A model for resource allocation using operational knowledge assets

A model for resource allocation using operational knowledge assets Purpose – The paper seeks to develop a business model that shows the impact of operational knowledge assets on intellectual capital (IC) components and business performance and use the model to show how knowledge assets can be prioritized in driving resource allocation decisions. Design/methodology/approach – Quantitative data were collected from 84 high‐tech federal contractors in the Washington DC metro area. Respondents in the target population were middle‐level and operations managers of business sectors holding positions as presidents, vice‐presidents, directors, engineering managers, operations managers, and analysts. Partial least squares (PLS) analysis was performed to develop a structural model between operational knowledge assets and IC components that maximizes explained variance for business performance. Operational assets were specified as formative constructs and IC and business performance were specified as reflective constructs. Findings – A parsimonious conceptually sound model with significant measured variables and path coefficients was developed that explains almost 40 percent of the variance in business performance. The model shows both the interrelationships between the IC components that drive performance and the operational assets as levers for each IC component, respectively. Research limitations/implications – The scope of the study was focused on the high‐tech federal contractors in the USA. However, the model can be applied and tested in different industry sectors. This would provide evidence of the different operational knowledge assets used as levers in different industry sectors. Practical implications – Senior executives and chief financial officers in particular are constantly challenged with making the optimum investment decisions given their budget constraints. The model offers a tool for developing and evaluating different resource allocation decisions based on an organization's strategic intent. In addition, the model can be useful in evaluating merger and acquisition decisions. In evaluating target companies the model can be used to identify the core capabilities or competency areas that the target company is leveraging and assess the impact or integration potential for the acquiring company. Originality/value – This is the first study in the field of IC that has adopted the use of formative indicators in specifying operational knowledge asset constructs. Previous research has focused on developing models with the use of proxy measures as reflective indicators. Therefore the emphasis so far has been on scale development. The use of formative items in this study fills both the business need and theory gap to understand better the causal relationships that exist between work and knowledge assets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Learning Organization Emerald Publishing

A model for resource allocation using operational knowledge assets

The Learning Organization, Volume 14 (4): 30 – Jun 5, 2007

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Publisher
Emerald Publishing
Copyright
Copyright © 2007 Emerald Group Publishing Limited. All rights reserved.
ISSN
0969-6474
DOI
10.1108/09696470710749272
Publisher site
See Article on Publisher Site

Abstract

Purpose – The paper seeks to develop a business model that shows the impact of operational knowledge assets on intellectual capital (IC) components and business performance and use the model to show how knowledge assets can be prioritized in driving resource allocation decisions. Design/methodology/approach – Quantitative data were collected from 84 high‐tech federal contractors in the Washington DC metro area. Respondents in the target population were middle‐level and operations managers of business sectors holding positions as presidents, vice‐presidents, directors, engineering managers, operations managers, and analysts. Partial least squares (PLS) analysis was performed to develop a structural model between operational knowledge assets and IC components that maximizes explained variance for business performance. Operational assets were specified as formative constructs and IC and business performance were specified as reflective constructs. Findings – A parsimonious conceptually sound model with significant measured variables and path coefficients was developed that explains almost 40 percent of the variance in business performance. The model shows both the interrelationships between the IC components that drive performance and the operational assets as levers for each IC component, respectively. Research limitations/implications – The scope of the study was focused on the high‐tech federal contractors in the USA. However, the model can be applied and tested in different industry sectors. This would provide evidence of the different operational knowledge assets used as levers in different industry sectors. Practical implications – Senior executives and chief financial officers in particular are constantly challenged with making the optimum investment decisions given their budget constraints. The model offers a tool for developing and evaluating different resource allocation decisions based on an organization's strategic intent. In addition, the model can be useful in evaluating merger and acquisition decisions. In evaluating target companies the model can be used to identify the core capabilities or competency areas that the target company is leveraging and assess the impact or integration potential for the acquiring company. Originality/value – This is the first study in the field of IC that has adopted the use of formative indicators in specifying operational knowledge asset constructs. Previous research has focused on developing models with the use of proxy measures as reflective indicators. Therefore the emphasis so far has been on scale development. The use of formative items in this study fills both the business need and theory gap to understand better the causal relationships that exist between work and knowledge assets.

Journal

The Learning OrganizationEmerald Publishing

Published: Jun 5, 2007

Keywords: Knowledge management; Intellectual capital; Business performance; Resource allocation; Mathematical modelling

References

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