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Unknowns of the Gig-Economy

By Brad Greenwood, Gordon Burtch, Seth Carnahan

Communications of the ACM, Vol. 60 No. 7, Pages 27-29

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Although gig-economy platforms like Uber, Postmates, and Thumb-Tack have captured the attention of policymakers and practitioners, research has only begun to tackle a $26B phenomenon17 that is estimated to grow dramatically in the coming years. Gig-economy platforms, defined as digital, service based, on-demand platforms that enable flexible work arrangements19 are a unique recent addition to the broader category of peer-to-peer platform business-models, which previously comprised intermediaries facilitating the exchange of tangible goods (for example, Etsy, eBay, or Alibaba).

New phenomena come with new research questions. To date, such research has examined a variety of important topics, including consumer surplus,14,16 drunk driving,12 disruption of industry incumbents,2,20 entrepreneurial activity,3 and the evolving nature of the employer-employee relationship.9,10 Importantly, authors of these works have been careful not to cast changes brought by the gig-economy in beneficial or pejorative terms, working instead towards the collection of a broad base of objective empirical facts that can inform policy. However, the collection of findings amassed thus far has arisen from disparate academic disciplines, conducting work across different levels of analysis, such as markets, firms, and individuals, which hampers their integration toward unified conclusions, and limits the identification of the most promising avenues for future work. Considering the critical implications for individuals, firms, and markets, we call for multidisciplinary research at each of these levels to both accelerate our understanding of the gig-economy, and to inform legislators of the potential benefits and pitfalls of the gig-economy (thereby facilitating the creation of effective policy). We anticipate that Communications readers will be heavily involved in research relating to the effective design and functioning of gig-economy markets, which will subsequently inform significant research addressing firm- and individual-level effects. We anticipate such effects will be of interest to colleagues in the social sciences, namely scholars of management, economics, and sociology.

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While research has begun to examine the effective design of gig economy markets, as well as the degree to which different industries will be affected by platform emergence, numerous questions remain. Perhaps the most intriguing and important questions relate to improvements in algorithm and market design. Algorithms help to match supply with demand on these platforms, and algorithms also help determine prices. While researchers like Chen and Sheldon4 highlight the effectiveness of current algorithms, others note the possibilities for more efficient outcomes from improved algorithms.11 What challenges will arise as gig-economy platforms expand and must integrate with existing markets and infrastructure? In addition to efficiency considerations, researchers need to consider the distributional consequences of algorithm and market design. For example, while providing platform participants with one another's names and photos allows for more efficient and pleasant interactions between buyers and sellers, it also creates the conditions that enable discrimination.9 What steps can algorithm and market designers take to provide platform participants with enough information about each other while simultaneously mitigating discriminatory behavior?

Gig-economy platforms also have interesting and unique strategic challenges, such as the management of labor.

Further, anecdotal accounts describe the spread of gig-economy models across different industries and geographies. Yet, we know little about which industries and locations are most likely to be affected. What impact might the emergence of P2P sharing platforms have on sales of durable goods in different industries?1 How will differences in labor laws, regulations, and economic opportunity affect the geographic expansion of gig-economy platforms?14 Uber, for example, moved its self-driving car operations to Arizona after changes in California law.a Legal scholars are also beginning to examine optimal regulatory policies for gig-economy markets,6 with the debate largely centered on the appropriate administrative unit (such as local, state, national, transnational) for regulating these markets, and with some scholars advocating platform self-regulation.6 Will regulation and algorithm design become increasingly linked over time? Should regulators police the contents of algorithms as a method for monitoring gig-economy platforms? Addressing these questions requires the combined talents of computer scientists and legal scholars.

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Several important questions arise when we consider the firm-level management challenges posed by growth of the gig-economy.7,8,13 At the broadest level, it is unclear why gig-economy innovations were primarily developed by startup entrants (including Uber, Airbnb, Prosper), rather than established players (suc as Yellow Cab, Marriott, Bank of America). Were established players simply unaware of the gig-economy approach to organizing, or did they evaluate and reject the idea? Going forward, which incumbents will make the transition to a platform business model,7,22 that is, from owning resources to renting them, and what will determine their success? Gig-economy platforms also face interesting and unique strategic challenges, such as the management of labor. As is well known, these platforms minimize costs by employing independent contractors. Yet, this gives rise to a host of challenges, such as ensuring there is sufficient labor to meet demand and extending the number of hours laborers work. To date, practice has focused on psychological manipulations of labor in order to extend working hours,18 which should continue to be of broad interest to behavioral economists and psychologists as means not only of affecting labor supply, but demand as well.21 Further, researchers should extend this line of work to consider the broader design of incentives structures. The classification of labor as independent contractors similarly poses challenges, inasmuch as gig-economy firms find themselves increasingly buffeted by labor-oriented lawsuits. How do firms manage these legal challenges, and why might some firms decide to convert independent contractors into employees? How do managers decide what level of risk the firm should absorb, versus what level of risk the independent contractor should absorb? As the decision to adopt a gig-economy business model is analogous to outsourcing part of the firm's human and/or physical capital, many of these firm-level questions relate to the classic line of inquiry about how managers demarcate the boundary between their firm and the market.

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At the individual level, important questions exist for scholars of organizational behavior, sociology, and labor economics. Who participates in the gig-economy? What are the longer-term implications of participation? Little is known about the demographic characteristics of gig-economy participants, such as gender, age, income level, prior occupation, education level, citizenship/immigration status (with a few notable exceptions).14,15 Does the absence of traditional employment benefits influence behavior in significant ways? While the Contingent Worker Supplement of the Current Population Survey will help shed light on these issues, it is not scheduled to go live until May 2017.b Obtaining this information will be critical for policy-makers as it will allow them to better understand whom the laws and regulations that shape the gig-economy might impact.

Which of these effects dominates, and for whom, is an important opportunity for future research.

The effect of gig-economy participation on long-term career outcomes is particularly unclear. A defining attribute of gig-economy jobs is that opportunities for "advancement" within the firm are limited. These jobs might therefore stagnate workers' career progressions, particularly if the gig-economy job requires the worker to make capital investments, such as the purchase of an automobile, which may require debt-based financing. At the same time, job flexibility may allow the worker to pursue other opportunities outside the gig-economy, such as education, which would allow her to improve career outcomes over the long term. Which of these effects dominates, and for whom, is an important opportunity for future research.

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The volume of open questions in this space implies the presence of a substantial blind-spot for practitioners and policymakers alike. It is not yet clear how the gig-economy influences social welfare, or how much total surplus is generated by these platforms. Although consumers appear to benefit from reduced prices,5 media accounts have repeatedly pointed out that working in these markets can have important drawbacks.c Understanding the implications of this new form of organizing is critical for scholars from many academic traditions. We therefore strongly urge researchers to consider these and other research questions at the confluence of business, technology, and society.

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1. Abhishek, V., Guajardo, J.A., and Zhang, Z. Business models in the sharing economy: Manufacturing durable goods in the presence of peer-to-peer rental markets. 2016; http://bit.ly/2pGF4nv.

2. Brazil, N. and Kirk, D.S. Uber and metropolitan traffic fatalities in the United States. American Journal of Epidemiology. (2016).

3. Burtch, G., Carnahan, S., and Greenwood, B.N. Can you gig it? An empirical examination of the gig-economy and entrepreneurial activity. (2017); http://bit.ly/2rmJnk5.

4. Chen, M.K. and Sheldon, M. Dynamic pricing in a labor market: Surge pricing and flexible work on the Uber platform. (Mimeo, UCLA), 2015.

5. Cohen, P. Using Big Data to Estimate Consumer Surplus: The Case of Uber. National Bureau of Economic Research, 2016.

6. Cohen, M. and Sundararajan, A. Self-regulation and innovation in the peer-to-peer sharing economy. U. Chi. L. Rev. Dialogue 82 (2015), 116.

7. Cusumano, M.A. How traditional firms must compete in the sharing economy. Commun. ACM 58, 1 (Jan. 2015), 32–34.

8. Cusumano, M.A. Platform wars come to social media. Commun. ACM 54, 4 (Apr. 2011), 31–33.

9. Edelman, B. and Luca, M. Digital discrimination: The case of Airbnb.com. Harvard Business School NOM Unit Working Paper (14-054), (2014)

10. Edelman B.G., Luca, M., and Svirsky, D. Racial discrimination in the sharing economy: Evidence from a field experiment. Harvard Business School NOM Unit Working Paper, (16-069), 2015.

11. Greengard, S. Smart transportation networks drive gains. Commun. ACM 58, 1 (Jan. 2015), 25–27.

12. Greenwood, B.N. and Wattal, S. Show me the way to go home: An empirical investigation of ride sharing and alcohol-related motor vehicle fatalities. MIS Quarterly 41, 1 (Jan. 2017), 163–187.

13. Hagiu, A. Strategic decisions for multisided platforms. MIT Sloan Management Review 55, 2 (2014), 71.

14. Hall, J.V. and Krueger, A.B. An analysis of the labor market for Uber's driver-partners in the United States. Mimeo, 2015.

15. Ipeirotis, P.G. Demographics of mechanical turk. 2010; http://bit.ly/2qsuJZD

16. Katz, L.F. and Krueger, A.B. The rise and nature of alternative work arrangements in the United States, 1995–2015 (2016); http://bit.ly/2pSZUuX

17. Malhotra A. and Van Alstyne, M. The dark side of the sharing economy... and how to lighten it. Commun. ACM 57, 11 (Nov. 2014), 24–27.

18. Scheiber, N. How Uber uses psychological tricks to push its drivers' buttons. The New York Times (Apr. 2, 2017).

19. Telles, R. Digital matching firms: A new definition in the "sharing economy" space. U.S. Department of Commerce Economics and Statistics Administration, 2016; http://bit.ly/1XBAFwc

20. Zervas, G., Proserpio, D., and Byers, J. The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry. Boston University School of Management Research Paper (2013–16), 2015.

21. Zhang, S., et al. Professional versus amateur images: Investigating differential impact on Airbnb property demand. In Proceedings of the Conference on Information Systems and Technology, 2016.

22. Zhu, F. and Furr, N. Products to platforms: Making the leap. Harvard Business Review 94, 4 (2016), 18.

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Brad N. Greenwood ([email protected]) is an assistant professor of Management Information Systems and the Richard J. Fox Faculty Fellow at Temple University's Fox School of Business.

Gordon Burtch ([email protected]) is an assistant professor of Information and Decision Sciences at the University of Minnesota Carlson School of Management.

Seth Carnahan ([email protected]) is the Sanford R. Robertson Assistant Professor in Business Administration in the Ross School of Business at the University of Michigan.

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a. http://cnnmon.ie/2qocUwb

b. The supplement was also included in 1995, 1997, 1999, 2001, and 2005, but was discontinued due to lack of funding.

c. http://bit.ly/2qTDaNi

Copyright held by author.

The Digital Library is published by the Association for Computing Machinery. Copyright © 2017 ACM, Inc.


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