Literature-related discovery (LRD): Potential treatments for Multiple Sclerosis

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 three papers in this Special Issue describe the application of ODS LRD to Raynaud's Phenomenon (RP), cataracts, and Parkinson's Disease (PD).Multiple Sclerosis (MS) is a progressive neurodegenerative disorder (typically preceded by periods of remission and relapse), affecting mainly people in their early-mid life. MS is characterized by changes in sensation (hypoesthesia), muscle weakness, abnormal muscle spasms, or difficulty to move; difficulties with coordination and balance (ataxia); problems in speech (Dysarthria) or swallowing (Dysphagia), visual problems (Nystagmus, optic neuritis, or diplopia), fatigue and acute or chronic pain syndromes, bladder and bowel difficulties, cognitive impairment, or emotional symptomatology (mainly depression).We selected the subject of MS 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 MS. 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’ and PD examples, 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 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): 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): 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 (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;

•

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: 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.     

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.     

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.     

Regulation and pricing of pharmaceuticals: Reference pricing or price cap regulation?

We study the relationship between regulatory regimes and pharmaceutical firms’ pricing strategies using a unique policy experiment in Norway, which in 2003 introduced a reference price (RP) system called “index pricing” for a sub-sample of off-patent pharmaceuticals, replacing the existing price cap (PC) regulation. We estimate the effect of the reform using a product level panel dataset, covering the drugs exposed to RP and a large number of drugs still under PC regulation in the time before and after the policy change. Our results show that RP significantly reduces both brand-name and generic prices within the reference group, with the effect being stronger for brand-names. We also identify a negative cross-price effect on therapeutic substitutes not included in the RP system. In terms of policy implications, the results suggest that RP is more effective than PC regulation in lowering drug prices, while the cross-price effect raises a concern about patent protection.     

Lifetime cost of everolimus vs axitinib in patients with advanced renal cell carcinoma who failed prior sunitinib therapy in the US

Abstract

Objective:

Everolimus and axitinib are approved in the US to treat patients with advanced renal cell carcinoma (RCC) after failure on sunitinib or sorafenib, and one prior systemic therapy (e.g., sunitinib), respectively. Two indirect comparisons performed to evaluate progression-free survival in patients treated with everolimus vs axitinib suggested similar efficacy between the two treatments. Therefore, this analysis compares the lifetime costs of these two therapies among sunitinib-refractory advanced RCC patients from a US payer perspective.

Research design and methods:

A Markov model was developed to simulate a cohort of sunitinib-refractory advanced RCC patients and estimate the cost of treating patients with everolimus vs axitinib. The following health states were included: stable disease without adverse events (AEs), stable disease with AEs, disease progression (PD), and death. The model included the following resources: active treatments, post-progression treatments, adverse events, physician and nurse visits, scans and tests, and palliative care. Resource utilization inputs were derived from a US claims database analysis. Additionally, a 3% annual discount rate was applied to costs, and the robustness of the model results was tested by conducting sensitivity analyses, including those on dosing scheme and post-progression treatment costs.

Results:

Base case results demonstrated that patients treated with everolimus cost an average of $12,985 (11%) less over their lifetimes than patients treated with axitinib. The primary difference in costs was related to active treatment, which was largely driven by axitinib’s higher dose intensity. Results remained consistent across sensitivity analyses for AE and PD treatment costs, as well as dose intensity and discount rates.

Conclusion:

The results suggest that everolimus likely leads to lower lifetime costs than axitinib for sunitinib-refractory advanced RCC patients in the US.     

Innovation and imitation as an interactive process

This article deals with the problem of the coexistence of innovators and imitators in a competitive market. The study proposes a model of innovation and diffusion of productive knowledge as an interactive process between innovators and imitators under conditions of dynamic uncertainty. The process can be modelled as a Stackelberg game, where the innovator acts as a leader in choosing whether to share knowledge or set up private protection and the imitator as a follower in choosing when becoming active. Under these conditions, activation thresholds are derived for both innovators and imitators. If protection policies are effective, the imitator can be trapped into an inaction region by the innovator. Thus, there will be two regimes without and with diffusion, according to whether the inaction region is enacted or not. Under these conditions, discovery and diffusion appear to be dynamic complements, as a higher speed of activation of innovating firms is favoured by a higher level of imitation and a higher speed of activation of imitating firms is favoured by a higher level of discoveries. In order to explore some of the quantitative implications of these results, the paper also proposes an application of the model to four European countries.     

Youth idleness in Eastern Europe and Central Asia before and after the 2009 crisis

ABSTRACT

Youth idleness is a significant problem in many countries, including in Eastern Europe and Central Asia (ECA) where it has rarely been studied. Labour market and education policies need to be based on a strong knowledge base on the Not in Employment, Education or Training (NEET) population. This paper uses micro-level data from the early 2000s through 2011 to fill knowledge gaps. NEET rates for different age intervals, gender and educational attainment are investigated for the ECA region and countries within. We find that the NEET rate in ECA was declining prior to the 2009 crisis and increased afterwards, with a more pronounced impact on males. Our findings reveal considerable heterogeneity across countries likely due to varying demographics, labour market conditions and education policies. Policies on idle youths in this region need to be tailored to varying national situations. This paper also suggests pathways for future research.

Abbreviations: NEET: Not in Employment, Education or Training; ECA: Eastern Europe and Central Asia     

Thinking inside the box: A participatory, computer-assisted approach to scenario discovery

Scenarios provide a commonly used and intuitively appealing means to communicate and characterize uncertainty in many decision support applications, but can fall short of their potential especially when used in broad public debates among participants with diverse interests and values. This paper describes a new approach to participatory, computer-assisted scenario development that we call scenario discovery, which aims to address these challenges. The approach defines scenarios as a set of plausible future states of the world that represent vulnerabilities of proposed policies, that is, cases where a policy fails to meet its performance goals. Scenario discovery characterizes such sets by helping users to apply statistical or data-mining algorithms to databases of simulation-model-generated results in order to identify easy-to-interpret combinations of uncertain model input parameters that are highly predictive of these policy-relevant cases. The approach has already proved successful in several high impact policy studies. This paper systematically describes the scenario discovery concept and its implementation, presents statistical tests to evaluate the resulting scenarios, and demonstrates the approach on an example policy problem involving the efficacy of a proposed U.S. renewable energy standard. The paper also describes how scenario discovery appears to address several outstanding challenges faced when applying traditional scenario approaches in contentious public debates.     

An empirical investigation of complexity and its management in new product development

New product development (NPD) programmes are increasingly complex and difficult to manage. The consequences of poorly managed development complexity can be highly visible and even lead to project failure. To effectively screen new product proposals and manage NPD projects more efficiently, NPD teams need to be equipped with the capacity to identify development complexity and possess the knowledge to manage it. Unfortunately, there have been few studies which specifically illuminate the challenges and experiences product developers face in dealing with complexity. Our research attempts to help fill this gap. Our study is based on exploratory field interviews with 32 project leaders and team members. We first focus on identifying the specific complexity issues encountered in NPD. We then identify what NPD teams actually do to minimise the potential adverse consequences of complexity. Finally, we examine whether a company's development process reduces or increases the complexities NPD teams encounter. Based upon our research, we present our results and conclude by offering several recommendations for complexity management as well as suggestions for future research.     

Knowledge-intensive entrepreneurship: sectoral patterns in a sample of European high-tech firms

This paper studies the relative importance of prior knowledge and resources available to a startup at the time of its founding across technologies. Our analysis is based on a survey submitted to the founders of new innovative ventures patenting in the biotech, electronics and medical devices technologies. Our findings show that pre-entry knowledge about customers’ needs and characteristics, about the technology and about potential suppliers and competitors differentially affect the technological and market entrepreneurial choice of the surveyed firms. These results suggest the existence of patterns of entrepreneurial activities that are technology-specific.     

Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern

Today’s companies still rely heavily on expert knowledge rather than quantitative data with a systematic approach to effectively identify and choose Research and Development (R&D) partners. It is advantageous to identify and select potential R&D partners using a Problem & Solution (P&S) pattern. This paper presents a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. First, we choose a thematic dataset that contains problems and quantitative data with relative topic terms. Then, we extract Subject-Action-Object semantic structures in a P&S pattern from the dataset, and identify various solutions to a technical problem, with each as a subject. In addition, we provide correlation mapping to visualise the text characters and identify R&D partners. Finally, we validate the proposed method through a case study of the dye-sensitized solar cells sector.     

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.