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This paper investigates how evolutions of complex technologies and networks of innovators affect the development of emerging innovations. Building upon the theories of technological evolution and socio-organizational dynamics, we develop propositions to examine the stability and change of networks punctuated by successive technological changes. We argue that incumbents who are early advocates of standards in complex technological environments are more likely to survive via alliances. Based on 150 firms and 319 alliances in the US data communications industry from 1985 to 1996, we found support for our propositions and the characteristics of central-periphery structure best describe the patterns of industry networks. 相似文献
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《Research Policy》2023,52(6):104761
Standard-setting organizations (SSOs) are collectively self-governed industry associations, formed by innovators and implementers. They are a key organizational form to agree on and manage technical standards, and form the foundation for many technological and economic sectors. We develop a model of endogeneous SSO participation that highlights different incentives for joining (namely licensing, learning, and implementation). We analyze equilibrium selection and conduct comparative statics for a policy parameter that is related to implementer-friendly Intellectual Property Rights policies, or alternatively, minimum viable implementation. The results can reconcile existing evidence, including that many SSO member firms are small. The extent of statutory participation of implementers in SSO control has an inverted U-shape effect on industry profits and welfare. 相似文献
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《Research Policy》2023,52(2):104661
Using patent data for a panel sample of European companies between 1995 and 2016 we explore whether the inventive success in Artificial Intelligence (AI) is related to earlier firms’ innovation in the area of Information and Communication Technology (ICT), and identify which company characteristics and external factors shape this performance. We show that AI innovation presents strong dynamic returns (learning effects) and benefits from complementaries with knowledge earlier developed in the area of network and communication technologies, high-speed computing and data analysis, and more recently cognition and imaging. AI patent productivity increases with the scale of firm innovation, and is lower for companies with narrow technological competences. There is evidence of knowledge spillovers from ICT innovators to AI innovators, but this effect is confined to the frontier firms of the new technological field. Our findings suggest that, with the take-off of the new technology, the technological lead of top AI innovators has increased due to the accumulation of internal competences and the expanding knowledge base. These trends help explain the concentration process of the world’s data market. 相似文献
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《Information processing & management》2020,57(2):102041
Trends change rapidly in today’s world, prompting this key question: What is the mechanism behind the emergence of new trends? By representing real-world dynamic systems as complex networks, the emergence of new trends can be symbolized by vertices that “shine.” That is, at a specific time interval in a network’s life, certain vertices become increasingly connected to other vertices. This process creates new high-degree vertices, i.e., network stars. Thus, to study trends, we must look at how networks evolve over time and determine how the stars behave. In our research, we constructed the largest publicly available network evolution dataset to date, which contains 38,000 real-world networks and 2.5 million graphs. Then, we performed the first precise wide-scale analysis of the evolution of networks with various scales. Three primary observations resulted: (a) links are most prevalent among vertices that join a network at a similar time; (b) the rate that new vertices join a network is a central factor in molding a network’s topology; and (c) the emergence of network stars (high-degree vertices) is correlated with fast-growing networks. We applied our learnings to develop a flexible network-generation model based on large-scale, real-world data. This model gives a better understanding of how stars rise and fall within networks, and is applicable to dynamic systems both in nature and society.Multimedia Links▶ Video ▶ Interactive Data Visualization ▶ Data ▶ Code Tutorials 相似文献
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在理论研究基础上,构建了创始人前摄性特质、社会网络与企业创新绩效之间的关系理论模型,运用多元回归分析方法,并通过对148份创新型企业问卷调查数据进行实证研究,探讨了社会网络对创新绩效的影响关系,以及社会网络在二者之间的中介作用机理。结果表明：社会网络均对创新绩效有显著正影响,且社会网络在前摄性与创新绩效之间起部分中介作用。此结论深化了对创始人前摄性特质、社会网络和创新绩效三者关系的理解,为企业在网络化发展模式中如何培养塑造创始人特质提供了理论参考。 相似文献
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《Information processing & management》2022,59(1):102755
General recommenders and sequential recommenders are two modeling paradigms of recommender. The main focus of a general recommender is to identify long-term user preferences, while the user’s sequential behaviors are ignored and sequential recommenders try to capture short-term user preferences by exploring item-to-item relations, failing to consider general user preferences. Recently, better performance improvement is reported by combining these two types of recommenders. However, most of the previous works typically treat each item separately and assume that each user–item interaction in a sequence is independent. This may be a too simplistic assumption, since there may be a particular purpose behind buying the successive item in a sequence. In fact, a user makes a decision through two sequential processes, i.e., start shopping with a particular intention and then select a specific item which satisfies her/his preferences under this intention. Moreover, different users usually have different purposes and preferences, and the same user may have various intentions. Thus, different users may click on the same items with an attention on a different purpose. Therefore, a user’s behavior pattern is not completely exploited in most of the current methods and they neglect the distinction between users’ purposes and their preferences. To alleviate those problems, we propose a novel method named, CAN, which takes both users’ purposes and preferences into account for the next-item recommendation. We propose to use Purpose-Specific Attention Unit (PSAU) in order to discriminately learn the representations of user purpose and preference. The experimental results on real-world datasets demonstrate the advantages of our approach over the state-of-the-art methods. 相似文献
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With the expansion of information on the web, recommendation systems have become one of the most powerful resources to ease the task of users. Traditional recommendation systems (RS) suggest items based only on feedback submitted by users in form of ratings. These RS are not competent to deal with definite user preferences due to emerging and situation dependent user-generated content on social media, these situations are known as contextual dimensions. Though the relationship between contextual dimensions and user’s preferences has been demonstrated in various studies, only a few studies have explored about prioritization of varying contextual dimensions. The usage of all contextual dimensions unnecessary raises the computational complexity and negatively influences the recommendation results. Thus, the initial impetus has been made to construct a neural network in order to determine the pertinent contextual dimensions. The experiments are conducted on real-world movies data-LDOS CoMoDa dataset. The results of neural networks demonstrate that contextual dimensions have a significant effect on users’ preferences which in turn exerts an intense impact on the satisfaction level of users. Finally, tensor factorization model is employed to evaluate and validate accuracy by including neural network’s identified pertinent dimensions which are modeled as tensors. The result shows improvement in recommendation accuracy by a wider margin due to the inclusion of the pertinent dimensions in comparison to irrelevant dimensions. The theoretical and managerial implications are discussed. 相似文献
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Social networks provide individuals with diverse or redundant information depending on the network structure. Both types of information offer advantages for generating new ideas. At the same time, network structure and network content are independent. As a result, two individuals with the same network position can access diverse or redundant content from their social peers. In this study, we investigate the function of social networks in innovative endeavors given individuals’ different kinds of information accessing behavior. In accordance with previous research, we argue that individuals with a broker status access more diverse information through non-redundant network structures and develop, on average, more novel ideas. We further propose that redundancy in content complements brokers’ structural non-redundancy by providing familiar knowledge elements and therefore interpretability, while non-redundancy in both content and structure leads to information overload. Thus, we hypothesize that brokers accessing more information depth, and independently, less information breadth generate newer ideas. To test our hypotheses, we collected data from a popular online maker community containing 18,146 ideas, 19,919 profiles, and 52,663 comments. We used topic modeling (Latent Dirichlet Allocation) to extract hidden knowledge elements and social network analysis to identify brokers. In line with our hypotheses, we find that information depth (breadth) strengthens (weakens) a favorable broker position. These findings have implications for the literature on idea generation in social networks and household sector innovation. 相似文献
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《Research Policy》2023,52(1):104618
Many standard setting organizations (SSOs) require participants to disclose patents that might be infringed by implementing a proposed standard, and commit to license their “essential” patents on terms that are fair, reasonable and non-discriminatory (FRAND). Data from SSO intellectual property disclosures have been used in academic studies to provide a window into the standard setting process, and in legal proceedings to assess the relative contribution of different parties to a standard. We describe the disclosure process, discuss the link between SSO rules and patent-holder incentives, and analyze disclosure practices using a novel dataset constructed from the disclosure archives of thirteen major SSOs. Our empirical results suggest that subtle differences in SSO policies influence which patents are disclosed, the terms of licensing commitments, and ultimately long-run citation and litigation rates for the underlying patents. Thus, while policy debates sometimes characterize SSOs as a relatively homogeneous set of institutions, our results point in the opposite direction – towards the importance of recognizing heterogeneity in SSO policies and practices. 相似文献
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《Research Policy》2021,50(10):104360
We investigate how U.S. Patent and Trademark Office (USPTO) patent examiner experience and seniority-based incentives affect the innovation ecosystem. First, we show that examiners respond to production incentives and demonstrate learning by increasing the use of examiner’s amendments in both experience and seniority, a mechanism not previously studied. Second, this examination procedure directly benefits innovators and firms by significantly reducing prosecution processing time without impacting patent quality. Finally, after considering examiner’s amendments, the negative relationship between examiner characteristics and patent examination quality found in the previous literature does not persist at first action, a decision point that allows for the clear measurement of examiner behavior. Our results demonstrate a need for reformulated policy recommendations related to the structure of examination at the USPTO. 相似文献
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综合运用多种软件技术和算法得到的中美机构合作网络,更清晰地揭示机构之间的合作网络关系。学术机构是中美能源技术领域机构合作网络中的最广泛创新主体;中美能源技术领域都与国际上许多国家存在着比较广泛的技术合作关系;政府和产业在两个合作网络中出现的数量相对比较少;美国能源技术领域的最大连通组网络比中国的更稠密一些;美国能源部和环保局等政府部门在美国能源技术合作网络中参与程度较高;参与美国能源技术合作网络研究的企业也相对比较多。美国能源技术领域的机构合作网络更具有三螺旋创新结构特征等。最后提出中国能源技术机构合作网络的发展,应加强政府与产业的参与程度。 相似文献
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《Information processing & management》2020,57(2):102064
We propose a setting for two-phase opinion dynamics in social networks, where a node’s final opinion in the first phase acts as its initial biased opinion in the second phase. In this setting, we study the problem of two camps aiming to maximize adoption of their respective opinions, by strategically investing on nodes in the two phases. A node’s initial opinion in the second phase naturally plays a key role in determining the final opinion of that node, and hence also of other nodes in the network due to its influence on them. However, more importantly, this bias also determines the effectiveness of a camp’s investment on that node in the second phase. In order to formalize this two-phase investment setting, we propose an extension of Friedkin–Johnsen model, and hence formulate the utility functions of the camps. We arrive at a decision parameter which can be interpreted as two-phase Katz centrality. There is a natural tradeoff while splitting the available budget between the two phases. A lower investment in the first phase results in worse initial biases in the network for the second phase. On the other hand, a higher investment in the first phase spares a lower available budget for the second phase, resulting in an inability to fully harness the influenced biases. We first analyze the non-competitive case where only one camp invests, for which we present a polynomial time algorithm for determining an optimal way to split the camp’s budget between the two phases. We then analyze the case of competing camps, where we show the existence of Nash equilibrium and that it can be computed in polynomial time under reasonable assumptions. We conclude our study with simulations on real-world network datasets, in order to quantify the effects of the initial biases and the weightage attributed by nodes to their initial biases, as well as that of a camp deviating from its equilibrium strategy. Our main conclusion is that, if nodes attribute high weightage to their initial biases, it is advantageous to have a high investment in the first phase, so as to effectively influence the biases to be harnessed in the second phase. 相似文献
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Mariana Giovanna Andrade Rojas Edgar Rogelio Ramirez Solis John JianJun Zhu 《Research Policy》2018,47(6):1111-1124
This study focuses on the multiplexity of firm R&D networks, and it investigates two types of boundary-spanning networks: the bipartite network between firms and government-sponsored institutions (GSIs), and the traditional firm–firm network. We apply a social network perspective to examine the effects that these kinds of networks have on firm innovativeness, in relation to the effects of the firm’s internal R&D efforts. We define the firm-GSI network as bipartite, and we investigate how the structural characteristics of this network (cohesion and centrality) affect innovativeness. We then decompose the innovational effects of firm–firm networks into two categories (intra- and inter-sector) to distinguish the effects of these collaboration networks. Furthermore, we investigate how these various external collaborative networks interact with a firm’s internal R&D efforts for driving innovativeness. Our empirical study of 420 manufacturing firms in Mexico evaluates evidence from surveys and secondary data. The findings indicate that the structural properties of both firm–GSI and firm–firm networks have positive effects on innovativeness, but firm–GSI network cohesion has a stronger negative interaction with R&D in influencing firm innovativeness. Moreover, intra-sector centrality in a firm–firm network has a stronger negative interaction with R&D than inter-sector centrality does in driving firm innovativeness. We contribute to the literature by integrating insights from the perspectives of network multiplexity, social embeddedness, and resource complementarity in regard to inter-organizational behavior. Our study also provides meaningful guidelines for both managers and policy makers. The study’s findings are robust to concerns of common method bias and alternative model specifications. 相似文献
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《Information processing & management》2022,59(6):103095
Modeling discussions on social networks is a challenging task, especially if we consider sensitive topics, such as politics or healthcare. However, the knowledge hidden in these debates helps to investigate trends and opinions and to identify the cohesion of users when they deal with a specific topic. To this end, we propose a general multilayer network approach to investigate discussions on a social network. In order to prove the validity of our model, we apply it on a Twitter dataset containing tweets concerning opinions on COVID-19 vaccines. We extract a set of relevant hashtags (i.e., gold-standard hashtags) for each line of thought (i.e., pro-vaxxer, neutral, and anti-vaxxer). Then, thanks to our multilayer network model, we figure out that the anti-vaxxers tend to have ego networks denser (+14.39%) and more cohesive (+64.2%) than the ones of pro-vaxxer, which leads to a higher number of interactions among anti-vaxxers than pro-vaxxers (+393.89%). Finally, we report a comparison between our approach and one based on single networks analysis. We prove the effectiveness of our model to extract influencers having ego networks with more nodes (+40.46%), edges (+39.36%), and interactions with their neighbors (+28.56%) with respect to the other approach. As a result, these influential users are much more important to analyze and can provide more valuable information. 相似文献
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Ismet Sahin 《Journal of The Franklin Institute》2006,343(2):168-180
This paper is concerned with deriving an optimal flow and routing control policy for two-node parallel link communication networks with multiple competing users. The model assumes that each user has a flow demand which needs to be optimally selected and routed on the network links. The flow and routing control policy for each user seeks to maximize the user's total throughput and minimize its expected delay. To derive such a policy, we consider a utility function for each user that combines these two objectives in an additive fashion with preference constants that can be adjusted to reflect the user's own preferences between maximizing throughput and minimizing delay. Additionally, we provide each user with the ability to make these preferences link-dependent to reflect the user's preferences for certain links over others in the network. A condition that depends on the link capacities and preference constants is derived to guarantee the existence and uniqueness of a non-symmetric flow and routing control policy solution which satisfies the Nash equilibrium of non-cooperative game theory. The Nash equilibrium is a desirable solution for such networks because it insures that no user can improve its utility by unilaterally deviating from its Nash control policy. We discuss in detail several interesting properties of this equilibrium and in particular, its relationship to the users’ preference constants. Two illustrative examples are also presented. 相似文献
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Weijing Qi Qingyang Song Xiangjie Kong Lei Guo 《Journal of The Franklin Institute》2019,356(2):766-790
Recently, Flying Ad-hoc Sensor Networks (FASNETs), which are basically ad hoc networks between Unmanned Aerial Vehicles (UAVs), have been playing a great role in many sensing fields. To further improve the utilization of network resources, researchers offer the possibility of applying Software Defined Networking (SDN) technology to FASNETs, enabling various applications to be supported over the same platform. Since these concurrently executed applications might have diverse Quality of Service (QoS) requirements, it brings new challenges to network management and resource scheduling. In this paper, we propose a novel clustering FASNET architecture with SDN cluster controllers and also a collaborative controller, in order to realize hierarchical management and unified dispatch. Based on our designed architecture, we also propose a centralized traffic-differentiated routing (TDR) executed in each cluster, which aims to guarantee the specific QoS requirements of delay-sensitive and reliability-requisite services. Different weights are assigned to various flows according to their sensitivity to delay and also levels of importance. In particular, we introduce a transmission reliability prediction model to TDR, in which we consider both link availability and node’s forwarding ability. Simulation results show our proposed TDR has good performances in terms of end-to-end delay, packet dropping ratio and network throughput. 相似文献
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Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks. 相似文献
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《Research Policy》2022,51(5):104497
This study empirically investigates the financial market's reaction to firms’ participation in standard setting organizations (SSOs) in terms of firms’ implied cost of equity capital – the discount rate applied by investors to a firm's expected future cash flows. Our analysis utilizes a panel of 3350 US public firms and their membership of 183 SSOs operating in a range of technology domains between 1996 and 2014. It shows a significantly lower cost of equity for SSO participants. We then empirically document a causal link between SSO membership and a firm's cost of equity, by exploiting exogenous variations in membership count linked to SSO closures and an instrumental variable measuring SSO availability. Our results underscore the important role of SSO membership in mitigating the perceived riskiness of a firm, particularly when it faces high degrees of technological uncertainty, product-market uncertainty, and information asymmetry. 相似文献
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Knowledge production and scientific research have become increasingly more collaborative and international, particularly in pharmaceuticals. We analyze this tendency in general and tie formation in international research networks on the country level in particular. Based on a unique dataset of scientific publications related to pharmaceutical research and applying social network analysis, we find that both the number of countries and their connectivity increase in almost all disease group specific networks. The cores of the networks consist of high income OECD countries and remain rather stable over time. Using network regression techniques to analyze the network dynamics our results indicate that accumulative advantages based on connectedness and multi-connectivity are positively related to changes in the countries’ collaboration intensity whereas various indicators on similarity between countries do not allow for unambiguous conclusions. 相似文献