Sharing economy: A systematic thematic analysis of the literature

Abstract

In this paper we present the results of our analyses of the existing literature on the sharing economy. It is important to understand the nature of the sharing economy, because its impacts on the larger economy, society, and governance are extensive and potentially deep and long lasting. Analyses of the 610 articles published between 2006 and 2018 show that there was a rapid increase in publications starting in 2014. Researchers in business, economics, technology, and environmental sciences were interested the most in the topic. There were relatively fewer publications in political science, public administration, and political science. Uber and Airbnb were the most studied platforms. The United States, China, and United Kingdom were the most commonly studied countries. A majority of the literature was a-theoretical. A majority of authors applied some empirical (qualitative and quantitative) research methods.

Keywords

Sharing economy collaborative consumption gig economy platform economy

1. Introduction

The growth of sharing economy practices have impacted economies and societies around the world in the 2010s. What is commonly known as the sharing economy (also known as collaborative consumption, gig economy, collaborative economy, crowd-based capitalism, e-lancing, on-demand economy, or platform economy) entails four groups of actors that are in networked relationships: service providers, who share their resources (e.g., Uber divers who share their cars); customers, who use such services (e.g., riders of Uber cars); platform operators, which facilitate the sharing of services through websites and applications in exchange for a portion of the proceeds (e.g., Uber); and governments, which try to regulate the networked relationships among these three groups of actors. So defined, the sharing economy may not be new, but another form of market economy. It can be argued, however, that the sharing economy is actually new and transformative. Internet technologies are used in intense and new ways to facilitate the interactions between service providers and customers and enhance an economy’s overall capacity to use previously idle capacity (Botsman & Rogers, 2010; Crittenden et al., 2017). The new ways of using idle capacity in the sharing economy are not the same as the old ways of second-hand economy, where ownership of goods is transferred (Beck, 2017).

The sharing economy deserves the attention of social scientists in general and public policy and administration scholars in particular for two reasons. First, its rapid growth in less than a decade has substantial implications for governments and economies (Ganapati & Reddick, 2018). In this short period, sharing economy startups have gained values in the hundreds of billions of dollars, which is indicative of their significance and potential impacts (Le Jeune, 2016). Second, the multiple actors involved in the sharing economy and their networked relationships create a new set of complex problems for governments. Governments in the United States and many other countries are challenged with whether or not and how to regulate platform providers and how to protect the rights of service providers and customers (Ganapati & Reddick, 2018).

There is a limited, but fast-growing, literature on the sharing economy and its implications for the larger economy and public policies. In this study, we evaluated the literature on the sharing economy to understand general patterns in the publications. Our aim was to develop a baseline understanding of the scholarly works on the sharing economy, which can be a basis for further studies of the literature. We aimed to answer the following research questions in our study.

1.
What is the trend in the number of publications in the academic literature? Is there an upward trend, as one would expect, given that there seems to be an increased use of sharing economy platforms and practices and that this new topic has been covered intensively in the media and social media?
2.
Which fields (disciplines) published the most numbers of papers? Sharing economy applications have a potential impact on various aspects of social life (economy, politics, policy, service delivery, etc.); one would expect that the researchers in these fields would be interested in them.
3.
What are the most studied services and platforms? One would expect that the most commonly used and talked about services (ride-sharing and accommodation sharing) and platforms (Uber and Airbnb) would be the primary topics in the literature.
4.
What is the geographic distribution of the publications? The applications of the sharing economy seem to be spreading around the world. It is important to find out the geographic distribution of the studies on it, as many countries are affected but research in a narrow set of environments will not yield comprehensive knowledge of the sharing economy’s global impact.
5.
Which theoretical/conceptual frameworks and methodologies have been applied in the studies? Rigorous academic studies should be guided by, or should be relevant to, some theoretical/conceptual frameworks and they should apply methodologies systematically. It is important to find out if there were frameworks and methodologies that were more commonly used by researchers and which academic fields applied particular frameworks and methodologies.

In the following sections, we first discuss how researchers characterize the sharing economy. Then, we present the methods we used and our findings. We conclude by discussing the meaning of these results for the study of the sharing economy and fruitful future directions for research.
2. Characterizing the sharing economy

The sharing economy is known mostly through the operations of ride-sharing companies (e.g., Uber and Lyft), accommodation-sharing companies (e.g., Airbnb), and bike-sharing companies (e.g., Zagster and LimeBike). It also includes other forms of sharing that involve nonprofit platform operators and direct person-to-person platforms of sharing, such as energy-sharing cooperatives, food-sharing, time-banking, and community gardens. One might argue that all these are different economic and social activities. Indeed, there are differences among them, but the sharing economy is a term that is commonly used to refer to these activities in general (Sundararajan, 2016, p. 30).

To gain an insight into the extent and depth of the impacts of the sharing economy, one can review how it is defined and characterized by scholars and relevant trade groups Ganapati and Reddick (2018) observe that the “sharing economy is broadly characterized by peer-to-peer exchanges for renting goods or services utilizing Internet platforms” (p. 77). Sharing Economy UK, a trade group of large and small companies that participate in the sharing economy activities in the United Kingdom (such as Airbnb, Zipcar, Stubhub, and others), characterizes it as using Internet technologies to connect groups of people and organizations and to make better use of goods, skills, services, capital and spaces (Boyle, 2016). In Botsman and Rogers’s (2010) view, the sharing economy has the following characteristics. First, there must be a critical mass of participants for an activity to occur. Second, they should have idling capacity (i.e., unused time in the resources and product they own: cars, tolls, time, etc.). Third, they should have belief in public/common goods/interest. And fourth, they should trust strangers to the degree that they can share their resources with them. Sundararajan (2016) uses the term “crowd-based capitalism” for the sharing economy and cites the following as its characteristics: largely market-based, using high-impact capital, using crowd-based networks (rather than hierarchical bureaucracies), blurring the lines between personal and professional, and blurring the lines between fully employed and casual labor.

We can summarize and rephrase the characterizations by these authors and the trade group as follows. The sharing economy is a system of sharing unused resources (goods, skills, services, capital, and spaces) among strangers whose interactions are facilitated by Internet technologies. For such a system to work, a sufficiently large number of people should participate in it and they should be able to trust one another (ter Huurne et al., 2017). In the sharing economy, technology is a critical element in connecting people, particularly strangers, to each other in such a way that they can trust each other. For technology to connect people in a way that allows for mutual trust, the technology should enable participants to police each other so that the resources can be shared fairly. Thus, the Internet enables not only the interactions among participants, but also provides the mechanisms (applications) to check on each other’s behavior (e.g., peer reviews of interactions).

The key elements of using idle capacity, using technology, and the ability to trust other participants are common in all the characterizations of shared economies Botsman and Rogers (2010) add two important and controversial elements to this core characterization of the sharing economy: belief in public/ common goods/interest and collaborative lifestyles. They further argue that sharing economy practices represent a communitarian and collaborative worldview and are examples of what Ostrom (2015) calls self-governing associations, or self-organized commons (p. xxii & 91).

Botsman and Rogers’s characterization of the sharing economy suggests that it is not merely a practice in certain sectors of economy, but its effects extend to large facets of life and it is potentially transformative of social life. The various names used interchangeably with the sharing economy (collaborative consumption, gig economy, collaborative economy, crowd-based capitalism, e-lancing, on-demand economy, platform economy, etc.) also indicate that it has a wide scope and can it affect various aspects of social life. Terms like gig economy and platform economy stress the role of technology in the sharing economy. The term elancing points to the effects the sharing economy is having in blurring the line between full employment and occasional work (Sundararajan, 2016).

The potentially wide extent of the contents and impacts of the sharing economy are not disputed in the literature, but the positive normative view projected by Botsman and Rogers – that the sharing economy represents a more collaborative, communitarian, and hence democratic worldview – has been critiqued by others. The critics argue that platform companies like Uber and Airbnb actually serve their own economic self-interests and they are predatory and exploitative. In one of the most pointed critiques of the positive views of the sharing economy, Slee (2017) argues that the rosy scenarios, such as the sharing economy creating a more equitable and democratic economic system, have not materialized. Sharing economy practices have promised to help powerless individuals take more control of their lives by becoming “micro-entrepreneurs.” Instead, the leading companies like Uber and Airbnb have turned into monopolies in unregulated markets and reduced entrepreneurs’ control of their lives. Further, these platform companies have brought in a new form of surveillance on their users (drivers, renters, and consumers). Uber has been accused of placing its interests over its drivers’ and Airbnb has been accused of driving up property prices and rents thereby having negative impacts on affordable housing (Bivens, 2019; Mishel, 2018).

Given the extent and potential impacts of sharing economy practices, it is important to assess the early efforts by scholars to understand them. Our review of the literature sheds light on the state of the studies on the sharing economy, from which one can draw conclusions for public policy and future directions and needs of empirical research. There are claims that there is very little empirical evidence about how the sharing economy works, upon which policymakers can create relevant policies and regulations (Quattrone et al., 2016). A review of the growing body of literature in the last few years can yield insights for policymakers. We turn now to presenting our methodology for reviewing this literature.

3. Methods

The search procedure we used in identifying papers to code and analyze is summarized in Fig. 1. As the figure shows, in our search for relevant papers, we used 11 keywords that are commonly used in relation to our topic: sharing economy, collaborative consumption, collaborative economy, peer-production economy, peer-to-peer economy, crowd-based capitalism, elancing, gig economy, mesh economy, on-demand economy, and platform economy. We collected papers in which one or more of these keywords were used in the ProQuest Multiple Databases. These databases include several main databases in a variety of academic fields of study, such as ABI/INFORM Collection and Social Science Premium Collection. In our searches, we selected only those papers that were published in scholarly peer-reviewed journals. These papers are typically higher quality and they include abstracts, which we used as the sources of information for our coding and analyses (see below). Figure 1 displays the numbers of papers we identified using each of the keywords. From the 1,153 papers we initially identified, we removed duplicates, which reduced the total number down to 1,041. Then we removed the papers published in languages other than English, which brought the number down to 898, and the ones that were published in 2019 (the year in which we conducted our study), which brought the number down to 740. Finally, we read through the abstracts and removed any that were not on topic and/or published in English. Our search and removal processes yielded 610 papers that were published between 2006 and 2018 to be coded and analyzed.

Figure 1.

Flow diagram for the literature search.

After the data collection, our research team reviewed the abstracts of the papers. We first conducted a pilot study to review a random selection of 30 papers to identify the fields/areas of study, services/ platforms studied, geographic areas of the studies, and the theories/concepts and research methods used in them. Then, we developed categories for our coding scheme based on the findings in these initial readings but kept the schemes flexible to be able to enter additional categories. Once we developed the initial coding scheme, we divided the entire set of 610 papers among the readers. Each paper was read by a first and a second reader. When there was a conflict between the codes of the two, a third reader made the final judgment.

To answer our research questions, which are presented in the introduction, we used two sources. To identify the year of publication and the journal it was published, we analyzed information that was yielded by our searches in ProQuest. We coded the information in the titles and abstracts of the papers to answer the questions about the theoretical/conceptual frameworks in the papers, specific platforms of services focused on, fields/areas in which the studies were conducted, countries and regions of the studies, and the theories and methodological approaches used in them.

Once we coded the information, we generated frequency tables, contingency tables, and charts to summarize and report the results. These results are presented in the following section. The database and code for reproducing the results are available at https://doi.org/10.7910/DVN/ZJCMYZ.

4. Results

4.1 Trend in the number of publications

Figure 2 shows the total count of publications in our dataset from 2006 to 2018. It is evident that there were very few publications on the sharing economy between 2006 and 2014; there was an exponential increase since then. The chart illustrates the rapid increase in the attention to the sharing economy in just a few years.

Figure 2.

Annual count of peer reviewed sharing economy publications.

4.2 Publishing fields

Table 1 presents the frequencies and percentages of papers published in the journals of academic fields or topical areas. In our analyses, we identified 37 fields and areas among the journals included in the dataset. This number indicates that the interest in the sharing economy is not concentrated in only a few fields/areas; it is quite wide. We present only those 17 fields/areas that had more than 1% of the total publications we analyzed in the table for brevity. The categories in the table are not mutually exclusive, so they do not reflect precise measurements of the publishing activity in various academic fields and topical areas, but the table is useful because it provides an approximation of the distribution of the activity.

Table 1
Publications by field/areas of study

Field/area	Frequency	Percentage
Business	167	27.4%
Technology	89	14.6%
Economics	49	8.0%
Environmental sciences/policy	49	8.0%
Tourism	44	7.2%
Law	35	5.7%
Sociology	27	4.4%
Labor studies	24	3.9%
Communications	17	2.8%
Urban/regional studies/planning	17	2.8%
Political science/public administration, public policy	17	2.8%
Geography	9	1.5%
General science (e.g., Nature)	9	1.5%
Transportation	8	1.3%
Architecture	6	1.0%
Psychology	6	1.0%
All other	38	6.0%
Total	610	100%

Table 1 shows that the largest number of articles were published in business (27.4% of total), which is followed by technology (general) (14.6%), economics (8.0%), environmental sciences (8.0%), tourism (7.2%), and law (5.7%). Sociology, labor relations, communications, urban/regional studies/planning, geography, political science/public administration/public policy, general science (e.g., publications in the journal Nature), transportation, architecture, and psychology are the other fields/areas that had 1% or higher of the publications. The fields and areas that had less than 1% of the publications are included in the category “All Other.” They are design, energy, healthcare, biology, education, music, agriculture, aviation, complex systems, criminology, engineering, futurology, gender studies, genetics, land use, museums, and religion.

The fact that the researchers in business and economics published large percentages of the publications can be explained by the observation that sharing economy technologies had their most disruptive effects in various areas of business and they have repercussions for the broader economy (Crittenden et al., 2017). Particularly, established businesses, such as taxis and hotels, have been affected substantially. The topics discussed in the business publications we identified lends support to this interpretation. The terms “disrupt,” “disruptive,” and “disruption” appear in 12 business abstracts. Across the entire 610 abstracts, 50 include one of these words at least once. The business abstracts also discuss topics that present themselves as alternatives to traditional business practices: alternative forms of production, socially driven business, consumer ownership, on-demand economy, de-ownership, gig employment, and socially responsible consumption. The topic of collaboration appears in 56 (34%) of the business abstracts and sustainability in 23 (14%). Common topics in the economics publications include implications for taxation, labor markets, transaction costs, the fragmentation of work, housing shortages, and social capital.

It is not surprising that we found a large percentage of publications were about technological issues. Sharing economy applications have been enabled by technological advances, particularly those in the Internet and communication technologies. These studies focus on such topics as applications in city planning, peer-to-peer networks, innovation management, scarcity, Internet access, digital platforms, users, infrastructure, crowdsourcing, blockchain technology, artificial intelligence, and big data.

The relatively large percentage of publications in tourism is also understandable because of the rising global influence of Airbnb and other accommodation-sharing platforms. The publications in this area are mainly about trust and reputation of providers, hospitality, accommodation amenities, perceived risks and psychological factors among users, experiential consumption, travel, the alternative accommodation market, and customer loyalty. The relationship between providers and users is a common theme in the publications on tourism. The topic of trust (between the platform providers and among renters and guests) appears in 25% of the tourism publications.

It is important to note the high percentage of the publications in environmental sciences and policy. A key concern discussed in many of these articles is what the sharing economy means for sustainable development. The topic of sustainability appears in 59% of the environmental journal papers. Some of the authors point out that if sharing of goods and services leads to lower consumption of raw materials and production of waste and pollution, there is potential for positive environmental externalities. As noted above, business journals have also published multiple papers on sustainability.

The relatively large percentage of the publications in law is also noteworthy. The fact that sharing economy practices, because of their novel and dynamic nature, create legal challenges was recognized in the papers in this category. Many of the law papers focus on the issue of employment. The authors note that the sharing economy not only affected businesses, but also employees. This is evident in the increasing discussion of the gig economy as an alternative to traditional employment, a topic also discussed in the economics publications. Many articles referred to the difficulties of regulating employees of the gig economy, particularly understanding employee rights and liabilities as opposed to treating them as independent contractors.

It’s also worth noting that there were relatively fewer publications in political science and even less in public administration and public policy. These low percentages may indicate that the scholars in these three related fields have not caught up with the significance of the impacts of the sharing economy on politics, policymaking, and public service delivery. Many of the publications in these fields focused on the regulatory aspects of the sharing economy, and a small number of public administration papers focused on sustainability. The dearth of publications in the journals of these three fields masks the number of publications on the regulatory issues related to the sharing economy in general, however. Those 17% of the papers that discuss government regulations are spread across the diverse fields included in our data set (particularly those in business, economics, law, and technology).

4.3 Most studied services and platforms

To our knowledge, there is no single comprehensive classification scheme for sharing economy services. We propose one that is presented in Table 2. The sharing economy is a dynamic and rapidly evolving set of activities, so it is challenging to definitively categorize all related activities. This table presents a rough classification of services and platforms with some illustrative examples. The results of our classifications of the 610 papers in our data set based on the categories in Table 2 are presented in Table 3. This table includes only those categories that are covered in 1% or higher of the papers.

Table 2
Examples of sharing economy services and platforms

Transportation: Car Pooling, Van Pooling, Amish Taxis	Collaborative websites: Wikipedia, Dropbox	Food: Olio, Food sharing
Car rentals: Zip Car, Easy Car, Car2Go	Energy: Cooperatives	Financing: LendingClub
Transportation network companies: Uber, Lyft	Arts & crafts: Etsy	Time-banking: Babysitting cooperatives
Bike sharing: Lime, Mobike, Ofo	Co-working spaces: Startup Harrisburg, LiquidSpace	Pet sitting: Cat In A Flat, DogVacay
Accommodations: Airbnb, Couch surfing. Love Home	Toiles: Airpnp	Clothing: Thredup, Neighborgoods, Tradesy
Shopping: Craigslist	Healthcare: Ease, Helparound, Stat	Labor: Mechanical Turk, Freelancer

Note: Collated from Botsman and Rogers (2010), Ganapati and Reddick (2018), Sundararajan (2016), and media reporting.

Table 3

Sharing economy services and platforms studied

Service/platform	Frequency	Percentage
General	292	47.9%
Accommodations	105	17.3%
Airbnb	65	10.7%
General	40	6.6%
Transportation	79	12.9%
Uber	33	5.4%
General	46	7.5%
Multiple services	48	7.9%
Food	13	2.1%
Clothing	8	1.3%
Financing	8	1.3%
Time-banking	8	1.3%
Energy	7	1.1%
Labor	7	1.1%
Workspace	6	1.0%
All other	30	4.9%
Total	610	100%

The results show that most of the papers published on the sharing economy do not focus explicitly on a specific service or platform (the “General” category in Table 3). Of all the articles, 47.9% had no service or platform mentioned in their abstracts. The following two categories are accommodations (17.3%) and transportation (12.9%). This finding is not surprising, given that the most prominent examples of the sharing economy around the world are Airbnb and Uber. Arguably, these two companies are the most influential sharing economy platforms in the economies of many counties: Airbnb is active in more than 160 countries, Uber operates in more than 80, and together they have received most of the media attention.

Services like food, clothing, financing, time-banking, energy, labor, and workspace sharing each represented in 1–2% of the sharing economy papers. The services and platforms that were covered in less than 1% of the publications were about sharing airplanes, art, bikes, durable goods, education, furniture, gifts, healthcare, home goods, infrastructure, music, shopping, and the technology of blockchain. We can interpret the lower frequencies of the publications on these services and platforms with the fact that these are more geographically restricted (local) activities and they are not operated or coordinated by large companies.

A cursory comparison between Tables 2 and 3 shows that much of the scholarly focus on services is narrow, mainly accommodations and transportation, and other services and platforms listed in Table 2 received less attention by researchers.

4.4 Geographical distribution of the studies

Of the 610 coded abstracts, 238 (39%) mentioned a specific country or region as the locus of their study. In total, 47 unique countries and regions were included in these studies. We did not include the passing references to countries that were occasionally provided as examples in this count. We included only those abstracts that clearly stated that a country or region was the focus. Figure 3 presents a map of all countries of focus in the publications; the shades in the figure indicate the total number of studies that focused on those countries. The map does not, however, capture the studies that mentioned large regions (e.g., Asia-Pacific) or supranational organizations like the European Union (13 of the 238 studies).

Figure 3.

Map of country-specific studies.

The most commonly studied country is the United States, with 67 papers. China and the United Kingdom are the next most frequently studied countries, with 24 and 20 papers respectively. After these top-three countries, there is a steep decline in the number of studies on specific countries. Of the 47 countries and regions studied, 20 were included in only a single paper. Figure 3 also reveals that countries in Africa and the former Soviet bloc are the most underrepresented in the sharing economy literature.

4.5 Theoretical frameworks and methodological approaches

Not many abstracts we studied included specific theoretical frameworks. Of the 610 abstracts, 65 (10.6%) included an identifiable theoretical framework in the abstract. This low number may mean that most of the studies were a-theoretical or that this information simply did not appear in the abstracts.

Table 4 presents the breakdown of the theoretical frameworks used in the papers in the fields/areas listed in Table 1. It can be observed in Table 4 that business, environmental sciences and policy, and tourism are the fields/areas whose authors used most of the theories. They are followed by technology, economics, law, sociology, and labor studies. The authors of the publications in political science, public administration, and public policy did not use any theories, or they were not reported in the abstracts.

It can also be observed in Table 4 that there was no emerging dominant theoretical framework for sharing economy studies and that most of the theories used were theories of economics and cognitive/behavioral psychology. Among the most frequently used theories were social capital theory, prospect theory, and transaction cost theory. Still, each theory was only used in a handful of studies. There were several papers published in business journals that attempted to develop theories of collaborative consumption, but many of the fields drew on theories that would be familiar to others in those fields. Perhaps the most eclectic field is tourism, which drew from a wide range of theories from economics, sociology, business, communications, psychology, and more. There were no discernable theories in the publications in political science, public policy, and public administration.

Table 4
Examples of theories identified in abstracts by publication field

Field	Theories
Business	Brand building, gift-giving, collaborative consumption, performativity, psychological contract theory, price theory, disruptive innovation, human capital, stakeholder, consumer culture, unified theory of acceptance and use of technology, theory of money, metatheory, co-optation, contingency, creative destruction, field theory, construal level theory
Technology	Nash bargaining, platform economics, collaborative consumption, neutralization
Economics	Share tenancy, transaction cost, social capital
Environmental	Agency theory, social psychological models, value theory, theory of platform governance, social capital, Polyani’s conceptualization of householding, technological acceptance model, theory of planned behavior
Tourism	Disruptive innovation, prospect theory, chaos theory, social exchange theory, motivation, social cognition, innovation diffusion, rhetorical theory, disruptive innovation
Law	Pluralism, Lemon’s Theory, Coase Theory
Sociology	Social exchange theory, Bernstein’s theory of pedagogic discourse
Labor studies	Scientific management
Political science, public	No Theories Reported in Abstracts
administration, public policy

Table 5 displays the breakdown of the methodological approaches the authors of the papers used. The methods are broken down by the six most-published fields, according to the numbers in Table 1 (business, technology, economics, environment, tourism, and law), and are included in Table 5. Also included are the fields of public affairs (political science, public administration, and public policy) because of our specific interest in these fields.

Table 5 shows that in 25.9% of the papers, no research methods were used. In these papers, authors were engaged in conceptual discussions (16.9%), provided literature reviews (4.9%), or offered their opinions (4.1%). In 13% of the paper abstracts, the methodological approach was not clear, so none could be discerned. Table 5 also shows that a variety of methodological approaches were used by the researchers. In 35.1% of the papers, the authors applied qualitative methods, 21.3% quantitative methods, and 4.4% mixed methods. The sub-categories of “general” under the qualitative and quantitative categories mean that the use of either empirical approach was evident, but not a specific method (e.g., the authors mention quantitative data without mentioning a specific statistical approach).

Table 5 shows that among the researchers who used qualitative methods, case studies, content/text/ discourse analysis, interviews, descriptive studies, ethnographic studies, and narrative observations were the most prominent ones. The qualitative methods that were used in less than 1% of the papers (concept mapping, grounded theory, life-cycle assessment, and qualitative comparative analysis) are not shown in the table. Among the quantitative methods researchers used, surveys, experiments, simulations, and regression analysis (various forms) top the list. Those quantitative methods that were used by less than 1% of the papers (structural equation modeling, cluster analysis, social network analysis, spatial analysis, difference-in-difference, factor analysis, game theory, matrix factorization, social media analytics, and time-series analysis) are not shown in the table.

Among the fields presented in Table 5, the authors of the studies in public affairs used the largest percentage of qualitative methods (particularly case studies), whereas the authors of tourism papers used the largest percentage of quantitative studies. The next most quantitatively oriented field is economics. The authors of the law articles used primarily case studies and offered their legal opinions.

Table 5

Research methodologies used in sharing economy studies

Method	Total		Business		Technology		Economics		Environment		Tourism		Law		Public affairs ${}^{*}$
Conceptual	103	(16.9%)	33	(19.7%)	14	(15.7%)	3	(6.1%)	7	(14.3%)	1	(2.3%)	8	(22.9%)	4	(23.5%)
Literature review	30	(4.9%)	12	(7.2%)	2	(2.2%)	2	(4.1%)	3	(6.1%)	1	(2.3%)	1	(2.9%)	0
Opinion	25	(4.1%)	7	(4.2%)	0		5	(10.2%)	0		0		7	(20.0%)	1	(5.9%)
Qualitative (total)	214	(35.1%)	65	(38.9%)	34	(38.2%)	14	(28.6%)	14	(28.6%)	14	(31.8%)	11	(31.4%)	7	(41.2%)
Case study	73	(12.0%)	15	(9.0%)	9	(10.1%)	4	(8.2%)	9	(18.4%)	2	(4.5%)	2	(5.7%)	5	(29.4%)
General (unspecified)	69	(11.3%)	19	(11.4%)	15	(16.9%)	6	(12.2%)	2	(4.1%)	3	(6.8%)	7	(20.0%)	1	(5.9%)
Interviews	19	(3.1%)	5	(3.0%)	3	(3.4%)	0		2	(4.1%)	1	(2.3%)	0		1	(5.9%)
Content/text/discourse analysis	17	(2.8%)	9	(5.4%)	1	(1.1%)	0		0		6	(13.6%)	0		0
Description	14	(2.3%)	2	(1.2%)	4	(4.5%)	3	(6.1%)	0		0		1	(2.9%)	0
Ethnography	8	(1.3%)	4	(2.4%)	0		0		0		1	(2.3%)	0		0
Narrative observation	7	(1.1%)	0		1	(1.1%)	1	(2.0%)	0		0		1	(2.9%)	0
All other	7	(1.1%)	11	(6.6%)	1	(1.1%)	0		2	(4.1%)	1	(2.3%)	0		0
Quantitative (total)	130	(21.3%)	29	(17.4%)	23	(25.8%)	18	(36.7%)	14	(28.6%)	20	(45.5%)	3	(8.6%)	2	(11.8%)
General (unspecified)	51	(8.4%)	14	(8.4%)	8	(9.0%)	10	(20.4%)	6	(12.2%)	5	(11.4%)	2	(5.7%)	2	(11.8%)
Surveys	27	(4.4%)	6	(3.6%)	3	(3.4%)	4	(8.2%)	3	(6.1%)	7	(15.9%)	0		0
Experiments	18	(3.0%)	4	(2.4%)	3	(3.4%)	1	(2.0%)	2	(4.1%)	1	(2.3%)	0		0
Simulations (agent-based, other)	9	(1.5%)	2	(1.2%)	2	(2.2%)	1	(2.0%)	0		0		1	(2.9%)	0
Regression (various)	6	(1.0%)	2	(1.2%)	0		0		1	(2.0%)	3	(6.8%)	0		0
All other	19	(3.1%)	1	(0.6%)	7	(7.9%)	2	(4.1%)	2	(4.1%)	4	(9.1%)	0		0
Mixed methods	27	(4.4%)	9	(5.4%)	3	(3.4%)	1	(2.0%)	6	(12.2%)	2	(4.5%)	0		0
Unclear	79	(13.0%)	22	(13.2%)	13	(14.6%)	6	(12.2%)	4	(8.2%)	6	(13.6%)	5	(14.3%)	3	(17.6%)
Total	610	(100%)	167	(100%)	89	(100%)	49	(100%)	49	(100%)	44	(100%)	35	(100%)	17	(100%)

${}^{*}$ Political Science, Public Administration, and Public Policy.

As in our findings about the theoretical frameworks, there is no dominant methodological approach in the sharing economy literature. It can be observed, however, that the most frequently used qualitative and quantitative methods are those that are most commonly used by social science researchers generally: case studies, interviews, surveys, experiments, and textual analyses.

4.6 Limitations

The most important limitation of this study is the use of abstracts, instead of full papers, for coding purposes. The use of abstracts is common for thematic systematic reviews (e.g., White, 1986), as the time and cost of reading and coding such a large body of research (610 articles in this case) is prohibitive. Another important limitation to consider, which relates to the first, is that abstracts are not always well written. Even with the movement towards requiring authors to write structured abstracts in the journals of some fields (Salager-Meyer, 1991), those writing systematic reviews note the poor quality of many abstracts (Riaz et al., 2010). It is possible that our systematic review may have undercounted the theories and methods used and the specific countries where the studies were conducted. Despite the limitations, the results of this study provide a good picture of the types of studies conducted in the first years of sharing economy research. These findings can help guide future studies.

5. Summary and conclusions

We can answer the five questions we asked in the introduction of this paper with the findings of our study and draw conclusions from them. We found that there was an exponential increase in the studies on the sharing economy in a short period of time, between 2014 and 2018 (question #1). This finding parallels the sharp increase in the use of sharing economy platforms in these years (Ganapati & Reddick, 2018) and the increased activity on collaborative consumption and crowd-based capitalism among academic/activists, as in networks like OuiShare (Sundararajan, 2016). It also confirms Slee’s (2017) observation that the sharing economy entered the mainstream of academic research in about 2014.

Researchers in fields like business, economics, tourism, and technology were the ones who published most of the studies on the sharing economy (question #2), which can be expected given that the sharing economy has had its most substantial effects in tourism and transportation businesses and the economy in general. Additionally, sharing economy platforms are enabled by advances in technology, particularly information and communication technologies. It is worth noting that environmental scientists and policy analysts are also interested in the sharing economy and their focus is on its impacts on the environment and sustainability.

Although the implications of the sharing economy for the public and public interest in general are obvious and the roles of governments in dealing with sharing economy applications need to be addressed, researchers in public affairs (politics, public policy, and public service delivery) do not seem to have paid sufficient attention to these issues in the period we studied. There were researchers in business, economics, law, and technology who focused on public policy-related issues, like regulations, however.

Whether and how to regulate sharing economy platforms and activities are important and complex problems for policymakers and public administrators. As Ganapati and Reddick (2018) observe, there are potentially conflicting implications of the sharing economy. They point to the innovative nature of the sharing economy and its ability to help actors utilize idle capacities in an economy. They note that regu- lating sharing economy practices, or overregulating them, may hamper innovations and consequently dampen the utilization of idle resources. They also point to the negative effects of the sharing economy and underscore the need for governments to interfere in the case of negative externalities.

Indeed, there are news reports and academic studies on the negative effects of sharing economy platforms, particularly Airbnb and Uber. These reports and studies address issues like Uber and Lyft drivers being deprived of employee rights and protections (Bales & Woo, 2016; Malin & Chandler, 2016; Poudel, 2016), the negative impacts of these platform providers on the business of taxi drivers in New York City (Berger et al., 2018; Cramer & Krueger, 2016) and on the utilization of public transit (Clewlow & Mishra, 2017; Hall et al., 2018). They also address the effects of Airbnb in rent increases (Boone, 2018), and neighborhood disruptions (Gurran & Phibbs, 2017).

With this background of potential positive and negative effects of sharing economy platforms and companies, one can ask the question, to what extent and how should governments get involved in taxing and regulating them. There already are some state and local government taxation and regulation schemes for sharing economy platform companies. For example, cities around the world (e.g., Singapore, London, Stockholm, Washington DC, Houston) have imposed congestion taxes (Centere for Public Impact, 2016). New York City, Philadelphia, and Washington, DC tax ride-sharing to fund public transit (Welle et al., 2018). Airbnb has fought back against numerous municipalities that have moved to implement additional home sharing taxes (Martineau, 2019). More than 30 U.S. states and more than 70 cities have passed legislation governing transportation network companies, like Uber and Lyft. These regulations are about fingerprinting drivers, pick-up locations, and fees (Lyft, 2019). New York City imposed a cap on the number of Uber and Lyft drivers (Honan, 2019). Officials in New York state and New York City have used the state’s Multiple Dwelling Law (MDGL) to regulate short term rentals, like those facilitated by Airbnb and VRBO (Inkson & Minnaert, 2018).

There are those who argue that governments should not overregulate sharing economy companies. For example, Cohen (2018) argues not only that government regulators should refrain from overregulating Airbnb and other similar companies; they can also learn from Airbnb about how to regulate larger sectors of the economy. He argues that governments should adopt looser and gentler regulations, and focus on outcomes in regulations, rather than procedural rules. Ganapati and Reddick (2018) argue for “adaptive governance” that would take advantage of the new opportunities that the sharing economy can bring (p. 77). The theoretical base of the arguments that governments should not regulate is the capacity of self-governing of common-pool resources in Ostrom’s (2015) work, according to Botsman and Rogers (2010, p. 91). Trade associations like Sharing Economy UK can be cited as an example of the self-regulating capacity of sharing economy platform companies. They use mechanisms like consumer-rating systems by platform providers, solving problems of platform providers, and developing and enforcing codes of conduct for providers.

The pro and con arguments for government involvement in regulating and taxing sharing economy companies are based mainly on existing legal structures and the conceptual frameworks of existing businesses and their economic functioning. Because sharing economy practices are relatively new and quite different than previous practices to some degree, a deeper understanding of this new system of interactions will lead to better forms of government involvement. Further, while it is encouraging that so many fields are engaging in at least some sharing economy research, there is the risk that disciplinary silos could inhibit a transdisciplinary study of the topic. This is a challenge for science writ large (Carolan, 2008), but the wide ranging impacts of the sharing economy for economics and governance warrant a truly transdisciplinary research approach.

We found that the sharing economy platforms most studied by researchers were Airbnb and Uber and that there were very few studies on others (question #3). The U.S., China, and the U.K. are the countries where the largest numbers of the studies were conducted, but it seems that the studies are spreading into other countries (question #4). This study only included papers published in English, which may have resulted in ignoring the studies in other countries. The lopsidedness in the sharing economy platforms and countries studied parallels the intensity of sharing economy activities, but future studies should focus on other forms and platforms of the sharing economy as well (see Table 2). Recent studies like Sallis’s (2019) study on electric scooters are encouraging, but there is clearly a great deal of room to expand scholarly research on sharing economy services and platforms beyond ride sharing and accommodation sharing.

The studies we identified in the literature seem to be mostly a-theoretical. The lack of theory or conceptualization may be a sign of lacking conceptual rigor in the studies, or it may be because of the emerging nature of the sharing economy, which does not allow it to fit into any existing frameworks.

There are some findings that have implications for future theoretical developments in this area of study. Our finding that among the few studies that used theory, most of them were theories of economics and cognitive/behavioral psychology is meaningful. It is likely that economic and cognitive/behavioral theories will continue to be utilized in this area in the future, given that there is still a lot to be learned about the economic and cognitive/behavioral underpinnings and implications of sharing economy activities. Arguably, the lack of any discernable theory in political science, public policy, and public administration publications is a deficiency. As Botsman and Rogers (2010) and Ganapati and Reddick (2018) point out, there are conceptual frameworks the researchers in these fields could use fruitfully: Ostrom’s (2015) framework for the capacity of self-governing of common-pool resources and “adaptive governance” conceptualizations.

The finding that in a large majority of the publications in our study the authors applied some empirical research methods, mostly qualitative but also some quantitative (question #5), is encouraging. Applications of rigorous methods in empirical studies, together with using rigorous theoretical frameworks, are necessary for more valid and useful findings.

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