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.

본 연구는 혁신주체 간 협력이 기술혁신 성과에 미치는 영향을 알아보기 위해 사회네트워크분석 및 분산분석 그리고 회귀분석을 실시하였다. 2009년부터 2012년까지 한국 특허청의 공동 출원인 자료를 토대로 네트워크 구조 변수 및 특성 변수를 도출하였다. 이를 통해 전체 네트워크의 구조적 유형, 혁신주체 별 역할 그리고 혁신 성과에 영향을 미치는 네트워크 특성 변수가 무엇인지를 실증 분석하였다. 분석결과를 요약하면 다음과 같다. 첫째, 특허 공동출원 인 네트워크는 비교적 소규모 그룹들이 산재해 있는 분산집중형 좁은세상 네트워크 구조이며 혁신주체들이 비교적 느슨하게 연결되어 있었다. 둘째, 특허 공동출원인 네트워크에서 가장 중심적인 역할을 하는 것은 대학교로 밝혀졌으나 협력 파트너의 다양성은 모든 혁신주체가 비슷하였다. 셋째, 익숙한 몇몇 협력 파트너로부터 얻는 신속하고 정확성이 높은 지식이 다양한 분야의 협력 파트너로부터 얻는 생소한 지식보다 성과를 창출하는 데에 보다 긍정적이었다.

These days, thanks to lots of smart devices and advanced communication technologies, consumer’s recognition and relations have been changed. They, beyond relying on information and services which are produced by experts, produce information and knowledge by themselves via SNS or web that they want to know. As consumer’s recognition is changing like this, SNS is evolving into social platform. Therefore, this paper is intended to clarify overall relationship between network characteristics in social platform, knowledge sharing, social capital, social innovation and customer’s value. This paper has clarified influences between variables related to consumer’s behaviors in social platform and the results are summarized as following: First, network characteristics in social platform are found to positively affect knowledge sharing efforts of social platform. Second, knowledge sharing has been found to positively affect social capital and innovation in social platform. However, enjoyment in helping others i.e a sub variable is found to positively affect social capital and innovation through anticipated reciprocal relationships. Third, social capital and innovation in social platform have affected customer value in social platform positively. Consequently, this paper is intended to solve various problems found from overall societies and industries through social innovation and also to advance them. For these purposes, social platform is believed to prompt sharing idea and knowledge based on interactions between users and social relationship. These actions become social capitals resulting in social innovation. Moreover, these would create new businesses and marketing opportunities across various areas in the processes that innovative activities form customer values.

This study wants to test the nexus among resource orientation, market orientation, social network, and knowledge sharing toward organizational innovation, and the nexus between organizational innovation and MSMEs performance. Questionnaires and interviews are conducted with some MSMEs actors in Central Java Province, Indonesia, in Klaten Regency and Pekalongan City. These two areas have creative MSMEs, especially Batik MSMEs that have been very large and known worldwide. The sampling technique is done purposively with certain criteria for the respondents. The data analysis technique is done using Partial Least Square. This study provides recommendations about strategic practice and policy (resource and market), social network, and knowledge sharing in increasing organizational innovation, and the impact of organizational innovation toward MSMEs performance. It also offers a comprehensive model of the determinant factor of organizational innovation by considering the aspect of strategic orientation, social network, and knowledge sharing. Other unique aspects that are also important to consider are social network and the importance of knowledge sharing in improving MSMEs Performance. The respondents are still limited in two areas, namely, Pekalongan and Klaten, so it still cannot represent the whole population. These areas also have different orientation of production process approach, namely, synthetic and natural dyes.

In this paper, firstly, from the input-output perspective of technological innovation, technological innovation process is divided into technological development process and technological achievements transformation process. On this basis, technological innovation efficiency can be decomposed into technological development efficiency and technological transformation efficiency. Secondly, on the basis of the two-stage technological innovation, by analyzing each stage in the process of technological innovation input and output elements, we design the input-output index system in each stage respectively, and ultimately get the high-tech industrial technology innovation efficiency evaluation index system. Thirdly, we build a chain network DEA model to evaluate the two-stage process of technological innovation of high-tech industry, include the overall efficiency of technological innovation and the efficiency of the two sub-stages. Finally, use chain network DEA model to calculate the technology innovation efficiency, technological development efficiency and technological transformation efficiency of high-tech industry of China's 25 regions in 2000-2010. According to the calculation results of h the empirical analysis of the efficiency of technological innovation of China's high-tech industry, and provide a reference for the provision of policy advice.

This study analyzed the probability that experiment stations, agricultural technology and extension centers, provincial agricultural research and extension services, central government organs, or civilian and other related organs will be the first choice of the compositional subjects of local innovation networks. While gender effect was statistically insignificant, educational level, income, main acquired information, sources of necessary information, and frequency of information acquisition sessions were significant, and the preference ranking model was highly relevant. According to the analysis, highly academic and business-related information was most likely to be acquired from the civilian sector; agricultural technology such as technology, crops/plants, storage, and circulation was most likely to be acquired from experiment stations and provincial agricultural research and extension services; and information on agricultural production was most likely to be acquired from agricultural technology centers.