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Look the Part? The Role of Profile Pictures in Online Labor Markets

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  • Isamar Troncoso

    (Department of Business Administration, Harvard Business School, Harvard University, Boston, Massachusetts 02163)

  • Lan Luo

    (Department of Marketing, Marshall School of Business, University of Southern California, Los Angeles, California 90089)

Abstract

Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer’s fit for the job (i.e., whether a freelancer “looks the part” for the job), as inferred from profile pictures, can impact hiring outcomes on such platforms. Leveraging computer vision techniques and choice models, we analyze six-month data from Freelancer.com (63,014 completed jobs that received 2,042,198 applications from 160,014 freelancers) and find that, above and beyond demographics and beauty, freelancers who “look the part” are more likely to be hired. Interestingly, we do not find a strong correlation between “looking the part” and job performance. Supplementing our large-scale observational study with two choice experiments, we find that (i) the effect of perceived job fit is stronger when reputation systems are not sufficiently diagnostic to differentiate candidates and (ii) that by considering perceptions of job fit, participants are more likely to choose freelancers with fewer reviews, lower ratings, and/or without certifications. Last, we find that “platform recommendations” can only partially mitigate the unintended consequences of profile pictures, and recommending multiple freelancers can further increase the role of “looking the part.”

Suggested Citation

  • Isamar Troncoso & Lan Luo, 2023. "Look the Part? The Role of Profile Pictures in Online Labor Markets," Marketing Science, INFORMS, vol. 42(6), pages 1080-1100, November.
    • Handle: RePEc:inm:ormksc:v:42:y:2023:i:6:p:1080-1100
    DOI: 10.1287/mksc.2022.1425
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    References listed on IDEAS

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    1. Jennifer L. Doleac & Luke C.D. Stein, 2013. "The Visible Hand: Race and Online Market Outcomes," Economic Journal, Royal Economic Society, vol. 123(11), pages 469-492, November.
    2. Lynch, John G, Jr & Marmorstein, Howard & Weigold, Michael F, 1988. "Choices from Sets Including Remembered Brands: Use of Recalled Attributes and Prior Overall Evaluations," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 169-184, September.
    3. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57946, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    5. Donald R. Davis & Jonathan I. Dingel & Joan Monras & Eduardo Morales, 2019. "How Segregated Is Urban Consumption?," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1684-1738.
    6. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 59346, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    7. Chen Liang & Zhan (Michael) Shi & T. S. Raghu, 2019. "The Spillover of Spotlight: Platform Recommendation in the Mobile App Market," Information Systems Research, INFORMS, vol. 30(4), pages 1296-1318, December.
    8. Benlian, Alexander & Titah, R. & Hess, Thomas, 2012. "Differential Effects of Provider and User Recommendations in e-Commerce Transactions: An Experimental Study," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 65605, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    9. Hema Yoganarasimhan, 2013. "The Value of Reputation in an Online Freelance Marketplace," Marketing Science, INFORMS, vol. 32(6), pages 860-891, November.
    10. Kohei Kawaguchi & Kosuke Uetake & Yasutora Watanabe, 2019. "Effectiveness of Product Recommendations Under Time and Crowd Pressures," Marketing Science, INFORMS, vol. 38(2), pages 253-273, March.
    11. Marios Kokkodis & Panagiotis G. Ipeirotis, 2016. "Reputation Transferability in Online Labor Markets," Management Science, INFORMS, vol. 62(6), pages 1687-1706, June.
    12. Jason Chan & Jing Wang, 2018. "Hiring Preferences in Online Labor Markets: Evidence of a Female Hiring Bias," Management Science, INFORMS, vol. 64(7), pages 2973-2994, July.
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