How to Find the Key Participants in Crowdsourcing Design? Identifying Lead Users in the Online Context Using User-Contributed Content and Online Behavior Analysis

Abstract

Lead users are the most valuable innovation sources in crowdsourcing design; how to identify these users is a research hotspot in the field of design and management. Existing approaches to discover lead users in the context of the online community, such as the manual method and ordering algorithm, have some limitations, for instance, low coverage and accuracy. To address these deficiencies, this article proposes a method that applies text-mining techniques, analysis of user behavior, and contributed content to identify lead users. We suggest a three-step analytical approach: First, a criterion system to evaluate the user’s leading-edge status is constructed. Second, we utilize a fuzzy analytical hierarchy process to assess the weighted value of each indicator and develop the reference sequence of the indicators. Third, grey relational analysis is employed to analyze the correlations between users’ indicators and reference sequences, and lead users are recognized based on each user’s correlation ranking. An empirical analysis is used to examine the effectiveness of the proposed method. The results reveal that the method has good precision and recall rate, can automatically process large-scale data, and has no strict requirements for respondents. Finally, the article discusses the limitations and provides possible directions for future research.

INFLUTRUST: Trust-Based Influencer Marketing Campaigns in Online Social Networks

“…A comprehensive examination of incentive mechanisms in socially aware crowdsourcing is presented in [24] by introducing a multi-leader and multi-follower Stackelberg game approach to model the strategic interactions among the OSN platforms and the influencers and integrating the social influence and strategic interconnections among the influencers and the OSN users. A crowdsourcing method utilizing text-mining techniques and user behavior analysis is proposed in [25] to identify influencers in OSNs.…”

Continued Participation in Crowdsourcing Innovation: The Role of Web-Specific Computer Self-Efficacy

“…The advancement of Internet technologies has offered a stable channel for customers to participate in innovation events, promoting crowdsourcing innovation (CI) development. CI allows enterprises to assign part or all the innovation tasks (e.g., interaction design, product design) to customers with appropriate resources and capabilities through online platforms (i.e., crowdsourcing innovation communities, CICs) [5]. Consumers are encouraged to contribute content (e.g., ideas and designs) by posting messages and topics within these communities, which is the primary channel to participate in CI [6].…”

Research on the Design of Surgical Auxiliary Equipment Based on AHP, QFD, and PUGH Decision Matrix

“…Some scholars have also applied different quantitative methods to product innovation design or product evaluation in the last two years. Oey et al [23] sought a way to accommodate user requirements in their product and process improvement, Lu et al [24] constructed a product form evolution design method integrating the TRIZ contradiction matrix to simplify the product form evolution process, Liu et al [25] proposed a conceptual design evaluation method based on Z-numbers, Yue et al [26] improved the evaluation system of the design of household medical products for the elderly, Zhang et al [27] proposed a lead user identification method based on user behavior data and contribution content analysis, Wang et al [28] proposed a product evaluation method that combines natural language processing techniques and fuzzy multicriteria decision-making, and Wurster et al [29] used consumers as a valuable source of information to specify features of the output of an innovative CE ecosystem. e overall comparison between this article and previous studies is shown in Table 1.…”