scholarly journals Buyer Uncertainty About Seller Capacity: Causes, Consequences, and a Partial Solution

2019 ◽  
Vol 65 (8) ◽  
pp. 3518-3540 ◽  
Author(s):  
John J. Horton

Employers in an online labor market often pursue workers with little capacity to take on more work. The pursuit of low-capacity workers is consequential, as these workers are more likely to reject employer inquires, causing a reduction in the probability that a job opening is ultimately filled. In an attempt to shift more employer attention to workers with greater capacity, the market-designing platform examined in this paper introduced a new signaling feature into the market. It was effective, in that when a worker signaled having high capacity, he or she received more invitations from employers, rejected a smaller fraction of those invitations, quoted lower prices to do the work, and was more likely to be hired. A back-of-the-envelope calculation suggests the signaling feature alone could increase market surplus by as much as 6%, both by increasing the number of matches formed and by helping to allocate projects to workers with lower costs. This paper was accepted by Lorin Hitt, information systems.

2020 ◽  
Vol 66 (9) ◽  
pp. 4047-4070 ◽  
Author(s):  
Moshe A. Barach ◽  
Joseph M. Golden ◽  
John J. Horton

Platform marketplaces can potentially steer buyers to certain sellers by recommending or guaranteeing those sellers. Money-back guarantees—which create a direct financial stake for the platform in seller performance—might be particularly effective at steering as they align buyer and platform interests in creating a good match. We report the results of an experiment in which a platform marketplace—an online labor market—guaranteed select sellers for treated buyers. The presence of a guarantee strongly steered buyers to these guaranteed sellers, but offering guarantees did not increase sales overall, suggesting financial risk was not determinative for the marginal buyer. This preference for guaranteed sellers was not the result of their lower financial risk, but rather because buyers viewed the platform’s decision to guarantee as informative about relative seller quality. Indeed, a follow-up experiment showed that simply recommending the sellers that the platform would have guaranteed was equally effective at steering buyers. This paper was accepted by Chris Forman, information systems.


Author(s):  
Nikhil Garg ◽  
Ramesh Johari

Problem definition: Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, ratings are often highly inflated and therefore, not very informative. In this paper, we first investigate whether the platform can obtain less inflated, more informative ratings by altering the meaning and relative importance of the levels in the rating system. Second, we seek a principled approach for the platform to make these choices in the design of the rating system. Academic/practical relevance: Platforms critically rely on rating systems to learn the quality of market participants, and so, ensuring these ratings are informative is of first-order importance. Methodology: We analyze the results of a randomized, controlled trial on an online labor market in which an additional question was added to the feedback form. Between treatment conditions, we vary the question phrasing and answer choices; in particular, the treatment conditions include several positive-skewed verbal rating scales with descriptive phrases or adjectives providing specific interpretation for each rating level. We then develop a model-based framework to compare and select among rating system designs and apply this framework to the data obtained from the online labor market test. Results: Our test reveals that current inflationary norms can be countered by reanchoring the meaning of the levels of the rating system. In particular, positive-skewed verbal rating scales yield substantially deflated rating distributions that are much more informative about seller quality. Further, we demonstrate that our model-based framework for scale design and optimization can identify the most informative rating system and substantially improve the quality of information obtained over baseline designs. Managerial implications: Our study illustrates that practical, informative rating systems can be designed and demonstrates how to compare and design them in a principled manner.


2019 ◽  
Vol 37 (3) ◽  
pp. 715-746 ◽  
Author(s):  
Stefano Banfi ◽  
Benjamín Villena-Roldán

PLoS ONE ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. e0229383
Author(s):  
Leib Litman ◽  
Jonathan Robinson ◽  
Zohn Rosen ◽  
Cheskie Rosenzweig ◽  
Joshua Waxman ◽  
...  

2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Laura Seppänen ◽  
Clay Spinuzzi ◽  
Seppo Poutanen ◽  
Tuomo Alasoini

Nordic working life studies have mostly focused on the precarious aspects of work mediated via online labor platforms. We follow a different approach and examine the potential of such work to benefit professionals by enhancing their job quality and learning. This qualitative, practice-based study applies the concept ‘co-creation’ to examine how a social form of creating value takes place in Upwork macrotask projects. It then investigates how platform features shape opportunities for co-creation. The data comprise interviews of 15 freelancers residing in Finland. The findings suggest that co-creation is possible in macrotask projects, but the platform does not seem to actively support co-creation. This paper provides insights into the discussion of job quality at platform work and how co-creation on platforms might be developed to support the Nordic labor market model.


2019 ◽  
Author(s):  
Leib Litman ◽  
Jonathan Robinson ◽  
Zohn Rosen ◽  
Cheskie Rosenzweig ◽  
Josh Waxman ◽  
...  

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks we analyze hourly wages by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender wage gap, including the tendency for women to select tasks that have lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.


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