Looking at the Gig Picture: Defining Gig Work and Explaining Profile Differences in Gig Workers’ Job Demands and Resources

Abstract

Gig workers are a growing portion of the workforce and of increased interest to researchers. Recent reports suggest one in four workers is involved in gig work to some extent. Additionally, gig work has been a trending topic in organizational psychology for the past few years; however, our systematic literature review revealed the need for more attention to address definitional ambiguity and consider the intricacies of gig work. Specifically, this article identified the following gaps in the extant literature: the need for a comprehensive definition of gig work, the creation of profiles to differentiate gig workers, and the application of organizational psychology theories to explain gig workers’ experiences. This conceptual article addresses these gaps by providing clarity with a definition for gig work that captures both the primary (e.g., shared by all gig workers) and secondary (e.g., shared by some gig workers) characteristics of gig work. Further, this article describes five gig worker profiles (i.e., Gig Service Providers, Gig Goods Providers, Gig Data Providers, Agency Gig Workers, and Traditional Gig Workers) based on combinations of secondary characteristics to identify different types of gig work. Using the definition provided in this article and applying the Job Demands-Resources (JD-R) model, propositions were developed to compare gig worker profiles based on the job demands and job resources they experience. Thus, this article serves as a foundation to advance the literature through a consistent definition of gig work that paves the way for future research to better understand gig workers through the JD-R model.

Keywords

gig work job demands-resources model employer–employee relationship gig economy

In today’s dynamic workforce, gig work is a growing segment of the nonstandard workforce with approximately one out of four workers having engaged in gig work to some degree (McKinsey Global Institute, 2019). Gig work has also quickly risen as a popular topic in organizational research. For example, gig work has moved up on the Society for Industrial and Organizational Psychology (SIOP) Top 10 Workplace Trends as the third most trending topic in 2020 from fourth and 10th, respectively, in the two prior years (SIOP, 2020). In the most recent SIOP release of Top 10 Workplace Trends for 2021, gig work is captured in the number one topic of the year (e.g., “remote work and flexible work arrangements”; SIOP, 2021). This is perhaps unsurprising as the novel coronavirus (COVID-19) pandemic has highlighted the importance of nonstandard work arrangements as an alternative to traditional full-time employment. Specifically, in light of COVID-19, mainstream media indicate that gig work has become a popular opportunity for those who were laid off in the pandemic or for workers who needed a more flexible work option in order to care for their children or other dependents during the traditional workday (Henderson, 2020).

Additionally, the general changing nature of work has facilitated the increase in gig work by making it appealing and accessible to both workers and employers. Scully-Russ and Torraco (2020) suggested that the rise of gig work is influenced by several factors. For example, technological advancements such as software development for mobile devices “allow efficient delivery of products and services and increase workforce productivity” (p. 76). Consumers also have increased their willingness to purchase goods and services via the Internet and to pay for short term or shared access to products instead of owning them, and workers have increased interest in flexible work through gigs rather than traditional roles within organizations. Finally, political, socioeconomical, and institutional shifts have further spurred changes such as the shift in labor markets from legally requiring employment policies that protect permanent workers to loosening regulation such that gig workers do not expect to receive the same protective policies as standard employees.

With the catchy term “gig economy” being relatively new and the digitalization of gig work piquing the interest of researchers, practitioners, and the media in the past decade, gig work can be misperceived to have not existed until recently. However, older forms of gig work such as direct selling companies (i.e., Avon, Tupperware, and Mary Kay) date back to the mid-to-late 1800s and others such as musicians have existed much longer than that. These types of gig work, as well as less obvious forms (i.e., substitute teachers and babysitters), are often overlooked in research today due to the heavy current emphasis on technology-driven gig work (Gleim, Johnson, & Lawson, 2019).

So, the question remains, what is gig work? Despite growing scholarly interest in gig workers, research on these individuals is hampered by definitional ambiguity. This is an obstacle to the progression of the literature as using different definitions of gig work limits accumulation of knowledge across studies and creates redundancy in literature where multiple terms refer to the same phenomenon (jingle fallacy, cf. Marsh, 1994). This highlights a prominent issue in the literature in which research tends to focus on specific types (i.e., contingent work) or platforms (i.e., Uber) of gig work as opposed to a broader definition of gig work that focuses on qualities shared by some/all gig workers that distinguish them from other types of workers. Additionally, there is a general lack of consensus about which types of nonstandard work (i.e., sharing economy and independent contractors) should be included in the gig economy. Thus, while research on specific types of gig workers is important, researchers may be able to produce more generalizable studies with a more complete and accepted definition of gig workers.

The goal of this conceptual article is to integrate previous research from our systematic literature review on gig work and related terms with a focus on four contributions. First, this article provides clarity on the definition of gig work by identifying its primary characteristics (e.g., common to all gig workers) as well as secondary characteristics that distinguish different forms of gig work. We offer a definition of gig work that captures both modern and older forms of gig work and allows clear determinations of whether a group of workers can be considered gig workers. Second, to our knowledge, no research to date has developed a typology that distinguishes specific types of gig workers. Given the continued growth of both the types of gig work and the percentage of gig workers in the workforce, differences in work experiences and outcomes between types of gig workers may become increasingly evident and will need to be acknowledged in the literature. Thus, building on our definition of gig work, we describe gig worker profiles that categorize groups of gig workers that can be differentiated based on each group’s secondary characteristics with the intent to inform future research. Future researchers conducting empirical studies will be able to use the definition provided in our article to justify the classification of their samples as gig workers or intentionally examine certain gig work profiles. Further, the profiles provide future gig work review articles and meta-analyses with a guiding framework for the inclusion criteria that should be used to determine the boundaries of gig work.

Third, based on our review, there is a need in the literature to apply organizational psychology theories to the gig work literature to explain theorized effects of gig work’s impact on workers and organizations. This article utilizes the Job Demands-Resources (JD-R) model to explain how job demands and job resources differ across gig worker profiles. Finally, our fourth contribution is providing a future research agenda that includes areas specific to extending our understanding of gig work in the JD-R theory as well as more general research areas such as potential moderators and methodological considerations.

Literature Search Process

Figure 1 provides an overview of the literature search process. To identify relevant nonstandard work articles, we used the following search terms: gig economy OR gig work OR gig workers, share economy OR sharing economy, platform economy OR platform workers OR digital platform OR virtual workers, contingent work OR contingent workers, and independent contractors. These keywords were searched in five databases: PsycINFO, Business Source Complete, Business Source Premier, PsycARTICLES, and the Psychology and Behavioral Sciences Collection. The inclusion criteria restricted the articles to scholarly (peer-reviewed) journals, written in English with a full text version available. No timeframe limitations were posed. At least two authors coded each article based on relevance by reading the abstract and skimming the body of text at each step. Inconsistencies in coding were discussed and resolved between coders. Articles were first coded by type of article (i.e., primary study and narrative review) and the construct(s) examined in the article (i.e., job satisfaction and commitment). Many of the articles from the initial search were excluded due a lack of social and behavioral focus. For example, several articles focused on developing business models or marketing strategies for gig work that were not relevant to the organizational psychology lens of this article. A total of 136 articles (70 primary studies, 61 narrative reviews, and 5 case studies) were included in the review. The full list of articles is available from the first author.

Figure 1.

Literature search process.

The lack of consistency across gig work articles (i.e., definitions used and outcomes studied) directed us to narrow our focus on using the literature search to build a narrative on “what is gig work?” rather than the conventional approaches of reviewing all included articles or conducting a meta-analysis. For example, the findings of our literature review indicated that a meta-analysis would not be appropriate due to a lack of studies focusing on the same constructs. The most frequently studied construct was job satisfaction; however, only 10 primary studies with relevant gig work samples examined job satisfaction. Other constructs (i.e., organizational commitment, job security, and performance) were examined in less than six primary studies, and constructs were not consistently measured from the perspective of the gig worker (e.g., measures sometimes targeted managers (Way, Lepak, Fay, & Thacker, 2010) in mixed workforces). Additionally, varying conceptualizations of gig work across the articles presented challenges concerning which studies used samples that should be classified as gig workers. Several definitions of gig work were used across different articles with many of them excluding traditional forms of gig work.

Thus, our systematic literature review supported the need for a clear and comprehensive definition of gig work that also lends itself to differentiation among types of gig workers. In response, we conducted a content analysis of definitions used for gig work and related terms (i.e., gig economy and platform workers) in the relevant articles including all narrative reviews, primary studies, and case studies. Each definition was coded by two authors for characteristics used to describe the term. The results of the coding were used to categorize existing definitions into three categories, develop primary and secondary characteristics that define gig work, and construct gig worker profiles based on these characteristics. Each of these contributions will be discussed in more detail below.

Existing Definitions

Based on our systematic literature review, the existing definitions of gig work (K = 28) can be categorized into three themes. These themes include definitions based upon characteristics of the type of work (e.g., technology enabled), those that are based on the way in which the work is arranged (e.g., project based), and those based on the tax status or legal classification of workers. There is some overlap between the various definitions, and each group of definitions comes with its own challenges and benefits.

Gig work definitions focusing on specific characteristics of work often include work that is enabled by a technology “platform” (K = 16) that facilitates the matching of supply and demand for services (e.g., Spreitzer, Lindsey, & Lyndon, 2017). For example, using this definition, individuals using the online platform Upwork would be considered gig workers. Other definitions proposed by researchers such as Gleim et al. (2019) have added to this by also including individuals who are engaged in direct selling (e.g., on Etsy). Although this definition extends gig work beyond the use of technology, it may still exclude many workers that should be considered gig workers such as musicians and substitute teachers.

The second definition theme reflects the way in which the work is arranged. All of the existing definitions of gig work (K = 28) referred to the nature of the work arrangement to some extent. For example, according to Dubal (2017), to be a gig worker, one must perform services or deliver goods on-demand and must lack the safety and security of employment benefits. Contrary to standard employees, these workers are not hired long term and do not earn a wage or a salary (e.g., freelancers and independent contractors). Although many gig workers fit these definitions, there is a lack of consensus among them, particularly regarding consistent terminology (Kilhoffer, Lenaerts, & Beblavý, 2017). For instance, terms such as “sharing economy” and “platform economy,” both of which are elements of the gig economy, are often conflated. Furthermore, the existing definitions do not elaborate sufficiently enough to determine whether a specific “gig” is considered gig work in their definition. Each aspect of these definitions needs to be thoroughly explicated to minimize definitional ambiguity.

The final theme of the definitions identifies gig workers based on their tax status or legal classification (K = 8). For instance, in the United States, gig workers are distinguished from standard employees by the different tax forms they receive (e.g., 1099 forms). Gig workers such as independent contractors and freelancers receive tax forms for the services they perform for a company; however, they are not considered company employees and lack the same benefits and legal protections as standard employees (Stone, 2006). As a result, companies may be motivated to cut costs by providing these workers with low wages and bypassing traditional safeguards (i.e., minimum wage and workers’ compensation) to protect employees (Steinberger, 2018).

Defining Gig Work

While the array of current definitions provides insight into the nature of gig work, a more parsimonious yet inclusive definition is needed. Our content analysis of previously used definitions of gig work allowed us to highlight the relative importance of gig work characteristics to determine what is (and what is not) gig work. Thus, we clarify the definition of gig work by identifying its primary and secondary characteristics and distinguish it from other types of nonstandard work.

We found 28 articles that defined “gig work” or “gig economy” specifically. It should be noted that many of the existing definitions were relatively vague, only briefly described the nature of gig work, and frankly appeared incomplete. However, based on characteristic frequency and discussion, we categorized the identified characteristics into primary characteristics (i.e., common to all gig workers) and secondary characteristics (i.e., shared by certain groups of gig workers). The majority of the definitions described gig work to be short term (73.08%), require the completion of finite assignments (84.62%), and allow loose boundaries for when and where people must work (80.77%). In the extant definitions that did not explicitly state these characteristics, the characteristics were generally implied in the article’s descriptions and examples of gig work. Therefore, temporary, project-based, and flexible were classified as core descriptors most used across definitions and were determined to be primary characteristics of gig work.

Primary Characteristics

As Figure 2 illustrates, gig workers may be viewed as a specific type of worker within the broader category of nonstandard work arrangements including temporary employees, part-time employees, and contracted employees (Dickson & Lorenz, 2009). To distinguish gig work from other nonstandard workers, we focus on three primary characteristics of gig work. To be considered a gig worker, all three characteristics must be present to some degree.

Figure 2.

Workforce diagram representing gig workers in relation to similar nonstandard work arrangements.

The first primary characteristic of gig work is project-based compensation. Rather than being given a salary, gig workers are compensated on a project-to-project basis. For example, an Airbnb host is compensated based on the number of days that their property is booked. If their property is not booked on a specific day, they do not receive income from that day. Similarly, Amazon’s Mechanical Turk workers (MTurkers) are compensated only when they complete a task. They are paid from project to project.

The second primary characteristic is that all gig work is temporary. This excludes employees who are hired by a company for a long-term position. It is important to note that this does not imply that individuals cannot work as a gig worker on a long-term basis; temporary refers to the nature of the work being conducted. For example, an independent contractor may be hired to complete a project for a company over a specified amount of time and once the job is completed, the worker moves on to the next “gig.” Similarly, a ridesharing driver is not hired by the individual requesting the ride to be their personal chauffeur. Rather, the driver transports the rider to their destination and moves on to the next individual requesting a ride. This characteristic adds to project-based compensation because of its temporal focus. Not all project-based compensation is gig work. For example, whereas an individual who works in sales and is paid a commission might be engaging in project-based work, a gig worker would work with no long-term commitment to the job. Both the gig worker and the organization understand this aspect of the nature of the work and the amount of time that they choose to be a gig worker is irrelevant.

Finally, the last primary characteristic of gig work is that it involves some level of flexibility in when/how/where the work is performed. Kossek and Michel (2011) define flexible work as having flexibility in the timing of work (when the work occurs), the location or place of work (where the work occurs), the amount of work (the amount of work or workload), and in work continuity (ability to allow for employment breaks or time off). Thus, based on these criteria, all gig workers have flexibility in their work. Many gig workers can decline jobs if they choose to do so. For example, substitute teachers may choose to not cover classes on specific days of the week. Additionally, gig workers often have flexibility in the location of work. Many gig workers choose to work from home or only specific locations. For example, MTurkers may choose to work at home, at a local café, or at a library. Next, the amount of work is also up to the individual gig worker. They have the freedom to reduce or increase the amount of work they take on as their work allows. Finally, regarding continuity, many gig workers have the option to take short-term breaks or time off when they choose. If an individual running a bed and breakfast on Airbnb would like to take a vacation, they can do so whenever they choose. There is no need to request a specific amount of time off because it is up to the individual worker.

Secondary Characteristics

In addition to the primary characteristics, we describe secondary characteristics of gig work. Secondary characteristics are common to many but not all gig workers. There are also workers whose jobs have some of these secondary characteristics but are not considered gig workers because they do not also have all three primary characteristics. Secondary characteristics of gig work include technologically enabled networks (76.92%), crowd work (30.77%), remote work (26.92%), and agency-based work (11.54%). Although a little over three-fourths of the gig work definitions described technologically enabled networks, we did not classify this as a primary characteristic as it clearly excluded many traditional forms of gig work (i.e., musicians and babysitters).

The first secondary characteristic is whether the work is facilitated using a technologically enabled network. This is defined as work that uses online platforms to digitally connect workers to consumers and is a central component to multiple definitions of gig work (e.g., de Ruyter, Keeling, & Ngo, 2018; Dubal, 2017; Gleim et al., 2019). Examples of technologically enabled work include ridesharing drivers or Esty workers who are enabled by the platform to generate income.

As a secondary characteristic, crowd work (e.g., crowdsourcing) is defined by Schulte, Schlicher, & Maier (2020) as digital work outsourced by a person or company to an open, anonymous crowd on the Web. This work varies in complexity and time required to complete and is on a job-to-job basis with no long-term contracts. These workers often use a third party to identify individuals with a specific need that the workers can fulfill typically through an application (app) accessible via phone or Internet (Rinne, 2017). For example, Airbnb brings individuals looking to book a place to stay to gig workers with available property. However, while it is generally agreed upon in the literature that crowd work is an element of gig work (Schulte et al., 2020), it does not apply to all gig workers (e.g., photographers). One important component of crowd work is that it requires workers to use their own resources or personal property to do the work. Thus, individuals who write for websites like Wikipedia are engaging in crowdsourcing but are not being paid and would not be considered gig workers.

The next secondary characteristic is working remotely. Many organizations allow individuals to work from home, on the road, in satellite offices, and a variety of other nontraditional settings (Barsness, Diekmann, & Seidel, 2005). Some of these individuals are employees, others are gig workers. MTurkers, for example, can work remotely from the home, the library, or a local coffee shop. However, while some gig workers can work remotely, others, such as individuals who babysit through Care.com, must be physically present at a specific time/place for the job. Conversely, many individuals who work remotely are not gig workers. For instance, many traditional workers shifted to working remotely due to the COVID-19 pandemic. However, they are not considered gig workers because they do not possess all three primary characteristics.

Finally, the last secondary characteristic is intermediary or agency-based work. Many gig workers are a part of an agency that they use to connect with clients or consumers. Although the agency-based characteristic was only included in 11.54% of the definitions specifically for “gig work” or “gig economy,” agency-based gig work was often captured in older articles that examined agency temporary work (K = 11) or within papers on contingent work. These gig workers are affiliated with the agency; however, the agency does not employ them in the traditional sense. Despite some gig work being agency-based, many gig workers chose not to use an intermediary or to be affiliated with an agency. Similarly, there are many agency-affiliated workers who are clearly not gig workers, such as physicians who affiliate with staffing services that help place them in full-time positions.

Distinguishing Gig Work from Related Nonstandard Work Arrangements

Now that both primary and secondary characteristics have been identified, we focus on categorizing four common types of nonstandard workers as presented in Figure 2. Our literature search indicated that the prevalence of articles published in the context of organizational psychology on these nonstandard work arrangements has changed over time. For example, from 1990 to 2015, contingent workers (K = 53) and independent contractors (K = 11) were popular groups of interest in organizational psychology but only seven and two articles, respectively, have specifically focused on these workers since 2015. On the contrary, contemporary forms of nonstandard work arrangements (e.g., gig work, sharing economy, platform economy, and crowd work) were absent in the organizational psychology literature until 2015. However, since 2015, 28 articles have been published on gig work, 17 on the sharing economy, six on crowd work, and three on the platform economy. These data reflected the boom in interest of gig work over the past 6 years as the changing nature of work has facilitated the popularity of gig work in both the academic literature and mainstream media. Additionally, in our systematic literature review and definition content analysis, we coded the descriptors of these related terms that are often conflated with gig work. This section builds on these results to describe what makes some of them gig workers and others not.

The first common type of nonstandard worker is the contingent worker. Contingent employment is typically defined as a job in which the worker does not have either an explicit or implicit contract for long-term employment (Coyle-Shapiro & Kessler, 2002) and often includes individuals who are self-employed or independent contractors, wage and salary workers, and temporary workers with less than a year of working (Chen, Yeh, & Madsen, 2019). This broad definition was reflected in our findings as contingent work was the most frequently defined term related to gig work (K = 60). Contingent workers who are not compensated based on project completion and receive a wage or a salary are not gig workers. Additionally, if the contingent worker does not also have flexibility, they are not considered a gig worker. Photographers are an example of gig workers who also may be considered contingent workers.

Self-employed workers also could be categorized as gig workers which was reflected by self-employment being used to describe gig work in approximately one-third of the existing definitions. Many self-employed workers easily satisfy both the project-based and flexible primary characteristics of a gig worker, but the temporary nature of gig work distinguishes many self-employed individuals from gig workers. Prottas and Thomson (2006) categorized self-employed workers into two groups: individuals who are self-employed and employ others and those who are self-employed and do not employ others. Self-employed individuals who also employ other workers are not gig workers. Additionally, many self-employed workers view their work as a long-term career. These individuals have invested their resources into their employment and are legally distinct from gig workers as well. If there is a legal distinction made that an individual is self-employed (i.e., via limited liability company or corporation status in the United States), then the worker is not considered a gig worker because the work is not temporary. This type of individual distinctly differs from the self-employed gig worker who, for example, is doing yard work as a temporary job.

The next two types of workers fall completely within our definition of gig work: sharing economy workers and platform workers. Platform workers utilize digital labor platforms as a means of work. Codagnone, Biagi, & Abadie (2016) define digital labor platforms as functioning:

“as digital marketplaces for non-standard and contingent work; where services of various nature are produced using preponderantly the labor factor (as opposed to selling goods or renting property or a car); where labor (i.e., the produced services) is exchanged for money; where the matching is digitally mediated and administered although performance and delivery of labor can be electronically transmitted or be physical” (p. 17).

Based on this definition, gig workers who use Etsy or Airbnb would be excluded due to their physical capital or goods component. As a result, while all platform workers are gig workers, not all gig workers are platform workers. Although our review indicated little attention has been given to platform workers specifically (K = 3), we made this distinction to prevent confusion stemming from the heavy use of online platforms in gig work.

Similarly, all sharing economy workers are gig workers, but not all gig workers are sharing economy workers. The sharing economy is a growing area in the organizational psychology literature (K = 15). Sharing economy workers are involved in the “IT-facilitated peer-to-peer model for commercial and noncommercial sharing of underutilized goods and service capacity through an intermediary without a transfer of ownership” (Schlagwein, Schoder, & Spindeldreher, 2020, p. 818). In other words, these individuals engage in the sharing of their assets with the help of technology. As a result, gig workers such as rideshare drivers who use their vehicles to provide transportation services are considered both sharing economy workers and gig workers based on the work being project-based, flexible, and temporary. However, although sharing economy work is frequently conflated with gig work, individuals such as substitute teachers are still gig workers despite not being part of the sharing economy.

Gig Worker Profiles

In addition to distinguishing gig work from other types of nonstandard work, clarifying the definition of gig work by identifying its primary and secondary characteristics allows us to address two additional gaps in the literature. First, the definition helps researchers clearly determine if a worker is engaged in gig work, ultimately facilitating research that compares gig workers to standard workers. Second, the nature of this definition allows us to compare different types of gig workers. To our knowledge, no research to date has constructed specific categories that capture the different types of jobs in the broad area of gig work. This article formally conceptualizes the distinctions across types of gig workers which serve as the groundwork for empirical testing of differences between the proposed gig worker profiles. Based on combinations of the secondary characteristics provided by our definition, we created five profiles reflecting different types of gig workers. These profiles reflect examples of modern gig work as well as older forms. Aligned with our definition of gig work, all of the gig work profiles must possess the three primary characteristics (i.e., temporary, flexible, and project-based). The five profiles of gig work are provided below and summarized in Table 1.

Table 1.

Defining characteristics and examples of gig worker profiles.

	Primary characteristic			Secondary characteristic				Example
	Project-based	Temporary	Flexible	Tech-enabled network	Crowd work	Remote work	Agency-based work	Example
Gig Service Providers	X	X	X	X	X			Ridesharing, Grubhub, TaskRabbit, and Rover
Gig Goods Providers	X	X	X	X		X		Etsy, Redbubble, and Pixapp
Gig Data Providers	X	X	X	X	X	X		Amazon’s Mechanical Turk and Survey Junkie
Agency Gig Workers	X	X	X				X	Models and temporary agency workers
Traditional Gig Workers	X	X	X					Substitute teachers and babysitters

Note: Table 1 represents the defining characteristics (both primary and secondary) of each gig worker profile and provides examples for each profile. X reflects the presence of a certain characteristic for the profile.

The first gig worker profile is Gig Service Providers. This group provides services through a technologically enabled network and crowdsourcing. Examples include most app-based sharing economy jobs such as Uber, Lyft, Airbnb, Grubhub, TaskRabbit, and Rover.

Gig Goods Providers require a technologically enabled network and provide goods to consumers. The goods must be created by the Gig Goods Provider and do not include the reselling of previously owned items (i.e., through eBay or Facebook Marketplace). Etsy, Redbubble, and Pixapp are examples of Gig Goods Providers.

Gig Data Provider gig work involves remote work; similar to Gig Service Providers, Gig Data Provider gig work utilizes a technologically enabled network and relies on crowdsourcing. However, unlike Gig Service Providers, Gig Data Providers do not necessarily provide a service to consumers. Examples include virtual platforms that pay workers for completing surveys such as Amazon’s Mechanical Turk, Survey Junkie, and Google Surveys.

Agency Gig Work is characterized by its agency-based nature and is not dependent on a technologically enabled network. In other words, Agency Gig Workers are assigned to projects through a third-party intermediary or agency and are not solely facilitated by an app. For example, models are Agency Gig Workers who work through an agency to be assigned modeling gigs.

Last, Traditional Gig Workers provide services and do not rely on a technologically enabled network or an agency to assign them to their gigs. Examples include substitute teachers, direct sellers (i.e., Mary Kay), comedians, babysitters, and some photographers, artists, videographers, and musicians. It is important to note though that for a photographer, artist, videographer, or musician to be considered a Traditional Gig Worker, they must not be formally self-employed (e.g., a wedding photographer with an LLC would not qualify as a Traditional Gig Worker or as a gig worker in general) and meet the other two core characteristics, project-based and flexible. Additionally, gig workers may perform the same work but be classified as a different profile depending on the peripheral characteristics of their job. For example, babysitters who work through technologically enabled platforms such as Care.com are Gig Service Providers, whereas babysitters who find their own babysitting gigs are Traditional Gig Workers.

Once we created the gig work profiles based on the initial systematic literature review, we followed up with a second round of coding to examine the extent to which the literature to date specifically examined the proposed profiles developed in this article. For the primary studies (K = 70), at least two authors coded for the gig worker profile(s) studied in the article. Some articles were coded as “not specific enough” when the sample description was too vague (i.e., if insufficient information was provided to determine if the types of contingent workers used met our three primary characteristics of gig workers) or “N/A” when the article was related to gig work but did not directly examine a sample of gig workers (i.e., if the study tested the perceptions of workforce mixing between gig workers and standard workers by sampling standard workers). As with the previous rounds of coding, discrepancies between coders were resolved through discussion.

Figure 1 reflects that only 27 of the 70 primary study articles used relevant samples with descriptions specific enough to be classified by the gig worker profiles proposed later in this article (e.g., 23 N/As, 20 not specific enough, six Gig Service Providers, 0 Gig Goods Providers, one Gig Data Providers, 13 Agency Gig Workers, and seven Traditional Gig Workers). It should be noted that the Gig Goods Provider is the only profile proposed in this article without any relevant samples used in previous empirical research as identified by our literature search; however, this type of work is relatively well-known by the general public, aligns with the characteristics of gig work, and examples of Gig Goods Providers have been recognized in narrative reviews on gig work (i.e., Chappa, Varghese, & Chandler, 2017). Thus, our literature search highlighted that researchers have given little attention to certain types of gig workers. Our article introduces Gig Goods Providers to the literature and provides a formal definition and theoretical basis for these gig workers as well as those of other profiles to be included in future research. Building on our definition of gig work to develop these profiles highlights the utility of considering both the primary and secondary characteristics of gig work. Further, these profiles allow researchers to be able to distinguish gig workers’ from standard employees as well as different types of gig workers’ based on their profiles.

Explaining Gig Worker Profile Differences

Our literature review revealed no agreed upon framework to explain the differences in subtypes of gig workers; however, researchers (e.g., Keith, Harms, & Long, 2020) have begun to examine gig work through the lens of the JD-R model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). Our article draws on the JD-R model as a general framework to understand the positive and negative experiences of gig work. We use the JD-R model to develop propositions about profile differences in demands and resources experienced by gig workers.

The JD-R model suggests that the balance of workers’ job demands and job resources influences their motivation and job strain, ultimately impacting work and health outcomes (Demerouti et al., 2001). The JD-R model also proposes that job demands and job resources initiate two underlying psychological processes that explain the development of the resources-motivation and demands–strain relationships: the motivational process and the health impairment process. The motivational process stems from resources. Access to job resources (i.e., social support) and personal resources (i.e., optimism) is expected to stimulate work engagement, resulting in improved positive work outcomes and reduced negative work outcomes (Schaufeli & Taris, 2014; Xanthopoulou, Bakker, Demerouti, & Schaufeli, 2007). Job demands (i.e., workload) initiate the health impairment process. Coping with chronic job demands expends workers’ physical and psychological resources; consequently, they may experience exhaustion due to depletion of energy and an increase in health problems (Bakker, Demerouti, & Schaufeli, 2003; Demerouti et al., 2001; Leiter, 1993). Although the JD-R model is a well-established organizational stress theory, we apply the model in the unique context of gig work.

Drawing from the JD-R model, Keith et al. (2020) discussed job demands and resources that may be particularly relevant for gig workers. Keith et al. (2020) proposed that some gig workers may be more susceptible to certain demands and resources due to the nature of their specific type of gig work; however, they did not extensively describe these differences. Thus, we extend Keith et al. (2020)’s work to further advance the gig work literature with three contributions: (1) creating gig worker profiles that include both technologically enabled forms of gig work and other forms of gig work not addressed in their article, (2) applying the JD-R model to the gig work profiles we created to expand the discussion of how job demands and job resources differ across profiles, and (3) describing additional resources beyond those included by Keith et al. (2020) that should be considered when comparing gig worker profiles. Table 2 summarizes the proposed associations between the profiles and job demand/resources.

Table 2.

Summary of the propositions.

	Job characteristic	Gig Service Providers	Gig Goods Providers	Gig Data Providers	Agency Gig Workers	Traditional Gig Workers
Job demands	Alienation			+ (P1)
	Emotional labor	+ (P2)				+ (P2)
	Overqualification	+ (P3)		+ (P3)
	Hours underemployment		+ (P4)		+ (P4)	+ (P4)
Job resources	Work scheduling autonomy	+ (P5)	+ (P5)	+ (P5)
	Decision-making autonomy	+ (P6)	+ (P6)			+ (P6)
	Workplace social support				+ (P7)	+ (P7)
	Task identity		+ (P8)		+ (P8)	+ (P8)

Note: Table 2 reflects the eight propositions outlined in this article. The plus signs (+) indicate that the specific gig worker profile is expected to be particularly likely to experience the job demand or job resource. The (P#) represents the proposition number that captures the relationship between the gig worker profile and job demand or job resource.

Job Demands

The job demands focused on in this article (e.g., alienation, emotional labor, and underemployment) were originally proposed by Keith et al. (2020). We extend their work by discussing differences in gig worker profiles and developing propositions about how and to what extent each profile experiences alienation, emotional labor, and underemployment. Based on the JD-R model, the proposed demands are expected to initiate the health impairment pathway and induce strain on gig workers; the potential implications of these demands for work outcomes will be addressed in the future research section.

Alienation

Alienation is a long-studied sociology concept first described by Karl Marx (e.g., Marx & Engels, 1837/1978) that remains relevant to organizational science, especially as the gig economy continues to expand, creating more jobs in which workers are likely to experience alienation. Work alienation refers “to the worker’s estrangement or distancing from the products of their labor as well as the society which that labor is supposed to be servicing” (Keith et al., 2020, p. 18; Marx & Engels, 1837/1978). Khan et al. (2019) tested work alienation in the JD-R model and found work alienation was mediated by burnout in predicting explorative and exploitative learning in organizations. Additionally, work alienation has been found to negatively relate to job satisfaction, organizational commitment, task performance, and contextual performance (Chiaburu, Thundiyil, & Wang, 2014), further justifying it as a demand for gig workers.

While the temporary and project-based nature of gig work increases workers’ susceptibility to experiencing alienation, some gig workers are more vulnerable than others (Keith et al., 2020). We propose that Gig Data Providers experience greater alienation than the other gig worker profiles. The work of Gig Data Providers fulfills both aspects of the definition of work alienation. Gig Data Providers are physically isolated as they work via virtual platforms, removing interactions with coworkers, customers, and supervisors and distancing Gig Data Providers from the society of their labor services (Webster, 2016). Additionally, Gig Data Providers tend to be estranged from the products of their work because they engage in small, repetitive tasks (i.e., completing similar surveys over time) but are not actually involved in the research process and usually do not receive feedback about the outcomes of their work.

Other gig worker profiles may experience work alienation but do not typically embody the full definition of work alienation. For example, Gig Goods Providers (i.e., Etsy artists) may be disconnected from society as they have little to no interaction with customers and do not have coworkers or supervisors, but they are closely connected to the products they create. Traditional Gig Workers are more connected to society. For example, substitute teachers work in the school systems interacting with students, coworkers, and administrators; however, especially when compared to full-time teachers, substitute teachers may not feel as connected to the products of their labor because they are not responsible for the continuous growth and development of the same students throughout the school year. Contrarily, musicians—another example of Traditional Gig Workers—may experience minimal work alienation as they tend to be closely affiliated with the audience they perform for and more connected with the products of their labor since performing is likely central to their identities. Thus, while the demand of work alienation varies across gig worker profiles, Gig Data Providers experience the most work alienation.

Proposition 1: Gig Data Providers experience more work alienation than other gig worker profiles.

Emotional labor

Workers engage in emotional labor when their job requires emotion regulation such as suppressing, faking, or intensifying emotions while at work (Grandey, 2000). Emotional labor is common, if not essential, in jobs that entail interpersonal interaction. Emotional labor poses a demand on workers that has been related to emotional exhaustion, job dissatisfaction, and negative health outcomes (Grandey & Melloy, 2017).

Differences in emotional labor associated with each gig worker profile reflect the extent to which each type of gig work involves interaction with others. Gig Service Providers and Traditional Gig Workers require the most emotional labor as they are in frequent contact with customers/clients and must follow display rules and norms concerning their expected emotions. For example, rideshare drivers are expected to maintain positive emotions even when dealing with difficult customers (i.e., intoxicated and noisy) and may be overly nice to customers in an attempt to receive a better rating or higher tips (Rosenblat & Stark, 2016). Additionally, Traditional Gig Workers, such as babysitters, often heavily rely on positive perceptions from their employer in order to be asked back for future jobs. They may engage in intensive emotional labor (i.e., managing their responses to misbehaving children) to make a good impression on the families they babysit for to increase their chances of future employment.

Agency Gig Workers’ required emotional labor depends on the type of agency gig work. For example, agency gig work assigning workers in the medical field (i.e., nursing assistants) may be expected to engage in more emotional labor when interacting with patients or patients’ families than Agency Gig Workers in information technology positions that tend to be less sociable. Thus, we make no predictions about emotional labor differences for Agency Gig Workers but compare Gig Service Providers and Traditional Gig Workers to Gig Data Providers and Gig Goods Providers. Last, Gig Data Providers and Gig Goods Providers engage in minimal to no interpersonal interaction and are not governed by display rules; therefore, these gig workers are least affected by the demand of emotional labor.

Proposition 2: Gig Service Providers and Traditional Gig Workers engage in more emotional labor than Gig Data Providers and Gig Goods Providers.

Underemployment

Underemployment is an economic stressor in which a worker experiences at least one of the following: insufficient use of skills, abilities, or qualifications, underpayment, and/or working fewer hours than desired (Feldman, 1996; Feldman & Maynard, 2011). Harari, Manapragada, and Viswesvaran (2017) meta-analysis showed that underemployed workers have reduced job satisfaction and organizational commitment as well as increased turnover intentions. Additionally, research suggests that underemployment is associated with decreased psychological well-being (McKee-Ryan & Harvey, 2011).

Keith et al. (2020) noted that certain dimensions of underemployment are particularly relevant to certain gig workers. Specifically, we focus on overqualification and hours underemployment as dimensions particularly relevant to differentiating gig worker profiles. Overqualification includes working outside of the field of one’s formal education or training (Feldman & Maynard, 2011). Gig Service Providers and Gig Data Providers are expected to be the most overqualified specifically regarding discrepancies in education and job field. Pew Research Center (2016) found that 51% of MTurkers held a college degree or more and another 36% have some college experience. Similarly, Uber (2015) reported 48% of their drivers had bachelor’s or advanced degrees and 24% had an associate’s degree or completed trade school, leaving only 28% of drivers without college degrees. These statistics support that Gig Services Providers and Gig Data Providers are overall relatively educated groups and overqualified for the general job requirements of their work (i.e., driving and completing surveys, respectively).

Other gig worker profiles may be somewhat overqualified for their jobs as this varies by individual; however, their types of gig work are more likely to be aligned with one’s field preference. For example, Traditional Gig Workers such as substitute teachers may be in school to become full-time teachers or substituting until they can find a full-time teaching job, Agency Gig Workers such as models may be working toward a modeling career, and Gig Goods Providers’ creations may reflect formal training through education or lessons related to art. Thus, it is expected that there are fewer discrepancies in the qualifications and job fields of these gig worker profiles relative to Gig Service Providers and Gig Data Providers.

Proposition 3: Gig Service Providers and Gig Data Providers are expected be more overqualified than other gig worker profiles.

In contrast, Gig Goods Providers, Agency Gig Workers, and Traditional Gig Workers are expected to experience more hours underemployment than the other two profiles. Although a general benefit of gig work is the flexibility in work schedule and hours, some gig workers may be less able to readily find work when desired than others. It should be noted that due to the project-based nature of gig work, it may be more important to focus on gig workers having a lack of gigs rather than a lack of hours per se. Regardless, discrepancies between a gig worker’s desired amount of work and actual amount of work reflect hours underemployment. For example, Gig Goods Providers are dependent on demand via their online selling platforms with little opportunity to seek out consumers on their own; if customers are not requesting for products to be made at the desired rate of the worker, Gig Goods Providers experience hours underemployment. Agency Gig Workers usually rely on the agency to schedule their hours, projects, or gigs. Models working for agencies may be able to request more gigs, but ultimately, they have limited control over whether they receive more work. Last, Traditional Gig Workers are often in similar situations as Agency Gig Workers but with their direct employer (i.e., children’s parents for babysitters and venues for bands).

On the other hand, Gig Services Providers and Gig Data Providers tend to have more opportunities to ensure they are working their desired number of hours or projects. Gig Service Providers can easily work for multiple platforms (i.e., Uber and Lyft; Grubhub and DoorDash) or intentionally work in more popular areas (i.e., drive in nearby cities or college towns vs. in rural areas) to increase their chances of finding work. Similarly, Gig Data Providers can work on multiple platforms (i.e., MTurk and Google Surveys) with little difficulty finding surveys to complete. Thus, Gig Goods Providers, Agency Gig Workers, and Traditional Gig Workers are more likely to experience hours underemployment and have less opportunities than Gig Service Providers and Gig Data Providers to reduce this demand of discrepancies in desired gigs.

Proposition 4: Gig Goods Providers, Agency Gig Workers, and Traditional Gig Workers are more likely to experience hours underemployment than other gig worker profiles.

Job Resources

We focused on three resources expected to differ across gig worker profiles: autonomy, social support, and task identity. Although gig workers are susceptible to certain job demands, they are able to utilize autonomy, social support, and task identity as resources. The extent to which each resource is utilized by the different types of gig workers varies, and particular resources are more applicable and beneficial to specific profiles than others.

Autonomy

Job design and work stress literature highlight the important influence of autonomy within the workplace. Job autonomy refers to “the degree to which the job provides substantial freedom, independence, and discretion to the employee in scheduling the work and in determining the procedures to be used in carrying it out” (Hackman & Oldham, 1975, p. 162). Job autonomy is included in Hackman and Oldham (1975) Job Characteristics Model (JCM) as one of the five core characteristics that evokes workers’ psychological reactions to the job. The JCM has been widely used to explain how core job characteristics influence work outcomes (Hackman & Oldham, 1975). Similarly, Karasek (1979) job demands-control model suggested that strain “results not from a single aspect of the work environment but from the joint effects of the demands of a work situation and the range of decision-making freedom (discretion) available to the worker facing those demands” (p. 287). Furthermore, Morgeson and Humphrey (2006) describe autonomy as “the extent to which a job allows freedom, independence, and discretion to schedule work, make decisions, and choose the methods used to perform tasks” (p. 1323).

Gig workers generally have more autonomy than traditional workers, and their rationale for doing gig work is often related to the autonomy gig work affords. We propose that Gig Data Providers, Gig Goods Providers, and Gig Service Providers will have greater work scheduling autonomy than Agency Gig Workers and Traditional Gig Workers. Greater work scheduling autonomy allows Gig Data Providers, Gig Goods Providers, and Gig Service providers to craft their work schedules around their personal life schedules. For example, if rideshare drivers (Gig Service Providers) have personal responsibilities during the day (i.e., childcare), they can work during the evening or nighttime. Gig Data Providers, such as MTurkers, can create their own work schedules and complete their tasks whenever they want. An individual who sells personalize products on Etsy (Gig Goods Provider) can schedule work at his or her own discretion based upon when products are available to sell. In contrast, Agency Gig Workers and Traditional Gig Workers have less work scheduling autonomy due to the nature of their work. For example, substitute teachers’ schedules are confined to normal school operating hours. Agency gig workers (i.e., models) are reliant on their agent to provide a work schedule and have little control over when they can complete each gig.

Proposition 5: Gig Data Providers, Gig Goods Providers, and Gig Service Providers have greater work scheduling autonomy than other gig worker profiles.

Additionally, we propose that Gig Goods Providers, Gig Service Providers, and Traditional Gig Workers will experience greater decision-making autonomy than Gig Data Providers and Agency Gig Workers. Decision-making autonomy includes a worker’s ability to utilize personal initiatives and judgments in completing work, as well as the ability to make work-related decisions (Morgeson & Humphrey, 2006). Gig Goods Providers and Gig Service Providers can decide when they want to work and can make some work-related decisions for themselves. An individual working for TaskRabbit (Gig Service Provider) can choose which task(s) to complete and while working can make the decisions regarding how to complete the task (i.e., building a desk for a home office). Gig Goods Providers are able to express themselves and personalize their work in order to generate valuable products to sell. Although Traditional Gig Workers tend to not have high levels of work scheduling autonomy, they are expected to have more decision-making autonomy. For example, musicians generally have the flexibility to play the songs of their choice, can perform in their attire of choice, and can make changes to their performance (play encores, repeat verses, talk to the crowd, etc.) at their discretion.

Agency Gig Workers and Gig Data Providers have less decision-making autonomy. As previously mentioned, models (Agency Gig Workers) have limited control over when they can complete their jobs, and they are often explicitly told what to wear and how to pose which undermines their ability to use personal initiative or judgment. Despite the work scheduling autonomy experienced by Gig Data Providers, they often have little to no control concerning their actual work tasks. Thus, levels of decision-making autonomy vary across the gig worker profiles, with greater decision-making autonomy experienced by Gig Goods Providers, Gig Service Providers, and Traditional Gig Workers.

Proposition 6: Gig Goods Providers, Gig Service Providers, and Traditional Gig Workers experience greater decision-making autonomy than other gig worker profiles.

Workplace social support

Workplace social support is defined as “the degree to which individuals perceive that their well-being is valued by workplace sources, such as supervisors and the broader organization in which they are embedded” (Kossek, Pichler, Bodner, & Hammer, 2011, p. 3). According to Kossek et al. (2011) conceptualization, workplace social support emanates from various sources including supervisors, coworkers, and organizations. Social support is a critical resource related to workers’ success on the job. Viswesvaran, Sanchez, and Fisher (1999) conducted a meta-analysis on the role of social support in the process of work stress and noted that “the rapidly changing workplace necessitates more attention to the role of support” (p. 329). Lesener, Gusy, Jochmann, & Wolter (2019) conducted a meta-analysis of longitudinal studies that examined antecedents of work engagement and determined work-related support resources that positively predicted work engagement exists at the group, supervisory, and organizational level, supporting its relevance to the JD-R model.

Workplace social support includes coworker, supervisor, and organizational support. Each source of workplace social support refers to the extent to which coworkers or supervisors or organizations value an employee’s contributions and care about an employee’s well-being (Kossek et al., 2011). For example, coworker support may be demonstrated by a colleague offering to help a coworker complete a difficult project. Family supportive supervisor behaviors, such as listening to and caring for employees’ work–family demands, provide workers with support from a supervisor level (Hammer et al., 2011). Employees may perceive higher levels of workplace social support when employers offer benefits packages that go beyond expected benefits such as medical coverage (Sinclair, Hannigan, & Tetrick, 1995).

The degree to which gig workers receive this support varies across profiles. We predict that Agency Gig Workers and Traditional Gig Workers experience more workplace social support than the other gig worker profiles. Traditional Gig Workers interact with their coworkers and leaders within the organization. For example, a substitute teacher that lives within a specific school district is likely to work within the same school. Over time, the substitute will form relationships with other teachers, faculty, and students. Therefore, a substitute teacher can receive support from coworkers, the principal (supervisor), and the school itself (organization). An agency nursing assistant (Agency Gig Worker) has the opportunity to form relationships with colleagues on the same hospital floor/unit. Nursing assistants also often have supervisors providing them with daily assignments. Over time, nursing assistants can perceive the organization to care about their well-being. Therefore, Agency Gig Workers are able to receive workplace social support from coworkers, supervisors, and the organization.

Due to the nature of their work, Gig Data Providers, Gig Goods Providers, and Gig Service Providers are limited in their interactions with coworkers and organizations. These gig worker profiles experience less workplace social support than Agency Gig Workers and Traditional Gig Workers. Rideshare drivers may communicate with other drivers online or within social circles and with passengers, which can provide some support, but there is a lack of supervisor or organizational support. Gig Data Providers (i.e., MTurkers) predominantly work alone when completing tasks, thus eliminating all three sources of workplace social support. Gig Goods Providers also generally do not work in environments structured by an organization, supervisor, and/or coworkers. Therefore, the amount of workplace social support varies across gig worker profiles.

Proposition 7: Agency Gig Workers and Traditional Gig Workers experience more workplace social support than other gig worker profiles.

Task identity

Task identity refers to “the extent to which an employee does an entire piece of work from beginning to end and can identify with the results of his or her efforts” (Lin & Hsieh, 2002, p. 156). Task identity is another of the five core characteristics in Hackman and Oldham (1975) JCM. Petriglieri, Ashford, and Wrzesniewski (2018) examined the personalization of work and identity through qualitative interviews with independent workers and analyzed responses to highlight recurring themes, which indicated that the participants viewed their work as “an avenue for self-expression” (p.136). Based on the view of work as an outlet for creativity, the type of gig work that a worker is involved in will influence an individual’s identification with their task. Petriglieri et al. (2018) results indicated that a participant’s work identity “depended on the discipline and opportunity to continue working” (p. 136). An advantage of gig work is the worker’s ability to continuously take on new projects or complete as many tasks as one desires. The projects that gig workers choose are influenced by personal preferences, knowledge, skills, and abilities. Using two studies, Jiang, Di Milia, Jiang, & Jiang (2020) examined the mediating role of thriving in the relationship between task identity and job satisfaction. Results indicated that task identity positively predicted thriving and “thriving mediated the positive relationships of task identity and autonomy with job satisfaction” (p.11).

Task identity is contingent upon an individual’s effort level from start to finish on a project and the consequential identification with the results of one’s effort. The different types of gig work require various types of tasks to be completed. Certain tasks provide opportunity for higher task identification than others. For example, drivers for a delivery service (Gig Service Providers) do not necessarily have high task identity as it would be difficult to connect driving with their personal identity or with the end results of their efforts, especially if the end result consists of driving to the next stop rather than witnessing a customer’s satisfaction with the delivery. Gig workers who sell their hand-crafted woodwork on Etsy or eBay (Gig Goods Providers) are more likely to experience greater task identity than those who take surveys on Google Survey or MTurk (Gig Data Providers) who are less likely to identify with the product of their efforts. A babysitter (Traditional Gig Worker) who works for the same family over an extended period of time is likely to have high task identity as a result of being emotionally attached to the children and watching them develop and learn new skills. Temporary nursing assistants with ambitions to become a registered nurse or doctor will experience greater task identity with their work because their efforts reflect the work they are passionate about and want to be involved in. Therefore, based on our gig worker profiles, we propose that Traditional Gig Workers, Agency Gig Workers, and Gig Goods Providers experience greater task identity than other gig worker profiles.

Proposition 8: Traditional Gig Workers, Agency Gig Workers, and Gig Goods Providers experience greater task identity than other gig worker profiles.

Future Research Agenda

Gig work is a fruitful area for future research. In addition to offering a general definition of gig work, developing gig worker profiles, and applying JD-R theory to propose profile differences in demands and resources, we promote the theoretical and empirical advancement of gig work research by providing a future research agenda. We first identify key areas of gig work research relevant to the JD-R model that need to be examined by future researchers and then provide future directions that will more broadly contribute to the gig work literature.

The JD-R model provides a starting point for testing propositions about links of gig work demands and resources to health and motivational processes. Future researchers should seek a comprehensive understanding of how gig work outcomes (i.e., job satisfaction, commitment, health, and well-being) differ across gig worker profiles as well as from other types of workers. More research is needed to determine if gig workers experience unique outcomes based on their profile or if they have similar outcomes but for different reasons. For example, do Gig Service Providers and Gig Data Providers experience similar levels of burnout due to their experience of the demands of emotional labor and alienation, respectively? Additionally, researchers should consider how competing demands and resources influence the effects of gig work. As suggested in the JD-R model, perhaps the higher social support experienced by Agency Gig Workers and Traditional Gig Workers compensates for the greater underemployment they experience.

A relatively recent addition to the JD-R model is the concept of job crafting (Bakker & Demerouti, 2017; Demerouti & Bakker, 2014; Wrzesniewski & Dutton, 2001). Bakker and Demerouti (2017) summarized the role of job crafting in the JD-R model as a way in which workers can proactively alter their job demands and resources to optimize their work environment and induce a gain spiral that leads to more job resources and work engagement. As outlined by Tims, Bakker, and Derks (2012), workers may job craft by increasing resources (i.e., seeking social support), increasing challenge job demands (i.e., learning a new skill), and decreasing hindrance job demands (i.e., reducing workload). Future researchers should consider how job crafting may occur in the context of gig work. There may be differences in the extent to which certain gig worker profiles engage in job crafting or in the types of job crafting behaviors used across profiles. For example, Traditional Gig Workers such as substitute teachers may job craft by seeking feedback on their performance (i.e., increasing resources) from more experienced teachers and may be more likely to use this type of job crafting than other gig worker profiles (i.e., Gig Data Providers and Gig Service Providers) whose work could not provide them with feedback. Gig Goods Providers may be more likely to increase their challenge job demands as Etsy artists may learn a new artistic technique to pursue more complex projects to sell.

Research is also needed to explore potential moderators of the proposed relationships in this article. Gig worker experiences do not occur in a vacuum. Other factors may contribute to gig workers’ perceptions of their demands and resources in the JD-R model, subsequently influencing the outcomes associated with the different profiles. For example, volition should be examined as a moderator such that gig workers who voluntarily chose this type of work should be more likely to have positive experiences. Being in gig work voluntarily should make the resource aspects of the job more salient as these gig workers likely chose the work for its benefits (i.e., flexibility) despite the demands. A component of whether a gig worker is voluntarily in the position may be their financial needs. The more economically dependent gig workers are on their job, perhaps the less likely they are to be in this type of work by choice. When this is the case, the demand aspects of the jobs are likely more salient, and gig workers are more likely to experience negative work outcomes. The presence of these moderators may differ across gig worker profiles, making some gig worker more susceptible to certain outcomes.

Future researchers should consider practical applications of JD-R theory to gig work such as through organizational assessments that can inform interventions (Bakker & Demerouti, 2017). Organizational assessments anonymously measure workers’ job demands and job resources (as well as other variables such as engagement, well-being, performance, etc.) at the individual level and then aggregate the scores to the company level which can be compared to national and/or sector standards (Bakker & Demerouti, 2017). Although we recognize organizational assessments may be applicable to specific gig workers (i.e., babysitters), the results of organizational assessments may be used to identify target areas for interventions to improve certain job resources or job demands. For example, as reflected in Proposition 2, Gig Service Providers and Traditional Gig Workers engage in emotional labor at work which would be expected to emerge as problem area for rideshare drivers and substitute teachers (as examples). In response, their organizations may consider emotional labor interventions that provide coping strategy training (e.g., Weaver, Allen, & Byrne, 2019) to help reduce the anticipated negative outcomes stemming from the demands of emotional labor.

Additionally, we call for researchers to expand the methodology used to examine gig workers. As shown in our systematic literature review, there is a need for more empirical studies on gig workers in general; however, most empirical studies to date on gig work have used cross-sectional designs, limiting the conclusions that can be made. Longitudinal designs allow researchers to track experiences of gig workers over time. For example, longitudinal designs may be useful in clarifying the relative impact of certain aspects of gig workers’ jobs (i.e., flexibility and volition) in predicting long-term gig worker health outcomes. Also, in line with Bakker and Demerouti (2017) suggestions for the future of the JD-R model, future researchers should employ experimental studies as a more rigorous test of causality. Experimental designs may be particularly helpful in testing the efficacy of JD-R interventions in improving gig worker’s outcomes as previously discussed.

Looking more broadly than JD-R theory, our systematic literature review showed that more research is needed on specific types of gig work. The extant research is often too vague in the sample descriptions to clearly determine the type of gig work being examined or was conducted prior to the term gig work being coined and uses samples that may or may not be considered gig workers (i.e., contingent workers and temporary workers) based on the definition provided in this article. Future researchers should apply the gig worker profiles posed in this article to provide clearer descriptions of their gig work samples so that the literature is able to explicitly capture the distinctions in gig worker experiences across profiles.

Conclusion

The changing nature of work has facilitated the rapid rise of gig work as a growing segment of the workforce and an increasingly popular topic for organizational science. A review of the existing gig work literature indicated that there are various definitions of gig work and a limited number of studies involving gig workers and their employment relationship. To offer clarity to the literature, this article provides a comprehensive definition of gig work that distinguishes primary and secondary characteristics. We created gig worker profiles based upon secondary characteristics to differentiate between types of gig work and applied the JD-R model to propose differences in the job demands and job resources experienced by the gig worker profiles. The definition and propositions provided in this conceptual article pave the way as a foundation of future research to improve organizational psychology’s understanding of gig workers’ experiences.

Footnotes

Acknowledgments

The authors thank Meredith Pool for her assistance related to this paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by a National Science Foundation Graduate Research Fellowship awarded to the first author.

ORCID iD

Gwendolyn Paige Watson

Associate Editor: Lucy Gilson

Author Biographies

Gwendolyn Paige Watson is a PhD student in Clemson University’s Industrial-Organizational Psychology program, a National Science Foundation Graduate Research Fellow, and the current president of Clemson’s Society for Industrial-Organizational Psychology. Paige’s research concerns occupational health and well-being. Specific research interests include economic stress, positive psychology, and the employer-employee relationship.

Lauren D. Kistler is a graduate student in Clemson University’s Industrial-Organizational Psychology program. Lauren’s research focuses on occupational health, the work-family interface, safety climate, and organizational leadership.

Baylor A. Graham is a graduate student in Clemson University’s Industrial-Organizational Psychology program. Her research interests are primarily in occupational health psychology and include economic stress, safety climate, and worker health and well-being.

Robert R. Sinclair is a Professor of Industrial-Organizational Psychology at Clemson University where he also serves as the graduate program coordinator for the Department’s PhD and MS programs in I-O Pysch. Dr. Sinclair is a founding member and past-president of the Society for Occupational Health Psychology, and he currently serves as the founding Editor-in-Chief of the journal Occupational Health Science and Associate Editor at the Journal of Business and Psychology. He has published over 90 articles and chapters in outlets such as the Journal of Applied Psychology, Journal of Organizational Behavior, and the Journal of Occupational Health Psychology on topics such as economic stress, organizational climate, and healthcare workers.

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