Literature-Related Discovery (LRD): Potential treatments for Parkinson's Disease

Literature-Related Discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous two papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP) and to cataracts.Parkinson's Disease (PD) is a progressive neurodegenerative disorder, affecting approximately 1% of individuals older than 60 years, and is characterized by resting tremor, rigidity, bradykinesia, and postural instability. We selected the subject of PD because of its global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery.Our first goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of PD. Our second goal was to demonstrate that we could again solve an ODS problem (using LRD) with no prior knowledge of any results or prior work (unlike the case of the RP problem). As in the ‘cataract’ example, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. Our third goal was to generate large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. The discovery generation methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for cataracts

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.This paper describes the second medical problem we addressed (cataract) using ODS LRD; the first problem addressed was Raynaud's Phenomenon (RP), and was described in the third paper of this Special Issue. Cataract was selected because it is ubiquitous globally, appears intractable to all forms of treatment other than surgical removal of cataracts, and is a major cause of blindness in many developing countries.The ODS LRD study had three objectives: a) identify non-drug non-surgical treatments that would 1) help prevent cataracts, or 2) reduce the progression rate of cataracts, or 3) stop the progression of cataracts, or 4) maybe even reverse the progression of cataracts; b) demonstrate that we could solve an ODS LRD problem with no prior knowledge of any results or prior work (unlike the case with the RP problem); c) determine whether large time savings in the discovery process were possible relative to the time required for conducting the RP study. To that end, we used the MeSH taxonomy of MEDLINE to restrict potential discoveries to selected semantic classes, as a substitute for the manually-intensive process used in the RP study to restrict potential discoveries to selected semantic classes. We also used additional semantic filtering to identify potential discovery within the selected semantic classes.All these goals were achieved. As will be shown, we generated large amounts of potential discovery in more than an order of magnitude less time than required for the RP study. We identified many non-drug non-surgical treatments that may be able to reduce or even stop the progression rate of cataracts. Time, and much testing, will determine whether this is possible. Finally, the methodology has been developed to the point where ODS LRD problems can be solved with no results or knowledge of any prior work.     

Literature-related discovery (LRD): Potential treatments for Raynaud's Phenomenon

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Raynaud's Phenomenon (RP) is a condition in which small arteries, most commonly in fingers and toes, contract and cause the skin to turn pale or a patchy red to blue. We selected the subject of RP for analysis by LRD because of RP's global prevalence, and its apparent intractability to all treatments except for palliative remediation mainly through drugs or surgery. Our main goal was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop the progression, or 4) maybe even reverse the progression, of RP. Our secondary goal was to compare our ODS LRD approach to the RP problem with other investigators who have addressed the RP problem since Swanson's pioneering 1986 ODS LRD paper on potential RP treatments [D.R. Swanson, Fish oil, Raynauds syndrome, and undiscovered public knowledge, Perspectives in Biology and Medicine 30 (1) (1986) 7-18].We used Medline from 1965-1985 to identify potential discovery for RP. We differ from all the other authors who have addressed this problem in two major respects: we make no numerically-based filtering assumptions, and we generate substantial potential discovery (∼ 130 potential discoveries). Further, we believe our reported results are the tip of the iceberg. Much more potential discovery is possible with an adequately resourced study using the lessons learned from this demonstration study and the other demonstration studies that follow in this Special Issue.     

Literature-Related Discovery (LRD): Introduction and background

Discovery in science is the generation of novel, interesting, plausible, and intelligible knowledge about the objects of study. Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery:

•

Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone.

•

Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.

LRD offers the promise of large amounts of potential discovery, for the following reasons:

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the burgeoning technical literature contains a very large pool of technical concepts in myriad technical areas;

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researchers spend full time trying to cover the literature in their own research fields and are relatively unfamiliar with research in other especially disparate fields of research;

•

the large number of technical concepts (and disparate technical concepts) means that many combinations of especially disparate technical concepts exist

•

by the laws of probability, some of these combinations will produce novel, interesting, plausible, and intelligible knowledge about the objects of study.

This Special Issue presents the LRD methodology and voluminous discovery results from five problem areas: four medical (treatments for Parkinson's Disease (PD), Multiple Sclerosis (MS), Raynaud's Phenomenon (RP), and Cataracts) and one non-medical (Water Purification (WP)). In particular, the open discovery systems (ODS) aspect of LRD (start with problem, generate potential solution(s), or vice versa) is addressed, rather than the closed discovery systems (CDS) aspect (start with problem and potential solution(s), generate linking mechanism(s)). In the presentation of potential discovery, a ‘vetting’ process is used that insures both requirements for ODS LBD are met: concepts are linked that have not been linked previously, and novel, interesting, plausible, and intelligible knowledge is produced.The potential discoveries for the PD, MS, Cataracts, and WP problems are the first we have seen reported by this ODS LBD approach, and the numbers of potential discoveries for the ODS LBD benchmark RP problem are almost two orders of magnitude greater than those reported in the open literature by any other ODS LBD researcher who has addressed this benchmark RP problem. The WP problem is the first non-medical technical topic to have been addressed successfully by ODS LBD.In all cases, but especially the medical, we have barely scratched the surface of quantity and quality of potential discovery that could be generated with adequately resourced studies. Based on the many potential discoveries we have obtained, and the promise of far more potential discoveries with adequately resourced studies, we have generated a new paradigm relative to discovery: while the key challenge in traditional discovery is finding a needle-in-a-haystack, the key challenge in ODS LRD (used appropriately) is handling the overwhelming amount of potential discovery available.Additionally, it is our thesis, as the specific ODS LBD studies will demonstrate, that synergistic combinations of our mainly individual potential discoveries are themselves potential discoveries. We demonstrate throughout this Special Issue the synergistic effects of combining a very few potential discoveries or interesting core literature concepts, and believe that these synergistic benefits are operable at larger scales of combination. In the final lessons-learned paper of this Special Issue, we also show that providing evidence for the synergistic benefits of large numbers of potential discoveries or interesting core concepts is very difficult due to the large numbers of potential combinations involved.One variant of the LAD operational mode (identifying disparate discipline recipients for Broad Agency Announcement (BAA) notifications in order to stimulate proposals of new ideas from these disparate disciplines) is presented for WP. Other possible applications of LAD include:

1.

Recipients of solicitation announcements (other solicitations similar to BAA, journal Special Issue calls for papers, etc),

2.

Participants in Workshops, Advisory Panels, Review Panels, Roadmaps, and War Games,

3.

Points of Contact for Field Science Advisors, Foreign Field Offices, Program Officer site visits, and potential transitions.

     

Literature-related discovery (LRD): Water purification

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). LRD has two main components that differ in their methodological approach to discovery: Literature-based discovery (LBD) produces potential discovery through analysis of the technical literature alone; Literature-assisted discovery (LAD) produces potential discovery through both analysis of the technical literature and use of selected authors of that literature. These authors generate potential discovery as proposers, workshop/panel participants, or in other active roles.The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The previous four papers in this Special Issue describe the application of ODS LRD (specifically, the ODS LBD variant) to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), and Multiple Sclerosis (MS).One goal of the present study was to determine whether LRD could be successfully applied (for the first time) to a challenging non-medical technical problem to generate potential discovery. The second goal was to explore the use of both LRD variants (LBD and LAD) to a non-medical technical problem. We selected the problem of water purification (WP) because of universal applicability and sponsor interest.We used LRD to identify purification concepts, technology components and systems that could lead to improved water purification techniques. We accessed many disparate disciplines to identify purification concepts from literatures not normally associated with water purification. We used two LBD approaches, Cluster Filtering and Latent Semantic Indexing (LSI), to search for potential discovery. We generated voluminous amounts of potential discovery, and believe we have only scratched the surface of what is possible. We also ran a short experiment using LAD to identify experts associated with potential discovery concepts, and use their expertise to generate potential discovery for water purification.     

Literature-related discovery (LRD): Lessons learned, and future research directions

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge (i.e., potential discovery). The open discovery systems (ODS) component of LRD starts with a problem to be solved, and generates solutions to that problem through potential discovery. We have been using ODS LRD to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications. The five immediately preceding papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP). We describe the lessons learned from each application, and how the techniques can be improved further.Generation of much potential discovery using ODS LRD is possible when the conceptual roadblocks to discovery are removed. Some of these roadblocks include use of numerical filters that are unrelated to generating discovery, and excessive reliance on literatures directly related to the problem literature of interest. The issue of how to handle large amounts of potential discovery has not been addressed in the literature, since most ODS LRD researchers have tried to find a relatively few potential discovery items. We present a development strategy that capitalizes on the large amounts of potential discovery we have identified.     

Literature-related discovery (LRD): Methodology

Literature-related discovery (LRD) is linking two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, plausible, and intelligible knowledge. LRD has two components: Literature-based discovery (LBD) generates potential discovery through literature analysis alone, whereas literature-assisted discovery (LAD) generates potential discovery through a combination of literature analysis and interactions among selected literature authors. In turn, there are two types of LBD and LAD: open discovery systems (ODS), where one starts with a problem and arrives at a solution, and closed discovery systems (CDS), where one starts with a problem and a solution, then determines the mechanism(s) that links them.The generic methodology for identifying potential discovery candidates through ODS LRD, focusing mainly on its ODS LBD component, is described in this paper. A comprehensive flow chart showing the details of our systematic potential discovery generation process, including the evolution of the flow chart steps through each of the studies performed, is presented. Also shown is a vetting procedure that insures potential discoveries claimed are potential discoveries realized. The semantic filters that replace the numerical filters of other ODS LBD approaches are overviewed. The rationale for addressing the five topics studied (Raynaud's Phenomenon (RP), Cataracts, Parkinson's Disease (PD), Multiple Sclerosis (MS), and Water Purification (WP)) is summarized.     

Literature-related discovery: Potential treatments and preventatives for SARS

Literature-related discovery (LRD) is the linking of two or more previously disjoint concepts in order to produce novel, interesting, plausible, and intelligible connections (i.e., potential discovery). LRD has been used to identify potential treatments or preventative actions for challenging medical problems, among myriad other applications.Severe acute respiratory syndrome (SARS) was the first pandemic of the 21st century. SARS was eventually controlled through increased hygienic measures (e.g., face mask protection, frequent hand washing, living quarter disinfection), travel restrictions, and quarantine. According to recent reviews of SARS, none of the drugs that were used during the pandemic worked.For the present paper, SARS was selected as the first application of LRD to an infectious disease. The main goal of this research was to identify non-drug non-surgical treatments that would 1) prevent the occurrence, or 2) reduce the progression rate, or 3) stop/reverse the progression of SARS. The MeSH taxonomy of Medline was used to restrict potential discoveries to selected semantic classes, and to identify potential discoveries efficiently. To enhance the volume of potential discovery, databases were used in addition to Medline. These included the Science Citation Index (SCI) and, in contrast to previous work, a full text database. Because of the richness of the full text, ‘surgical’ queries were developed that targeted the exact types of potential discovery of interest while eliminating clutter more efficiently.     

Systematic acceleration of radical discovery and innovation in science and technology

A systematic two-component approach (front-end component, back-end component) to bridging unconnected disciplines and accelerating potentially radical discovery and innovation (based wholly or partially on text mining procedures) is presented. The front-end component has similar objectives to those in the classical literature-based discovery (LBD) approach, although it is different mechanistically and operationally. The front-end component will systematically identify technical disciplines (and their associated leading experts) that are directly or indirectly-related to solving technical problems of high interest. The back-end component is actually a family of back-end techniques, only one of which shares the strictly literature-based analysis of the classical LBD approach. The non-LBD back-end techniques (literature-assisted discovery) make use of the human experts associated with the disparate literatures (disciplines) uncovered in the front-end to generate radical discovery and innovation.Specifically, in the literature-assisted discovery operational mode, these disparate discipline experts could be used as:

1. Recipients of solicitation announcements (BAA, SBIR, MURI, journal Special Issue calls for papers, etc.),

2. Participants in Workshops, Advisory Panels, Review Panels, Roadmaps, and War Games,

3. Points of Contact for Field Science Advisors, Foreign Field Offices, Program Officer site visits, and potential transitions.

Disruptive technology roadmaps

Disruptive technologies create growth in the industries they penetrate or create entirely new industries through the introduction of products and services that are dramatically cheaper, better, and more convenient. These disruptive technologies often disrupt workforce participation by allowing technologically unsophisticated individuals to enter and become competitive in the industrial workforce. Disruptive technologies offer a revolutionary change in the conduct of processes or operations.Disruptive technologies can evolve from the confluence of seemingly diverse technologies or can be a result of an entirely new technological investigation. Existing planning processes are notoriously poor in identifying the mix of sometimes highly disparate technologies required to address the multiple performance objectives of a particular niche in the market. For a number of reasons, especially the inability to look beyond short-term profitability, and the risk/return tradeoff of longer term projects, it is suggested that current strategic planning and management processes promote sustaining technologies at the expense of disruptive technologies.We propose a systematic approach to identify disruptive technologies that is realistic and operable and takes advantage of the text mining literature. This literature-based discovery process is especially useful in identifying potential disruptive technologies that may require the input from many diverse technological and management areas. We believe that this process holds great potential for identifying projects with a higher probability of downstream success. Further, we suggest a process to take the identified potential disruptive technology from the “idea stage” through to the development of a potentially feasible product for the market. This second stage makes use of workshops and roadmapping to codify the ideas of technological and management experts, who were identified in the literature-based discovery stage. Our goal is to describe and explain the pragmatic steps suggested by our innovative and practical process.The proposed process could identify technologies whose eventual development and application to specific problems would generate innovative products. The goal is to isolate technologies that have the potential to redefine an industry, or alternatively, have the potential to create an entirely new industrial setting. Use the text-mining component of literature-based discovery to identify both the technical disciplines that are likely candidates for disruptive technological products, and experts in these critical technical and managerial disciplines. While we know that this is but one way to investigate nascent disruptive technologies we feel it is imperative that the representatives of these potentially critical technical disciplines are included in the roadmap development process, either as implementers or as consultants.Every firm is looking for “the next great thing”. Literature-based discovery offers a starting point for identifying at least a portion of the major contributory technical and managerial disciplines necessary for potential disruptive technologies and discontinuous innovations. Combining literature-based discovery with a practical workshop/roadmap process dramatically enhances the likelihood of success.     

基于科学的创新失败挽救：扩展适应视角

基于科学的创新具有失败率极高的特点，对其失败（项目）进行挽救可减少创新资源浪费，提升相关产业创新绩效，具有可观的经济价值和社会意义。采用多案例方法，从扩展适应视角研究发现，基于科学的创新失败挽救过程由新功能意义建构、基于扩展适应的商业化实现及持续扩展适应3个阶段构成；在基于科学的创新领域，扩展适应是一种重要的失败挽救机制；机缘巧合触发基于扩展适应的挽救机会形成，已存在扩展适应的创新生态系统是挽救创新失败的实现条件，多次、持续的扩展适应可不断挖掘和释放失败项目的创新价值。结论可丰富基于科学的创新和创新失败挽救相关理论研究，为创新管理实践、政策制定等提供理论参考。     

An Entrepreneurial Perspective of Institutional Change

Utilizing Kirzner's theory of entrepreneurial discovery, Schumpeter's two types of economic responses and the Austrian theory of institutions as building blocks, this paper constructs an entrepreneurial theory of institutional change. Focusing on the coordinating role of human institutions, this paper argues that entrepreneurial extraordinary discovery destroys the stability of institutions and creates uncertainty in the market (creative response). As a result, institutions are incapable of coordinating economic activities because market participants' stocks of knowledge are no longer adequate to solve new problems. Hence, profit gaps or mismatches of market participants' plans occur. Given new technologies, new relative prices and tastes, imitative entrepreneurs soon identify and capitalize on the opportunities created by Schumpeterian extraordinary discoveries (adaptive response). Imitators improve production methods, modify rules and alter property rights in order to improve coordination. Through learning, experimentation, trial and error, the more rewarding methods are then selected. Successful actions are imitated and repeated in the market, and gradually crystallized into new institutions which once again serve as social coordinators.     

高校学科知识发现系统建设研究--基于元数据仓储

对学科知识的概念、特点和研究现状进行分析，通过对各学科知识中不同格式规范的元数据进行搜集、整理、分析、存贮、归档、公布并形成元数据仓储，从而对学科知识进行有效地组织、分配、开发和评价。从学科知识规划、学科知识元数据采集、学科知识组织与存储、学科知识发现系统门户四个方面，结合元数据仓储技术详细阐述高校学科知识发现系统的建设方案。     

Entrepreneurial Alertness and Discovery

The purpose of this paper is to elaborate Kirzner's concepts of entrepreneurial alertness and discovery in the subjectivist perspective. Specifically, it argues that the entrepreneurial discovery process is associated with the actor's interpretation framework, or the stock of knowledge, which is derived from everyday life experiences. Discovery in this context means that the actor interprets incoming information in a way different from perceptions of the general public. Two kinds of entrepreneurial discovery, namely ordinary and extraordinary, are discussed. In terms of mental constructs, ordinary discovery is a backward interpretation in a sense that the entrepreneur endeavours to exploit profit opportunities by doing some things better. This type of discovery largely promotes change within an existing situation. Extraordinary discovery is a forward interpretation that involves a new dimension of interpreting events. In this case, the entrepreneur explores profit opportunities by doing some things drastically different from the traditional. This type of discovery enhances revolutionary change to the economy. Inertia is explained, in the subjectivist perspective, as a result of actors taking knowledge for granted and being locked inside the old interpretation frameworks. The argument developed is applied to explain (1) why firms vertically integrate and, (2) why the socialist system impedes entrepreneurial alertness and discovery.     

发明者中间人角色对二元创新的影响——知识多样化的中介作用

将中间人划分为内部联系中间人和外部联系中间人，基于2010-2018年华为公司与中兴通讯发明专利数据，研究发明者中间人角色对二元创新的影响机理。利用Stata软件，对移动面板数据进行零膨胀负二项回归分析。研究结果表明：内部联系中间人角色与相关知识多样化呈正相关关系，相关知识多样化与利用式创新呈正相关关系，相关知识多样化具有中介效应；外部联系中间人角色与非相关知识多样化具有正相关关系，非相关知识多样化与探索式创新呈倒U型关系，非相关知识多样化对外部联系中间人角色与探索式创新关系发挥中介作用。     

Entrepreneurial Discovery and the Demmert/Klein Experiment: Another Attempt at Creating the Proper Context

Friedrich Hayek conjectured that the free enterprise system is the most effective in making discoveries, and Israel Kirzner refines the conjecture by saying that profit opportunities evoke entrepreneurial discovery. Demmert and Klein (2003) present the first attempt to demonstrate the Hayek/Kirzner conjecture. On the whole, Demmert and Klein (2003) classify the results as disappointing but fruitful. In contrast we argue that additional experimental evidence might yield a demonstration of the conjecture. We continued the diligence and good-faith effort started by Demmert and Klein (2003) to devise an experimental setting that would create a genuine context for entrepreneurial discovery, yet the conjecture eludes our efforts at controlling the experiment. We duplicated the experiment at a Business School in Germany, with two simple variations. First, Demmert and Klein (2003) recruited only male students. We include male and female students. Second, Demmert and Klein (2003) used a payment schedule that includes a flat rate for participation and additional earnings depending on the presented performance. We drop the flat rate and slightly reduce the earnings per unit outperformance. Our results show that overall money matters. Our results are rather like those of Demmert and Klein (2003) and do not seem to be influenced by a baseline payment. Moreover, there are gender specific divergences showing male students earning significant higher additional earnings than their female fellow students.     

Information Linkages between Chinese and World Copper Futures Markets

In this paper, we examine the price discovery process and volatility spillover effects in informationally linked futures markets. Using synchronous trading information from the Shanghai Futures Exchange (SHFE), the New York Mercantile Exchange (NYMEX), and the London Metal Exchange (LME) for copper futures from 2000 to 2012, we show that the cointegration relationships of these futures markets changed during 2006–2008. The results indicate that there is a bidirectional relationship in terms of price and volatility spillovers between the LME and NYMEX and the SHFE, with a stronger effect from the LME and NYMEX to the SHFE (versus the effect from the SHFE to the LME and NYMEX) prior to 2006. Our results also highlight the increasingly prominent role of the SHFE in the price formation process and cross-volatility spillover effects since 2008. Finally, we show that volatility spillover has important implications for constructing optimized portfolios for copper investors.     

产学合作关系对企业创新绩效的影响研究——基于案例研究的概念模型与解释

基于对关系嵌入性已有文献和企业知识观理论的研究评述,提出了产学合作的关系嵌入性→外部知识获取→企业创新绩效的理论预设。并以理论预设中的3个研究构面为讨论原点,结合4个企业和大学合作项目的探索性案例分析,构建了纳入细致考察变量的概念模型,提出并解释了一系列基于经验研究的理论命题,从而进一步揭示了理论预设中3个研究构面的关系链机理。研究发现,产学合作的关系嵌入性(信任、信息共享、控制)通过企业获取外部知识的两种主要机制(显性知识转移、共同解决问题)间接影响企业创新绩效,并进一步解释了其中的关联路径与影响方式。     

企业外部知识搜寻多维结构与创新质量关系研究

以63家美国医药上市企业和173家中国医药上市企业发明专利为样本，研究企业多维知识搜寻结构与创新质量的关系。结果表明：①不同搜寻结构对企业创新质量的影响存在差异性，尤其是同时沿着多个维度进行远距离知识搜寻能够显著提高企业创新质量；②各维度远距离搜寻对创新质量的影响不同，在中美医药企业之间也存在一定差异；③对美国医药企业而言，知识搜寻的3个维度对创新质量的影响程度依次为认知维度>时间维度>地理维度，而中国医药企业则是认知维度>地理维度>时间维度。