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Wang, Y.; Qi, G. Sustainable Knowledge Contribution. Encyclopedia. Available online: https://encyclopedia.pub/entry/23910 (accessed on 16 November 2024).
Wang Y, Qi G. Sustainable Knowledge Contribution. Encyclopedia. Available at: https://encyclopedia.pub/entry/23910. Accessed November 16, 2024.
Wang, Yujie, Guijie Qi. "Sustainable Knowledge Contribution" Encyclopedia, https://encyclopedia.pub/entry/23910 (accessed November 16, 2024).
Wang, Y., & Qi, G. (2022, June 10). Sustainable Knowledge Contribution. In Encyclopedia. https://encyclopedia.pub/entry/23910
Wang, Yujie and Guijie Qi. "Sustainable Knowledge Contribution." Encyclopedia. Web. 10 June, 2022.
Sustainable Knowledge Contribution
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This entry is adapted from the peer-reviewed paper 10.3390/su14116536

In open innovation platforms, users learn external knowledge through network interaction, and their position in the interactive network has an impact on the user’s sustainable knowledge contribution. Due to the gap in knowledge level, users’ absorption and utilization efficiency of external knowledge is not consistent.

open innovation platform knowledge absorption sustainable knowledge contribution

1. Introduction

With the rapid development of the information economy, the role of product users has changed, from a passive product receiver to an active value co-creator [1]. To take advantage of these user innovations, more and more companies have established an open innovative platform (OIP) to collect user ideas [2]. Open innovation was proposed by Chesbrough [3], and the central idea is to crowdsource knowledge to the public [4]; that is, user groups participate in creative tasks and collaborate to generate innovative ideas [5]. The open innovation platform is a platform sponsored by a company that uses the Internet as a medium, and is mainly aimed at providing an interactive space for companies and end users [6][7]. Users contribute knowledge to the company through the OIP. Companies acquire a large amount of external knowledge, develop knowledge and skills at a lower cost, and improve company innovation performance [8]. There are also some problems in the operation of OIP, and the number of ideas contributed by users is insufficient. Some OIPs have even been closed, such as IdeaStorm of Dell and My Starbucks of Starbuck. Therefore, improving user vitality and promote user idea contribution is a joint research problem of academics and enterprises.
The learning and creation of knowledge occur at the individual level, so effective management and rational use of individual knowledge are essential to the sustainable development of companies [9]. In OIP, user interaction is the main way for users to obtain external knowledge [10][11], and the network location of the user is very important for sustainable knowledge contribution [12]. From the perspective of social network analysis, the existing research believes that in a better position in the network, more and better knowledge of the platform can be obtained. However, different scholars have different judgments on the network location. In the interaction characteristics from the perspective of social networks, scholars believe that users in better network locations can obtain more external knowledge and thus provide more contributions; however, different scholars have different definitions of better network locations. For example, Peng et al. believe that extensive connections will result in more knowledge, thereby promoting user sustainable knowledge contributions [13], whereas Garriga et al. believe that integrating knowledge of extensive network connections will lead to coordination difficulties and reduce users’ motivation to contribute knowledge [14]. Freeman et al. believe that deep embedding of the network helps users to more quickly grasp all aspects of the platform’s knowledge [15], whereas Tang believes that if the user’s network is embedded too deeply, users will have less desire to explore new knowledge [16]. The possible reason for this is that these studies implicitly assume that knowledge acquired by users through network locations can be automatically absorbed, that is, knowledge acquisition is equivalent to knowledge absorption. Previous literature on OIP has noted the importance of network location for knowledge contribution from the perspective of social networks, but little attention has been paid to the importance of user knowledge absorption.
According to the knowledge absorption theory, internal knowledge constitutes the basis of individual knowledge absorption capacity and reflects the ability to absorb external knowledge [16]. The effective sharing and integration of internal knowledge is the key to knowledge absorption [17]. Therefore, for users with different knowledge levels, the influence of network location on sustainable knowledge contribution may be different, because users with different knowledge levels have different efficiencies in absorbing and using external knowledge. Examining only the impact of network location on users’ sustainable knowledge contribution behavior, while ignoring whether users’ knowledge levels are able to absorb external knowledge acquired through the network location, may yield an inaccurate impact on the perception of users’ sustainable knowledge contribution. Therefore, it is particularly important to study user sustainable knowledge contribution from the perspective of knowledge absorption. From the perspective of knowledge absorption theory, researchers explore the role of knowledge absorptive capacity of OIP users in the influence of network location on knowledge contribution. In the model, the user’s network location provides access to external knowledge beyond their knowledge boundaries, and the level of internal knowledge reflects the user’s ability to absorb external knowledge.

2. The Influence of Network Location on Users’ Sustainable Knowledge Contribution

Social learning theory believes that social members will have learning behaviors under the influence of others, and this influence may be direct interaction or indirect observation [18]. In an OIP, users can increase their skill reserves by interacting with other users, thereby enhancing creativity and promoting sustainable knowledge contributions. The interaction in an OIP creates a network relationship to obtain external knowledge. In the paper, network breadth refers to the degree of connection between users and other users, and network depth refers to the level of embeddedness in the platform’s interactive network.
Network breadth refers to the range of knowledge gained by users through network connections. Through the interactive network, users can directly connect with other users in the network and obtain external knowledge of other users. The social capital theory shows that the collection of all knowledge resources in a person’s relationship network can strongly affect the degree of interpersonal knowledge sharing [19]. Knowledge is very important for innovation, but the cost of acquisition is very high. The interaction between members of the OIP provides a cost-effective way to obtain a wider range of knowledge sources. The more social interactions users have, the greater the intensity, frequency, and breadth of information exchange [20]. The more connections a user has on the network, the more innovative resources and external information they can access during the innovation process. The more innovative knowledge exchanges with other users, the higher the innovation performance may be [21]. Extensive network connections can provide users with new insights, reduce cognitive effort, and increase the rate of creation of new knowledge [13].
Network depth refers to the contact distance with other users in the interactive network, that is, the degree of embeddedness in the network. In other words, network depth means the distance from a user to other users connected directly or indirectly in the network. When deeply embedded in the network, users can quickly access relevant information. However, an excessively embedded network will cause users to access redundant information, which limits their ability to effectively explore new knowledge in the network [22]. Proximity to other users’ network locations may cause similar or redundant information loops, confining users to their perceptions. This kind of cognitive lock-in may inhibit users’ motivation to explore new knowledge from external networks and hinder their motivation to continue to innovate, resulting in a decline in sustainable knowledge contribution. When the user is deeply embedded in the network, the user’s motivation to explore new ideas and create new knowledge from the network will be weakened. As old knowledge becomes obsolete, users further lose their motivation to continue to innovate, which hinders their sustainable knowledge contribution [16].

3. The Influence of Knowledge Diversity on User’s Sustainable Knowledge Contribution

Knowledge diversity refers to the abundance of individual knowledge, experience, and skills, and is a measure of the user’s internal knowledge level [23]. The collection of knowledge elements owned by each individual and the relationship between these collections constitute a personal knowledge base. Innovation is the process of reorganizing the knowledge elements in the knowledge base [24]. In an OIP, users have different professional levels and experience, and each person’s knowledge inventory is also different, and there is a gap in their innovative ability. Users with diverse knowledge are more able to promote knowledge transfer and sharing [25]. According to Nonaka’s research, diverse knowledge will stimulate users’ innovative thinking and produce more practical ideas [26].
Users with different knowledge levels have different motivations to contribute to their knowledge. Generally speaking, users with low knowledge levels only contribute knowledge to obtain platform rewards, whereas users with high levels not only gain platform revenue but also gain new knowledge [25]. According to the theory of planned behavior, in an OIP, users’ perception and control of knowledge creation are determined by their knowledge level. If users do not have enough knowledge, even if they have the willingness to create new knowledge, they will not undergo sustainable knowledge contribution behavior [27]. In other words, the willingness to innovate alone is not enough, and the knowledge and ability to support the generation of innovation is also required [28]. In an OIP, users need a series of knowledge related to products and services to propose ideas.

4. Difference Analysis of Knowledge Absorption Effect to User Sustainable Knowledge Contribution

Absorptive capacity is the ability to recognize, digest, transform, and develop and utilize knowledge. Cognitive and behavioral science research shows that absorbing knowledge is the process of using the knowledge through the evaluation of external knowledge, establishing connections with pre-concepts, and associating existing knowledge after possessing internal knowledge [16]. The process of interactive digestion of external knowledge and internal knowledge is the process of knowledge absorption. The knowledge absorption effect expresses the degree of utilization of knowledge after the interaction between internal knowledge and external knowledge [16]. For OIP users, based on existing internal knowledge, they can use external knowledge by establishing network connections with other users.
According to cognitive load theory, each user’s attention is limited. When internal knowledge is highly diversified, the value of acquiring external knowledge may be more limited, because a wealth of internal knowledge can provide enough new perspectives. The high network breadth will increase the cost for users to integrate knowledge from different sources. This diverse external knowledge may collide with the existing internal knowledge, resulting in difficult coordination [29]. When users accept a wide range of external knowledge, the direction of internal knowledge may be the opposite. Users need to spend more time and energy to coordinate this knowledge, which will significantly reduce the speed of the sustainable knowledge contribution. For users with a high level of knowledge, an extensive network may also increase the complexity of the integration of internal and external knowledge, which reduces the level of user knowledge absorption and leads to a decrease in user sustainable knowledge contribution [30]. For users with low knowledge levels, extensive network connections can help users obtain more external knowledge, and the problem of insufficient internal knowledge can be alleviated through extensive external knowledge.
Deeply embedded networks can obtain overall network information more easily and quickly, and achieve better performance with “less information transmission, shorter time, and lower cost” [15]. The extensive knowledge base helps users evaluate development trends from different perspectives. When deeply embedded in the network, users can quickly develop the best knowledge about technological trends and related expertise [15][16]，and obtain the best development direction. Although excessive network embedding may cause knowledge redundancy for users, for users with high knowledge levels with diverse knowledge, their broad knowledge and multi-angle thinking can alleviate this cognitive lock-in. The deeply embedded network helps users better and more quickly understand the overall status of the OIP network and grasp the latest trends [16]. The smaller the knowledge distance, the fewer intermediate steps the knowledge receiver takes to absorb knowledge, and the efficiency of knowledge absorption increases. For users with high knowledge levels, by quickly acquiring external knowledge, understanding the most promising development direction, and gathering their diversified knowledge, they can create more new knowledge more quickly. For users with low levels of knowledge, based on insufficient internal knowledge, deep embedding in the network makes it worse. Redundant external knowledge can only limit the development of their thinking and is not conducive to their sustainable knowledge contribution.

References

  1. Wooten, J.O.; Ulrich, K.T. Idea Generation and the Role of Feedback: Evidence from Field Experiments with Innovation Tournaments. Prod. Oper. Manag. 2017, 26, 80–99.
  2. Liu, Q.; Du, Q.; Hong, Y.; Fan, W.; Wu, S. User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community. Inf. Syst. J. 2020, 30, 899–927.
  3. Chesbrough, W.H. Open Innovation: The New Imperative for Creating and Profiting from Technology; Harvard Business School Press Books: Boston, MA, USA, 2006.
  4. Schlagwein, D.; Cecez-Kecmanovic, D.; Hanckel, B. Ethical norms and issues in crowdsourcing practices: A Habermasian analysis. Inf. Syst. J. 2018, 29, 811–837.
  5. Chiu, C.-M.; Liang, T.-P.; Turban, E. What can crowdsourcing do for decision support? Decis. Support Syst. 2014, 65, 40–49.
  6. Guo, W.; Liang, R.Y.; Wang, L.; Peng, W. Exploring Sustained Participation in Firm-Hosted Communities in China: The Effects of Social Capital and Active Degree. Behav. Inf. Technol. 2017, 36, 223–242.
  7. Burger-Helmchen, T.; Cohendet, P. User Communities and Social Software in the Video Game Industry. Long Range Plan. 2011, 44, 317–343.
  8. Taylor, J.; Joshi, K.D. Joining the Crowd: The Career Anchors of Information Technology Workers Participating in Crowdsourcing. Inf. Syst. J. 2019, 29, 641–673.
  9. Hulland, J. Organizational learning: The contributing processes and the literatures. Organ. Sci. 1999, 2, 119–126.
  10. Wang, X.; Ow, T.T.; Liu, L.; Feng, Y.; Liang, Y. Effects of Peers and Network Position on User Participation in A Firm-Hosted Software Community: The Moderating Role of Network Centrality. Eur. J. Inf. Syst. 2020, 29, 521–544.
  11. Chen, W.; Wei, X.; Zhu, K.X. Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model. MIS Q. 2018, 42, 83–100.
  12. Yang, J.; Sia, C.L.; Liu, L.; Chen, H. Sellers Versus Buyers: Differences in User Information Sharing on Social Commerce Sites. Inf. Technol. People 2016, 29, 444–470.
  13. Peng, G. Co-Membership, Networks Ties, and Knowledge Flow: An Empirical Investigation Controlling for Alternative Mechanisms. Decis. Support Syst. 2019, 118, 83–90.
  14. Garriga, H.; Krogh, G.V.; Spaeth, S. How Constraints and Knowledge Impact Open Innovation. Strateg. Manag. J. 2013, 34, 1134–1144.
  15. Freeman, L.C. Centrality in Social Networks: Conceptual Clarification. Soc. Netw. 1979, 1, 215–239.
  16. Tang, T.Y.; Fang, E.E.; Qualls, W.J. More Is Not Necessarily Better: An Absorptive Capacity Perspective on Network Effects in Open Source Software Development Communities. MIS Q. 2020, 44, 1651–1678.
  17. Zahra, S.A.; George, G. Absorptive Capacity: A Review, Reconceptualization, and Extension. Acad. Manag. Rev. 2002, 27, 185–203.
  18. Heyes, C.M. Social Learning in Animals: Categories and Mechanisms. Biol. Rev. Camb. Philos. Soc. 2010, 69, 207–231.
  19. Phua, J.; Jin, S.V.; Kim, J.J. Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Comput. Hum. Behav. 2017, 72, 115–122.
  20. Larson, A. Network Dyads in Entrepreneurial Settings: A Study of the Governance of Exchange Relationships. Adm. Sci. Q. 1992, 37, 76–104.
  21. Zheng, H.; Li, D.; Jing, W.; Yun, X. The Role of Multidimensional Social Capital in Crowdfunding: A Comparative Study in China And US. Inf. Manag. Amst. 2014, 51, 488–496.
  22. Hwang, E.H.; Singh, P.V.; Argote, L. Jack of All, Master of Some: Information Network and Innovation in Crowdsourcing Communities. Inf. Syst. Res. 2019, 30, 389–410.
  23. Pollok, P.; Amft, A.; Diener, K.; Luettgens, D.; Piller, F.T. Knowledge diversity and team creativity: How hobbyists beat professional designers in creating novel board games. Res. Policy 2021, 50, 104174.
  24. Yayavaram, S.; Ahuja, G. Decomposability in Knowledge Structures and Its Impact on the Usefulness of Inventions and Knowledge-base Malleability. Adm. Sci. Q. 2008, 53, 333–362.
  25. Zhang, J.; Zhang, J.; Zhang, M. From free to paid: Customer expertise and customer satisfaction on knowledge payment platforms. Decis. Support Syst. 2019, 127, 113140.
  26. Nonaka, I. The Knowledge-Creating Company; Harvard Business Review Press: Boston, MA, USA, 2015.
  27. Wasko, M.M.; Faraj, S. Why Should I Share? Examining Social Capital and Knowledge Contribution in Electronic Networks of Practice. MIS Q. 2005, 29, 35–57.
  28. Hsu, M.H.; Ju, T.L.; Yen, C.H.; Chang, C.M. Knowledge Sharing Behavior in Virtual Communities: The Relationship between Trust, Self-Efficacy, And Outcome Expectations. Int. J. Hum. Comput. Stud. 2007, 65, 153–169.
  29. Temizkan, O.; Kumar, R.L. Exploitation and Exploration Networks in Open Source Software Development: An Artifact-Level Analysis. J. Manag. Inf. Syst. 2015, 32, 116–150.
  30. De Luca, L.M.; Atuahene-Gima, K. Market Knowledge Dimensions and Cross-Functional Collaboration: Examining the Different Routes to Product Innovation Performance. J. Mark. 2007, 71, 95–112.
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