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Interplay of Network Structure and Neighbour Performance in User Innovation ARTICLE https://doi.org/10.1057/s41599-019-0383-x OPEN Interplay of network structure and neighbour performance in user innovation Kunhao Yang1*, Itsuki Fujisaki1,2 & Kazuhiro Ueda1* ABSTRACT Previous studies showed that regular users have become an important source of innovation (called user innovation). Previous studies also suggested that two factors have significant impacts on user innovation: the social network structure of the users and neigh- ’ 1234567890():,; bours innovation performance (neighbours mean users having interactions with the focal user). However, in these studies, the influence of the two factors were only discussed separately, and it remained unclear whether these two factors interdependently affected a focal user’s innovation. To examine the interplay between the network structure and the neighbours’ innovation performance, we harnessed data sets from “Idea Storm”, which col- lects data on user network and idea submission. Through panel regression analyses, we found that—within an open-network structure—the higher innovation performance of neighbours has a larger positive impact on the focal user’s innovation ability. Conversely, in an enclosed network structure, neighbours’ higher performance has a larger negative impact on the focal user’s innovation ability. Our findings filled an important gap in understanding the interplay between the network structure and the neighbours’ performance in user innovation. More broadly, these results suggested that the interplay between the neighbours and the network structures merits attention that even goes beyond user innovation. 1 Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan. 2 Research Fellowship for Young Scientists (DC2), Japan Society for the Promotion of Science (JSPS), Tokyo, Japan. *email: [email protected]; [email protected] PALGRAVE COMMUNICATIONS | (2020) 6:7 | https://doi.org/10.1057/s41599-019-0383-x | www.nature.com/palcomms 1 ARTICLE PALGRAVE COMMUNICATIONS | https://doi.org/10.1057/s41599-019-0383-x Introduction any recent studies have shown that regular users, rather because it brings heterogeneous neighbours to the ego (Newman Mthan specialists or organisations, have begun to capture and Dale, 2007; Prell et al., 2009; Rogers, 2010). These neighbours a leading role in innovation (Gustafsson et al., 2012; are in the various sub-groups within the network. Therefore, they Kratzer and Lettl, 2008; Lüthje, 2004; Reichwald et al., 2004; Von can provide the nonredundant information for the ego (Burt, Hippel, 1986). This new perspective on innovation is called cus- 2009; Capaldo, 2007; Kratzer and Lettl, 2008; Perry-Smith and tomer innovation or user innovation (Gustafsson et al., 2012; Von Mannucci, 2017). Furthermore, the different knowledge back- Hippel, 1986). User innovation occurs when a non-professional grounds and behavioural patterns of these neighbours become the group of people contributes to innovation in a professional field source of the nonredundant information for the ego (Newman (Howe, 2008). Because of its high effectiveness and low cost, it is and Dale, 2007; Prell et al., 2009; Rogers, 2010). The diversity and considered a key approach to innovation (Hallikainen et al., 2019). newness of nonredundant information will become the important One important question about user innovation revolves around resource for the ego’s innovation. By contrast, the enclosed net- what features of an agent (person) affect her/his innovation work structure tends to undermine the ego’s innovation because ability, and how. Previous studies (Grosser et al., 2017; Perry- it results in highly homogeneous neighbours (Newman and Dale, Smith and Mannucci, 2017; Perry-Smith and Shalley, 2003; 2007; Prell et al., 2009; Rogers, 2010). Therefore, they can only Phelps et al., 2012; Shah et al., 2018) showed that two features provide the highly redundant information for the ego (Burt, 2009; have a significant impact: (1) the social network structure around Granovetter, 1983). Moreover, the enclosed network causes the the focal agent and (2) innovation abilities of other agents who so-called echo chamber phenomenon, which often happens have direct interactions with the focal agent (hereafter, we call the among highly homogeneous neighbours (e.g., Pentland, 2015; focal agent ego, and these other agents neighbours). Researchers Colleoni et al., 2014; Treviranus and Hockema, 2009). Under this found that an open-network structure, which comprises neigh- phenomenon, egos behave in the same way as their neighbours, bours in groups without ties between one another (See Fig. 1), resulting in a significant decline in innovation performance. benefited the innovation capability of an ego as the open-network On the other hand, the intensity of the network structure’s structure provides a diverse information set to the ego (Perry- influence will vary depending on the neighbours’ performance. Smith and Mannucci, 2017; Phelps et al., 2012; Shah et al., 2018). First, the innovation performance of neighbours indicates the In addition, researchers showed that neighbours with a high quantity of the (redundant or nonredundant) information innovation performance also benefited the innovation of an ego resource neighbours can provide (Grosser et al., 2017; Shah et al., as they provided valuable experiences and resources (Grosser 2018; Lin, 2002). High-performance neighbours significantly et al., 2017; Shah et al., 2018). However, all these studies discussed affect the ego because she/he receives most of the information the network structure separately from the innovation capability of resources from them. In contrast, low-performance neighbours the neighbours. In contrast, some theorists in network science can hardly provide enough information resources to influence the (Lin, 2002; Pentland, 2015) suggested that there should be an ego. Subsequently, the ego will judge the underlying quality of this interplay between these two factors. In other words, the effects of information when she/he is affected by her/his neighbours’ the network structure and of the neighbours will affect each other, information (De Clercq and Dimov, 2008; Dimov and Milanov, dependently. 2010; Geringer, 1988; Podolny, 2001). The information from In this respect, on one hand, the direction of neighbours’ effect high-status neighbours will be evaluated more highly than that will differ depending on the openness of the network structure. from low-status. Accordingly, it will affect the ego’s innovation The open-network structure will benefit the ego’s innovation more strongly. In addition, it is considered that, in a user inno- vation process, a neighbour’s status is decided by her/his previous innovation performance (Lin, 1999; Lin, 2002). Therefore, neighbours with high performance will be considered as highly creditable, and their information will have a larger impact on the ego’s innovation. In contrast, the information from neighbours without high performance will not be highly evaluated by the ego and will only have a small effect on the ego’s innovation (Perry- Smith and Shalley, 2003; Lin, 1999; Lin, 2002). We combined the above two aspects to build the complete the- oretical prediction about the interplay of the network structure and neighbours’ innovation performance: the network structure mod- erates the direction of the effect, and the neighbours’ innovation performance adjusts the intensity of the effect (as shown in Fig. 2). As shown in Fig. 2, the information from high-performance neighbours will have a large effect on the ego’s innovation; if the Fig. 1 The illustration of the open-network structure and the enclosed network structure is open, the effect will be positive; if the net- network structure. Both a and b show network structures of egos with the work structure is enclosed, it will be negative. On the contrary, ego as a blue node, neighbours as yellow nodes and other nodes (users) the information from low-performance neighbours will have less having ties with neighbours as grey nodes. In a typical open-network impact on the ego’s innovation, and the direction is decided by structure (a), neighbours (in yellow) have no tie between one another, the network structure, in the same way as above. therefore, no neighbour can be approached through other neighbours of the Based on this theoretical prediction, we hypothesised as follows: ego, positioning the ego as a bridge connecting several (three in a) in an open-network structure, neighbours with a higher innovation unconnected sub-groups in which her/his neighbours are. In a typical performance have a larger positive effect on an ego’s innovation; enclosed network structure (b), neighbours (in yellow), conversely, have contrastingly, in an enclosed network structure, neighbours’ higher ties between one another. As a result, every neighbour in (b) can be performance will have a larger negative effect on an ego’s innova- approached through other neighbours, indicating that the ego is stuck in tion (hereafter denoted as the research hypothesis). one dense sub-group and has no chance of linking with other sub-groups in In the remainder of the paper, we conducted a panel regression the network. to test whether and how the interaction between the network 2 PALGRAVE COMMUNICATIONS | (2020) 6:7 | https://doi.org/10.1057/s41599-019-0383-x | www.nature.com/palcomms PALGRAVE COMMUNICATIONS | https://doi.org/10.1057/s41599-019-0383-x ARTICLE Here, we did not distinguish the “vote” ties from the “comment” ties because the previous research (Bayus, 2013; Huang et al., 2014) using the same data from the “Idea Storm” suggested that both the “comment” and the “vote” have the same essence: the signal of preference for information and interaction. We also confirmed this argument using our data: We found that nearly all of the users (1,000 in 1,057) who sent a comment to another user also voted for the same user. This high synchronicity between the “comment” and the “vote” implies their essential similarity. In the idea submission data, 326 users among 6,333 submitted 493 ideas defined as effective by Dell from 2007 to 2018.
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