Stochastic Sequential Allocations for Creative Crowdsourcing

Abstract

C reative crowdsourcing is an innovative online business model in which a platform marshals independent professionals (e.g., designers) to conduct creative work projects. Typically, clients submit project requests stochastically to a platform which possesses a pool of registered designers. For each submitted project, designers decide whether to participate and attempt to submit a design, and the client either chooses a winner among all submissions, or rejects them all, based on subjective criteria. In general, platforms cannot control individual designers directly because of the nature of the freelance market, incurring possible mismatches between designers and arriving projects. To tackle the problem, we present a dynamic control policy applied to the maximum number of participants for each arrived project. Our study reveals that the optimal policy follows an inverted-U-shaped function of the project value, highlighting the importance of applying a stronger restriction on the number of participants for some sufficiently high-valued projects. In addition, the optimal policy we have developed allows the platform to gain higher rewards judiciously even when the market is more volatile. Furthermore, extensive numerical studies have been conducted to glean managerial insights. Specifically, the optimal policy becomes more beneficial even when more designers are available, which is counter-intuitive to the common-sense notion that control should be more valuable when designers are scarcer; and the optimal policy is further shown to be robust when the objective is changed from maximizing the total reward to maximizing the total number of successful projects.

Performance enhancement in two‐stage innovation contests: Feedback and elimination schemes

“…For example, Hu and Wang (2021) study the contest design problem on the product with two substitute attributes and investigate the optimal contest structure, and Chen et al (2022) extend the discussion to products with both substitute and complementary attributes. Bimpikis et al (2019) study two-stage dynamic contests, and Tian et al (2022) investigate the contest design problem with stochastic arriving projects.…”

Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond

“…For example, Tian et al. (2021) investigate the “talent crowdsourcing” platform operations. The authors derive the optimal mechanism of assigning the registered talents to the projects.…”

“…It will be interesting to conduct analyses on them to provide insights which may help the related stakeholders to better deploy the new technologies. For example, Tian et al (2021) investigate the "talent crowdsourcing" platform operations. The authors derive the optimal mechanism of assigning the registered talents to the projects.…”

Discrete‐item inventory control involving unknown censored demand and convex inventory costs

“…Dating back to Scarf (1960), the optimality of an (s, S)-policy for a traditional system with fixed setup costs and by specialization that of a base-stock policy for one without were usually established for a convex average inventory cost 𝔼 F [ Ĩ(x − D)], which can be traced back to a convex Ĩ(⋅). Many authors dealt with quadratic, piecewise-linear convex, or generally convex inventory costs; see, for example, Porteus (1990), Gerchak and Wang (1997), Shi et al (2019), Sillanpaa et al (2021), Katehakis et al (2016), andTian et al (2022) Our policy would work for convex inventory costs without relying on strong censoring indicators or assuming a finite demand support. It has some semblance to Huh et al's (2011) policy.…”