Identification of the unique attributes and topics within Smart Things Open Innovation Communities

Olmedilla, M.; Send, Hendrik; Toral, S. L.

doi:10.1016/j.techfore.2019.05.004

Cited by 20 publications

(8 citation statements)

References 77 publications

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“…Martinez-Torres and Olmedilla (2016) applied particle swarm optimization to identify the innovation solvers in open innovation communities. Olmedilla et al (2019) suggested a novel methodology called co-occurrence differential analysis to identify the unique attributes and topics in the open innovation communities. Liao and Xu (2020) proposes a process approach to understand multiple open source innovation contests in the android developer challenges.…”

Section: Research Model and Hypothesismentioning

confidence: 99%

Understanding users’ contribution in open innovation communities: a social capital perspective

Zhou

2021

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Purpose As users often lack the motivation to contribute their ideas and knowledge in open innovation communities, it is necessary to identify the determinants of users’ contribution. This paper aims to examine users’ contribution in open innovation communities based on the social capital theory. Design/methodology/approach The authors collected 474 valid responses from a survey and adopted structural equation modeling (SEM) to conduct data analysis. Findings The results indicated that social interaction, which includes informational and emotional interaction, has a significant effect on social capital, which in turn affects users’ contribution. Research limitations/implications The results imply that companies need to facilitate users’ interactions and develop social capital to promote their contribution in open innovation communities. Originality/value Although previous research has found the effect of individual motivations such as perceived benefits and behavioural control on innovation community users’ behaviour, it has seldom considered the effect of social capital embedded within the social relationship networking. This research tries to fill the gap and the results disclosed the mechanism underlying open innovation community users’ contribution.

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Section: Research Model and Hypothesismentioning

confidence: 99%

Understanding users’ contribution in open innovation communities: a social capital perspective

Zhou

2021

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“…Li, Zhang, & Han, 2021). Furthermore, crowd member behavior tends to influence how individuals in the crowd interact with ideas and what persuasions they form (Olmedilla et al, 2019). When applying ELM to VCCs, idea-relevant information typically consists of credible, tangible cues that directly relate to the content and quality of the idea, while peripheral cues represent the supporting elements associated with the idea (C. Li et al, 2021).…”

Section: Elaboration Likelihood Modelmentioning

confidence: 99%

“…Qin and Liang (2019) found that users with different numbers of friends have different influence on the likelihood of their ideas being adopted by the community. Similarly, Olmedilla et al (2019) empirically studied online crowdsourcing activities and found that users' peer recognition and followership significantly influence the evaluation of other community members' ideas. Typically, all user contributions, such as ideas for product innovation or suggestions, can be evaluated by the community or other users for content quality and feasibility.…”

Section: Community Recognition Of Usersmentioning

confidence: 99%

“…As a result, VCCs are increasingly viewed as a critical source of sustainable competitive intelligence due to their ability to elicit collective knowledge that is both novel, i.e., original or unexpected, and valuable, i.e., applicable and relevant to user needs and preferences (Akman, Plewa, & Conduit, 2019;Romero & Molina, 2009). Recognizing their valuable contribution to the development of innovation and entrepreneurship, a growing number of companies such as Microsoft, Tableau, and Google have established their own VCCs and focused on how to motivate users to contribute ideas and innovation solutions to their communities (Olmedilla, Send, & Toral, 2019). In all of these cases, community hosting companies seek to provide a collaborative innovation environment to foster creativity and facilitate online communication between companies and their customers for crowdsourced ideas, and then use these insights to better implement innovation-driven development strategies and improve internal research and development (R&D) capabilities (Q.…”

Section: Introductionmentioning

confidence: 99%

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The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities

Daradkeh

2021

International Journal of Knowledge Management

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Building on the elaboration likelihood model (ELM) and absorptive capacity, this study develops a four-dimensional model of idea adoption in Virtual Crowdsourcing Communities (VCCs) and examines the influence of different persuasion cues on idea adoption. The research model was tested using hierarchical logistic regression based on a dataset from the Tableau community. The results show that both community recognition of users and community recognition of ideas are positively related to idea adoption. Proactive user engagement has a significant positive impact on idea adoption, while reactive user engagement has no significant impact. Idea content quality, represented by idea length and supporting arguments, has an inverted U-shaped relationship with idea adoption. Community absorptive capacity positively moderates the curvilinear relationship between idea content quality and idea adoption. These results contribute to a better elucidation of the persuasion mechanisms underlying idea adoption in VCCs, and thus provide important implications for open innovation research and practice.

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“…A personality trait is a psychological structure that can make people’s behavior tendency show the characteristics of persistence, stability, and consistency, which reflects the consistency and regularity of individual behavior [ 13 ]. The open innovation community is an internet-based virtual network platform created by enterprises [ 14 ]. It diverges from the structure of traditional enterprise organizations, as it is distinguished by the voluntary engagement of users, absence of central authority, and an atmosphere of freedom [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Impacts of User Personality Traits on Their Contributions in Idea Implementation: A Moderated Mediation Model

Mi,

Zhang,

2024

Behavioral Sciences

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In the realm of open innovation, users have emerged as a significant external source of innovation that enterprises cannot afford to overlook. Implemented ideas play a pivotal role in driving the iterative innovation of products within enterprises. However, the existing literature still lacks an exploration of specific impact mechanisms on contributions in idea implementation. This study presents a model that delineates the impact of user personality traits on idea implementation contributions, drawing upon theories such as personality trait theory, user engagement perspective, and trait activation theory. Empirical research was carried out by utilizing user data obtained from the Chinese high-tech company Xiaomi’s MIUI community. Personality trait indicators were developed through the application of text mining and machine learning techniques. To evaluate the models, a negative binomial regression model, which is well-suited for handling discrete data, was employed. The findings of this study indicate that user openness and conscientiousness positively influence their idea implementation contribution, whereas neuroticism has a negative impact on implementation contribution. Additionally, it is observed that user engagement plays a partial mediating role in the relationship between openness, conscientiousness, neuroticism, and idea implementation contribution. Community incentives can positively moderate the impact of user engagement on the relationship between conscientious personality and idea implementation contribution. This study expands the analysis of the impact mechanism of user idea implementation contributions, which has important theoretical guidance and practical implications for accurately identifying leading users in open innovation communities and enhancing user innovation contributions.

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Identification of the unique attributes and topics within Smart Things Open Innovation Communities

Cited by 20 publications

References 77 publications

Understanding users’ contribution in open innovation communities: a social capital perspective

Understanding users’ contribution in open innovation communities: a social capital perspective

The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities

Impacts of User Personality Traits on Their Contributions in Idea Implementation: A Moderated Mediation Model

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