The impact of employee participation in online innovation communities on idea quality

Yu, Jing; Liu, Chen

doi:10.1108/k-04-2020-0228

Cited by 11 publications

(12 citation statements)

References 48 publications

(89 reference statements)

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“…As a popular application, open innovation community has received attention from information systems researchers. Open innovation community reflects a platform that enables users to share their innovative ideas on product and services with enterprises, which can adopt these ideas into the research and development processes and improve the products and services (Yu and Liu, 2021). Thus, users' contribution, which means their posting, commenting and updating of innovative ideas, is crucial to the success of open innovation communities (Ma et al, 2019) semiconductor industry and found that virtual brand communities can be used to implement open innovation.…”

Section: Open Innovation Community User Behaviourmentioning

confidence: 99%

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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: Open Innovation Community User Behaviourmentioning

confidence: 99%

“…Zheng et al (2018) noted that creator, project and user participation characteristics affect project success in co-innovation communities. Similarly, Yu and Liu (2021) reported the effect of employee participation on idea quality in innovation communities.…”

Section: Research Model and Hypothesismentioning

confidence: 99%

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

Zhou

2021

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“…Moreover, past comments have the highest effect on idea quality, compared with all the other factors researched. We can thus conclude that constantly participating and gathering experience in ideation platforms is a good approach for users to improve the potential value of their ideas (Yu and Liu, 2020), especially by learning and commenting on others' ideas.…”

Section: Discussion and Future Workmentioning

confidence: 93%

“…The mean score across these criteria was used to measure overall quality. Other scholars, however, hold that an idea ought to be actually implemented by an organization (Bayus, 2013;Beretta, 2019;Liu et al, 2020;Yu and Liu, 2020), which is an objective means to evaluate idea quality (Bayus, 2013). In fact, an idea should be novel, useful and feasible in order to be implemented by a company (Franke et al, 2006;Hwang et al, 2019).…”

Section: Ejim 263mentioning

confidence: 99%

Identifying high quality ideas in the online context: evidence from a meta-analysis

2021

EJIM

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PurposeWith the rapid development of web-based ideation platforms, firms are increasingly reaching outside their boundaries to obtain ideas, which makes idea evaluation tricky and ineffective. Numerous studies have investigated the antecedents of idea quality, but few have focused on the fragmented and contradictory conclusions these studies have drawn. Thus, the purpose of this paper is to disentangle conflicting findings regarding factors affecting idea quality.Design/methodology/approachA total of 41 independent samples from 40 articles published between 2009 and 2020 were retrieved and encoded to conduct a meta-analysis. By using Comprehensive Meta-analysis 3.0 (CMA 3.0) software, the relationship between identified influencing factors and idea quality, and the moderating effects of potential variables were investigated.FindingsFactors affecting idea quality were summarized and divided into three categories: idea-related factors, ideator-related factors and platform-related factors. The results indicated that all proposed factors (idea length, supporting evidence, past success, past submissions, past comments, votes received and comments received) had significant positive effects on idea quality. Moreover, the measurement of idea quality and idea type was the causative factor for the inconsistent findings, whereas the moderating effect of platform type was not significant.Originality/valueThis study systematically synthesized the existing research on the factors influencing idea quality and helped to reconcile the mixed results. The findings serve to enrich current knowledge of idea evaluation in the online context, which could have important implications for both scholars and practitioners.

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“…First, the study contributes to a stream of research that emphasizes the importance of influencing factors from two previous dimensions. Although prior research has investigated the concept of the quality of creative information, much of that literature only stresses the information or information publishers (Yu and Liu, 2020). This study extends the existing literature and suggests information characteristics, and information publishers characteristics are both critical factors, and then combine the two dimensions exploring how that factor influences the quality of creative information (Shi et al , 2020).…”

Section: Discussionmentioning

confidence: 99%

How user personality and information characteristics influence the creative information quality on open innovation platforms: an elaboration likelihood model

Zhou

Zhang

Zhao

et al. 2021

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PurposeLarge volumes of users' creative information have rapidly become vital resources in the open innovation platforms, so it is crucial to identify high-quality information from massive creative information. However, the existing literature on the quality of creative information only focuses on the information characteristics or publishers' features.Design/methodology/approachIn this paper, the authors used the elaboration likelihood model to examine the joint effect of central route factors (information characteristics: timeliness, readability and sentiment) and peripheral route factors (source characteristics: personality traits, past successful experiences and social network location) on the quality of creative information. Furthermore, the author explored the moderating roles of companies' support between central and peripheral route factors on the quality of creative information. Finally, binary logistic regression was adopted to test the research hypotheses on the empirical data from Salesforce.FindingsThe results indicated that users with high extroversion, conscientiousness, social centrality and prior success rate tended to propose high-quality information. Meanwhile, information timeliness, readability and sentiment also negatively influence the quality of creative information.Originality/valueDifferent from previous studies, the study findings not only provide insights on identifying the quality of creative information from an information perspective, but also promotes the awareness of the intrinsic personality traits of information users and innovative support efforts by platforms and their managers.

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The impact of employee participation in online innovation communities on idea quality

Cited by 11 publications

References 48 publications

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

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

Identifying high quality ideas in the online context: evidence from a meta-analysis

How user personality and information characteristics influence the creative information quality on open innovation platforms: an elaboration likelihood model

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