' hygiene protective behaviors. There was a significant difference between accommodation sharing and ride sharing customers in their psychological mechanism of how perceived risks influence their self-protective behaviors.Practical implicationsThe findings of this study help SE platforms and service providers better understand their customers' perceived risks of their services and suggest them to promote their customers' self-protective behaviors so that perceived risks can be mitigated, thereby generating strong behavior intentions. As the results indicated that there is a significant difference between the two major forms of SE (i.e. accommodation sharing and ride sharing) in their customers' perceived risks and self-protective behavior, SE platforms can further refine their operational and marketing efforts based on the findings.Originality/valueThis study offers a comprehensive understanding of SE customers' self-protective behaviors by examining the effects of SE customers' different perceived risks on their self-protective behaviors during the unprecedented pandemic. Furthermore, the comparison of the two most popular forms of SE (i.e. accommodation sharing and ride sharing) provides new perspectives to understand customers' behavior in the SE context.

Effective and Privacy-Preserving Estimation of the Density Distribution of LBS Users under Geo-Indistinguishability

With the widespread use of mobile devices, location-based services (LBSs), which provide useful services adjusted to users' locations, have become indispensable to our daily lives. However, along with several benefits, LBSs also create problems for users because to use LBSs, users are required to disclose their sensitive location information to the service providers. Hence, several studies have focused on protecting the location privacy of individual users when using LBSs. Geo-indistinguishability (Geo-I), which is based on the well-known differential privacy, has recently emerged as a de-facto privacy definition for the protection of location data in LBSs. However, LBS providers require aggregate statistics, such as user density distribution, for the purpose of improving their service quality, and deriving them accurately from the location dataset received from users is difficult owing to the data perturbation of Geo-I. Thus, in this study, we investigated two different approaches, the expectation-maximization (EM) algorithm and the deep learning based approaches, with the aim of precisely computing the density distribution of LBS users while preserving the privacy of location datasets. The evaluation results show that the deep learning approach significantly outperforms other alternatives at all privacy protection levels. Furthermore, when a low level of privacy protection is sufficient, the approach based on the EM algorithm shows performance results similar to those of the deep learning solution. Thus, it can be used instead of a deep learning approach, particularly when training datasets are not available.

Application-Based Cab Services in India: Commuters' Barriers due to COVID-19

The outbreak of the novel Coronavirus pandemic has brought the world to a standstill. The constant increase in the rise of cases and deaths has compelled nearly all countries to impose lockdowns and other restrictive measures. The restrictions on travel and other non-essential activities have raised some serious business concerns for ridesharing, carpooling, and cab rental services. This study aims to identify, analyze, and prioritize the commuters' barriers to App-based Ridesharing Services during COVID-19's first and second waves, and potential ways of adaptation for an anticipated third wave in Indian contexts. The hierarchy of barriers is established using the responses from sixty respondents and their analysis using the multi-criteria decision-making (MCDM) technique, the Analytic Hierarchy Process (AHP). ‘Safety from contagion' was found to be the most significant and strong factor followed by the desire for personal space and personal security as the most important inhibitors for not choosing ridesharing services during COVID-19. Socio-economic status and the lack of reliability of service were not given much importance by the respondents. The current and potential implications for sustainable business and the environment are also discussed.

Research on Matching Mechanism and Route Planning of Intercity Carpool

Under the influence of COVID-19, intercity ride-sharing has become more and more popular due to its relatively little contact and low price and has gradually become one of the important ways of intercity transportation. The ride-sharing platform provides functions of information interaction among passengers and drivers, allocating the transportation tasks and recommending the optimal route planning. Existing ride-sharing platforms fail to take user's personalized needs into account when assigning tasks, and users have low satisfaction with the planned routes. This paper designs an allocation algorithm (Allocation Algorithm 4 Inter-city Carpool) for intercity carpool and proposes a pricing function related to the detour distance and user's satisfaction, so as to ensure the optimal benefits for ride-sharing platforms and drivers, as well as the optimal passenger satisfaction. The AA4IC algorithm is proved to be incentive compatible and budget balanced theoretically, and the effectiveness of allocation scheme generation and path planning is verified by experiments. When the algorithm is iterated 1000 times, the time is less than 200 s, and the task assignment under the optimal user satisfaction can be achieved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Reorienting the sharing economy for social benefit: the nonprofit digital platform business model

Purpose>Digital platforms enable the sharing economy and have become dominant business models in many industries. Despite their many benefits, negative externalities associated with the growth of for-profit digital platforms, such as Uber and Google, have ignited concerns among market participants, policymakers and society as a whole, without corrective market forces in sight. One way to address this problem is through a combination of government regulation, criminal enforcement actions and private antitrust litigation. This study aims to analyze an alternative approach, called the nonprofit digital platform (NDP), which is an emerging business model capable of unleashing free-market forces and enhancing the sharing economy’s social benefits.Design/methodology/approach>This study documents the negative externalities (actual and potential) of for-profit digital platforms, uses the product attributes model to explain the market position and strategy of NDPs with respect to for-profit digital platforms and provides recommendations for the successful launch and management of NDPs.Findings>An NDP is a market-based alternative to antitrust, regulation and litigation that enhances the social value created by the sharing economy, but its success requires startup-like management that attracts and retains talent, capital, effective advertising and positive network externalities.Social implications>NDPs can force free-market adjustments in the industries they enter, reduce the negative spillovers of for-profit digital platforms and increase social value by incrementally raising the level of competition.Originality/value>This study conceptually explores the value that nonprofits could bring to the sharing economy in fulfilling its promise and provides strategic recommendations for social-digital entrepreneurs and nonprofits.

RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing Services

Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world;and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54%, utilization of available vehicles increased by 6%, and a 5 × increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services' availability and surge pricing. © 2022 ACM.

Travel Mode Choice Analysis with Shared Mobility in Context of COVID-19

To analyze the impact of COVID-19 on the travel mode choice behavior with diverse shared mobility services, this study designed the stated preference (SP) questionnaire for the multi-modal transportation system which include conventional travel modes, ride hailing, ride sharing, car sharing, and bike sharing. The mixed Logit models with panel data were proposed to investigate the travel mode choices before and during COVID-19. The influence differences of explanatory variables are compared, and the joint effects of perceived pandemic severity and mode choice inertia are examined. Based on the elasticity analysis, the mode choice preferences are predicted corresponding to different management policies under COVID-19 pandemic. The results indicate that the perception to pandemic severity has significant impacts on the ridership of ride sharing and car sharing, and the mode choice inertia obviously affects the usage of ride hailing, car sharing, and bike sharing. When the perceived pandemic severity reduces to 30%~50%, the strategy of increasing parking charge to 1.6~3.0 times would reduce the usage of private car to pre-pandemic condition, and the car sharing with lower close contact risk could become a main substitute. When the perceived pandemic severity is higher than 60%, the strategy of increasing the travel safety of ride sharing to 1.4~3.6 times would improve the ridership. Copyright © 2022 by Science Press.

Exploring non-users' intention to adopt ride-sharing services: Taking into account increased risks due to the COVID-19 pandemic among other factors.

The coronavirus disease 2019 (COVID-19) outbreak has a substantial negative effect on the global transportation industry. Ride-sharing is an innovative means of transportation that is also affected by the COVID-19. How and when individuals adopt ride-sharing services under the COVID-19 context should be explored to reduce the influence of the COVID-19 on ride-sharing and promote the development of ride-sharing services. This research investigates the effect of ambiguity tolerance and environmental concern on potential users' intention toward adopting ride-sharing services and further examines how the COVID-19 affects their intention toward adopting ride-sharing services. Data from 964 potential users of ride-sharing services suggest that ambiguity tolerance and environmental concern directly and positively influence potential users' intention toward adopting ride-sharing services. In addition, both indirectly affect consumers' intention toward adopting ride-sharing services through perceived ease of use and perceived usefulness. Moreover, the perceived health threat negatively moderates the effect of ambiguity tolerance and environmental concern on consumers' intention toward adopting ride-sharing services. This study enriches the research on how and when ambiguity tolerance and environmental concern influence consumers' intention toward adopting ride-sharing services. Furthermore, this study highlights the moderating effect of perceived health threat under the COVID-19 context. Based on the empirical findings, practical implications are proposed for the providers and facilitators of ride-sharing services.

UV-LEDs Based Photocatalytic Cabin IAQ System to Eliminate Viruses Encountered in a Conditioned Space

The author has been conducting research on UV based photocatalytic air purifier systems for the past 5 years to eliminate living organic germs, bacteria, pathogens, etc. from the cabin air. An HVAC system has been developed by using a filter impregnated by titanium di-oxide (TiO2) with UV lights to improve and maintain cabin air quality. The designed system can be used for conventional vehicles, EVs, ride sharing and for autonomous vehicles. The author has designed and constructed a 3rd generation HVAC unit for cabin air purification for automobiles that is based on UV photocatalytic process by using UV-C LEDs to eliminate viruses that typically exist in conditioned space. The author has conducted tests with the following viruses and bacteria that are typically encountered in a conditioned environment:(i)Staph Epidermititus: Infections in wounds (Anthrax)(ii)Erwinia Herbicola: Bacteria (Infection in soil and water)(iii)MS2: RNA, COVID-19(iv)Phi-174: DNA, Herpes and HIV(v)Bacillus Globigii: Virus - influenza(vi)Aspergillus Niger: Mold spore (Black mold) Percentage destruction rates for the above viruses and bacteria at 15, 30, 45 and 60 minute intervals are presented in this paper. The developed system is able to reduce the viruses by almost 99.99#x00025;(log 4 reductions) in first 15 minutes of unit operation. Aspergillus Niger destruction rates were lower - approx. 90.3#x00025;in 15 minutes (log 1 reduction). The developed system out performs the industry standard of log 4 reductions in 60 minutes! Using a UV-C and UV-A LED light sources with titanium dioxide filter makes this a unique application for automobile HVAC systems. Additional tests have been planned in 2022 to ensure the developed system is able to eliminate Omicron, a variant of COVID-19. © 2022 SAE International. All Rights Reserved.

Determinants of on-demand ridesharing: the role of awareness of environmental consequences

Purpose>The primary goal of this study is to determine the predictors of on-demand ridesharing intention in an emerging economy. For this purpose, the study uses the theoretical underpinnings of the theory of planned behavior (TPB).Design/methodology/approach>The study surveyed 347 frequent users of ridesharing services using a set of pre-validated scales. The resulting data were analyzed using covariance-based structural equation modeling (CB-SEM).Findings>The results of SEM analysis disclosed that the significant factors contributing to ridesharing intention are awareness of environmental consequences, subjective norm, perceived behavioral control and attitude (towards ridesharing).Practical implications>This empirical research provides statistically robust insights for developing marketing strategies that attract more individuals toward ridesharing services.Originality/value>This research has remarkable significance as it is one of the pioneering studies that critically examine the determinants of ridesharing intention from a South Asian emerging economy. Further, the extended TPB framework proposed in this study explains 71.4% variance in ridesharing intention, which is significantly higher than existing studies, with none of them explaining more than 70% variance.

Crowd Models for Last Mile Delivery in an Emerging Economy

The dramatic rise in online shopping means that the last mile delivery (LMD) task is becoming extremely important. However, last mile delivery faces many economic, social, and environmental challenges. A fast-growing innovative solution is Crowd Logistics Delivery (CLD). This study investigates how CLD is meeting these challenges in a rapidly emerging economy (Saudi Arabia). It uses semi-structured interviews to analyse CLD from the perspectives of multiple stakeholders, focusing on its implementation, benefits to different stakeholders, and its limitations. While the findings of this study broadly support the work of other studies in this area, it provides several new insights. It observed three different business models being used for CLD: B2B, B2C, and C2C. It identified the internal success factors of each business model, including registration, assigning orders, compensation, and the payment model. It revealed the motivations for stakeholders to use CLD as a last mile delivery solution, such as LMD-related benefits and the social impact on society. In addition, the study highlighted the four main challenges these CLD implementations face that impede their success: legislation, availability of supply/drivers, trust, and culture. These results add to the rapidly expanding field of CLD.

The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China.

The COVID-19 pandemic has brought unprecedented disruptions to many industries, and the transportation industry is among the most disrupted ones. We seek to address, in the context of a ride-sharing platform, the response of drivers to the pandemic and the post-pandemic recovery. We collected comprehensive trip data from one of the leading ride-sharing companies in China from September 2019 to August 2020, which cover pre-, during-, and post-pandemic phases in three major Chinese cities, and investigate the causal effect of the COVID-19 pandemic on driver behavior. We find that drivers only slightly reduce their number of shifts in response to increased COVID-19 cases, likely because they have to make a living from providing ride-sharing services. Nevertheless, conditional on working, drivers exhibit strong risk aversion: As the number of new cases increases, drivers strategically adjust the scope of their search for passengers, complete fewer trips, and as a result, make lower daily earnings. Finally, our heterogeneity analyses indicate that the effects appear to vary both across drivers and over time, with generally stronger effects on drivers who are older, more experienced, more active before the pandemic, and higher-status within the firm. Our findings have strong policy implications: These drivers tend to contribute more to the focal company, and also rely more on providing ride-sharing services to make a living. Therefore, they should be prioritized in stimulus plans offered by the government or the ride-sharing company.