Regulation and governance of the sharing economy by U.S. local governments

Abstract

The sharing economy has disrupted industries and raises inquiries into the logic of existing regulations. Despite the varying levels of legislative status of the sharing economy, systematic research on governance that frames regulatory issues on such innovations has been lacking in the academic literature, particularly in the public administration field. In this research, we (1) examine determinants that influence the legislative status of regulations with a focus on sharing economy innovations in the U.S., and (2) explore the stakeholder groups that are perceived to be influential in shaping the regulatory environment among U.S. local government policymakers. The results indicate that stakeholder groups actively engaged in legislative lobbying as well as jurisdictional characteristics such as geographical region are associated with whether a local government is active in regulating the local sharing economy. This article offers insights into how policymakers govern the innovations taking place in their communities.

Keywords

Innovation sharing economy regulation public engagement theory of organizational innovation policy innovation framework

1. Introduction

The sharing economy has given rise to a broad array of new regulatory issues for the public sector. New business models such as Airbnb ${}^{\text{TM}}$ and Uber ${}^{\text{TM}}$ have received strong pushback from incumbents (Alderman, 2017). Industry incumbents claim that the sharing economy takes advantage of technology and evades existing regulatory requirements. In the transportation market, for example, the taxi industry has claimed that Uber ${}^{\text{TM}}$ , Lyft ${}^{\text{TM}}$ , and other transportation platform companies circumvent the licensing fees that taxicab companies must pay. The regulatory questions raised by the rapid growth of the sharing economy are extensive. They span the entire map of the legal world, including tax collection, permitting, licensing, zoning, safety and health, quality, and consumer protection.

As controversy about regulations continues to swirl around the sharing economy, local and state government officials in the U.S. have been struggling with how to regulate emerging and dynamically evolving business environments appropriately (Koopman et al., 2015). They face challenges in balancing the competing interests of stakeholders in the context of broader public interests (Helmer, 2017). Local and state government officials have introduced legislation focusing on regulating the sharing economy. For example, the Public Utility Commission in California offered legal status for ride-hailing companies and allowed them to operate legally, but hearings continue around the U.S. at the local municipal levels (Geron, 2013). In Austin (TX), the city government prevented Uber ${}^{\text{TM}}$ and other ride-hailing services from operating under rules that govern limousine services (Batheja, 2015). Furthermore, the U.S. federal structure combined with state preemption of local government actions make effective and appropriate regulation even more complicated.

Scholars and pundits alike have shown that the sharing economy has disrupted industries while also raising questions about the efficacy of existing regulations (Lobel, 2016). Systematic research on governance that frames regulatory issues in these new, technologically innovative spaces has been lacking in academic literature, particularly in the tourism field. An examination may require a review of basic principles and a new look at market innovation and public intervention at a local level, the scale where business licenses and operational protocols are governed. This research starts at a local level – the city in which sharing economy service providers operate. From a local government perspective, several broad research questions arise. Who are the stakeholders in the community (citizens, businesses, unions, other organizations, etc.)? Do city leaders share the vision of these stakeholders? What characteristics of cities and their local governments influence innovations like the sharing economy? Some of these questions posed can be answered by delving into the nexus of innovation in tourism, particularly accommodation and transportation, and its governance (regulations).

The article (1) examines determinants that influence local legislative status with a focus on sharing economy innovations in the U.S. using survey data collected from a sample of local government officials in U.S. cities and counties, and (2) assesses local government officials’ perceptions of stakeholder groups that may be influential in the regulatory environment. Specifically, the survey asked local government chief administrative officers (CAOs) about how they perceived the regulatory environment for the sharing economy in their jurisdiction, thus focusing on the perspectives of city or county top administrative officials (e.g., mayors or managers). These local government officials act as useful sources about the current dynamics of legislation emerging in their jurisdictions and the regulatory environment. The present study provides insight into how these local government officials perceive and react to these substantive disruptive innovations over a relative short time span (a few year).

The article provides a brief background to this policy domain and related research. This highlights how the sharing economy, as an example of innovation creating a dynamic shock to existing environments, presents unique regulatory challenges to local governments. The next section then presents a conceptual framework, the hypotheses to be tested, and research methods. The article then concludes with a presentation of the results, discussion, limitations, and suggestions for continued future research.

2. Background

To address the topics of determinants of legislative status and government officials’ perceptions of stakeholder influence in the governance of the sharing economy, literature is reviewed in three main areas: the sharing economy as a unique innovation in the tourism industry, balancing innovation and regulation, and the government regulatory framework.

2.1 Sharing economy as a unique innovation

In the sharing economy, individuals are gaining temporary access to or use of products and services in exchange for a fee (Cheng, 2016; Guttentag et al., 2017; Tussyadiah & Pesonen, 2016). The sharing economy encourages new entrants into industries already occupied by incumbents (industry actors grounded in earlier technological environments and norms). With reduced overhead costs, the start-up costs are relatively low. This can facilitate new market entrants and the potential for higher levels of financial success. The large sharing economy companies promote that there are virtually zero start-up costs to become a provider, essentially a micro-entrepreneur, on already-existing platforms (Rifkin, 2014). If individuals want to become a bed and breakfast host, a driver, or a chef, they simply list their services on an existing platform and wait for an offer from consumers. The companies’ costs of advertising are often embedded in a transaction fee and earned with a credit card payment. Thus, the sharing economy has introduced a new supply chain for small and medium entrepreneurs to sell their services and goods.

As it takes advantage of the reduced barriers to entry, the sharing economy poses serious threats to incumbents who are governed and regulated by local, state, and federal laws and policies, including licensing fees and taxes. In the case of accommodations, the increased number of Airbnb ${}^{\text{TM}}$ listings has led to a decrease in quarterly hotel revenues in the state of Texas (Zervas et al., 2017). Budget hotels have taken the brunt of the declines (Fan et al., 2016; Zervas et al., 2017). In the transportation market, taxicab rides continued to plummet, down to 14% of business trips, compared to 37% of the market two years ago (Jones, 2016). The sharing economy has introduced innovation in the existing industry, including hotels, taxis, and short-term rentals.

A challenge for policy makers is the argument that local government officials must balance the competing interests of stakeholders with the introduction and growth of new innovations. In response to the rising phenomenon of the sharing economy, academic literature has highlighted its impacts at the community and government level (Dredge & Gyimóthy, 2015; Fan et al., 2016; Hartl et al., 2016; Rauch & Schleicher, 2015). Despite recent advances in sharing economy studies, research on governance in regulating the sharing economy is particularly scant in the public administration literature.

2.2 Balancing innovation and regulation

The emergent dynamics of innovation and regulation in the sharing economy raises concerns over the effectiveness of administrative mechanisms and appropriate governmental regulation in an information age. Innovation is a complex process that can trigger a need for regulatory intervention to protect the public. However, regulatory public agencies taking such actions should be balance in a way such that the benefits from the innovation are not stifled. A better understanding of the innovation process is essential to determine the impact of legal instruments on the innovation process. Generally, regulations are administrative instruments used to implement economic and social objectives. The main justification for governmental regulation has broadly been the protection of the public welfare. Elected officials and administrative officers enact legislation to impose regulations designed to: (1) protect a marketplace; (2) enforce established professional standards; (3) safeguard community space and ecosystems; (4) protect scarce resources; (5) protect workers and contractors in a particular industry; (6) ensure equitable access to public accommodations; (7) generate revenue by imposing taxes on certain industries; or (8) require consumers to purchase certain goods and services such as insurance (Griffith, 2018).

While new technology has long challenged the flexibility of regulatory systems, platform companies have consistently pushed back against establishing new regulations over the sharing economy. The counterargument to regulation is an approach to privilege self-regulation (Bolton et al., 2004; Kuwabara, 2015). In the sharing economy, there is often an extensive reputation system of feedback mechanisms. The sharing economy rests upon its reputation system rather than external enforcement and administrative system. For example, Airbnb ${}^{\text{TM}}$ offers apartment owners the opportunity to have certified photos taken of their apartments. Uber ${}^{\text{TM}}$ and Lyft ${}^{\text{TM}}$ show a driver’s profiles and ratings.

However, empirical research demonstrates the flaws and shortcomings of the “don’t regulate” approach. For instance, in internet markets like e-Bay, research on feedback mechanism suggests that dissatisfied consumers often do not leave feedback (Nosko & Tadelis, 2015) and that consumers can be deterred from truthful reporting by the threat of retaliatory feedback (Bolton et al., 2004). On digital platforms, the majority of feedback trends positive, and average feedback scores do not vary much (Horton & Golden, 2015). Heavy reliance on feedback scores raises the concern that users manipulate scores (Mayzlin et al., 2014), raising doubts about the extent to which reputation and feedback systems function well. These doubts call for external enforcement, such as screening or quality certification.

Generally, regulations emerge in response to public demand triggered by market failures that have led to inefficient or inequitable outcomes. In the sharing economy, scholars have identified three common forms of market failure: information asymmetry, negative externalities, and consumer protection from unfair business practices (Sundararajan, 2016). First, with respect to the lack of information symmetry, Uber ${}^{\text{TM}}$ drivers know far more about their driving capabilities and the condition of their car than do prospective passengers. Likewise, Airbnb ${}^{\text{TM}}$ hosts possess information about the cleanliness of the accommodations offered, whereas prospective guests merely rely on previous peer ratings for such information. Therefore, short-term rentals and ride-hailing services, facilitated through a third-party platform, are prone to market failures.

Second, negative externalities arise from sharing economy businesses. Mobility and vehicle sharing services lead to a greater use of automobiles in highly congested urban areas. Ride-hailing services in New York City averaged 15 million passengers per month in fall 2016, tripling their level of service from spring 2015 (Schaller, 2017). Home sharing services may produce negative externalities. When residents rent out accommodations to guests through digital platforms, they introduce strangers into a community and their behaviors may not conform to established neighborhood norms. Housing that otherwise would be available for long-term rental can be taken off the market if the rate of return is greater on a short-term rental basis. Home sharing services aggravate affordable housing in certain areas (Lee, 2016). Moreover, residents who rent out their homes or vacation homes most likely cannot offer their guests the type of safety and fire protection that licensed hotels provide.

The third market failure threat is consumer protection from unprofessional or unfair business practices. Liability issues could be raised, such as: who ought to be liable if a driver for a ride-hailing firm runs over a pedestrian? Or a room-sharing host attacks a guest? Studies have shown significant evidence of discrimination among sharing economy companies (Edelman et al., 2015; Karlsson et al., 2017). Who is responsible when minorities or the disabled are denied access to the new services? These problems could be solved by administrative mechanisms designed to protect public safety (Sundararajan, 2016).

Overall, new platform technologies challenge the existing U.S. federal and state legal systems in many ways. At the same time, local government officials are struggling to decide the rules by which sharing economy companies should operate in their communities as their respective state legislatures consider laws to preempt local government actions regarding certain sharing economy activities (Swindell et al., 2020). The challenge for policymakers is establishing regulations that work while not impeding innovation. Nonetheless, the goal of regulation in the sharing economy is two-fold. First, regulation can involve correction of market failures. Second, regulation can play a role in making an arrangement to motivate the stakeholders to compete fairly within the market. Regulations can be designed to address public safety, quality controls, and liability concerns.

3. Conceptual framework

Several scholars have extensively examined the process by which a new program or policy spreads from one jurisdiction to another (Balla, 2001; Berry & Berry, 1990; Mintrom, 1997; Mohr, 1969; Walker, 1969; Walker et al., 2015). A policy innovation represents a “program or policy which is new to [the state or local governments] adopting it.” (Walker, 1969, p. 881). In our study on public governance in the sharing economy, we use an alternative and generally more inclusive definition. Based on foundational work by Mohr (1969), here we define innovation more broadly as “the successful introduction into an applied situation of means or ends that are new to that situation” (p. 112). Accordingly, a policy innovation in the sharing economy era entails a local government’s institutional adaptation to new technological advances through regulatory adjustment.

The conceptual framework of the current research builds on previous scholarship centered around the three studies on diffusion of innovations (Berry & Berry, 1990; Mohr, 1969; Rogers, 2003). The main theoretical tenet of the present study rests on Berry and Berry’s (1990) policy innovation framework. Built on Mohr’s (1969) theory of organization innovation, Berry and Berry (1990) argue that the propensity to innovate is positively associated with the motives to innovate, but inversely related to the strength of obstacles to innovation. Their analysis of policy innovations lays a foundation for building a conceptual framework that combine the motivation to innovate with the strength of obstacles.

Another theoretical tenet of the present study is that stakeholders provide an opportunity to incorporate information about the policy-making process into the diffusion (Balla, 2001; Mintrom, 1997). Stakeholders may influence diffusion in two ways. First, stakeholders provide local government officials with opportunities to learn about current issues in regulatory regime by engaging in public comment periods and hearings. Second, stakeholders provide institutional foundations for policy development. They establish an ongoing process or protocol wherein they get involved with developing policies recommended for adoption. Lastly, Rogers (2003) also argues that the innovation decision process is a function of socio-economic, demographic, and geographic characteristics of the decision-making unit. Rogers (2003) offered important insights into the mechanism that facilitates the spread of certain new ideas or practices, but it needs to be adjusted to explain in the policy and governance context.

Our conceptual framework rests on these three research streams: Berry and Berry (1990), Mohr (1969), and Rogers (2003). We integrate Berry and Berry’s (1990) framework in two ways. First, the current study considers two institutional factors as the primary motives to innovate within the existing regulatory/policy environment: organizational adaptation to changes (innovativeness) and a local government’s orientation towards public engagement. Second, we integrate the obstacles to innovation, such as the lack of internal expertise, lack of information to proceed, lack of access to funding, and state or federal regulations. Meanwhile, our conceptual model also combines the role of stakeholders who actively advocate for or against regulating sharing economy businesses. Our empirical model goes further and incorporates jurisdiction characteristics (e.g., population size, metropolitan area, education, income, form of government, geographical region) predicated on Rogers’ (2003) theory of diffusion of innovations. The next section presents hypothetical relationships with these characteristics as exogenous independent variables that we test.

In the present study, we categorize the dependent variable, localized sharing economy legislation, into legislative status on two levels: legislation in place or in development versus no legislative action. The central question in the early stage of innovation and regulation is when to regulate. The timing of when regulators should act has not received much attention in the current debate about regulating the sharing economy. Sharing economy businesses can grow very quickly. One difficulty in trying to regulate rapidly evolving businesses is that regulations cannot be easily changed or withdrawn, so rules that look sensible at the time they were imposed may appear outdated or misguided in the short order. As a result, local government officials may be concerned that by delaying action, they may miss the opportunity for public intervention. The following sections examine the factors that impact regulation and governance and present the hypotheses we test.

3.1 Factors that impact regulation and governance

To understand a policy innovation framework, several factors emerge as possible predictive variables to legislative positions. These factors include: motivations to innovate, obstacles to innovate, and jurisdictional characteristics. We test models created to reflect stakeholder involvement in sharing economy legislation status.

3.1.1 Motives to innovate

The current study postulates that two institutional factors function as primary motives to innovate existing regulatory regimes. The first dimension of motives to innovate is the local government organization’s internal culture of innovation or “innovativeness.” Organizational innovation can be broadly defined as an organization’s propensity to innovate (Walker et al., 2015) or the capacity to change its structure, strategy, and administrative processes (Damanpour, 1991; Thompson, 1965). We conceptualize organizational innovativeness as the capacity or willingness to respond effectively and efficiently to external changes, share information within the organization and obtain information on successful new practices from other local governments. In the sharing economy, local governments’ reactions to adjust existing regulatory regimes may depend on their own organizational orientation towards innovation (Davidson & Infranca, 2018). This yields the following hypothesis:

Hypothesis 1: A local government’s organizational orientation toward innovation (“innovativeness”) is positively related to active local regulation of the sharing economy.

The second dimension of motives to innovate focuses on a local government organization’s orientation toward public engagement. Public engagement requires an organizational culture that supports openness to citizen participation. Openness toward stakeholders also enables learning and better adaption to emerging policy challenges (Mahler, 1997), thus increasing the likelihood of balancing public interests and addressing local concerns. The current study captures the two types of the local government organizational orientation towards public engagement. The negative orientation represents how local governments meet the minimum legal requirements for public engagement. The procedural practices such as public comment periods and hearings are primary examples. It allows a low level of public participation, according to Arnstein’s famous ladder of citizen participation (1969). On the other hand, the positive orientation toward public engagement facilitates a higher level of participation. It represents genuine efforts at meaningful citizen engagement. It encompasses the extent to which local governments develop partnerships with neighborhood and community organizations and establish a process or protocol that involves stakeholders.

The concept of positive orientation towards public engagement is aligned with ancillary innovation (Damanpour, 1987; Walker et al., 2015). Ancillary innovation reflects the shift to partnership and networking in the delivery of public services, and a literature has developed on collaborative governance and the new public governance (Ansell & Torfing, 2014; Hartley et al., 2013). It differs from other innovations because it is concerned with working across boundaries with other service providers, users, or other public agencies (Damanpour, 1987). Thus, its successful implementation is reliant upon stakeholders or constituent groups in local government jurisdictions. To our knowledge, the specific effects of “local government orientation towards public engagement” on a policy adoption in the arena of the sharing economy have not yet been investigated in empirical studies. This leads to the following hypotheses:

Hypothesis 2a: The local government’s negative orientation towards public engagement is negatively related to local regulation of the sharing economy. Hypothesis 2b: The local government’s positive orientation towards public engagement (ancillary innovation) is positively related to local regulation of the sharing economy.

3.1.2 Obstacles to innovation

The current analyses include the extent to which policy makers assess factors that impede new practices or initiatives in their local government. Drawn from the literature on the sharing economy and policy adoption, four factors emerge as hindrances to innovating the preexisting regulatory regimes of the sharing economy. First, Rogers (2003) found that the availability of funding to invest in policy design and implementation is crucial in policy adoption. Other scholars have consistently found that financial capacity facilitates or impedes the early adoption of innovations (Boehmke & Skinner, 2012; Gray, 1973; Walker, 1969).

The lack of internal expertise is a second factor that can hinder initiating new practices. In terms of government institutions, legislators in highly professionalized chambers may be better able to identify and evaluate new policy options (Boushey, 2010; Gailly, 2011; Shipan & Volden, 2006). Conversely, legislators with less expertise may have difficulties in dealing with disruptions in regulatory issues of the sharing economy. Third, the challenges in coordinating state or federal regulations and policy are another factor that scholars claim is a major concern in the area of law and policy. The externalities generated by the sharing economy businesses (such as conflicts over land use in residential areas and impacts of alternative mobility services to local public transportation), at least in the United States, largely devolved to the local level (Griffith, 2018; Light, 2018). Given that local, state, and federal governments in the U.S. have different domains and roles to play, scholars indicate that establishing uniformity and oversight up the geographic scale is increasingly central to understanding the regulatory landscape, particularly given the variation in state legislative preemption activities across certain state related to the sharing economy (Davidson & Infranca, 2018; Griffith, 2018; Swindell et al., 2020).

Lastly, the lack of information on how to proceed is another obstacle to implementing new practices and initiatives. The development of an innovation implies both potentials and risks associated with the lack of information inherent to the development of new products and services. Uncertainty may leave legislators with few facts on which to base their regulations (Ranchordás, 2015). The absence of information as to the potentials and risks of the sharing economy may prevent legislators from assessing whether the pre-existing regulatory framework delays or impedes the diffusion of innovation, particularly in the initial stages of innovation development. Based on these previous findings, the models here test the following hypotheses:

Hypothesis 3a,b,c,d: The obstacles to innovation, including (a) availability of funding, (b) lack of internal expertise, (c) challenges in coordinating state or federal regulations and policy, and (d) the lack of information how to proceed, are each negatively related to local regulation of the sharing economy.

3.1.3 Jurisdictional characteristics

Policy adoption may be more likely to take place in jurisdictions with certain characteristics. The current analyses use previous research to inform and test the effects of socio-economic, demographic, geographic, and form of government characteristics. Scholars have found that more populous states and states with higher urbanization levels tend to be more innovative (Boehmke & Skinner, 2012; Walker, 1969). The current analyses, therefore, include the natural log of the jurisdiction population and a dichotomous variable that indicates whether a local government is located within a Metropolitan or Micropolitan area based on the U.S. Office of Management and Budget. Scholars have also found that socio-economic traits such as education and income are associated with innovativeness (Boushey, 2010; Kouser, 2005; Walker, 1969). One such socio-economic characteristic in the analyses in our models is the percentage of each jurisdiction’s population who are high school graduates. In addition, the models include the median household income of the jurisdictions.

While findings are mixed, the form of government is another variable that scholars have examined extensively in studies of local government (Svara & Nelson, 2015). Some research has found that council-manager governments are more likely to adopt or innovate (Li & Feeney, 2014; Moon, 2002). As such, the local governments with professional chief administrative officials (e.g., city managers) may be more likely to adopt regulation of the sharing economy.

Geographical region may capture differences in political cultures affecting innovation. One might expect less likelihood of a policy adoption in the South, for example, with its more hierarchical and traditional political culture (Elazar, 1972). The West, on the other hand, has a history of direct democracy through ballot initiatives and referenda (Tolbert, 2003), and this may be reflected in a policy adoption.

Hypothesis 4a,b,c,d,e,f: Jurisdiction characteristics, including (a) population size, (b) metropolitan area, (c) education, (d) income, (e) form of government, and (f) geographical region are each associated with local regulation of the sharing economy.

3.1.4 Stakeholder influences

In the current analyses, we estimate a separate model account for the impact of various stakeholders on legislation status. The interests of stakeholders are the important factor in the literature on policy adoptions (Clark & Little, 2002; Karch et al., 2016; Mintrom, 1997). For example, existing research on the diffusion of policy innovations highlights impacts of interest groups (Clark & Little, 2002; Karch et al., 2016). Professional associations and interest groups enable various levels of support for policy ideas (Balla 2001; Karch et al., 2016). For example, homeowners associations (HOAs), the Taxicab, Limousine and Paratransit Association (TLPA), or union groups are active in efforts to effect the regulation of the sharing economy. Legislative leaders may value and incorporate the information and positions of these stakeholders (Clark & Little, 2002). The present study takes into account different stakeholders or a variation on the stakeholder dimension.

Many individual residents fear that short-term rentals, such as Airbnb ${}^{\text{TM}}$ , will negatively affect residential life and property values. In the context of regulations on homesharing services, residents have encouraged several jurisdictions to take actions by either prohibiting short-term rental use or implementing a permitting scheme (Lee, 2016; Scanlon, 2017). The first issue individual residents and neighborhood associations have raised is that homesharing services threatens quality of residential life (Scanlon, 2017). Many residents have complained of a variety of negative consequences for the neighborhoods, including an undue burden on public services without compensation, rising rents, and a loss of community and neighborhood cohesion. Residents in popular Airbnb ${}^{\text{TM}}$ neighborhoods may find parking limited and roads crowded (Scott & Brown, 2017). Thus, individual residents claim that tough regulations are necessary to deal with loud parties to preserve neighborhood character (Miller, 2019). Another regulatory issue is the property and zoning laws. The purpose of zoning ordinances is to divide up the locality’s geographic area into different categories of land use that are inconsistent with each other. Thus, residential areas are kept separate from commercial and industrial areas (Jefferson-Jones, 2015). The sharing economy blurred the boundary between residential and commercial use in zoning laws, challenging traditional zoning provisions (Kassan & Orsi, 2012; Rauch & Schleicher, 2015; Scanlon, 2017).

The incumbents, including the existing hotel or taxi companies, require that sharing economy businesses comply with the same requirements that other industry players follow. These requirements encompass the regulatory issues, such as tax collection, permitting, licensing, and employment laws (Einav et al., 2016; Hall & Krueger, 2018; Weil & Goldman, 2016). Conventional hotel operators are facing pressures from the emergence of this new competitor for tourist accommodations. The traditional hotel industry has claimed that Airbnb ${}^{\text{TM}}$ has an unfair advantage because its listings are not required to meet the same commercial standards and the transient occupancy tax (Oswald, 2015). Rideshare services have also been controversial, as illustrated in clashes and tension between Uber ${}^{\text{TM}}$ drivers and taxi drivers. In almost all major cities, taxi or cab drivers are required to obtain a special permit to act as a taxi driver; a medallion issued by the Taxi and Limousine Commission (Kim et al., 2018). On the contrary, becoming an Uber ${}^{\text{TM}}$ driver does not require such a license. Therefore, there has been controversy on how to regulate such a technology-equipped new entrant that bypasses laws in a traditionally regulated market. Such controversies suggest the need for a closer examination on the relationship between the incumbents and sharing economy businesses.

In this study, local government officials are more likely to adopt policy for which the various stakeholders actively advocate. Thus, we test the following hypothesis:

Hypothesis 5: Stakeholder groups, including (a) individual residents, (b) homeowners associations, (c) local taxi industry, (d) local tourism industry, and (e) individual businesses, each positively influence local regulation of the sharing economy.

4. Methods

4.1 Data collection and procedure

To examine how local governments regulate the sharing economy, we analyze the results of the Innovations and Emerging Practices in Local Government 2016 survey. This article is the first to utilize questions in this nationally-representative survey addressing the sharing economy and local governments, along with an array of variables related to emerging practices and issues in local government. The International City/County Management Association (ICMA) conducted the survey in partnership with Arizona State University’s Center for Urban Innovation. The sample frame included all general purpose local governments (i.e., municipalities and counties) serving populations greater than 250,000, and one out of three general purpose local governments smaller than that. ICMA sent paper surveys (with an online option) to a total sample of 5,451 U.S. cities and counties, targeting the jurisdiction’s chief administrative officer. The effort yielding complete and usable responses from 599 of the governments surveyed, generating an 11% response rate with an overall margin of error of $\pm$ 3.9%. Of those responding, approximately 16% were counties with the remaining 84% being municipalities. The jurisdictions captured in the sample match the population size and region of the country for all cities and counties in the nation on these two characteristics. Table 1 shows descriptive statistics on responding jurisdictions.

Table 1
Survey response

Classification	Number of responses	% of responses
Total	599	100
Municipalities	505	84	.3
Counties	94	15	.7
Population group
Over 1,000,000	6	1	.0
500,000–999,999	7	1	.2
250,000–499,999	19	3	.2
100,000–249,999	25	4	.2
50,000–99,999	36	6	.0
25,000–49,999	47	7	.9
10,000–24,999	101	17	.9
5,000–9,999	118	19	.7
2,500–4,999	147	24	.5
Under 2,500	93	15	.5
Form of government
Cities
Mayor-council	173	28	.9
Council-manager	303	50	.7
Commission	6	1	.0
Town meeting	21	3	.5
Rep. town meeting	2	0	.3
Counties
Commission	36	6	.0
Council-administrator	43	7	.2
Council-elected executive	14	2	.3
Metro status
Metro statistical area	354	59	.1
Micro statistical area	90	15	.0
New england city & town	18	3	.0
Undesignated	137	22	.9
Geographic region
Northeast	112	18	.7
North-central	249	41	.6
South	145	24	.2
West	93	15	.5

Table 2

Measures

Variables	Description	Source
Dependent variable:
1. Legislation status	Categorical variable coded 1 for “legislation in place,” or “legislation in pursuit,” and 0 for “non-legislation”.	ICMA survey (2016) Q18
Independent variables:
Motivation to innovate
2. The local government’s innovativeness	The average score of five items from Q1 (a) to (e). The extent to which respondents agree with the five statements regarding the local government’s innovativeness (ranging from 1-strongly disagree to 5 $=$ strongly disagree).	ICMA survey (2016) Q1 (a) to (e)
3. Negative orientation towards public participation	The average score of two items from Q13 (a) and (b). The extent to which respondents rate the jurisdiction’s negative orientations towards citizen participation (ranging from 1 $=$ strongly disagree to 5 $=$ strongly agree).	ICMA survey (2016) Q13 (a), (b)
4. Positive orientation towards public participation	The average score of three items from Q13 (c), (d), and (f). The extent to which respondents rate the jurisdiction’s positive orientations towards citizen participation (ranging from 1 $=$ strongly disagree to 5 $=$ strongly agree).	ICMA survey (2016) Q13 (c), (d), and (f)
Obstacles to innovation
5. Availability of funding	The extent to which respondents rate the significance of availability of funding in hindering the implementation of new practices (ranging from 1 $=$ not significant to 4 $=$ very significant).	ICMA survey (2016) Q4 (a)
6. Lack of internal expertise	The extent to which respondents rate the significance of lack of internal expertise in hindering the implementation of new practices (ranging from 1 $=$ not significant to 4 $=$ very significant).	ICMA survey (2016) Q4 (a)
7. State or federal regulations or policy	The extent to which respondents rate the significance of state or federal regulations or policy in hindering the implementation of new practices (ranging from 1 $=$ not significant to 4 $=$ very significant).	ICMA survey (2016) Q4 (a)
8. Lack of information how to proceed	The extent to which respondents rate the significance of lack of information how to proceed in hindering the implementation of new practices (ranging from 1 $=$ not significant to 4 $=$ very significant).	ICMA survey (2016) Q4 (a)
Jurisdiction characteristics
9. Ln (Population)	Transformed natural log of population.	U.S. Census Bureau (2011)
10. Metropolitan	Dummy-coded measure of metropolitan statistical area (0 $=$ non-metropolitan, 1 $=$ metropolitan).	U.S. Office of Management and Budget
11. Income	Median income of jurisdiction.	ACS (2016)
12. Education	Percentage of adults (age 25 $+$ ) graduated from high school.	ACS (2016)
13. Form of local government	Dichotomous variable coded 1 if the jurisdiction is with professional chief administrative officers (city managers), otherwise 0.	ICMA survey (2016)
14. Geographical region	Categorical variable coded 1 for “Northeast,” 2 for “West,” 3 for “South,” and 4 for “North Central.” The North Central region is the base category.	U.S. Census Bureau (2011)
Stakeholder groups actively advocating for regulations
15. Individual residents	Categorical variable coded 1 if individual residents actively advocate for the regulations on the sharing economy, otherwise 0.	ICMA survey (2016) Question 19 (a)
16. Homeowners associations	Categorical variable coded 1 if homeowners associations actively advocate for the regulations on the sharing economy, otherwise 0.	ICMA survey (2016) Question 19 (b)
17. Individual businesses	Categorical variable coded 1 if individuals actively advocate for the regulations on the sharing economy, otherwise 0.	ICMA survey (2016) Question 19 (c)
18. Local taxi industry	Categorical variable coded 1 if local taxi industry actively advocates for the regulations on the sharing economy, otherwise 0.	ICMA survey (2016) Question 19 (d)
19. Local tourism industry	Categorical variable coded 1 if local tourism industry actively advocates for the regulations on the sharing economy, otherwise 0.	ICMA survey (2016) Question 19 (e)

4.2 Measures

The dependent variable, legislative status, is a categorical variable, coded 0 for legislation neither in place nor in development (no legislation) and 1 for the presence of legislation (or the pursuit of legislation). To examine the factors that account for legislative status, we include three groups of independent (explanatory) variables: motives to innovate, obstacles to innovation, and stakeholder influences. The estimation model controls for a series of demographic and socio-economic characteristics, as well as the form of government. Table 2 summarizes these operational measures.

4.2.1 Motives to innovate

The literature on policy innovations and organizational studies highlight the local government’s ability to implement new ideas and willingness to work across the boundaries with other stakeholders. To capture motives to innovate, the survey included a series of five statements to which respondents indicated their agreement on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). These five items focused on: how quickly they respond to change, how effectively they respond to change, how extensively new information spread throughout the local government, how the organization obtains information on new practices from other local governments, and how they share their new practices with other organizations. These measures align with those of Oliveira and Welch’s (2013) measure of innovativeness.

Second, the survey included six questions to evaluate the nature of public engagement in jurisdictions. Two of these represent negative orientations towards public engagement (the degree to which the local government focuses on minimum legal requirements for civic engagement such as public comment periods and hearings). The other four survey questions capture the positive orientation towards public participation: organizational expectations for more deliberative public engagement, adopting formal procedures, developing partnerships, and establish ongoing body. These four questions align with higher levels of Arnstein’s ladder of citizen participation (1969), as policymakers are actively orientated towards civic participation that promotes collaboration with stakeholders. We constructed composite variables by averaging scores for the negative and positive measures.

4.2.2 Obstacles to innovation

Based on the literature on policy innovations and the sharing economy, we identify four factors as the major obstacles impeding or delaying local regulation of the sharing economy: availability of funding, lack of internal expertise, state or federal regulation or policy, and lack of information how to proceed (Boehmke & Skinner, 2012; Boushey, 2010; Davidson & Infranca, 2018; Griffith, 2018; Rauch & Schleicher, 2015). In the survey, respondents assessed how significant each item hindered the implementation of new practices or initiatives in their jurisdiction. The respondents scored items on four-point Likert scales, ranging from 1 $=$ “not significant” to 4 $=$ “very significant”.

4.2.3 Stakeholder influences

Drawn from recent literature on the sharing economy (Davidson & Infranca, 2018; Ganapati & Reddick, 2018; Scott & Brown, 2016), stakeholder groups are considered in the analyses. Local government respondents evaluated whether each stakeholder group was an advocate, non-advocate, if they were not sure, or if it was not applicable to them. Five external stakeholder groups emerged for this analysis: individual residents, homeowners associations, local taxi industry, local tourism industry, and individual businesses. In the estimation model, we included five categorical variables coded 1 if a stakeholder actively advocates for the regulations on the sharing economy, otherwise 0.

4.2.4 Jurisdictional characteristics

The demographic, socio-economic, and geographical variables came from the secondary data. Population size came from 2016 U.S. Census Bureau estimates. The estimation model employs the transformed natural log of population to address concerns of heteroscedasticity. Metropolitan status came from the U.S. Office of Management and Budget. It is a dichotomous variable coded 1 if the jurisdiction is metropolitan and 0 otherwise. A series of regional dummy variables are also included in the estimation model, given that technology development has been unevenly distributed across the U.S. (Rogers, 2003). Based on the U.S. Census Bureau classification, geographical region is classified into four sub-regions: Northeast, West, South, and North Central regions. The base category in the model is North Central. For all measures, we use the concept of the sharing economy broadly and do not delineated across accommodations, ride-sharing, or other service categories. The form of government is a dichotomous variable coded 1 if the jurisdiction is governed by a professional chief administrative office (i.e., city/county manager), otherwise 0.

The 2016 American Community Survey data (ACS) provided data for the median household income and the percentage of the jurisdiction population who are high school graduates for the 599 sample jurisdictions. As a nationwide survey conducted by U.S. Census Bureau, the ACS provides broad information on socioeconomic and demographic characteristics about the U.S. population and communities every year (U.S. Census Bureau, 2017).

4.3 Data analysis

To examine the hypotheses, we employ a logistic regression approach for the binary outcome of regulatory status (Hair et al., 2010; Long & Freese, 2014). We applied this approach to estimate three logistic models. The first model regresses legislative status on the two dimensions of motives to innovate and obstacles to innovation. The second model adds jurisdiction characteristics as controls to the first model. The third model tests the impact of stakeholder influences, along with the other explanatory variables and control variables included in the first and second models.

5. Results

Table 3 presents the descriptive statistics for the measures in the estimated models. Table 4 shows the bivariate correlations for localized sharing economy legislation and independent variables.

Table 3
Descriptive statistics

Variable	N	Yes (%)	Mean	Std dev	Min	Max
1. Legislation status (legislation in place and in development)	549	87 (15.8%)	0.16	0.37	0	1
Motives to innovate
2. The local government’s innovativeness	592	–	3.62	0.65	1	5
3. Negative orientation towards public engagement	542	–	2.78	1.01	1	5
4. Positive orientation towards public engagement	541	–	3.37	0.68	1	5
Obstacles to innovation
5. Availability of funding	589	–	3.29	0.80	1	4
6. Lack of internal expertise	591	–	2.33	0.83	1	4
7. State or federal regulations or policy	587	–	2.66	0.86	1	4
8. Lack of information how to proceed	586	–	2.48	0.82	1	4
Jurisdiction’s characteristics
9. Ln (Population)	551	–	9.24	1.64	4.96	15.18
10. Metropolitan	599	–	0.59	0.49	0	1
11. Income	550	–	57,585	23,707	14,745	232,981
12. Education	548	–	88.63	7.55	48.37	99.72
13. Form of local government (professional CAOs)	598	388 (64.9%)
Stakeholder groups advocating for regulations
15. Individual residents	303	74 (24.4%)	0.24	0.43	0	1
16. Homeowners associations	267	41 (15.4%)	0.15	0.36	0	1
17. Individual businesses	285	60 (21.1%)	0.21	0.41	0	1
18. Local taxi industry	248	38 (15.3%)	0.15	0.36	0	1
19. Local tourism industry	275	59 (21.5%)	0.21	0.41	0	1

Table 5 presents the results of the three binary logit models examining the influences on local sharing economy legislative status in U.S. municipalities and counties. The dependent variable is the outcome of legislative status. A jurisdiction scored 1 if regulatory legislation was in place at the time or the survey or was being discussed/developed. Jurisdictions with no plans for such legislation received a code of 0. The table shows the coefficients for each variable on legislative status. The estimates for the impacts of motives to innovate and obstacles to innovate appear in the first group of rows, jurisdiction characteristics in the second group of rows, and the stakeholder groups advocating for local regulation of the sharing economy in the third group of rows.

Model I shows the results of legislative status regressed on the two dimensions of motives to innovate and obstacles to innovation. The results suggest that there is no statistically meaningful association between a local government’s organizational orientation to innovation and legislation, failing to support Hypothesis 1. The coefficient of a local government’s negative orientation toward engagement is also not statistically significant, failing to support Hypothesis 2a. However, the estimated effect of a local government’s positive orientations towards public engagement is positive and highly significant at the 1% level, supporting Hypothesis 2b. This estimated impact of the local government’s positive orientations toward public engagement largely remains intact and strong even in the presence of other covariates. None of the variables associated with obstacles to innovation exhibit significant effects on legislation, as shown in Models I and II. The signs of “influence of availability of funding” and “lack of internal expertise” are negative, but not statistically significant, failing to offer evidence supporting Hypothesis 3. This finding suggests that the effect of obstacles to innovation may simply be overwhelmed by the explanatory power of motives to innovation.

Model II includes a series of jurisdiction characteristics. The coefficient of positive orientations towards public engagement remains positive and significant at the conventional level. The robust effect of this factor highlights that a local government’s willingness to work with constituent groups in the jurisdiction impacts a policy innovation. Among the control variables (jurisdiction characteristics), the geographical region is positively associated with the legislation. The result indicates that local government officials in the West are more likely to adopt regulations on the sharing economy, partially supporting Hypothesis 4.

Model III tests Hypothesis 5. It includes stakeholder groups actively advocating for local regulation of the sharing economy in jurisdictions. The third column of Table 5 presents the result of Model III. In Model III, the statistically significant influence of local government’s positive orientation disappears when the covariates of external stakeholder groups are included. Table 6 provides odds ratio and confidence intervals of the independent variables of Model III. Among the stakeholder groups, the coefficient of local taxi industry is positive and highly significant at the 1% level. As expected, incumbents are most likely to generate legislative action in the area of the sharing economy. Individual residents are significantly associated with local regulation of the sharing economy at the conventional level. In other words, when local taxi industry and individual residents actively get involved, the local government officials are more likely to adopt localized regulation of the sharing economy. Local tourism industry and individual businesses do not have any significant association with legislative status to regulate the sharing economy businesses. The coefficient of “lack of information to proceed” does achieve statistical significance and is positive.

Table 4

Bivariate correlations for localized sharing economy legislation and independent variables

Variable	1		2		3		4		5		6		7		8		9		10		11		12		13		14		15		16		17		18
1	–
2		0.09 ${}^{*}$	–
3	$-$	0.05	$-$	0.27 ${}^{**}$	–
4		0.18 ${}^{**}$		0.38 ${}^{**}$	$-$	0.33 ${}^{**}$	–
5	$-$	0.04	$-$	0.08		00.1	$-$	0.06	–
6	$-$	0.04	$-$	0.22 ${}^{**}$		0.18 ${}^{**}$	$-$	0.18 ${}^{**}$		0.30 ${}^{**}$	–
7		0.02		0.09 ${}^{*}$		0.06		0.14 ${}^{**}$		0.20 ${}^{**}$		0.20 ${}^{**}$	–
8		0.01	$-$	0.15 ${}^{**}$		0.13 ${}^{**}$	$-$	0.12 ${}^{**}$		0.35 ${}^{**}$		0.54 ${}^{**}$		0.33 ${}^{**}$	–
9		0.20 ${}^{**}$		0.14 ${}^{**}$	$-$	0.27 ${}^{**}$		0.20 ${}^{**}$	$-$	0.01	$-$	0.14 ${}^{**}$	$-$	0.05	$-$	0.14 ${}^{**}$	–
10		0.08		0.10 ${}^{*}$	$-$	0.16 ${}^{**}$		0.06		0.03	$-$	0.06		0.01	$-$	0.07		0.32 ${}^{*}$	–
11		0.04		0.09 ${}^{*}$	$-$	0.14 ${}^{**}$		0.06	$-$	0.11 ${}^{**}$	$-$	0.09 ${}^{*}$	$-$	0.10 ${}^{*}$	$-$	0.12 ${}^{**}$		0.11 ${}^{**}$		0.22 ${}^{**}$	–
12		0.02	$-$	0.03	$-$	0.10 ${}^{*}$		0.03	$-$	0.10 ${}^{*}$	$-$	0.08	$-$	0.02	$-$	0.07	$-$	0.05		0.10 ${}^{*}$		0.52 ${}^{**}$	–
13		0.02		0.03	$-$	0.06		0.07	$-$	0.01		0.01		0.01	$-$	0.01		0.21 ${}^{**}$		0.06	$-$	0.09 ${}^{*}$	$-$	0.06	–
15		0.53 ${}^{**}$		0.06	$-$	0.07		0.19 ${}^{**}$	$-$	0.05	$-$	0.07		0.00	$-$	0.08		0.26 ${}^{**}$		0.11		0.11		0.03		0.12 ${}^{*}$	–
16		0.47 ${}^{**}$		0.03	$-$	0.05		0.18 ${}^{**}$		0.04		0.02		0.09		0.03		0.29 ${}^{**}$		0.08		0.02		0.04		0.08		0.70 ${}^{**}$	–
17		0.47 ${}^{**}$	$-$	0.02		0.04		0.17 ${}^{**}$		0.01	$-$	0.02		0.06		0.00		0.23 ${}^{**}$		0.02	$-$	0.06	$-$	0.04		0.10		0.74 ${}^{**}$		0.66 ${}^{**}$	–
18		0.51 ${}^{**}$		0.01	$-$	0.11		0.21 ${}^{**}$	$-$	0.01	$-$	0.11	$-$	0.04	$-$	0.15 ${}^{*}$		0.42 ${}^{**}$		0.12	$-$	0.02	$-$	0.01		0.12		0.57 ${}^{**}$		0.46 ${}^{**}$		0.62 ${}^{**}$	–
19	$-$	0.40 ${}^{**}$		0.03	$-$	0.03		0.19 ${}^{**}$		0.02	$-$	0.05		0.04	$-$	0.04		0.17 ${}^{**}$	$-$	0.03	$-$	0.15 ${}^{*}$	$-$	0.09		0.16 ${}^{**}$		0.62 ${}^{**}$		0.62 ${}^{**}$		0.75 ${}^{**}$		0.49 ${}^{**}$	–

Note. ${}^{*}p<$ 0.05, ${}^{**}p<$ 0.01.

Table 5

Logistic regression model results

	Model I			Model II			Model III
Variable	B		SE	B		SE	B		SE
Motives to innovate
The local government’s innovativeness	0.	15	(0.22)	0.	02	(0.25)	0.	17	(0.52)
Negative orientation towards public participation	0.	06	(0.13)	0.	16	(0.15)	$-$ 0.	35	(0.34)
Positive orientation towards public participation	0.	70 ${}^{**}$	(0.21)	0.	55 ${}^{*}$	(0.23)	0.	50	(0.56)
Obstacles to innovation
Availability of funding	$-$ 0.	16	(0.16)	$-$ 0.	16	(0.18)	$-$ 0.	45	(0.41)
Lack of internal expertise	$-$ 0.	11	(0.19)	$-$ 0.	22	(0.21)	$-$ 0.	81	(0.45)
State or federal policies	$-$ 0.	01	(0.15)	$-$ 0.	40	(0.18)	$-$ 0.	64	(0.42)
Lack of information how to proceed	0.	22	(0.20)	0.	23	(0.22)	1.	13 ${}^{*}$	(0.52)
Jurisdiction characteristics
Ln (Population)				0.	31	(0.09)	$-$ 0.	20	(0.26)
Metropolitan				0.	32	(0.33)	0.	78	(0.76)
Income				0.	00	(0.00)	0.	00	(0.00)
Education				0.	02	(0.03)	0.	11	(0.06)
Form of local government				$-$ 1.	34	(0.31)	$-$ 0.	16	(0.75)
Geographical region
Northeast				0.	50	(0.40)	1.	30	(1.02)
West				0.	79 ${}^{*}$	(0.39)	1.	33	(0.92)
South				0.	06	(0.39)	0.	59	(0.94)
Stakeholder groups advocating for regulations
Individual residents							2.	59 ${}^{*}$	(1.15)
Homeowners associations							0.	63	(1.12)
Individual businesses							0.	17	(1.19)
Local taxi industry							2.	61 ${}^{**}$	(1.03)
Local tourism industry							$-$ 0.	29	(1.37)
Intercept	$-$ 4.	63	(1.27)	$-$ 8.	24	(2.73)	$-$ 6.	40	(6.98)
$n$	524			469			177
-2LL	423.138			359.78			79.98
Nagelkerke $R^{2}$	0.06			0.13			0.53

Note. For geographical region, the North Central region is the base category. ${}^{*}p<$ 0.05, ${}^{**}p<$ 0.01.

Table 6

Logit model predicting localized sharing economy legislation (Model III)

Variable	B		SE	OR	95% CI	Wald statistic	$p$
Motives to innovate
The local government’s innovativeness	0.	17	0.52	1.19	[0.43, 3.27]	0.11	0.74
Negative orientation towards public participation	$-$ 0.	35	0.34	0.71	[0.36, 1.38]	1.03	0.31
Positive orientation towards public participation	0.	50	0.56	1.65	[0.55, 4.95]	0.80	0.37
Obstacles to innovation
Availability of funding	$-$ 0.	45	0.41	0.64	[0.28, 1.43]	1.20	0.27
Lack of internal expertise	$-$ 0.	81	0.45	0.45	[0.19, 1.07]	3.27	0.07
State or federal policies	$-$ 0.	64	0.42	0.53	[0.23, 1.19]	2.37	0.12
Lack of information how to proceed	1.	13	0.52	3.10	[1.13, 8.52]	4.82	0.03
Jurisdiction characteristics
Ln (Population)	$-$ 0.	20	0.26	0.82	[0.50, 1.36]	0.58	0.45
Metropolitan	0.	78	0.76	2.18	[0.49, 9.65]	1.06	0.30
Income	0.	00 ${}^{*}$	0.00	1.00	[1.00, 1.00]	3.41	0.07
Education	0.	11	0.06	1.09	[0.95, 1.24]	1.55	0.21
Form of local government	$-$ 0.	16	0.75	0.89	[0.20, 3.92]	0.02	0.88
Geographical region
Northeast	1.	30	1.02	3.65	[0.49, 26.94]	1.61	0.20
West	1.	33	0.92	3.78	[0.62, 23.08]	2.08	0.15
South	0.	59	0.94	1.80	[0.28. 11.40]	0.39	0.53
Stakeholder groups advocating for regulations
Individual residents	2.	59 ${}^{*}$	1.15	13.38	[1.42, 126.35]	5.13	0.02
Homeowners associations	0.	63	1.12	2.52	[0.24, 26.12]	0.60	0.44
Individual businesses	0.	17	1.19	1.52	[0.08, 29.31]	0.08	0.78
Local taxi industry	2.	61 ${}^{**}$	1.03	13.53	[1.79, 102.02]	6.39	0.01
Local tourism industry	$-$ 0.	29	1.37	0.75	[0.05, 10.92]	0.04	0.83
Intercept	$-$ 6.	40	6.98	13.38	–	0.84	0.36

Note. For geographical region, the North Central region is the base category. $n=$ 177. Nagelkerke $R^{2}=$ 0.53. CI $=$ confidence interval for odds ratio (OR). ${}^{*}p<$ 0.05, ${}^{**}p<$ 0.01.

6. Discusson and implications

The present research sheds light on local regulation of the sharing economy that has not received much attention in the current debate about industry innovation and government regulation. The present study responds to Ganapati and Reddick’s (2018) call for more research on how government agencies better adapt to the digital platform economy. In the phenomenon of the sharing economy, the digital era is marked by remarkable growth of platform technology defying existing regulatory landscape. Government officials face challenges in addressing market failures, such as information asymmetry, negative externalities, and consumer protection from unfair business practices (Rauch & Schleicher, 2015; Sundararajan, 2016). These challenges signal the need to develop effective administrative mechanisms to motivate the stakeholders to compete fairly within the market.

This research, which represents a national representative sample of local city administrators, provides an important proxy of individual municipalities’ policy agendas in a landscape defined by different and divergent interests involved in a given industry, such as accommodation sharing and mobility sharing services. Sharing economy firms or emerging entrepreneurs can benefit from this analysis to evaluate support for their business models and challenges they may face to operate locally.

6.1 Theoretical implications

Theoretically, this paper is one of the first studies to investigate how a local government’s orientation towards public engagement influences legislative status in the context of the sharing economy. The finding indicates that the local governments are more likely to innovate the existing regulatory regime of the sharing economy, if they are open to civic engagement. More specifically, introducing new practices or initiatives into the realm of the sharing economy depends on the extent to which local government officials support public engagement in their jurisdiction. Our finding may explain more about the processes in which local government officials engage stakeholders in the sharing economy and ultimately decide the appropriate levels of local regulation of the sharing economy. The finding also signifies the importance of collaborative governance (Ansell & Torfing, 2014; Hartley et al., 2013) that the literature on e-government or open government adoptions recently discussed (Grimmelikhuijsen & Feeney, 2017; Zhao et al., 2019).

Moreover, the study contributes to the identification of stakeholder groups who are perceived to be influential in innovating the existing regulatory regime, and an estimation of whether they are active or not on regulating the sharing economy. For instance, individual residents and the local taxi industry are more likely to advocate for regulating the sharing economy businesses. The results facilitate the development of the balanced regulation strategy of the sharing economy depending on the size or region of the cities. It provides the insights on how local government officials balance competing interests of stakeholders and under what conditions they act on new legislation.

6.2 Managerial implications

This study offers managerial implications for public sector agencies. Ride-sharing services are often considered a cost-effective alternative form of transportation, but currently in many municipalities, ride-sharing firms’ practices challenge existing municipal regulations. Concurrently, there is a strident criticism coming from incumbent, well established groups such as taxi unions and hotels, which face unprecedented competition from disruptive sharing services. Given the growing popularity of innovative sharing economy services, it is critically important that local governments need to be fully cognizant of the implications of these changes relative to their regulatory regimes and proactively consider the development and implementation of regulations for providers and customers of the sharing economy. In other words, instead of merely establishing mandatory environmental regulations and penalizing organizations’ disobedient behaviours, public agencies should expand services that offer public value. Government agencies, including public transportation agencies, should bring parties together and make an effort to partner with ride sharing platforms (such as Uber ${}^{\text{TM}}$ or Lyft ${}^{\text{TM}}$ ). A collaboration can provide greater integration of innovative public services, effective regulations, and use of technologies. Successful governance of the sharing economy may prove to create a net positive impact, a high service quality with public safety oversight, and may dissolve significant barriers such as taxi license issues and individual privacy and security issues. Alternatively, the present study implies that ride-sharing platforms should seek to understand other values that can be delivered to consumers and more broadly to their citizenry. Successful governance of the sharing economy can lead to increased stakeholder involvement and growth of industry and branding of a municipality as innovative.

6.3 Limitation and future studies

There are limitations on this study. Local government chief administrative officers provided the data through the survey instrument as a proxy of the local situation. While these positions can provide a broad overview of a topic or situation, they may not completely capture the full range of other people’s support or knowledge of issues. Another limitation is that this survey was at one point in time. Sharing economy regulations are constantly changing and need frequent observations to monitor. Longitudinal studies can track this group one or two years from now as the sharing economy continues to innovate and evolve, as well as face or require legislation at municipal and state levels. Future research is also needed to study those cities with no plans to add legislation. Without legislation, lost taxes and a lack of consumer protections may be an undesirable outcome.

Footnotes

Appendix. Item descriptions and factor loadings from principal component factor analysis

Item description	M (SD)	$\lambda$	$\alpha$
Local government’s innovativeness			0.81
The organization generally responds quickly to external changes.	3.51 (0.94)	0.74
The organization generally responds effectively to external changes.	3.75 (0.80)	0.76
Information on successful new practices is easily shared within the organization.	3.67 (0.86)	0.77
The organization regularly obtains information on successful new practices from other local governments.	3.63 (0.84)	0.73
The organization regularly shares information on successful new practices with other local governments.	3.52 (0.88)	0.79
Negative orientation towards public participation			0.84
Attention is primarily focused on the minimum legal requirements for public engagement, including public comment periods and hearings.	2.78 (1.11)	0.93
There are few public engagement practices beyond minimum requirements, and they vary by department.	2.79 (1.07)	0.90
Positive orientation towards public participation			0.69
There are expectations that more extensive and deliberate public engagement beyond minimum requirements will be used for local decision-making.	3.51 (0.91)	0.50
There is an adopted set of principles that generally define and encourage the use of effective and inclusive public engagement when/as appropriate.	3.28 (0.94)	0.85
Partnerships are developed with neighborhood and community organizations to involve the public in appropriate public engagement activities over time.	3.50 (0.93)	0.69
There is an established and ongoing body, process, or protocol that provides community representatives with input into the direction, operation, and adaptation of a public engagement plan or set of practices.	3.18 (1.01)	0.74

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Regulation and governance of the sharing economy by U.S. local governments

Abstract

Keywords

1. Introduction

2. Background

2.1 Sharing economy as a unique innovation

2.2 Balancing innovation and regulation

3. Conceptual framework

3.1 Factors that impact regulation and governance

3.1.1 Motives to innovate

3.1.2 Obstacles to innovation

3.1.3 Jurisdictional characteristics

3.1.4 Stakeholder influences

4. Methods

4.1 Data collection and procedure

Table 1 Survey response

4.2.1 Motives to innovate

4.2.2 Obstacles to innovation

4.2.3 Stakeholder influences

4.2.4 Jurisdictional characteristics

4.3 Data analysis

5. Results

Table 3 Descriptive statistics

6.1 Theoretical implications

6.2 Managerial implications

6.3 Limitation and future studies

Footnotes

Appendix. Item descriptions and factor loadings from principal component factor analysis

References

Table 1
Survey response

Table 3
Descriptive statistics