A Hierarchical Innovation-Related Crowdsourcing Decision in Fast Fashion Industry

Abstract

Due to scarcity of designers in fast fashion industry and proliferation of the Internet, small- and medium-sized garment makers have gradually turned to external designers to enhance their innovation efficiency via crowdsourcing initiative. However, few have investigated the issue of fast fashion customized-design matching decision in the crowdsourcing context. Different from previous works, we split crowdsourcing matching decision process into three hierarchical submodels in terms of three key factors, namely, surplus, due date, and goodwill. From a dynamic perspective, we first develop a two-sided matching model where garment makers and online designers select one another by maximizing their total surpluses with an aim to reach robust final pairs and derive the corresponding conditions under which the optimal pairs can be obtained. Then, the extensions of the matching model are conducted by incorporating the critical factors of due date and garment makers’ goodwill, respectively. Followed by that, an improved Gale–Shapley algorithm is devised to solve the crowdsourcing matching problems. The results illustrate when garment makers exceed online designers in number, crowdsourcing design tasks without due-date constraint are more attractive for designers’ participation than those with due-date constraint, and garment makers intend to share the incremental surpluses with designers to maximize the total surpluses. By contrast, when online designers surpass garment makers in number, designers prefer due-date design tasks to those without it. In addition, regardless of whether under the irregular or regular case, the model with goodwill concern always outperforms the two others. Moreover, celebrated garment makers are invited to post design tasks, thus enabling to entice more designers’ engagement in crowdsourcing activities, which in turn facilitating to transfer myopic designers to strategic ones. Finally, sensitivity analysis further verifies the models are stable and robust.

Optimal Information Acquisition and Sharing Decisions

“…Information acquisition and sharing are crucial for supply chain members. It is common for high-tech firms to enhance product innovation by obtaining and sharing information among various agents (Li et al, 2020). A body of related literature focuses on information obtaining/sharing from the competitive or co-petitive perspective.…”

An optimal strategy of advertising and electronic word-of-mouth with considering rebates

“…Additionally, with rebates, consumers will decide whether to accept eWOMrebates or not, and to make purchase decision according to their valuation on products. If consumers obtain a non-negative utility, consumers will make the deal (Li et al, 2020b). Meanwhile, the effect of eWOM generated by consumers in period 1 will affect the late consumers' behavior purchase in period 2 (Yang and Dong, 2018;Zhen et al, 2019).…”

“…Additionally, with rebates, consumers will decide whether to accept eWOM-rebates or not, and to make purchase decision according to their valuation on products. If consumers obtain a non-negative utility, consumers will make the deal (Li et al. , 2020b).…”

Open Collaboration and Digital Technologies in the Context of Environmental Innovations

“…In this sense, different crowdsourcing configurations are identified in organizations: internal crowdsourcing; community crowdsourcing; open crowdsourcing; and crowdsourcing via a broker (Simula & Ahola, 2014) Crowdsourcing allows improving the efficiency of innovation (Li, Bian, Liu, & Wu, 2020), democratize the innovative process, promote creativity and use external knowledge as a response to the challenges of the organization (Forbes, Han, & Schaefer, 2020). Furthermore, crowdsourcing as a new pattern of innovation allows companies to reduce risks and costs (Meng et al, 2019) and generate creative ideas through the interaction of different users (Acar, 2019;Cheng et al, 2020;Forbes et al, 2020;Seltzer & Mahmoudi, 2013).…”

“…Improves the efficiency of innovation (Li et al, 2020) Democratizes the innovative process, promotes creativity and use external knowledge as a response to the challenges of the organization (Forbes et al, 2020) Allows companies to reduce risks and costs (Meng et al, 2019) Allows to generate creative ideas through the interaction of different users (Acar, 2019;Cheng et al, 2020;Forbes et al, 2020;Seltzer & Mahmoudi, 2013) Allows a greater globalization in innovation sourcing (Bakici, 2020) Makes users co-creators of new products Allows access a set of relevant knowledge (Pohlisch, 2020) Crowdsourcing…”