Evidence-based modelling of strategic fit: An introduction to RCaRBS

Beynon, Malcolm James; Andrews, Rhys; Boyne, George Alexander

doi:10.1016/j.ejor.2010.05.024

Cited by 9 publications

(20 citation statements)

References 50 publications

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“…In this subsection we report an RCaRBS analysis on the business training and performance dataset [see Beynon et al (2010a) for details of the technical aspects of the RCaRBS technique]. The type of data represented in table 4 is analysed without any external management of the '-' TM not-used values present.…”

Section: Rcarbsmentioning

confidence: 99%

“…Figure 3 shows the simplex plot stage of the RCaRBS-based modelling of the SMEs' increased business performance. Each circle shown in the simplex plot represents the simplex coordinate form of an SME's fi nal business performance body of evidence (BOE), m j (), found from the combination of its series of training-met BOEs, containing the evidence from the T1, T2, … , T9 questions [see Beynon et al (2010a) (see table 4 and surrounding discussion). The fewer the number of TMs utilised by an SME, the more the 'not-used' values affect the resultant business performance BOE (the higher up in the simplex plot a business performance BOE would be positioned).…”

Section: Rcarbsmentioning

confidence: 99%

“…The type of data represented in table 4 is analysed without any external management of the '-' TM not-used values present. The RCaRBS technique through its methodology of uncertain reasoning (Beynon et al, 2010a), can accommodate the presence of a '-' TM not-used value without needing to externally manage the data. Consequently, it can allow a multivariate-regression-type analysis to take place on the sparse dataset.…”

Section: Rcarbsmentioning

confidence: 99%

“…The RCaRBS technique was introduced by Beynon et al (2010a), as a development on the Classifi cation and Ranking Belief Simplex (CaRBS) technique (Beynon, 2005a;2005b), to undertake multivariate-regression-type analyses. The previous employment of RCaRBS, and CaRBS, include in the areas of medicine Jones et al, 2006), animal biology (Beynon and Buchanan, 2004), marketing (Beynon et al, 2010b), management (Beynon and Andrews, 2011;Beynon et al, 2010a) and e-learning (Jones and Beynon, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the Impact of Different Training Methods on SME Business Performance

Jones

Beynon

Pickernell

et al. 2013

Environ Plann C Gov Policy

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With this study we evaluate the impact of diff erent training methods (TMs) on small and medium-enterprises' (SMEs') business performances, utilising a dataset of 3521. We consider the relationship between the satisfaction SMEs have towards their employees' training needs being met by a diverse range of TMs and the levels of impact the training has had on business performance. To investigate both the interdependence and dependence of satisfaction levels of training needs met with business performance, we undertake bivariate correlations and multivariate regression-type analyses (using Regression-type Classifi cation and Ranking Belief Simplex). The results presented off er novel insights into the relationships between TMs and business performance.

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Section: Rcarbsmentioning

confidence: 99%

Section: Rcarbsmentioning

confidence: 99%

Section: Rcarbsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the Impact of Different Training Methods on SME Business Performance

Jones

Beynon

Pickernell

et al. 2013

Environ Plann C Gov Policy

View full text Add to dashboard Cite

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“…The re-sampling undertaken here was based on performing multiple runs of the NCaRBS technique using identified in-samples and out-of-samples of cases. Here, 10 runs were performed, in each run 90% of cases (3,846) were used as the in-sample on which the NCaRBS was run to configure a model, and 10% of cases (427) were used an out-of-sample. The contribution of the individual variables to the social capital blends following the re-sampling procedure can be illustrated graphically as for the one-off analysis using all of the data (see Fig.…”

Section: Validation Analysis Of Ncarbs Results (Using Re-sampling)mentioning

confidence: 99%

Evidence-Based Modelling of Organizational Social Capital with Incomplete Data: An NCaRBS Analysis

Beynon

Andrews

2014

Belief Functions: Theory and Applications

Self Cite

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Abstract. Organizational social capital is critical to effective organizational functioning. Yet, different aspects of social capital are likely to be present to varying degrees within any given organization. In this study, alternative blends of structural, relational and cognitive social capital are modelled using a range of key organizational variables drawn from an incomplete dataset. A novel evidence-based approach to the ambiguous classification of objects (N-state Classification and Ranking Belief Simplex or NCaRBS) is used for the analysis. NCaRBS is uniquely able to capture the full range of ambiguity in the antecedents and effects of social capital, and to do so by incorporating incomplete data without recourse to the external management of the missing values. The study therefore illustrates the multi-faceted potential of analytical techniques based on uncertain reasoning, using the Dempster-Shafer theory of evidence methodology.

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The Application of NCaRBS to the Trendelenburg Test and Total Hip Arthroplasty Outcome

2015

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This paper compares the frontal plane hip function of subject’s known to have had hip arthroplasty via either the lateral (LA) or posterior (PA) surgical approaches and a group of subjects associated with no pathology (NP). This is investigated through the Trendelenburg test using 3D motion analysis and classification. Here, a recent development on the Classification and Ranking Belief Simplex (CaRBS) technique, able to undertake n-state classification, so termed NCaRBS is employed. The relationship between post-operative hip function measured during a Trendelenburg Test using three patient characteristics (pelvic obliquity, frontal plane hip moment and frontal plane hip power) of LA, PA and NP subjects are modelled together. Using these characteristics, the classification accuracy was 93.75% for NP, 57.14% for LA, 38.46% for PA. There was a clear distinction between NP and post-surgical function. 3/6 LA subjects and 6/8 PA subjects were misclassified as having NP function, implying that greater function is restored following the PA to surgery. NCaRBS achieved a higher accuracy (65.116%) than through a linear discriminant analysis (48.837%). A Neural Network with two-nodes achieved the same accuracy (65.116%) and as expected was further improved with three-nodes (69.767%). A valuable benefit to the employment of the NCaRBS technique is the graphical exposition of the contribution of patient characteristics to the classification analysis.

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Evidence-based modelling of strategic fit: An introduction to RCaRBS

Cited by 9 publications

References 50 publications

Evaluating the Impact of Different Training Methods on SME Business Performance

Evaluating the Impact of Different Training Methods on SME Business Performance

Evidence-Based Modelling of Organizational Social Capital with Incomplete Data: An NCaRBS Analysis

The Application of NCaRBS to the Trendelenburg Test and Total Hip Arthroplasty Outcome

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