Knowledge Generation Process Research Articles

Building Industries by Building Knowledge: Uncertainty Reduction over Industry Milestones

Scholars have long been interested in new industry emergence, highlighting that it could often be impeded by uncertainty across four dimensions: technology, demand, ecosystem, and institutions. Building on the insight that uncertainty stems from partial knowledge, we develop a conceptual framework that utilizes a temporal and a process perspective for knowledge generation and aggregation. Industry emergence through key milestones—commercialization, firm takeoff, and sales takeoff—is made possible by knowledge-generation processes by diverse actors within and across uncertainty dimensions, and knowledge-aggregation processes with appending, selecting, and collective mechanisms at play. Our conceptual framework integrates across disciplinary perspectives to shed light on both the development of an industry poised for future growth, and the bottlenecks that may delay or even impede industries from emergence.

Designing a program evaluation for a medical-dental service for adults with autism and intellectual disabilities using the RE-AIM framework.

IntroductionRobust evaluation of service models can improve the quality and efficiency of care while articulating the models for potential replication. Even though it is an essential part of learning health systems, evaluations that benchmark and sustain models serving adults with developmental disabilities are lacking, impeding pilot programs from becoming official care pathways. Here, we describe the development of a program evaluation for a specialized medical‐dental community clinic serving adults with autism and intellectual disabilities in Montreal, Canada.MethodUsing a Participatory Action‐oriented approach, researchers and staff co‐designed an evaluation for a primary care service for this population. We performed an evaluability assessment to identify the processes and outcomes that were feasible to capture and elicited perspectives at both clinical and health system levels. The RE‐AIM framework was used to categorize and select tools to capture data elements that would inform practice at the clinic.ResultsWe detail the process of conceptualizing the evaluation framework and operationalizing the domains using a mixed‐methods approach. Our experience demonstrated (1) the utility of a comprehensive framework that captures contextual factors in addition to clinical outcomes, (2) the need for validated measures that are not cumbersome for everyday practice, (3) the importance of understanding the functional needs of the organization and building a sustainable data infrastructure that addresses those needs, and (4) the need to commit to an evolving, “living” evaluation in a dynamic health system.ConclusionsEvaluation employing rigorous patient‐centered and systems‐relevant metrics can help organizations effectively implement and continuously improve service models. Using an established framework and a collaborative approach provides an important blueprint for a program evaluation in a learning health system. This work provides insight into the process of integrating care for vulnerable populations with chronic conditions in health care systems and integrated knowledge generation processes between research and health systems.

Determinants of innovation performance

Purpose The purpose of this paper is to analyse the relationship between organisational learning capabilities (OLCs) and innovation performance (IP) in organisational contexts where knowledge creation and exploitation are the business’ main source of competitive advantage. Design/methodology/approach The study hypotheses are tested on a unique sample of 74 high-performance businesses operating in knowledge-intensive business services (KIBS) industries and non-knowledge intensive sectors in 2016. The study employs a sequential deductive triangulation analysis (QUAN → qual) based on linear regression models and qualitative interviews. Findings The results indicate that OLCs positively impact IP. Additionally, the findings reveal that this relationship is stronger in organisations where knowledge creation and exploitation constitute the main source of competitive advantage, namely, KIBS firms. Research limitations/implications This paper offers insights into how the innovation outcomes of OLCs are heterogeneous across industries. This study contributes to a better understanding of the conditions under which the effects of developing learning-enhancing strategies occur in businesses operating in different industries. Practical implications Both knowledge generation and exploitation processes are critical for business success, and OLCs play a decisive role in this process. In this sense, the results suggest that managers need to turn their attention to the characteristics of business operations when considering the development of strategies aimed at enhancing OLCs. Originality/value The paper further explores the influence of OLCs on IP by analysing how organisational learning strategies interact with relevant organisational characteristics – that are linked here to the exploitation of knowledge-based resources – to yield superior IP.

Model detecting learning styles with artificial neural network

Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach.

A long-term comparative analysis of the direction and congruence of technological change

AbstractTechnological congruence identifies the coherence between the direction of technological change and input costs in an economy and measures its effects on Total Factor Productivity (TFP). The search for technological congruence is constrained by the systemic characteristics of the countries where it takes place with special attention to the types of industrial relations and the role of the labor force in the accumulation of technological knowledge. Constrained technological congruence enables to interpret the heterogeneous long-term evidence of five advanced countries. The analysis confirms that the direction of technological change and its effects are heterogeneous across countries and time and reflect the specific conditions of their internal and external labor markets and of their knowledge generation processes.

Building Industries by Building Knowledge: Uncertainty Reduction through Experimentation, Knowledge Release & Knowledge Acquisition

Scholars have long been interested in new industry emergence, highlighting that it could often be impeded by uncertainty across four dimensions: technology, demand, ecosystem, and institutions. Building on the insight that uncertainty stems from partial knowledge, we develop a conceptual framework that utilizes a temporal and a process perspective for knowledge generation and aggregation. Industry emergence through key milestones — commercialization, firm takeoff and sales takeoff — is made possible by knowledge generation processes by diverse actors within and across uncertainty dimensions, and knowledge aggregation processes with appending, selecting, and collective mechanisms at play. Our conceptual framework integrates across disciplinary perspectives to shed light on both the development of an industry poised for future growth, and the bottlenecks that may delay or even impede industries from emergence.

A logic-based relational learning approach to relation extraction: The OntoILPER system

Relation Extraction (RE), the task of detecting and characterizing semantic relations between entities in text, has gained much importance in the last two decades, mainly in the biomedical domain. Many papers have been published on Relation Extraction using supervised machine learning techniques. Most of these techniques rely on statistical methods, such as feature-based and tree-kernels-based methods. Such statistical learning techniques are usually based on a propositional hypothesis space for representing examples, i.e., they employ an attribute–value representation of features. This kind of representation has some drawbacks, particularly in the extraction of complex relations which demand more contextual information about the involving instances, i.e., it is not able to effectively capture structural information from parse trees without loss of information. In this work, we present OntoILPER, a logic-based relational learning approach to Relation Extraction that uses Inductive Logic Programming for generating extraction models in the form of symbolic extraction rules. OntoILPER takes profit of a rich relational representation of examples, which can alleviate the aforementioned drawbacks. The proposed relational approach seems to be more suitable for Relation Extraction than statistical ones for several reasons that we argue. Moreover, OntoILPER uses a domain ontology that guides the background knowledge generation process and is used for storing the extracted relation instances. The induced extraction rules were evaluated on three protein–protein interaction datasets from the biomedical domain. The performance of OntoILPER extraction models was compared with other state-of-the-art RE systems. The encouraging results seem to demonstrate the effectiveness of the proposed solution.

Integrative LHS for precision medicine research: A shared NIH and Taiwan CIMS experience.

IntroductionPrecision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease‐based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care.MethodsThe LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care.ResultsThe implementation of a shared‐design, full‐cycle LHS with an enhanced CIMS, combined with hospital‐based real‐world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high‐quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full‐cycle LHS for TCVGH's dementia research to improve the care of elderly patients.ConclusionsKnowledge generation requires high‐quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated.

Linking social–ecological knowledge with rural communities in Mexico: lessons and challenges toward sustainability

AbstractFor decades, ecologists have stressed the need to establish and strengthen the exchange of knowledge between scientists and different stakeholders in order to construct better management practices that mitigate or provide solutions to environmental problems. Using a framework that links knowledge generation with social appropriation processes, we present an 18 yr’ experience of social–ecological research and outreach activities carried out in a place where scientists have generated a great amount of knowledge: the Chamela Biological Research Station. This site was created in 1971 as a place for research, teaching, and to conserve tropical dry forests in Mexico, and it has played a relevant role in the advancement of tropical science as well as in undergraduate and graduate education. For many years, researchers and students from Mexico and many other countries have used the facility that is part of the National Autonomous University of Mexico. Here, we measured the extent to which social–ecological knowledge responds to information needs of rural people and found that very little of it has been shared with the local inhabitants. Local needs regarding pasture and forest management are covered by local people's own knowledge. In order to help bridging the gap between social–ecological knowledge and rural inhabitants, we describe and evaluate ongoing communication and educational projects. We propose a model that shows how knowledge generation, exchange, and use processes can be linked to facilitate the work of scientists and their institutions. Our aim is that the experience contributes to promote and strengthen linkage processes between scientists and rural people around the world in order to better transit toward sustainable social–ecological systems.

Regional Innovation Systems in Policy Laboratories

Innovation policy and business strategy often expect that investing in private and public research and development will immediately produce a flow of products and processes with high commercial and social returns. Policymakers and managers implicitly follow the logic underlying most linear innovation models assuming a well-defined and uni-directional relationship between R&D spending as input and innovation rents as output of the innovation process. Modern innovation economics dismisses the simplified approximation of knowledge by R&D investment and, instead, considers complex knowledge generation and diffusion processes in innovation networks. From this angle, the disappointing performance of traditional approaches is traced back to strong limits of conventional steering, control, and policy instruments. In this paper, we show that the new view of knowledge generation and diffusion in innovation networks allows for an alternative and has led to systemic approaches in innovation analyses. Combined with computational approaches like agent-based modeling, this new view enables today innovative tools in policy consulting. Using the example of regional innovation policy, we introduce a policy laboratory in which innovation processes can be analyzed in depth to see the impact of different innovation policy instruments in-silico. This ex-ante evaluation helps considerably to improve the understanding of innovation processes and with it the performance of innovation policy.

Research landscape of the BRICS countries: current trends in research output, thematic structures of publications, and the relative influence of partners

This article provides the comprehensive analysis of research landscape in BRICS countries in different aspects: level of their publication activity and contribution to the global process of knowledge generation; thematic structure of publications of BRICS countries, their scientific specialization; quality of articles measured by citation indicators; similarity of thematic structures of publications; international research collaboration profiles; and finally closeness and relative influence of each country in intra-BRICS collaborating pairs. Special sections of the article are devoted to review of the literature, which discusses the main articles on various aspects of BRICS countries’ publication activity and their international research collaboration and to description the database and set of various bibliometric indicators, used in our analysis. We use Scopus database and the timespan of our research covers 2001–2015 years that allows us to identify key points in development of research landscapes of BRICS countries. The empirical part of the article is structured as follows. First, we provide the overview of publication activity and thematic structure of BRICS countries. Second, we measure the closeness of thematic structure of publications versus each other and versus general research agenda in the world using different indices of structural difference. Third part is the analysis of research collaboration with clear visualization of its thematic structure, identification of potential areas of collaboration and detection the influential countries in intra-BRICS collaborating pairs. We use wide range of bibliometric indicators: citation indicators; indices of structural difference; indicators of scientific collaboration. We apply different approaches to visualise data in form of different illustrative graphs including colored tables to do our research easy-to-read-and understand. The results of the study may be of interest to decision makers in determining the conscientious research story of the BRICS countries and priorities setting for multilateral scientific and technological cooperation, as well as for researchers dealing with relevant problems.

Assessing data analysis performance in research contexts: An experiment on accuracy, efficiency, productivity and researchers’ satisfaction

Any knowledge generation process involves raw data comprehension, evaluation and inferential reasoning. These practices, common to different disciplines, are known as data analysis, and represent the most important set of activities in research contexts. Researchers use data analysis software methods and tools for generating new knowledge in their daily data analysis. In recent years, data analysis software has been incorporating explicit references in modelling of cognitive processes, in order to improve the assistance offered in data analysis tasks. However, data analysis software commercial suites are still resisting this inclusion, and there is little empirical work done in knowing more about how cognitive aspects inclusion in software helps researchers in analyzing data.In this paper, we evaluate the impact produced by the explicit inclusion of cognitive processes in the assistance logic of software tools design and development. We conducted an empirical experiment comparing data analysis performance using traditional software versus data analysis performance using software-assistance tools which incorporate cognitive processes in their design. The experiment is designed in terms of accuracy, efficiency, productivity and user satisfaction during the data analysis made by researchers. It allowed us to find some clear benefits of the cognitive inclusion in the software designed for research contexts, with statistically significant differences in terms of accuracy, productivity and researcher's satisfaction in support of this explicit inclusion, although some efficiency weaknesses are detected. We also discuss the implications of these results for the priority of cognitive inclusion in the software tools design for research contexts data analysis.

Who produces knowledge? Transforming undergraduate students’ views of science through participatory action research

AbstractUndergraduate research experiences (UREs) that include interdisciplinary, engagement‐oriented, and collaborative experiences have the potential to benefit students in unique ways. Through these innovative experiences, students are able to see the social impact and importance of their work—a factor known to strengthen the academic and career persistence of students traditionally underrepresented in science, technology, engineering, and mathematics (STEM) fields. In the present study, students assumed simultaneous roles as students, interdisciplinary researchers, and community liaisons as they facilitated a community‐engaged participatory research project. Using interpretive phenomenological analysis, we explore students’ evolving perspectives of knowledge generation and social change processes before, during, and after the URE. Findings emphasize participants’ altered views of the respective roles of academic researchers and community members in generating knowledge, the value of interdisciplinary research, and the potential of innovative UREs to foster positive change—in academic and community contexts.

International Organizations as ‘Experts’ on Indicators and Measurements: Understanding the Politics of OECD’s Technocratization of Innovation

AbstractInternational Organizations (IOs) act as powerful actors in construction and constitution of knowledge. This role becomes even more critical as new agendas emerge for global governance in a multi-actor world, making the process of knowledge generation and dissemination even more contentious. One of the tools to exercise such organizational power is through rankings and ratings. These indicators, designed scientifically using expert knowledge, are increasingly being used as tools of statecraft. As good governance synonymizes good business, a technocratic lens has become a preferred practice of present day policymaking. This article attempts to exploreIOs’ role as authoritative knowledge actors, designing indicators for governmentality and policy advocacy. It particularly looks atOECD’s ‘utilitarian’ measurement of innovation based on a technocratic approach.

Design Social | Technology . Activism . Anti-Social

The emergence of social media and the networked society, as exemplified by The Internet of Things (IoT) (Ashton 1999), generates enormous potential that repositions design as a means to synthesise emerging social complexities into new constellations. One of the ways design in this context becomes reconfigured is as the dynamic interconnections of people, practices, and artefacts. The propinquity of this lineage leads to relational rather than objectified forms of design. Such approaches tend to be process-driven rather than outcome-based, and activate design’s potential within both knowledge generation and knowledge transfer processes. This in itself can be understood as “information” or as designbefore- design and design-after-design that provides pathways for innovation in the development of new processes, systems, networked, and relational outcomes (Deforge and Cullars 1990).

Benennen – Verorten – Abgrenzen: Sprachliche Praktiken zur Konstitution neuer Ansätze als Teil der Germanistischen Linguistik

Abstract The process of scientific knowledge generation is accompanied by an on-going transformation of the corresponding scientific discipline. Scientists develop new theories and methods and discard old ones. Thus, scientific disciplines can be considered as a result of negotiations within the scientific community. The paper focuses on the following questions: Which theories, methods and sub-disciplines do scientists regard as part of a scientific discipline? How do scientists label and define these theories, methods and sub-disciplines? Which linguistic practices do scientists apply to authorize new scientific approaches? With recourse to “The Genesis and Development of a Scientific Fact” by Ludwik Fleck as well as “Epistemic cultures” and “The manufacture of knowledge” by Karin Knorr-Cetina, the paper shows why it is reasonable to see science as a social practice. It also discusses the role language plays when linguists try to establish new approaches and focuses on the linguistic practices of labeling, locating and defininig.

Nursing and midwifery use, perceptions and barriers to evidence-based practice: a cross-sectional survey.

The study aimed to explore how nurses and midwives obtain, use and embed evidence in everyday practice. The study design was cross-sectional survey method. The setting was one local health district in metropolitan Sydney, Australia. All nurses and midwives working within the local health district, with access to an email account, were invited to participate in the study. An online survey questionnaire was distributed to explore how evidence is obtained, used and embedded within the clinical setting. The data were analysed using descriptive statistics (frequency and percentages). Pearson's Chi-square tests were used for comparison between groups. There were 204 survey respondents. The findings identified that the majority (n = 157; 76.96%) of respondents obtained evidence primarily from clinical practice guidelines. The majority (n = 149; 73.04%) of respondents reportedly searched databases and used evidence related to general clinical practice. There was a statistical difference (χ = 17.069; df = 8; P = 0.029) when comparing leadership positions and other registered practitioner groups in the frequency of searching for evidence. Most respondents (n = 138; 67.65%) were confident in their ability to change practice on the basis of available evidence. Thematic analysis identified four barriers to sustaining evidence-based practice, which included: the need for time; the need for organizational and management support; the need for educational opportunities and challenges to accessing evidence. The study provided an understanding of how nurses and midwives obtain, use and embed evidence into everyday practice. More importantly, the role of leadership is significant to support a process of knowledge generation, research translation and the implementation of evidence into clinical settings.

Participatory Research and Empowerment: A Conceptual Revisit of the Debate on Alternative Social Science Research

After the 1970s, there has been a growing emphasis on participatory research aimed at capturing people’s lived realities of everyday lives. The proponents of participatory research (also called alternative research) build on a critique of what is called “extractive,” top-down, and so-called objective empirical research of positivist kind. In contrast, alternative research method embeds research with empowerment and regards survey based conventional research as instrumental. This paper first introduces basic premises of alternative research method together with its philosophical underpinning. Drawing arguments from Robert Chambers the following section compares and contrasts the conventional and alternative research methods. Further, a paradigm shift in social science research in terms of reversals of frame, reversals of modes, reversal of relation and reversal of power is dealt. The final section draws a conclusion that compared to survey based, “objective,” conventional research method, participatory researches are superior in facilitating knowledge generation process, eventually empowering the people.Dhaulagiri Journal of Sociology and Anthropology Vol.11, 2017; 115-129

Citizen science for hydrological risk reduction and resilience building

In disaster risk management (DRM), an emerging shift has been noted from broad‐scale, top‐down assessments toward more participatory, community‐based, bottom‐up approaches. Arguably, nonscientist local stakeholders have always played an important role in knowledge risk management and resilience building within a hydrological context, such as flood response and drought alleviation. However, rapidly developing information and communication technologies such as the Internet, smartphones, and social media have already demonstrated their sizeable potential to make knowledge creation more multidirectional, decentralized, diverse, and inclusive. Combined with technologies for robust and low‐cost sensor networks, a ‘citizen science’ approach has recently emerged as a promising direction in the provision of extensive, real‐time information for risk management. Such projects work best when there is community buy‐in, when their purpose(s) are clearly defined at the outset, and when the motivations and skillsets of all participants and stakeholders are well understood. They have great potential to enhance knowledge creation, not only for data collection, but also for analysis or interpretation. In addition, they can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. Here, we review the state‐of‐the‐art of citizen science within the context of hydrological risk reduction and resilience building. Particularly when embedded within a polycentric approach toward risk governance, we argue that citizen science could complement more traditional knowledge generation practices, and also enhance innovation, adaptation, multidirectional information provision, risk management, and local resilience building. WIREs Water 2018, 5:e1262. doi: 10.1002/wat2.1262This article is categorized under: Engineering Water > Planning Water Science of Water > Water Extremes

Nature-based care opportunities and barriers in oncology contexts: a modified international e-Delphi survey

ObjectiveTo develop recommendations regarding opportunities and barriers for nature-based care in oncology contexts using a structured knowledge generation process involving relevant healthcare and design experts.DesignFour-round modified electronic Delphi study. Oncology...