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.

This study examines consumers’ online activities according to the categories of goods and services, applying foraging theory, and explore online information structure perceived by consumers, determining the degree of information overload. Consumers are confronted by nearly unlimited amounts of information when they gather information to make purchase decisions in an online environment. Few studies have focused on the behavioral pattern of information acquisition to reduce information overload. The available studies are limited by particular conditions under a normative perspective (e.g., cognitive limitation with item specific information only and overemphasis on the quantity dimension of information structure). An improvement may be a holistic approach that allows freedom of information acquisition, and includes an ecological perspective, which emphasizes an interaction between minds and immediate environments (Todd & Gigerenzer, 2007). In other words, to provide a better explanation of information overload phenomenon, the research includes quantity information as well as quality and environmental information in the information structure. This has been overlooked in an information overload paradigm (for example, the question of whether more or less information is better) (Scheibehenne, Greifeneder, & Todd, 2010). Moreover, Xia and Monroe (2005) argued that the majority of research about information acquisition has overemphasized information searching while overlooking information browsing, although both activities occur concurrently during processes of information acquisition. The foraging theory (Stephen and Kreb, 1986), which originated from behavioral ecology, can help explain a continuum of browsing and searching behavior through utilizing the patch framework (Kim & Hantula, 2016). The patch framework provides a different perspective for information structure in terms of the amount of data as a combination of within-patch (searching) and between-patch (browsing), thereby covering the issue of the browse-search continuum to explain issues of amount for information overload. (e.g., Detlor, Sproule, & Gupta, 2003). The current study applied foraging theory into the online behavior of information acquisition and explored the information structure that consumers establish and consider in their process of information foraging across categories of goods and services (i.e., durables, nondurables, and services). This theoretical integration would proffer some clues for information structure to reduce information overloads through browsing and searching information online. The investigations consist of ordinary activities and purchase-related activities online, frequently purchased items and consideration to make purchase decisions, strategic information foraging patterns, and perceived decisional difficulty.