Search results for: Uber

Authors: Audie Daniel Wood

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In the spring of 2020, the country and the economy came to a halt due to an outbreak of the novel coronavirus, SARS-2, virus known as COVID-19. One of the hardest hit sectors of the economy was the gig-sector, which includes Lyft, Uber, Door-Dash, and other services. In this study, we examined the effects of the independent contractor status of laborers in this field to see how a near-complete economic shut-down affected the lives of laborers who are denied access to health-care and unemployment benefits due to their status as independent contractors. What the study found was there was no 'life-altering' change to the lives of the workers who used gig-work as supplementary income during the economic shut-down, but those who relied on Lyft and Uber, etc. as their sole source of income were more heavily impacted by the economic shut-down than part-time workers. The second significant finding of the study was that across all genders and races, the idea of having to seek unemployment or help was something that none of the workers wanted. They all felt as if unemployment and social-insurance were for those who could not work. While the findings are not generalizable due to this being a small qualitative study consisting of 27 participants, the findings suggest that the economic and social impact of COVID-19 on those that work in the gig-industry warrants further discussion and research.

Keywords: gig-work, Covid-19, independent contractor, Uber

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Authors: Ana Hol

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Digital systems are in the cognitive wave of the eTransformations and are now extensively aimed at meeting the individuals’ demands, both those of customers requiring services and those of service providers. It is also apparent that successful future systems will not just simply open doors to the traditional owners/users to offer and receive services such as Uber for example does today, but will in the future require more customized and cognitively enabled infrastructures that will be responsive to the system user’s needs. To be able to identify what is required for such systems, this research reviews the historical and the current effects of the eTransformation process by studying: 1. eTransitions of company websites and mobile applications, 2. Emergence of new sheared economy business models as Uber and, 3. New requirements for demand driven, cognitive systems capable of learning and just in time decision making. Based on the analysis, this study proposes a Cognitive eTransformation Framework capable of guiding implementations of new responsive and user aware systems.

Keywords: system implementations, AI supported systems, cognitive systems, eTransformation

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Authors: Raúl Bajo-Buenestado

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This paper studies the impact of the intensity of market competition on firms' willingness to adopt green technologies —which has become particularly relevant in the light of the debate on whether competition policies should be relaxed to achieve certain environmental targets. We exploit the staggered rollout of different rail-hailing platforms (most notably, Uber) across different metropolitan areas in Spain as a natural experiment that provides time and city-specific exogenous variation in the intensity of competition to study the impact on taxi drivers' decisions to purchase “green” or “dirty” vehicles. It was shown that the entry of these platforms significantly increased the takeout of green vehicles among professional drivers in incumbent (dominant) conventional taxi companies and decreased that of dirty vehicles. The exact opposite effect is observed in the cities where these platforms were extremely unlikely to enter. Back of the envelope calculations suggest that the entry of Uber is associated with an extra green vehicle purchase in every four among taxi drivers, resulting in a substantial drop in the level of emissions from the taxi fleet —still mostly dominated diesel vehicles.

Keywords: technological change, green technology adoption, market competition, diffusion of technology, environmental externalities

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Authors: Ricardo Daziano

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In the analysis and planning of the mobility ecosystem, preferences for ride-hailing over incumbent street-hailing services need better understanding. In this paper, a seminonparametric discrete choice model that allows for flexible preference heterogeneity is fitted with data from a discrete choice experiment among adult commuters in Montreal, Canada (N=760). Participants chose among Uber, Teo (a local electric ride-hailing service that was in operation when data was collected in 2018), and a standard taxi when presented with information about cost, time (on-trip, waiting, walking), powertrain of the car (gasoline/hybrid) for Uber and taxi, and whether the available electric Teo was a Tesla (which was one of the actual features of the Teo fleet). The fitted flexible model offers several behavioral insights. Waiting time for ride-hailing services is associated with a statistically significant but low marginal disutility. For other time components, including on-ride, and street-hailing waiting and walking the estimates of the value of time show an interesting pattern: whereas in a conditional logit on-ride time reductions are valued higher, in the flexible LML specification means of the value of time follow the expected pattern of waiting and walking creating a higher disutility. At the same time, the LML estimates show the presence of important, multimodal unobserved preference heterogeneity.

Keywords: discrete choice, electric taxis, ridehailing, semiparametrics

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Authors: Jorge Cortés, Alejandra Martínez, Carlos Pérez, Anaid Simón

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The presence of government, news media, and general citizenry in social media allows considering interactions between them as a form of political communication (i.e. the public exchange of contradictory discourses about politics). Twitter’s asymmetrical following model (users can follow, mention or reply to other users that do not follow them) could foster alternative democratic practices and have an impact on Mexican political culture, which has been marked by a lack of direct communication channels between these actors. The research aim is to assess Twitter’s role in political communication practices through the analysis of interaction dynamics between government, news media, and citizens by extracting and visualizing data from Twitter’s API to observe general behavior patterns. The hypothesis is that regardless the fact that Twitter’s features enable direct and horizontal interactions between actors, users repeat traditional dynamics of interaction, without taking full advantage of the possibilities of this medium. Through an interdisciplinary team including Communication Strategies, Information Design, and Interaction Systems, the activity on Twitter generated by the controversy over the presence of Uber in Mexico City was analysed; an issue of public interest, involving aspects such as public opinion, economic interests and a legal dimension. This research includes techniques from social network analysis (SNA), a methodological approach focused on the comprehension of the relationships between actors through the visual representation and measurement of network characteristics. The analysis of the Uber event comprised data extraction, data categorization, corpus construction, corpus visualization and analysis. On the recovery stage TAGS, a Google Sheet template, was used to extract tweets that included the hashtags #UberSeQueda and #UberSeVa, posts containing the string Uber and tweets directed to @uber_mx. Using scripts written in Python, the data was filtered, discarding tweets with no interaction (replies, retweets or mentions) and locations outside of México. Considerations regarding bots and the omission of anecdotal posts were also taken into account. The utility of graphs to observe interactions of political communication in general was confirmed by the analysis of visualizations generated with programs such as Gephi and NodeXL. However, some aspects require improvements to obtain more useful visual representations for this type of research. For example, link¬crossings complicates following the direction of an interaction forcing users to manipulate the graph to see it clearly. It was concluded that some practices prevalent in political communication in Mexico are replicated in Twitter. Media actors tend to group together instead of interact with others. The political system tends to tweet as an advertising strategy rather than to generate dialogue. However, some actors were identified as bridges establishing communication between the three spheres, generating a more democratic exercise and taking advantage of Twitter’s possibilities. Although interactions in Twitter could become an alternative to political communication, this potential depends on the intentions of the participants and to what extent they are aiming for collaborative and direct communications. Further research is needed to get a deeper understanding on the political behavior of Twitter users and the possibilities of SNA for its analysis.

Keywords: interaction, political communication, social network analysis, Twitter

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Authors: Raja Kannusamy

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The research focuses on the applicable technologies in the automobile industry and their effects on the productivity and annual revenue of the industry. A study has been conducted on 6 major automobile manufacturing industries represented in this research as M1, M2, M3, M4, M5 and M6. The results indicate that M1, which is a pioneer in technological applications, remains the market leader, followed by M5 & M2 taking the second and third positions, respectively. M3, M6 and M4 are the followers and are placed next in positions. It has also been observed that M1 and M2 have entered into an agreement to share the basic structural technologies and they maintain long-term and trusted relationships with their suppliers through the Keiretsu system. With technological giants such as Apple, Microsoft, Uber and Google entering the automobile industry in recent years, an upward trend is expected in the futuristic market with self-driving cars to dominate the automobile sector. To keep up with the market trend, it is essential for automobile manufacturers to understand the importance of developing technological capabilities and skills to be competitive in the marketplace.

Keywords: automobile manufacturing industries, competitiveness, performance improvement, technological applications

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Authors: Mayank Raj, V. G. Narendra

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Ever since the DARPA challenge on autonomous vehicles in 2005, there has been a lot of buzz about ‘Autonomous Vehicles’ amongst the major tech giants such as Google, Uber, and Tesla. Numerous approaches have been adopted to solve this problem, which can have a long-lasting impact on mankind. In this paper, we have used Deep Learning techniques and TensorFlow framework with the goal of building a neural network model to predict (speed, acceleration, steering angle, and brake) features needed for navigation of autonomous vehicles. The Deep Neural Network has been trained on images and sensor data obtained from the comma.ai dataset. A heatmap was used to check for correlation among the features, and finally, four important features were selected. This was a multivariate regression problem. The final model had five convolutional layers, followed by five dense layers. Finally, the calculated values were tested against the labeled data, where the mean squared error was used as a performance metric.

Keywords: autonomous vehicles, deep learning, computer vision, artificial intelligence

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Authors: Ridwan Dewayanto Rusli

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We show how prisoner's dilemma-type competition problems can be mitigated through rapid platform diversification and ecosystem expansion. We analyze a ride-hailing company in Southeast Asia, Gojek, whose network grew to more than 170 million users comprising consumers, partner drivers, merchants, and complementors within a few years and has already achieved higher contribution margins than ride-hailing peers Uber and Lyft. Its ecosystem integrates ride-hailing, food delivery and logistics, merchant solutions, e-commerce, marketplace and advertising, payments, and fintech offerings. The company continues growing its network of complementors and App developers, expanding content and gaining critical mass in consumer data analytics and advertising. We compare the company's growth and diversification trajectory with those of its main international rivals and peers. The company's rapid growth and future potential are analyzed using Cusumano's (2012) Staying Power and Six Principles, Hax and Wilde's (2003) and Hax's (2010) The Delta Model as well as Santos' (2016) home-market advantages frameworks. The recently announced multi-billion-dollar merger with one of Southeast Asia's largest e-commerce majors lends additional support to the above arguments.

Keywords: ride-hailing, prisoner's dilemma, platform and ecosystem strategy, digital applications, diversification, home market advantages, e-commerce

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Authors: Babak Mohajeri

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In recent years, the conventional business model of ownership has been changed towards accessibility in a variety of markets. Two trends can be observed in the evolution of this rental-like business model. Firstly, the technological development that enables the emergence of new business models. These new business models increasingly become agile and flexible. For example Spotify, an online music stream company provides consumers access to over millions of music tracks, conveniently through the smartphone, tablet or computer. Similarly, Car2Go, the car sharing company accesses its members with flexible and nearby sharing cars. The second trend is the increasing communication and connections via social networks. This trend enables a shift to peer-to-peer accessibility based business models. Conventionally, companies provide access for their customers to own companies products or services. In peer-to-peer model, nonetheless, companies facilitate access and connection across their customers to use other customers owned property or skills, competencies or services .The is so-called the sharing economy business model. The aim of this study is to investigate into a new and emerging type of the sharing economy model in which role of customers and service providers may dramatically change. This new model is called Collaborative Service Networks. We propose a mechanism for Collaborative Service Networks business model. Uber and Airbnb, two successful growing companies, have been selected for our case studies and their business models are analyzed. Finally, we study the emergence of the collaborative service networks in the manufacturing domain. Our finding results to a new manufacturing paradigm called social manufacturing.

Keywords: sharing economy, collaborative service networks, social manufacturing, manufacturing development

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Authors: D. Tappi

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In the past decades, the idea of innovation moved from being considered the result of development to being seen as its means. Innovation and its diffusion are indeed keys to the development and economic catch-up of a country. However, the process of diffusing existing innovation in low income countries has demonstrated dependent on inadequate infrastructures and institutions. The paper examines the role of disruptive innovation in bridging the technology gap between high- and low-income countries, overcoming the lack in infrastructures and institutions. In particular, the focus of this paper goes to the role of disruptive innovation in people transportation in Nigeria. Uber, Taxify, and Smartcab are covering a small and interesting market that was underserved, between the high-end private driver markets, the personal car owners and the low-priced traditional cab and the Keke (tricycle). Indeed the small Nigerian middle class and international community have found in the sharing people transportation market a safe, reasonably priced means of transportation in Nigerian big cities. This study uses mainly qualitative data collection methods in the form of semi-structured interviews with major players and users and quantitative data analysis in the form of a survey among users in order to assess the role of these new transportation modes in shaping the market and even creating a new niche. This paper shows how the new sharing economy in people transportation is creating new solutions to old problems as well as creating new challenges for both the existing market players and institutions. By doing so, the paper shows how disruptive innovations applied to low income countries, not only can overcome the lacking infrastructure problem but could also help bridge the technology gap between those and high income countries. This contribution proves that it is indeed exactly because the market presents these obstacles that disruptive innovations can succeed in countries such as Nigeria.

Keywords: development, disruptive innovation, sharing economy, technology gap

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Liam Fassam, Pouria Liravi, Jacquie Bridgman

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Introduction: The way we travel is rapidly changing, car ownership and use are declining among young people and those residents in urban areas. Also, the increasing role and popularity of sharing economy companies like Uber highlight a movement towards consuming transportation solutions as a service [Mobility of a Service]. This research looks to bridge the knowledge gap that exists between city mobility, smart cities, sharing economy and social entrepreneurship business models. Understanding of this subject is crucial for smart city design, as access to affordable transport has been identified as a contributing factor to social isolation leading to issues around health and wellbeing. Methodology: To explore the current fit vis-a-vis transportation business models and social impact this research undertook a comparative analysis between a systematic literature review and a Delphi study. The systematic literature review was undertaken to gain an appreciation of the current academic thinking on ‘social entrepreneurship and smart city mobility’. The second phase of the research initiated a Delphi study across a group of 22 participants to review future opinion on ‘how social entrepreneurship can assist city mobility sharing models?’. The Delphi delivered an initial 220 results, which once cross-checked for duplication resulted in 130. These 130 answers were sent back to participants to score importance against a 5-point LIKERT scale, enabling a top 10 listing of areas for shared user transports in society to be gleaned. One further round (4) identified no change in the coefficient of variant thus no further rounds were required. Findings: Initial results of the literature review returned 1,021 journals using the search criteria ‘social entrepreneurship and smart city mobility’. Filtering allied to ‘peer review’, ‘date’, ‘region’ and ‘Chartered associated of business school’ ranking proffered a resultant journal list of 75. Of these, 58 focused on smart city design, 9 on social enterprise in cityscapes, 6 relating to smart city network design and 3 on social impact, with no journals purporting the need for social entrepreneurship to be allied to city mobility. The future inclusion factors from the Delphi expert panel indicated that smart cities needed to include shared economy models in their strategies. Furthermore, social isolation born by costs of infrastructure needed addressing through holistic A-political social enterprise models, and a better understanding of social benefit measurement is needed. Conclusion: In investigating the collaboration between key public transportation stakeholders, a theoretical model of social enterprise transportation models that positively impact upon the smart city needs of reduced transport poverty and social isolation was formed. As such, the research has identified how a revised business model of Mobility of a Service allied to a social entrepreneurship can deliver impactful measured social benefits associated to smart city design existent research.

Keywords: social enterprise, collaborative transportation, new models of ownership, transport social impact

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Authors: Loha Hashimy, Isabella Castillo

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In today's rapidly evolving technological landscape, the importance of precise navigation and mapping systems cannot be understated. As various sectors undergo transformative changes, the market potential for Advanced Mapping and Management Systems (AMMS) emerges as a critical focus area. The Galileo/GNSS-Based Autonomous Mobile Mapping System (GAMMS) project, specifically targeted toward high-definition mapping (HDM), endeavours to provide insights into this market within the broader context of the geomatics and navigation fields. With the growing integration of Autonomous Vehicles (AVs) into our transportation systems, the relevance and demand for sophisticated mapping solutions like HDM have become increasingly pertinent. The research employed a meticulous, lean, stepwise, and interconnected methodology to ensure a comprehensive assessment. Beginning with the identification of pivotal project results, the study progressed into a systematic market screening. This was complemented by an exhaustive desk research phase that delved into existing literature, data, and trends. To ensure the holistic validity of the findings, extensive consultations were conducted. Academia and industry experts provided invaluable insights through interviews, questionnaires, and surveys. This multi-faceted approach facilitated a layered analysis, juxtaposing secondary data with primary inputs, ensuring that the conclusions were both accurate and actionable. Our investigation unearthed a plethora of drivers steering the HD maps landscape. These ranged from technological leaps, nuanced market demands, and influential economic factors to overarching socio-political shifts. The meteoric rise of Autonomous Vehicles (AVs) and the shift towards app-based transportation solutions, such as Uber, stood out as significant market pull factors. A nuanced PESTEL analysis further enriched our understanding, shedding light on political, economic, social, technological, environmental, and legal facets influencing the HD maps market trajectory. Simultaneously, potential roadblocks were identified. Notable among these were barriers related to high initial costs, concerns around data quality, and the challenges posed by a fragmented and evolving regulatory landscape. The GAMMS project serves as a beacon, illuminating the vast opportunities that lie ahead for the HD mapping sector. It underscores the indispensable role of HDM in enhancing navigation, ensuring safety, and providing pinpoint, accurate location services. As our world becomes more interconnected and reliant on technology, HD maps emerge as a linchpin, bridging gaps and enabling seamless experiences. The research findings accentuate the imperative for stakeholders across industries to recognize and harness the potential of HD mapping, especially as we stand on the cusp of a transportation revolution heralded by Autonomous Vehicles and advanced geomatic solutions.

Keywords: high-definition mapping (HDM), autonomous vehicles, PESTEL analysis, market drivers

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