A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

Abstract

The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion and attention mechanisms. Our method stratifies the OIC and creates user node embeddings based on different relationship types. Node embeddings from different layers are then merged to form a global representation of user fusion embeddings based on a semantic attention mechanism. The embedding learning of nodes is optimized using a multi-objective optimized node representation based on the Deep Graph Infomax (DGI) algorithm. Finally, the k-means algorithm is used to form clusters of users and partition them into distinct segments based on shared features. Experiments conducted on datasets collected from four OICs of business intelligence and analytics software show that our method outperforms multiple baseline methods based on unsupervised and supervised graph embeddings. This study provides methodological guidance for user segmentation based on structured community data and semantic social relations and provides insights for its practice in heterogeneous OICs.

Navigating Value Co-Destruction in Open Innovation Communities: An Empirical Study of Expectancy Disconfirmation and Psychological Contracts in Business Analytics Communities

“…Third, these communities have a large and diverse user base, with a growing number of users searching, contributing, and exchanging knowledge within the community every day. For example, as of January 2023, the Microsoft Power BI Community has more than 50 million registered users, with more than 10,000 users active every day [ 2 , 3 , 57 ]. Therefore, it is convenient to find a sufficient sample of contributors to study and compare the interaction dynamics between the motivating factors of value co-destruction in the OIC.…”

“…The basis for decentralized cooperation among members is formed through the openness of knowledge and the exclusion of proprietary exploitation patterns, characterizing the form of coordination of innovation communities. Moreover, these communities are directly focused on the products and services of the individual companies, and members contribute by suggesting innovative ideas or simply by sharing their preferences and needs [ 3 , 4 , 5 ].…”

Lurkers versus Contributors: An Empirical Investigation of Knowledge Contribution Behavior in Open Innovation Communities

“…In the context of OICs, social learning is based on collaboration and is a part of community learning; however, not all community learning involves social learning [62]. Social learning in OICs is stage-based, where learning is seen as a trajectory into the community, emphasizing the importance of social engagement in shaping member identities, with significant shifts in knowledge and skills bringing about corresponding shifts in identity and motivation [1].…”

“…As a result, rather than exclusively relying on their internal research and development (R&D) capabilities and resources, today's companies are increasingly tapping into external knowledge and expertise by developing open innovation models. With the shift in innovation technology paradigms and the growth of Web 2.0 applications, open innovation communities (OICs) are increasingly being embraced by many companies as a means to augment their open innovation capabilities and generate a wealth of ideas and innovative products [1]. As such, an increasing number of companies are establishing OICs to gather knowledge and feedback from relevant stakeholders.…”

“…For example, Microsoft's Power BI community collects customer intelligence and knowledge contributions to improve business intelligence and analytics products and solutions. In OICs, heterogeneous knowledge bases can be bundled and accessed by different stakeholders, leading to the development of new products, processes, or business models [1,6].…”