A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows

M.J. Beynon, Paul Jones, D. Pickernell, G. Packham

    Research output: Contribution to journalArticle

    5 Citations (Scopus)
    19 Downloads (Pure)

    Abstract

    This study demonstrates a novel form of business analytics, respecting the quality of the data available (allowing incompleteness in the data set), as well as engaging with the uncertainty in the considered outcome variable (inclusive of Don’t Know (DK) responses). The analysis employs the NCaRBS technique, based on the Dempster–Shafer theory of evidence, to investigate the relationship between Small and Medium-sized Enterprise (SME) characteristics and whether they intended to undertake future innovation. The allowed outcome response for intended innovation was either, Yes, No and DK, all of which are considered pertinent responses in this analysis. An additional consequence of the use of the NCaRBS technique is the ability to analyse an incomplete data set, with missing values in the characteristic variables considered, without the need to manage their presence. From a soft computing perspective, this study demonstrates just how exciting the business analytics field of study can be in terms of pushing the bounds of the ability to handle real ‘incomplete’ business data which has real, and sometimes uncertain, outcomes. Further, the findings also inform how different notions of ignorance in evidence are accounted for in such analysis.
    Original languageEnglish
    Pages (from-to)97–112
    JournalOmega
    Volume59
    Issue numberA
    Early online date12 Jun 2015
    DOIs
    Publication statusPublished - Mar 2016

    Fingerprint

    Innovation
    Small and medium-sized enterprises
    Ignorance
    Uncertainty
    Soft computing
    Missing values
    Incompleteness
    Incomplete data

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, [VOL 59, ISSUE A, (2015)] DOI: 10.1016/j.omega.2015.04.018

    © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Keywords

    • SME
    • NCaRBS
    • Don’t Know
    • Innovation

    Cite this

    Beynon, M. J., Jones, P., Pickernell, D., & Packham, G. (2016). A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows. Omega, 59(A), 97–112. https://doi.org/10.1016/j.omega.2015.04.018

    A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows. / Beynon, M.J.; Jones, Paul; Pickernell, D.; Packham, G.

    In: Omega, Vol. 59, No. A, 03.2016, p. 97–112.

    Research output: Contribution to journalArticle

    Beynon, MJ, Jones, P, Pickernell, D & Packham, G 2016, 'A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows' Omega, vol. 59, no. A, pp. 97–112. https://doi.org/10.1016/j.omega.2015.04.018
    Beynon, M.J. ; Jones, Paul ; Pickernell, D. ; Packham, G. / A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows. In: Omega. 2016 ; Vol. 59, No. A. pp. 97–112.
    @article{4b71712f9ea248258dbf3a3ab1cec1c9,
    title = "A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows",
    abstract = "This study demonstrates a novel form of business analytics, respecting the quality of the data available (allowing incompleteness in the data set), as well as engaging with the uncertainty in the considered outcome variable (inclusive of Don’t Know (DK) responses). The analysis employs the NCaRBS technique, based on the Dempster–Shafer theory of evidence, to investigate the relationship between Small and Medium-sized Enterprise (SME) characteristics and whether they intended to undertake future innovation. The allowed outcome response for intended innovation was either, Yes, No and DK, all of which are considered pertinent responses in this analysis. An additional consequence of the use of the NCaRBS technique is the ability to analyse an incomplete data set, with missing values in the characteristic variables considered, without the need to manage their presence. From a soft computing perspective, this study demonstrates just how exciting the business analytics field of study can be in terms of pushing the bounds of the ability to handle real ‘incomplete’ business data which has real, and sometimes uncertain, outcomes. Further, the findings also inform how different notions of ignorance in evidence are accounted for in such analysis.",
    keywords = "SME, NCaRBS, Don’t Know, Innovation",
    author = "M.J. Beynon and Paul Jones and D. Pickernell and G. Packham",
    note = "NOTICE: this is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, [VOL 59, ISSUE A, (2015)] DOI: 10.1016/j.omega.2015.04.018 {\circledC} 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",
    year = "2016",
    month = "3",
    doi = "10.1016/j.omega.2015.04.018",
    language = "English",
    volume = "59",
    pages = "97–112",
    journal = "Omega",
    issn = "0305-0483",
    publisher = "Elsevier",
    number = "A",

    }

    TY - JOUR

    T1 - A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows

    AU - Beynon, M.J.

    AU - Jones, Paul

    AU - Pickernell, D.

    AU - Packham, G.

    N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Omega. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Omega, [VOL 59, ISSUE A, (2015)] DOI: 10.1016/j.omega.2015.04.018 © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    PY - 2016/3

    Y1 - 2016/3

    N2 - This study demonstrates a novel form of business analytics, respecting the quality of the data available (allowing incompleteness in the data set), as well as engaging with the uncertainty in the considered outcome variable (inclusive of Don’t Know (DK) responses). The analysis employs the NCaRBS technique, based on the Dempster–Shafer theory of evidence, to investigate the relationship between Small and Medium-sized Enterprise (SME) characteristics and whether they intended to undertake future innovation. The allowed outcome response for intended innovation was either, Yes, No and DK, all of which are considered pertinent responses in this analysis. An additional consequence of the use of the NCaRBS technique is the ability to analyse an incomplete data set, with missing values in the characteristic variables considered, without the need to manage their presence. From a soft computing perspective, this study demonstrates just how exciting the business analytics field of study can be in terms of pushing the bounds of the ability to handle real ‘incomplete’ business data which has real, and sometimes uncertain, outcomes. Further, the findings also inform how different notions of ignorance in evidence are accounted for in such analysis.

    AB - This study demonstrates a novel form of business analytics, respecting the quality of the data available (allowing incompleteness in the data set), as well as engaging with the uncertainty in the considered outcome variable (inclusive of Don’t Know (DK) responses). The analysis employs the NCaRBS technique, based on the Dempster–Shafer theory of evidence, to investigate the relationship between Small and Medium-sized Enterprise (SME) characteristics and whether they intended to undertake future innovation. The allowed outcome response for intended innovation was either, Yes, No and DK, all of which are considered pertinent responses in this analysis. An additional consequence of the use of the NCaRBS technique is the ability to analyse an incomplete data set, with missing values in the characteristic variables considered, without the need to manage their presence. From a soft computing perspective, this study demonstrates just how exciting the business analytics field of study can be in terms of pushing the bounds of the ability to handle real ‘incomplete’ business data which has real, and sometimes uncertain, outcomes. Further, the findings also inform how different notions of ignorance in evidence are accounted for in such analysis.

    KW - SME

    KW - NCaRBS

    KW - Don’t Know

    KW - Innovation

    U2 - 10.1016/j.omega.2015.04.018

    DO - 10.1016/j.omega.2015.04.018

    M3 - Article

    VL - 59

    SP - 97

    EP - 112

    JO - Omega

    JF - Omega

    SN - 0305-0483

    IS - A

    ER -