Introduction Many extant studies in the strategic management literature show that a firm’s network influences its innovation outcomes (Ahuja, Lampert, & Tandon, 2008). Networks are characterized by strong and weak ties in terms of the combination of the amount of time, intensity, intimacy, and reciprocal services (Granovetter, 1973). There is, however, a continuing debate about the relative advantages of strong and weak ties. These equivocal findings suggest that the relationship between tie strength and a firm’s innovation outcome is complex, and call for a more detailed examination of this relationship. The implications of networks for a firm’s innovation outcomes are quite significant. Nevertheless, the majority of research studies still examine networks using simple dyadic relationships (e.g., Capaldo, 2007). In reality, a firm’s networks are composed of more than a single dyadic relationship and are much more complex. Thus, dyadic approaches are limited in providing understanding of networks on a firm’s innovation performance. As such, we will take the perspective of a focal firm in a triad network. While still relatively simple, the triad network approach allows us to identify key relationships previously unexplored in network tie configuration, and to shed light on the equivocal results in the extant literature. Specifically, we will examine the position of the strong or weak ties among the firms, and also whether the strong or weak ties are adjacent or non-adjacent to the focal firm. Breakthrough innovation is defined as the basic invention, which leads to the evolution of many subsequent technological developments (Ahuja & Lambert, 2001). This definition suggests that novel and unique knowledge is required to create breakthrough innovation. Indeed, recent research shows that firms need novel knowledge created by network partners to create breakthrough innovation (e.g., Srivastava & Gnyawali, 2011). As such, we investigate how different levels of novel and diverse knowledge arising from the position of network ties impact a focal firm’s breakthrough innovation. Nevertheless, obtaining diverse and novel knowledge from networks does not guarantee the creation of successful breakthrough innovation. A firm needs a capability to learn, absorb and integrate the new knowledge into its works, which is its absorptive capacity. Thus, we examine the moderating role of a firm’s absorptive capacity on the relationship between the impact of the configurations arising from the position of strong/weak ties and a firm’s breakthrough innovation in a triad network relationship. Conceptual Framework We posit six different types of network configurations based on tie strength and position of strong/weak ties that are adjacent or non-adjacent to the focal firm in a triad (Figure 1). For example, Configuration 1 has three strong ties and Configuration 6 has three weak ties. Configuration 2 has two strong ties that are adjacent to a focal firm and one weak tie that is non-adjacent to a focal firm in a triad. Importantly, we use two theories (i.e., network theor and relational theory) to elaborate the impact of six configurations on a firm’s breakthrough innovation, considering the tie strength, the position of strong/weak ties, and whether the strong or weak ties are adjacent or non-adjacent to the focal firm. The first of these is network theory. Network theory and relational theory assert different effects of strong versus weak ties on a firm’s breakthrough innovation. To resolve the ambiguities in the literature on this issue, we combine network theory and relational theory and investigate the implication of the position of the strong or weak ties. We argue that the position of strong/weak ties must be considered to explain the impact of tie strength on firm breakthrough innovation in a triad context. For example, Configuration 2 has two strong ties adjacent to focal firm and one weak tie non-adjacent to focal firm (Figure 1). The non-adjacent weak tie provides potential for diverse and novel knowledge and reduced knowledge redundancy. Also, adjacent two strong ties provide the benefits of commitment and trust, and rich information flow. Additionally, these adjacent strong ties facilitate the transfer of novel knowledge generated from the non-adjacent weak tie. Thus, we argue that Configuration 2 has a potentially positive influence on firm breakthrough innovation. Configuration 5 has two adjacent weak ties and one non-adjacent strong tie. The non-adjacent strong tie has high knowledge redundancy and high trust and commitment between the two actors B and C (Figure 1). The non-adjacent strong tie between B and C induces potential opportunistic tendencies toward focal firm and inhibits information sharing with the focal firm. This indicates that the two firms form an alliance to the detriment of the focal firm. Further, adjacent weak ties provide novel and diverse information while maintaining less commitment and trust with the focal firm. Importantly, diverse information from adjacent weak ties is degraded because of the knowledge redundancy generated by non-adjacent strong tie between B and C. Thus, we argue that Configuration 5 has potentially negative influence on firm breakthrough innovation. The above discussion suggests that one cannot determine which type of network ties will tend create a firm’s breakthrough innovation simply by counting the numbers of weak and strong ties in the configurations. One must also consider the position of the strong and weak ties within the network and the focal firm’s absorptive capacity. For example, though there is a high level of knowledge redundancy created by the non-adjacent strong tie in Configuration 5, if the focal firm has strong ability to learn, the focal firm can still capture and use the limited new knowledge, depending on its absorptive capacity. Next, we explain how firm’s absorptive capacity influences the relationship between six configurations and breakthrough innovation. Methods We will collect a full sample of data from three main data sources: alliance data from the Securities Data Company (SDC) on Joint and Alliances, patent data from the National Bureau of Economic Research (NBER) U.S. Patent Citation 1997-2016, and financial data from COMPUSTAT. The initial sample will consist of all firms that announced a triad of strategic alliance firms across industries from 1997 to 2016 in the United States. Implications Our study makes two key contributions. First, we investigate the impact of various triad strong/weak tie network configurations on a firm’s breakthrough innovation. We test various effects accrued from the position of strong/weak ties that are adjacent or non-adjacent to a focal firm on the focal firm’s breakthrough innovation in the triad network. By examining these relationships, we uncover critical implications of the tie strength previously unexplored in the network literature. We provide conceptual advancement of Granovetter’s notion of the strength of weak ties, showing the importance of the position of strong or weak ties as a critical driver to influence a firm’s breakthrough innovation. Second, we investigate how a focal firm’s absorptive capacity moderates the impact of network configurations on a firm’s breakthrough innovation. This provides a more precise and fine-grained understanding of how a firm’s capability to absorb outside knowledge influences the relationship between network configurations and a focal firm’s breakthrough innovation. Combined, our two contributions help to resolve some of the equivocal results in the extant literature regarding the effects of strong or weak ties on breakthrough innovation.

Despite the importance of innovation and customer participation for both practitioners and academics, the effects of the integration between innovation and customer participation has rarely been addressed in consumers’ perspectives. Accordingly, the authors first examine separately the impact of the two breakthrough innovation types (technology-based innovation vs. market-based innovation) and two forms of customer participation (as information providers vs. as co-developers) on brand attitude. Following this, the interaction effect between the two variables is also tested. We used a 2x2x2 mixed subjects design. We employed a 2 (breakthrough innovations: T-INNO, M-INNO) x 2 (customer participation: CPI, CPC) between-subjects design for independent variables and the dependent variable had a 2 (brand attitude: pre-brand attitude, post-brand attitude) within-subject design. The hypotheses were tested for a cell phone product category by pretest. Participants were 148 university students from Seoul, Korea. The results show that both breakthrough innovation and customer participation positively influence the brand attitudes held by customers, though neither the two forms of breakthrough innovation nor the two forms of customer participation differ from each other in terms of the strength of this relationship. However, when technology-based innovation is combined with customer participation in the form of co-development, a stronger positive impact on brand attitude is observed than when customers are treated as information providers. Conversely, when market-based innovation is combined with customer participation in the form of information provision, a stronger positive impact on brand attitude is observed than when the customers act as co-developers. These results have a number of theoretical contributions. First, prior innovation research has mostly focused on the impact on firm performance. Even though a few researchers have conducted several studies about the impact of innovation in terms of consumers’ perspectives, they did not consider the specific type of innovation. The present study focuses on comparing the impact of two types of breakthrough innovation based on customers’ perspectives. Second, prior customer participation or co-creation research has mostly looked at the positive impact on performance from both the firm’s and consumer’s perspective. However, they did not consider the specific type of customer participation which can affect differently performance. In this study, the differential impact of each type of customer participation was explored. Third, previous studies have not focused on the interaction effect between two types of innovation and customer participation. We found that the interaction effect can be significant when they are combined together. This study has also managerial implications. First, when firm managers utilize both breakthrough innovation and customer participation strategies, they need to consider the most effective combination of the forms of innovation and participation available. Second, this interaction effect should be considered not only in the innovative product development process but also in the communication activities in their customers. Finally, the limitations and further research directions of this results are discussed.