Platform effects on traditional public transportation

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	===='''The Sharing Economy in Social Media: Analyzing Tensions Between Market and Non-market Logics'''====		===='''The Sharing Economy in Social Media: Analyzing Tensions Between Market and Non-market Logics'''====
	*'''''Context'''''		*'''''Context'''''
		+	This in-depth study explores the complex world of Sweden's sharing economy and analyzes its dynamics using social media data. It explores the intricacies and uncertainties surrounding its conception while examining the ways in which market and non-market logics merge inside this dynamic organizational world. The study emphasizes the conflicts that exist between commercial and sharing-oriented initiatives, revealing how profit-driven organizations co-opt oppositional viewpoints into the discourse. It reveals the subtle aspects of user-generated material through content analysis of social media posts, emphasizing the contrast between real sharing behaviors and commercial transactions. It highlights the need for a more complex definition that includes both market-driven processes and communal values inside the sharing economy framework by investigating the stakeholder landscape.
	*'''''Overview'''''		*'''''Overview'''''
-	Sharing-economy platforms are increasingly popular due to their innovative value creation, effective resource utilization, and technological disruption. However, there are still disagreements and uncertainties in the subject. Online activities such as file sharing, collaborative encyclopedias, content sharing, and open-source software have led to the rise of the sharing economy, which is increasingly linked to platform capitalism. Unresolved challenges of tax evasion and regulatory compliance pose a disruption to established enterprises and existing institutions in the sharing economy.	+	Sharing-economy platforms are gaining popularity among many enterprises due to their ability to create new value, efficiently utilize resources, and disrupt the market with technology. Still, there are differences of opinion and ambiguities around the topic. The sharing economy was spawned by collaborative encyclopedias, open-source software, file sharing, content sharing, and other online activities driven by both non-financial and financial motivations. However, it's becoming increasingly associated with platform capitalism, wherein profit-driven companies create two-sided markets and profit from the product and service exchange between buyers and sellers. The sharing economy's well-established businesses and institutions are at risk from the unresolved issues of tax evasion and regulatory compliance. This study looks at the sharing economy in Sweden with a focus on its non-market and market practises, and it also suggests a larger definition for it.The purpose of the study question is to find out how market and non-market logics related to the sharing economy are represented in social media interactions. It is believed that two institutional logics that are continually altering the boundaries of the sharing economy represent a recently emerged organizational domain. Actor behaviour are contingent upon every facet of the organizational field, which is constructed through a dynamic process. Three aspects are investigated: the state of the field at the moment, the manner in which meanings circulate among actors, and the dynamics among actors. The coexistence of many institutional logics enables for changes within the sharing economy, as there aren't yet established institutionalized practices in place. Institutional change is articulated through the actors' ongoing struggles for meaning, and change is largely dependent on their ability to inspire and coordinate.This study looks into who is driving the growth of the sharing economy and how it is framed using social media analysis, or SMA. Researching contemporary issues like the sharing economy is made easier by SMA's interdisciplinary approach, which incorporates social media data analysis techniques. In order to collect data in an organized manner, the study made use of Notified, a technology that collects user-generated content from numerous social media platforms. The data collection was created over a two-month period by gathering 1034 postings using the Swedish phrases for "sharing economy" and "the sharing economy." Both organized and unstructured content in the data were examined using content analysis. The study did not distribute data across social media platforms because its objective was to record the framing of the sharing economy on these platforms. The sequential analysis paradigm guided the data set's institutional analysis.The study looks into how the sharing economy is now doing as an organizational sector by looking at thematic categories and institutionalized practices in user-generated content. The five main topic areas that were selected were the sharing economy as a phenomenon, user experiences related to the sharing economy, the sharing economy in connection to specific participants, sharing economy industries, and sharing economy societal repercussions. Furthermore, the analysis looked at 21 separate difficulties and found 31 unique sharing economy participants. The results were categorized into two phases: a first look at the institutionalized practises, problems, and organizational field; and a second look at the actors and their positions within the framing.The sharing economy is a relatively new phenomena with unclear societal implications that is the subject of many social media material. The most frequent activity is selling, and most postings on the sharing economy are commercial exchanges. The sharing economy is in a precarious position due to tensions between market and non-market logics. The term "sharing economy" is being used to describe activities that are not related to sharing, and there are continuous discussions about what exactly it means. More than half of the book is devoted to the topic of taxes and regulations, with an emphasis on official organizations involved in the sharing economy. A market logic has generated controversy, and the sharing economy is characterized by tensions between non-market and market logics. Distinct standards and values indicate an ongoing institutional change.In Sweden, there are various stakeholder groups that shape the sharing economy, with content controlled by profit-driven private corporations like Uber and Airbnb. The meanings and values associated with the organizational realm of the sharing economy are enhanced by these actors. Some users, however, disagree with them and argue they shouldn't be considered part of the phenomenon. The field is currently fragmented, with many actors contributing to the collective frame. This ambiguity stems from the multiplicity of actor groups and the potential incentives they have to build credibility for their organizations. The sharing economy and collaborative consumption have coexisted with two institutional logics. Given that the findings are only partially consistent with Möhlmann's (2015) definition, the results highlight the need for a new definition of the sharing economy that takes into consideration both market and non-market logics and practises.This research examines how the sharing economy is conceptualized in Sweden and reveals the various ways it functions by applying both non-market and market logics.
-		+
-	This study proposes a broader definition of the sharing economy and examines the sharing economy in Sweden with an emphasis on its non-market and market practices. The study uses Social Media Analysis (SMA) to investigate how the sharing economy is framed and who is driving its development. Notified, a tool that gathers user-generated information from many social media platforms, was used to gather data over two months. Content Analysis was used to examine both structured and unstructured material in the data.	+
-		+
-	The study identified five primary subject categories: the sharing economy as a phenomena, user experiences related to the sharing economy, the sharing economy in relation to certain players, sectors of the sharing economy, and societal ramifications of the sharing economy. Additionally, 31 distinct sharing economy participants were identified and 21 different challenges were examined.	+
-		+
-	The findings highlight the need for a new definition of the sharing economy that takes into account both market and non-market logics and practices. The findings are only partially consistent with Möhlmann's (2015) definition, as the field is dispersed and numerous actors are involved in the collective framing. The multitude of actor groups and motivations they could have to establish credibility for their organizations contribute to this ambiguity.	+
-		+
-	The study acknowledges four drawbacks of the study: small data collection, the dispersed social media landscape, and the inclusion of only publicly posted user-generated content. Subsequent studies should focus on understanding the nascent state, societal ramifications, institutional constraints, and tactics employed by participants in the sharing economy.	+
	*'''''Strengths and Weaknesses'''''		*'''''Strengths and Weaknesses'''''
		+	This article identifies four weaknesses in the study on the sharing economy: limited data collecting, a dispersed social media environment, and the use of only publicly available user-generated content. It does, however, draw attention to several of its strong points, including its thorough technique and social media analysis that examines the sharing economy from a variety of perspectives while taking non-market logics into account.
	*'''''Assessment'''''		*'''''Assessment'''''
		+	In conclusion, through an analysis of social media interactions, the study explores the complexities of Sweden's sharing economy and highlights the conflicts between market and non-market logics. It reveals the fractured environment of conflicting opinions and the growing impact of profit-driven organizations such as Uber and Airbnb by dissecting the interplay among stakeholders. Although it acknowledges the ongoing changes in institutions, it also highlights the need for a more precise definition that takes into account the complexities of this changing economic environment. This will improve understanding by including both non-market and market logics into the conceptualization of the sharing economy.

---

Revision as of 00:58, 5 December 2023

Context Statement: Uber's on-demand, personalized transportation has significantly impacted urban mobility, influencing traditional public transport systems' perception and usage. However, it has also raised concerns about viability, affordability, and adaptability.

Disruptive Change in the Taxi Business: The Case of Uber. The American Economic Review

Cramer, J., & Krueger, A. B. (2016). Disruptive Change in the Taxi Business: The Case of Uber. The American Economic Review, 106(5), 177–182.

DOI: 10.1257/aer.p20161002

Article Link: https://doi.org/10.1257/aer.p20161002

• Context

This study uses a combination of aggregated and micro-level data to examine and compare the capacity utilization rates of Uber and taxi drivers in various cities. The study determines the capacity utilization rate by analyzing the work hours of drivers and the amount of time they spend with fare-paying customers. Because UberX drivers are so dominant in the Uber ecosystem, special attention is given to them in this study. This analysis is based on data from five cities in Uber's administrative database, as well as data from Seattle and Los Angeles. The study takes into consideration a period prior to Uber's notable influence on the transportation business, which is noteworthy. It also adjusts for temporal differences between cab and Uber data. With possible explanations ranging from regulatory inefficiencies in the taxi industry to Uber's scale and technological advantages, the findings highlight notable differences in utilization rates between UberX and taxi drivers, raising concerns about the productivity of for-hire drivers.

• Overview

The purpose of the study is to compile information on the capacity utilization rates of Uber and taxi drivers in various cities. The information is obtained from aggregated data from Seattle and Los Angeles as well as micro-level daily data on the work hours and time of taxi drivers. Individual-level data on drivers' work hours and the number of hours they had a fare-paying passenger in their car can be used to calculate the capacity utilization rate. The same days of the week and proximity to the Boston Marathon are reflected in the data for Uber drivers. Because they make up the largest and fastest-growing group of Uber drivers, the study focuses on UberX drivers. Based on Uber's administrative database for drivers in five cities, the information is presented. Because the cab data comes from a year earlier than the Uber data and covers a time before Uber made major inroads into the industry, the study also takes timing into account.

With the exception of New York, all five cities' UberX drivers had substantially greater capacity utilization rates than taxi drivers, according to the report. While taxi drivers in Boston had passengers in their cars anywhere from 32% of the time to nearly half of the time in New York City, UberX drivers spend around half of their working hours with passengers in their cars. In addition, UberX drivers have a greater mileage-based capacity utilization rate (Fm) than taxi drivers. In terms of the percentage of miles driven with a passenger in the car, UberX drivers in Seattle outperform taxis by 41%. UberX drivers in San Francisco had a greater utilization rate than taxi drivers at all hours, with the narrowest margin between 4 and 8 pm. The variations in the mean capacity utilization rates are not caused by a small number of drivers.

For a variety of reasons, UberX drivers may attain better capacity utilization rates than taxi drivers. These include ineffective taxi licensing laws, Uber's flexible labor supply model, its bigger scale, network efficiencies from scale, and its more effective driver-passenger matching technology. UberX drivers who put in at least seven hours a day had nearly comparable occupancy rates in New York, Seattle, and Los Angeles. This implies that the entry and exit of UberX drivers during the day balances the market, such that the utilization rate remains constant for all drivers, regardless of how long they work. In New York City, UberX drivers' usage rate is 3.5 percent greater than that of taxi drivers', indicating that the little variation in capacity usage rate may be explained by variations in driver-passenger matching technology. The results have an impact on for-hire drivers' productivity because UberX drivers may charge 28% less per hour than taxi drivers while still making the same money.

• Strengths and Weaknesses

This article has both strengths and weaknesses that should be considered. The article's first strength is its extensive data collection, which uses individual level data on drivers' work hours and passenger presence in addition to aggregated and micro-level data. This thorough technique makes accurate evaluation possible. An additional advantage is the transparent way that their conclusions are presented; the study employs specific percentages and comparable measures to make the results clearly understandable. One of the disadvantages that comes to mind is the restricted geographic scope. Even though the study looks at a number of cities, it doesn't offer a worldwide viewpoint on the problem. Another weakness is the possible bias in the data. The reason there is a possible bias is due to the fact that the article only focuses on UberX, meaning that different options in uber could have different rates such as UberGreen, UberXL, and Comfort.

• Assessment

This research carefully evaluates the capacity usage rates of Uber and taxi drivers in different cities by employing a combination of aggregated and personal data. It finds a pattern in which UberX drivers use the service at substantially higher rates than taxi drivers, with the exception of New York. The study identifies a number of possible causes for this inequality, such as operational benefits enjoyed by Uber and differences in regulations. It effectively examines the market balance and driver behavior, exposing the factors shaping the transportation environment. The productivity of for-hire drivers may be impacted by these findings, which emphasize in particular how UberX drivers retain higher rates even when they charge less. The study offers insightful information about how the field of urban mobility is changing.

Uber, Public Transit, and Urban Transportation Equity: A Case Study in New York City

• Context

The article looks at how Uber and public transport interact in New York City, with a particular emphasis on the app's distribution and effects on the equity of urban transit. It indicates places like Manhattan and Queens where Uber either competes with or enhances current services. Uber's role in improving transit equity is called into doubt by the report, which emphasizes how unevenly distributed it is when compared to public transportation.

• Overview

This article uses 2014 Uber pickup data to examine the spatiotemporal link between Uber and public transportation in New York City. Uber complements public transport while also competing with it, according to the data, albeit competition is more pronounced in New York City. When public transport is available and competitive during most of the day, Uber complements it; yet, when public transport is scarce or unavailable after midnight, it competes with it. Uber's services are distributed in a very unequal manner, and the company has no impact on increasing transportation equity. In addition to evaluating how Uber impacts urban transportation equity, the article attempts to quantify the spatiotemporal relationship between Uber and public transportation. There is little quantitative data to back up this claim that the relationship between ridesourcing and public transport has not been thoroughly investigated.The literature on transport equity and policy emphasizes both horizontal and vertical equity, with horizontal equity emphasizing the equitable distribution of costs and benefits among individuals and groups.

Ridesourcing equity studies look into how different socioeconomic classes and income levels can access ridesourcing services. When combined with public transportation, Uber might not increase transportation equity, even though it might increase accessibility to transit in low-income areas. Higher population density, shorter commute times, and denser road networks all correspond with improved accessibility, according to research on ridesourcing accessibility.The interaction between Uber and public transportation is investigated in three buffer regions in this study: the 100-meter buffer, the 100- to 400- or 800-meter buffer, and the outside buffer. In these regions, Uber pickup sites are viewed as either complementing or competing with public transportation. The spatial link between Uber pickups and scheduled public transportation pickups is measured at the Central Business District (CBG) scale using spatial cross-correlation, taking distance decay into consideration. Whereas a negative SCI denotes a complementary relationship, a positive SCI suggests a competitive interaction between Uber and public transportation. The Lorenz curve is used to visually depict the distribution of transportation supply throughout the population, while the Gini Coefficient is used to evaluate the distribution of transportation provision among populations. Uber's association with public transport services and different socioeconomic factors is examined through the calculation of the Pearson correlation coefficient.This study investigates the interaction between Uber and public transport in New York City, with a particular emphasis on the weekly and daily variations in both services.

Findings indicate that there is a plentiful supply of public transportation, a sharp drop in late-night and early-morning Uber pickups, and a steady volume of Uber journeys all week long, especially on Friday and Saturday nights. The survey also looks at Uber pickups and public transportation coverage, and it finds that Queens has the least amount of coverage while Manhattan has the most. In order to examine the connection between the quantity of Uber pickups and scheduled public transportation pickups, the study additionally computes a spatial cross-correlation (SCI). With the exception of midnight, Uber competes with public transportation most of the time in Manhattan; in Queens, however, it enhances it. According to the study's findings, there may be competition between the two forms of transportation given Uber's presence in Queens and Manhattan.The contribution of Uber to equitable urban transit in New York City is examined in this article. Compared to Uber and taxis, the Gini coefficient of public transport is substantially lower, suggesting a more equitable distribution among the populace. Uber's distribution is somewhat more egalitarian than that of public transportation. According to Lorenz curves, 20% of residents use 95% of Uber's services, indicating that the company's distribution is further from the ideal equality line. Uber plays a negligible influence in enhancing transport fairness; the Gini coefficient is only marginally smaller when it is added to the transportation system. Transportation supply and population, income, and minority groups do not significantly correlate, according to Pearson correlation coefficients.

• Strengths and Weaknesses

This article's thorough analysis is its strongest point. The study thoroughly examines how Uber and public transport interact in New York City. The emphasis on equity is another excellent job that this article provides. By concentrating on transport equality, it highlighted the differences in the distribution of services between socioeconomic classes, adding to a crucial component of the conversation about urban transport. It does, however, have a few weaknesses. The article's length is the first; it is rather lengthy. Another weakness is the dated data, this article uses data from 2014, which can alter the overall findings.

• Assessment

To sum up, this study uses Uber pickup data from 2014 to thoroughly examine how the service interacts with public transportation in New York City. By analyzing how Uber competes with and enhances public transportation in different locations and times, it reveals differences in the way that locals receive services. Using a variety of quantitative techniques, the study reveals relationships between Uber's existence and socioeconomic characteristics while also highlighting transportation equity. Uber services are not distributed equally, according to the study, but it has little effect on improving transportation equity.

The Sharing Economy in Social Media: Analyzing Tensions Between Market and Non-market Logics

• Context

This in-depth study explores the complex world of Sweden's sharing economy and analyzes its dynamics using social media data. It explores the intricacies and uncertainties surrounding its conception while examining the ways in which market and non-market logics merge inside this dynamic organizational world. The study emphasizes the conflicts that exist between commercial and sharing-oriented initiatives, revealing how profit-driven organizations co-opt oppositional viewpoints into the discourse. It reveals the subtle aspects of user-generated material through content analysis of social media posts, emphasizing the contrast between real sharing behaviors and commercial transactions. It highlights the need for a more complex definition that includes both market-driven processes and communal values inside the sharing economy framework by investigating the stakeholder landscape.

• Overview

Sharing-economy platforms are gaining popularity among many enterprises due to their ability to create new value, efficiently utilize resources, and disrupt the market with technology. Still, there are differences of opinion and ambiguities around the topic. The sharing economy was spawned by collaborative encyclopedias, open-source software, file sharing, content sharing, and other online activities driven by both non-financial and financial motivations. However, it's becoming increasingly associated with platform capitalism, wherein profit-driven companies create two-sided markets and profit from the product and service exchange between buyers and sellers. The sharing economy's well-established businesses and institutions are at risk from the unresolved issues of tax evasion and regulatory compliance. This study looks at the sharing economy in Sweden with a focus on its non-market and market practises, and it also suggests a larger definition for it.The purpose of the study question is to find out how market and non-market logics related to the sharing economy are represented in social media interactions. It is believed that two institutional logics that are continually altering the boundaries of the sharing economy represent a recently emerged organizational domain. Actor behaviour are contingent upon every facet of the organizational field, which is constructed through a dynamic process. Three aspects are investigated: the state of the field at the moment, the manner in which meanings circulate among actors, and the dynamics among actors. The coexistence of many institutional logics enables for changes within the sharing economy, as there aren't yet established institutionalized practices in place. Institutional change is articulated through the actors' ongoing struggles for meaning, and change is largely dependent on their ability to inspire and coordinate.This study looks into who is driving the growth of the sharing economy and how it is framed using social media analysis, or SMA. Researching contemporary issues like the sharing economy is made easier by SMA's interdisciplinary approach, which incorporates social media data analysis techniques. In order to collect data in an organized manner, the study made use of Notified, a technology that collects user-generated content from numerous social media platforms. The data collection was created over a two-month period by gathering 1034 postings using the Swedish phrases for "sharing economy" and "the sharing economy." Both organized and unstructured content in the data were examined using content analysis. The study did not distribute data across social media platforms because its objective was to record the framing of the sharing economy on these platforms. The sequential analysis paradigm guided the data set's institutional analysis.The study looks into how the sharing economy is now doing as an organizational sector by looking at thematic categories and institutionalized practices in user-generated content. The five main topic areas that were selected were the sharing economy as a phenomenon, user experiences related to the sharing economy, the sharing economy in connection to specific participants, sharing economy industries, and sharing economy societal repercussions. Furthermore, the analysis looked at 21 separate difficulties and found 31 unique sharing economy participants. The results were categorized into two phases: a first look at the institutionalized practises, problems, and organizational field; and a second look at the actors and their positions within the framing.The sharing economy is a relatively new phenomena with unclear societal implications that is the subject of many social media material. The most frequent activity is selling, and most postings on the sharing economy are commercial exchanges. The sharing economy is in a precarious position due to tensions between market and non-market logics. The term "sharing economy" is being used to describe activities that are not related to sharing, and there are continuous discussions about what exactly it means. More than half of the book is devoted to the topic of taxes and regulations, with an emphasis on official organizations involved in the sharing economy. A market logic has generated controversy, and the sharing economy is characterized by tensions between non-market and market logics. Distinct standards and values indicate an ongoing institutional change.In Sweden, there are various stakeholder groups that shape the sharing economy, with content controlled by profit-driven private corporations like Uber and Airbnb. The meanings and values associated with the organizational realm of the sharing economy are enhanced by these actors. Some users, however, disagree with them and argue they shouldn't be considered part of the phenomenon. The field is currently fragmented, with many actors contributing to the collective frame. This ambiguity stems from the multiplicity of actor groups and the potential incentives they have to build credibility for their organizations. The sharing economy and collaborative consumption have coexisted with two institutional logics. Given that the findings are only partially consistent with Möhlmann's (2015) definition, the results highlight the need for a new definition of the sharing economy that takes into consideration both market and non-market logics and practises.This research examines how the sharing economy is conceptualized in Sweden and reveals the various ways it functions by applying both non-market and market logics.

• Strengths and Weaknesses

This article identifies four weaknesses in the study on the sharing economy: limited data collecting, a dispersed social media environment, and the use of only publicly available user-generated content. It does, however, draw attention to several of its strong points, including its thorough technique and social media analysis that examines the sharing economy from a variety of perspectives while taking non-market logics into account.

• Assessment

In conclusion, through an analysis of social media interactions, the study explores the complexities of Sweden's sharing economy and highlights the conflicts between market and non-market logics. It reveals the fractured environment of conflicting opinions and the growing impact of profit-driven organizations such as Uber and Airbnb by dissecting the interplay among stakeholders. Although it acknowledges the ongoing changes in institutions, it also highlights the need for a more precise definition that takes into account the complexities of this changing economic environment. This will improve understanding by including both non-market and market logics into the conceptualization of the sharing economy.