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

David Pickernell Orcid Logo, Malcolm J Beynon, Paul Jones, David Pickernell, Gary Packham, Paul Jones Orcid Logo

Omega, Volume: 59, Pages: 97 - 112

Swansea University Authors: David Pickernell Orcid Logo, Paul Jones Orcid Logo

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DOI (Published version): 10.1016/j.omega.2015.04.018

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 te...

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Published in: Omega
ISSN: 0305-0483
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa43284
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spelling 2021-01-19T10:55:53.7469394 v2 43284 2018-08-13 A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows 913bd73da00d7df4f5038f6f144b235e 0000-0003-0912-095X David Pickernell David Pickernell true false 21e2660aaa102fe36fc981880dd9e082 0000-0003-0417-9143 Paul Jones Paul Jones true false 2018-08-13 BBU 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. Journal Article Omega 59 97 112 0305-0483 SME; NCaRBS; Don’t Know; Innovation 1 3 2016 2016-03-01 10.1016/j.omega.2015.04.018 http://dx.doi.org/10.1016/j.omega.2015.04.018 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2021-01-19T10:55:53.7469394 2018-08-13T11:01:21.3964742 Faculty of Humanities and Social Sciences School of Management - Business Management David Pickernell 0000-0003-0912-095X 1 Malcolm J Beynon 2 Paul Jones 3 David Pickernell 4 Gary Packham 5 Paul Jones 0000-0003-0417-9143 6 43284__17597__85f6d1ea0c234db2881379c3f30f176f.pdf 43284.pdf 2020-06-29T14:07:40.0279123 Output 1318596 application/pdf Accepted Manuscript true Licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
spellingShingle A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
David Pickernell
Paul Jones
title_short A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
title_full A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
title_fullStr A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
title_full_unstemmed A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
title_sort A NCaRBS analysis of SME intended innovation: Learning about the Don’t Knows
author_id_str_mv 913bd73da00d7df4f5038f6f144b235e
21e2660aaa102fe36fc981880dd9e082
author_id_fullname_str_mv 913bd73da00d7df4f5038f6f144b235e_***_David Pickernell
21e2660aaa102fe36fc981880dd9e082_***_Paul Jones
author David Pickernell
Paul Jones
author2 David Pickernell
Malcolm J Beynon
Paul Jones
David Pickernell
Gary Packham
Paul Jones
format Journal article
container_title Omega
container_volume 59
container_start_page 97
publishDate 2016
institution Swansea University
issn 0305-0483
doi_str_mv 10.1016/j.omega.2015.04.018
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
url http://dx.doi.org/10.1016/j.omega.2015.04.018
document_store_str 1
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description 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.
published_date 2016-03-01T03:54:33Z
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score 11.012924

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