Mohammad Mosaddek Hussain :
(From previous issue)
Various researchers have approached the issue from different perspectives that we have classified into five categories: (a) accounts and/or audit type of studies; (b) studies based on the balanced scorecard; (c) studies that evaluate and measure the impact; (d) quantitative measures studies; and (e) studies of the causal relations between KM and perforemance with or without the involvement of information technology in the activities. Under a separate section certain important parameters of every measurement system that do not fall under any of the above KM measurement perspectives.
Auditing its Value
As Larsen et al (1999) and others studied the intellectual capital accounting statements of five organizations, utilizing specific metrics. The creation of intellectual accounts by means of specific metrics, the proposed by Larsen et al (1999) model merits some credit. It provides a matrix with definitions of human, structural and customer capital that demonstrates how the intellectual capital can be made visible with the use of the proposed metrics. They are based on Statistical Information), Internal Key Indicators and Effect Measures, all three elements tightly coupled during the creation of the intellectual accounts. Larsen et al (1999) in a self critical way conclude that “… there is no set model for intellectual capital statements, and they do not provide a bottom-line indicator of the value of intellectual capital.” According to the authors “… intellectual capital statements are situational … they are not concerned merely with metrics, do not disclose the value of the firm’s intellectual resources. Instead, they disclose aspects of the firm’s knowledge management activities.” But they declare that intellectual capital accounting statements “… do not just ‘measure’, they also ‘report’ and ‘act’ for future activities and planning business goal.
Further the other researchers consider that knowledge audits play a key role in identifying both knowledge assets and the appropriate KM strategy. As Liebowitz et al (2000) conducting a knowledge audit is one of the first critical steps in the knowledge management area. In the same way that a traditional manufacturing company will first inventory its physical assets, an aspiring knowledge organization should inventory its intellectual capital assets. The knowledge audit they propose a study to a small company, is focused on determining what knowledge is needed, what is available and missing, who needs this knowledge and how it will be applied to work. The audit instrument they apply consists of two sets of rather complicated questions aiming to provide answers for the first two steps of the audit. Due to this complexity only a small sample was addressed and an even smaller return rate was achieved. As the authors themselves recognize, analysis of the questionnaire results alone is not enough, and follow-up questions via interviews are needed for the third and final step of the audit to be completed carefully.
Maintaining Balanced Scorecard
Moreover, Return on Investment (ROI) and Economic Value Added (EVA), used by senior management in several organizations, Knight (1999) proposes a Balanced Performance Measurement System (BPMS) based on the Kaplan and Norton (1992) Balanced Scorecard. According to the author, increasing ROI attracts new investors and drives stock prices high, while EVA, which aims to improve profits and market value by keeping low-cost dept, when used alone, disregards intellectual assets and long term investments in training and information technologies.
Knight argues that “Leveraging intellectual capital requires a company to become a knowledge-based organization and to revise its performance measures accordingly” . His model functions in three levels. The first two provide all information needed for making a business case for the company’s KM project. In the final level, BPMS is used to measure and leverage the organization’s intellectual capital and its financial performance that involves the level of profitability and growth achieved. Based on the equation: Market Value = Book Value + Intellectual Capital, Knight proposes generic performance indicators that can be used by almost any organization, to evaluate measurable performance objectives. The model provides for more indicators to be developed and used by middle level management in order to face unique situations. Unfortunately, the model has been applied on a hybrid organization -based on real-life experiences, as the author claims- lacking, in this way, credential for serious generalization.
Measurement of the Impact
This approach that has gained greater support and appreciation within the business world. Cohen (1998) reports Jan Torsilibri saying that “… the value of knowledge cannot be directly measured, but it is possible to measure outcomes: changes in profitability, efficiency, or rate of innovation that follow from knowledge efforts.”. And he gives the example of Buckman Laboratories that “… has used the increase in percentage of sales from new products as a measure of innovation and attributes the improvement to the firm’s development of a better knowledge culture and infrastructure as a whole.
Proposal from Firestone (2001),as the Comprehensive Benefit Estimation (CBE) framework that presents the basic concepts, methodology and tools for producing improved KM benefit estimates. CBE is firmly coupled to corporate goals, and distinguishes benefits according to their relative importance. He claims that various degrees of comprehensiveness are appropriate for different corporate situations, but might not be practical in other situations. So, instead of a single methodology he is proposing an ‘abstract pattern’ of CBE that could easily be tailored, in different ‘ideal type’ situations, to achieve a feasible estimation procedure. He suggested three ideal situations as presented in his paper.
Firestone admits that CBE is not the best solution. As an alternative he proposes the use of the Analytical Hierarchy Process (AHP, Saaty 1990) a method that does not need prior measured data to work except data generated by AHP itself. Firestone considers the second case as a better developed business environment. He proposes to once again apply the AHP, but this time using the real data. Finally, case three (where a Balanced Scorecard or EPM system is already available) is recognized as the most favorable situation for implementing CBE, as most of the data gathering and measurement will have already been completed.
The works of Cohen (1998) centering on corporate profitability and efficiency, and Firestone (2001) centering on corporate goals and benefits, are both in the direction of our investigation, but they do not provide a direct link to business performance.
Through Quantitative Measures
It shows that Return on Investment (ROI) is probably the most popular among the quantitative measures of KM project impact. Compared to cost value it is considered a much better tool for the assessment of the business performance. Anderson (2002) demonstrates the use of ROI in a case study of a large equipment manufacturer that had invested in deploying a company-wide, Internet-based, knowledge management capability. Using proven measurement methodology (Phillips, 1997) the model estimates the annualized cost of knowledge management and the financial benefits produced in five areas: personal productivity, the productivity of others, speed of problem resolution, cost savings and quality.
Anderson concludes with a number of recommendations aiming to increase the business benefits of knowledge management. As Kingsley (2002) studied’ profit models, the costs of KM systems and document reuse statistics, thatdevelops a framework for measuring the Return on Investment (ROI) and the Cost of Information (COI) and proposes tools to evaluate alternative knowledge-sharing strategies. He sees ROI as the return (or incremental gain) from a project minus its cost and proposes its use for measuring ‘hard’ returns. In addition, and for measuring ‘soft’ benefits, Kingsley utilizes COI, a figure of specific value to law offices that calculates the expense of knowledge sharing by comparing the per-document cost of the system to the average rate of reuse.
Causal Relations
Other researchers, Nelson and Cooprider (1996) studied and highlighted the causal effects of knowledge shared between Information System (IS) groups and their line customers, to the performance of the IS group. They base their empirical study on data collected through interviews and questionnaires addressed to managers of 86 IS groups and their line customers, in the USA. In the proposed shared knowledge model they introduce mutual trust and mutual influence as the two components or antecedents of shared knowledge, an idea we have adopted for our model. Despite the obvious connection of information technology (IT) to both the sharing of knowledge and the performance of the IS group, IT is not included in the Nelson and Cooprider model.
Besides this, Lee and Choi (2003) propose a method to measure organizational performance, built upon a rather complicated research model that interconnects knowledge management factors, such as enablers and processes, with performance. The model links seven KM enablers with Nonaka’s knowledge creation model and organizational creativity, in order to measure their impact on organizational performance. The questionnaire-based survey was conducted among 58 major Korean companies .
The use of information technology as an enabler, affecting knowledge creation, has been adopted in the model, and also investigate the impact of Information Technology on business result. The Lee and Choi method illustrates cause and effect links among the proposed model components in a similar way.
Other Issues
Certain other issues that are related to KM and the measurement of its effect on business performance that do not fall under any of the above KM measurement perspectives. For example, Davenport and Prusak (2000) have observed the increased interest in knowledge management among Human Resources managers and they interpret this “… as a sign that organizations are realizing the vital connection between knowledge-oriented behavior and overall employee performance.”
Time is also a measurement issue: Not only ‘what’ we measure but ‘when’ we expect measurable results must be part of the measurement system.
The American Productivity and Quality Center (APQC) during its 2000 consortium implemented a multi-client benchmark among some of the most advanced early knowledge management adopters from both the US and Europe.
(To be continued)
According to the report of American Productivity and Quality Center (APQC) that appears in 2001, although they recognize five stages of KM project implementation in the so-called KM Measurement Bell Curve, only during the more structured ones measurement is considered of importance. During the early implementation stage, measurement rarely takes place, but interviewing key stakeholders -the methodology used in our study- is recommended. As companies move into more advanced stages the need for measurement steadily increases and during the latest stages, when KM becomes a way of doing business, the importance of KM-specific measures diminishes.
APQC recognize that measuring knowledge management is not simple, and is in fact analogous to measuring the contribution of marketing, employee development or any other management or organizational competency.
Nowadays, there is an effective link between human resource, information technology and performance measurement system throughout the business world that helps in the development of personnel in various departments, sections, divisions and finally in the process of organizational development as a whole.
(Mohammad Mosaddek Hussain is a management consultant.)
(From previous issue)
Various researchers have approached the issue from different perspectives that we have classified into five categories: (a) accounts and/or audit type of studies; (b) studies based on the balanced scorecard; (c) studies that evaluate and measure the impact; (d) quantitative measures studies; and (e) studies of the causal relations between KM and perforemance with or without the involvement of information technology in the activities. Under a separate section certain important parameters of every measurement system that do not fall under any of the above KM measurement perspectives.
Auditing its Value
As Larsen et al (1999) and others studied the intellectual capital accounting statements of five organizations, utilizing specific metrics. The creation of intellectual accounts by means of specific metrics, the proposed by Larsen et al (1999) model merits some credit. It provides a matrix with definitions of human, structural and customer capital that demonstrates how the intellectual capital can be made visible with the use of the proposed metrics. They are based on Statistical Information), Internal Key Indicators and Effect Measures, all three elements tightly coupled during the creation of the intellectual accounts. Larsen et al (1999) in a self critical way conclude that “… there is no set model for intellectual capital statements, and they do not provide a bottom-line indicator of the value of intellectual capital.” According to the authors “… intellectual capital statements are situational … they are not concerned merely with metrics, do not disclose the value of the firm’s intellectual resources. Instead, they disclose aspects of the firm’s knowledge management activities.” But they declare that intellectual capital accounting statements “… do not just ‘measure’, they also ‘report’ and ‘act’ for future activities and planning business goal.
Further the other researchers consider that knowledge audits play a key role in identifying both knowledge assets and the appropriate KM strategy. As Liebowitz et al (2000) conducting a knowledge audit is one of the first critical steps in the knowledge management area. In the same way that a traditional manufacturing company will first inventory its physical assets, an aspiring knowledge organization should inventory its intellectual capital assets. The knowledge audit they propose a study to a small company, is focused on determining what knowledge is needed, what is available and missing, who needs this knowledge and how it will be applied to work. The audit instrument they apply consists of two sets of rather complicated questions aiming to provide answers for the first two steps of the audit. Due to this complexity only a small sample was addressed and an even smaller return rate was achieved. As the authors themselves recognize, analysis of the questionnaire results alone is not enough, and follow-up questions via interviews are needed for the third and final step of the audit to be completed carefully.
Maintaining Balanced Scorecard
Moreover, Return on Investment (ROI) and Economic Value Added (EVA), used by senior management in several organizations, Knight (1999) proposes a Balanced Performance Measurement System (BPMS) based on the Kaplan and Norton (1992) Balanced Scorecard. According to the author, increasing ROI attracts new investors and drives stock prices high, while EVA, which aims to improve profits and market value by keeping low-cost dept, when used alone, disregards intellectual assets and long term investments in training and information technologies.
Knight argues that “Leveraging intellectual capital requires a company to become a knowledge-based organization and to revise its performance measures accordingly” . His model functions in three levels. The first two provide all information needed for making a business case for the company’s KM project. In the final level, BPMS is used to measure and leverage the organization’s intellectual capital and its financial performance that involves the level of profitability and growth achieved. Based on the equation: Market Value = Book Value + Intellectual Capital, Knight proposes generic performance indicators that can be used by almost any organization, to evaluate measurable performance objectives. The model provides for more indicators to be developed and used by middle level management in order to face unique situations. Unfortunately, the model has been applied on a hybrid organization -based on real-life experiences, as the author claims- lacking, in this way, credential for serious generalization.
Measurement of the Impact
This approach that has gained greater support and appreciation within the business world. Cohen (1998) reports Jan Torsilibri saying that “… the value of knowledge cannot be directly measured, but it is possible to measure outcomes: changes in profitability, efficiency, or rate of innovation that follow from knowledge efforts.”. And he gives the example of Buckman Laboratories that “… has used the increase in percentage of sales from new products as a measure of innovation and attributes the improvement to the firm’s development of a better knowledge culture and infrastructure as a whole.
Proposal from Firestone (2001),as the Comprehensive Benefit Estimation (CBE) framework that presents the basic concepts, methodology and tools for producing improved KM benefit estimates. CBE is firmly coupled to corporate goals, and distinguishes benefits according to their relative importance. He claims that various degrees of comprehensiveness are appropriate for different corporate situations, but might not be practical in other situations. So, instead of a single methodology he is proposing an ‘abstract pattern’ of CBE that could easily be tailored, in different ‘ideal type’ situations, to achieve a feasible estimation procedure. He suggested three ideal situations as presented in his paper.
Firestone admits that CBE is not the best solution. As an alternative he proposes the use of the Analytical Hierarchy Process (AHP, Saaty 1990) a method that does not need prior measured data to work except data generated by AHP itself. Firestone considers the second case as a better developed business environment. He proposes to once again apply the AHP, but this time using the real data. Finally, case three (where a Balanced Scorecard or EPM system is already available) is recognized as the most favorable situation for implementing CBE, as most of the data gathering and measurement will have already been completed.
The works of Cohen (1998) centering on corporate profitability and efficiency, and Firestone (2001) centering on corporate goals and benefits, are both in the direction of our investigation, but they do not provide a direct link to business performance.
Through Quantitative Measures
It shows that Return on Investment (ROI) is probably the most popular among the quantitative measures of KM project impact. Compared to cost value it is considered a much better tool for the assessment of the business performance. Anderson (2002) demonstrates the use of ROI in a case study of a large equipment manufacturer that had invested in deploying a company-wide, Internet-based, knowledge management capability. Using proven measurement methodology (Phillips, 1997) the model estimates the annualized cost of knowledge management and the financial benefits produced in five areas: personal productivity, the productivity of others, speed of problem resolution, cost savings and quality.
Anderson concludes with a number of recommendations aiming to increase the business benefits of knowledge management. As Kingsley (2002) studied’ profit models, the costs of KM systems and document reuse statistics, thatdevelops a framework for measuring the Return on Investment (ROI) and the Cost of Information (COI) and proposes tools to evaluate alternative knowledge-sharing strategies. He sees ROI as the return (or incremental gain) from a project minus its cost and proposes its use for measuring ‘hard’ returns. In addition, and for measuring ‘soft’ benefits, Kingsley utilizes COI, a figure of specific value to law offices that calculates the expense of knowledge sharing by comparing the per-document cost of the system to the average rate of reuse.
Causal Relations
Other researchers, Nelson and Cooprider (1996) studied and highlighted the causal effects of knowledge shared between Information System (IS) groups and their line customers, to the performance of the IS group. They base their empirical study on data collected through interviews and questionnaires addressed to managers of 86 IS groups and their line customers, in the USA. In the proposed shared knowledge model they introduce mutual trust and mutual influence as the two components or antecedents of shared knowledge, an idea we have adopted for our model. Despite the obvious connection of information technology (IT) to both the sharing of knowledge and the performance of the IS group, IT is not included in the Nelson and Cooprider model.
Besides this, Lee and Choi (2003) propose a method to measure organizational performance, built upon a rather complicated research model that interconnects knowledge management factors, such as enablers and processes, with performance. The model links seven KM enablers with Nonaka’s knowledge creation model and organizational creativity, in order to measure their impact on organizational performance. The questionnaire-based survey was conducted among 58 major Korean companies .
The use of information technology as an enabler, affecting knowledge creation, has been adopted in the model, and also investigate the impact of Information Technology on business result. The Lee and Choi method illustrates cause and effect links among the proposed model components in a similar way.
Other Issues
Certain other issues that are related to KM and the measurement of its effect on business performance that do not fall under any of the above KM measurement perspectives. For example, Davenport and Prusak (2000) have observed the increased interest in knowledge management among Human Resources managers and they interpret this “… as a sign that organizations are realizing the vital connection between knowledge-oriented behavior and overall employee performance.”
Time is also a measurement issue: Not only ‘what’ we measure but ‘when’ we expect measurable results must be part of the measurement system.
The American Productivity and Quality Center (APQC) during its 2000 consortium implemented a multi-client benchmark among some of the most advanced early knowledge management adopters from both the US and Europe.
(To be continued)
According to the report of American Productivity and Quality Center (APQC) that appears in 2001, although they recognize five stages of KM project implementation in the so-called KM Measurement Bell Curve, only during the more structured ones measurement is considered of importance. During the early implementation stage, measurement rarely takes place, but interviewing key stakeholders -the methodology used in our study- is recommended. As companies move into more advanced stages the need for measurement steadily increases and during the latest stages, when KM becomes a way of doing business, the importance of KM-specific measures diminishes.
APQC recognize that measuring knowledge management is not simple, and is in fact analogous to measuring the contribution of marketing, employee development or any other management or organizational competency.
Nowadays, there is an effective link between human resource, information technology and performance measurement system throughout the business world that helps in the development of personnel in various departments, sections, divisions and finally in the process of organizational development as a whole.
(Mohammad Mosaddek Hussain is a management consultant.)
