In the rapidly evolving technological landscape and increasingly competitive market environment, enterprises have become acutely aware that relying solely on internal R&D is no longer sufficient to meet the ever-changing market demands. Faced with this challenge, there is an urgent need for enterprises to transform their innovation methods by tapping into external sources of innovation. Users, not only as the ultimate consumers of products but also as vital participants in innovation, provide invaluable insights and directions for innovation through their needs and feedback during the product usage process. Moreover, the innovative consciousness of users serves as an essential driver for enhancing corporate innovation capabilities. Therefore, it is necessary to investigate the innovation value, specifically the contributions in idea implementation by users within open innovation communities. Existing research has predominantly focused on the impact of the characteristics of ideas themselves, yet it lacks a systemic logical framework at the individual level that can represent individual characteristics. This study utilizes personality traits to characterize individual differences among users and introduces a framework grounded in the theory of cognitive-affective personality systems to examine the impact pathways and boundary conditions of user personality traits on implementation contributions.
This study focuses on users within the representative open innovation community, specifically the Xiaomi MIUI community, as its research sample. The MIUI community is an open and successful innovation community and has attracted a large number of Xiaomi fans. Xiaomi users are free to comment and provide feedback on product innovation and development, such as creative solicitation, version testing, or marketing. The effective integration of user ideas and internal resources of Xiaomi has driven the iterative innovation of MIUI mobile operating system. Research data from a total of 48 354 Xiaomi users was obtained for the study. Utilizing text mining techniques to extract data from user comments, the study constructs scores for users' personality traits using the Linguistic Inquiry and Word Count (LIWC) dictionary method. Additionally, natural language processing techniques are employed to obtain scores for users' emotional inclinations. It then validates the chain mediating effect of cognitive ability, emotional orientation, and user engagement behavior on the contribution of different personality traits to idea implementation contributions. Furthermore, the study investigates the moderating effect of peer recognition by defining variables and constructing a negative binomial regression model.
The findings reveal that users' openness significantly and positively influences idea implementation contributions, whereas neuroticism has a significant negative impact. Cognitive ability and user engagement behavior act as chain mediators in the relationship between openness and idea implementation contributions, while negative emotions and user engagement behavior mediate the relationship between neuroticism and idea implementation contributions. Moreover, peer recognition significantly and positively moderates the mediating role of cognitive ability in the link between openness and idea implementation contributions. Peer recognition also significantly and positively moderates the mediating role of negative emotions and user engagement behavior in the relationship between neuroticism and idea implementation contributions.
This study constructs an interdisciplinary theoretical framework to explain how personality traits influence users’ idea implementation contributions. Drawing upon the cognitive-affective personality system theory, it introduces a chain-mediated effect of cognition/emotion and user engagement behavior, while also considering the moderating effect of peer recognition within community contexts. In order to enhance innovation capabilities through self-built open innovation communities, enterprises should give priority to incentivizing users with open personality traits, while managers should adopt more refined and personalized strategies to activate users' cognitive abilities, emotional expressions, and engagement behaviors, and the innovative value in users' negative emotions should be explored. This study contributes significantly to understanding the mechanisms underlying users’ idea implementation contributions, and offers insights for optimizing user management in open innovation communities for enterprises. Future research can further validate the accuracy of measuring personality traits by conducting comparative verification through the distribution of questionnaires, and more investigations and studies can be conducted in other open innovation communities to further enhance the reliability of the research. In addition, the study concludes that the mediation results show only partial mediation effects, therefore, there are other mediation effects that have not been confirmed and could be further explored in future research.
[1] GRUNER R L, HOMBURG C, LUKAS B A. Firm-hosted online brand communities and new product success[J]. Journal of the Academy of Marketing Science, 2014,42(1): 29-48.
[2] BRADONJIC P,FRANKE N, LUTHJE C. Decision-makers′ underestimation of user innovation[J]. Research Policy, 2019,48(6): 1354-1361.
[3] 秦敏, 梁溯. 在线产品创新社区用户识别机制与用户贡献行为研究:基于亲社会行为理论视角[J]. 南开管理评论, 2017,20(3):28-39.
[4] OLMEDILLA M, SEND H, TORAL S L. Identification of the unique attributes and topics within smart things open innovation communities[J]. Technological Forecasting & Social Change, 2019,146: 133-147.
[5] MISCHEL W, SHODA Y A. Cognitive-affective system theory of personality: reconceptualizing situations, dispositions, dynamics, and invariance in personality structure[J]. Psychological Review, 1995,102(2): 246-268.
[6] FAST L A, FUNDER D C. Personality as manifest in word use: correlations with self-report, acquaintance report, and behavior[J]. Journal of Personality and Social Psychology, 2008,94(2): 334-346.
[7] MAVIS G, TOROSLU I H, KARAGOZ P. Personality analysis using classification on Turkish Tweets[J]. International Journal of Cognitive Informatics and Natural Intelligence, 2021,15(4): 1-18.
[8] VON HIPPEL E. Democratizing innovation: the evolving phenomenon of user innovation[J]. Journal für Betriebswirtschaft, 2005,55(1): 63-78.
[9] 顾美玲, 迟铭, 韩洁平. 开放式创新社区治理机制对用户知识贡献行为的影响——虚拟社区感知的中介效应[J]. 科技进步与对策,2019,36(20): 30-37.
[10] MAHR D, LIEVENS A. Virtual lead user communities: drivers of knowledge creation for innovation[J]. Research Policy, 2012,41(1): 167-177.
[11] OGINK T, DONG J Q. Stimulating innovation by user feedback on social media: the case of an online user innovation community[J]. Technological Forecasting & Social Change, 2019,144: 295-302.
[12] LIU Q, DU Q, HONG Y, et al. User idea implementation in open innovation communities: evidence from a new product development crowdsourcing community[J]. Journal of Management Information Systems, 2020,30(5): 899-927.
[13] YANG M, HAN C. Stimulating innovation: managing peer interaction for idea generation on digital innovation platforms[J]. Journal of Business Research, 2021,125: 456-465.
[14] HOORNAERT S, BALLINGS M, MALTHOUSE E C, et al. Identifying new product ideas: waiting for the wisdom of the crowd or screening ideas in real time[J]. Journal of Product Innovation Management, 2017,34(5): 580-597.
[15] WANG N, TIBERIUS V, CHEN X, et al. Idea selection and adoption by users——a process model in an online innovation community[J]. Technology Analysis & Strategic Management,2021,33(9): 1036-1051.
[16] LI M, KANKANHALLI A, KIM S H. Which ideas are more likely to be implemented in online user innovation communities? an empirical analysis[J]. Decision Support Systems, 2016,84: 28-40.
[17] BIRCH K, COCHRANE D T, WARD C. Data as asset? the measurement, governance, and valuation of digital personal data by big tech[J]. Big Data & Society,2021,8(1):135-150.
[18] ZHAI M, CHEN Y. How do relational bonds affect user engagement in e-commerce livestreaming? the mediating role of trust[J]. Journal of Retailing and Consumer Services, 2023,71: 103-119.
[19] ZHANG X, DING X, WANG G, et al. Investigating the influences of social overload and task complexity on user engagement decrease[J]. Total Quality Management & Business Excellence, 2020,31(15-16): 1774-1787.
[20] 李黎飞, 卫旭华, 程德俊. 职场负面八卦对被八卦员工行为的影响:基于认知—情感人格系统理论的元分析[J].心理科学进展,2022,12(30):2681-2695.
[21] SUTIN A R, STEPHAN Y, LUCHETTI M, et al. Five-factor model personality traits and cognitive function in five domains in older adulthood[J]. BMC Geriatrics, 2019,19(1): 343.
[22] MOUSSA S, ACHKOUTY I, MALAEB D, et al. Personality traits and perceived cognitive function in Lebanese healthcare professionals[J]. BMC Psychology, 2023,11(1): 90.
[23] LETZRING T D, ADAMCIK L A. Personality traits and affective states: relationships with and without affect induction[J]. Personality and Individual Differences, 2015,75: 114-120.
[24] ION A. Primary affective systems and personality: disentangling the within person reciprocal relationships[J]. Journal of Personality,2024,92(6):12908.
[25] BARRICK M, MOUNT M. The big five personality dimensions and job performance: a meta-analysis[J]. Personnel Psychology, 1991,44(1): 1-26.
[26] HARRIS E G.Re-examining salesperson goal orientations:personality influencers, customer orientation,and work satisfaction[J].Journal of the Academy of Marketing Science,2005,33(1):19-35.
[27] WOOLLEY A W, CHABRIS C F, PENTLAND A, et al. Evidence for a collective intelligence factor in the performance of human groups[J]. Science, 2010,330(6004): 686-688.
[28] GRANT A M, BERRY J W. The necessity of others is the mother of invention: intrinsic and prosocial motivations, perspective taking, and creativity[J]. Academy of Management Journal, 2011,54(1): 73-96.
[29] SERVAAS M N, VAN DER VELDE J, COSTAFREDA S G, et al. Neuroticism and the brain: a quantitative meta-analysis of neuroimaging studies investigating emotion processing[J]. Neuroscience & Biobehavioral Reviews, 2013,37(8): 1518-1529.
[30] BAAS M, DE DREU C K W, NIJSTAD B A, A Meta-analysis of 25 years of mood-creativity research: hedonic tone, activation, or regulatory focus[J]. Psychological Bulletin, 2008,134(6): 779-806.
[31] ROMANI S, GRAPPI S, DALLI D. Emotions that drive consumers away from brands: measuring negative emotions toward brands and their behavioral effects[J]. International Journal of Research in Marketing, 2012,29 (1): 55-67.
[32] BHANDARI M, RODGERS S. What does the brand say? effects of brand feedback to negative EWOM on brand trust and purchase intentions[J]. International Journal of Abrasive Technology, 2018,37(1): 125-141.
[33] PORTYANKO S, REINMOELLER P, HUSSELS S, et al. Peer effects and intentional entrepreneurial behavior: a systematic literature review and research agenda[J]. International Journal of Management Reviews, 2023,25(3): 515-545.
[34] BLACK P W. The effect of peer-to-peer recognition systems on helping behavior: the influence of rewards and group affiliation, accounting[J]. Organizations and Society,2023,109:101454.
[35] VAN DEN BROEK T, LANGLEY D J, EHRENHARD M L, et al. When do evaluators publicly express their legitimacy judgments? an inquiry into the role of peer endorsement and evaluative mode[J]. Organization Science, 2023,34(6): 2143-2162.
[36] LUO L, WANG Y, DUAN S, et al. Continuous knowledge contribution in social Q&A communities: the moderation effects of self-presentation and motivational affordances[J]. Information Technology & People, 2024,37(5):1950-1982.
[37] SCHWARTZ H A, EICHSTAEDT J C, KERN M L, et al. Personality, gender, and age in the language of social media: the open-vocabulary approach[J]. Plos One, 2013,8(9): e73791.
[38] SHAHBAZNEZHAD H, DOLAN R, RASHIDIRAD M. The role of social media content format and platform in users' engagement behavior[J]. Journal of Interactive Marketing, 2021,53(1): 47-65.
[39] DONG L, HUANG L, HOU J, et al. Continuous content contribution in virtual community: the role of status-standing on motivational mechanisms[J]. Decision Support Systems, 2020,132: 113-131.
[40] MARTIN L L, WARD D W, ACHEE J W, et al. Mood as input: people have to interpret the motivational implications of their moods[J]. Journal of Personality and Social Psychology, 1993,64(3): 317-326.
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