Intellectual Capital: More Than the Interaction of Competence x Commitment

Intellectual Capital: More Than the

Interaction of Competence x Commitment

Abstract:

Ulrich (1998) has suggested that intellectual capital is a product of competence and

commitment. This broad proposition, though intuitively appealing, does not identify

theoretical links between these variables, and has little empirical foundation. This

paper draws on organisational behaviour theory to propose a model that specifies

mechanisms, intermediate linkages and boundary conditions that predict intellectual

capital. In doing so, we respond to a recent call for research that is specific about

human resource management–firm effectiveness relationships. Moderated

relationships between competence, commitment and control are proposed as

predictors of intellectual capital. Implications for future theory and practice are

highlighted.

Keywords:

INTELLECTUAL CAPITAL; COMPETENCE; COMMITMENT; CONTROL.

1. Introduction

The advance  resource-based view of the firm suggests that intellectual capital and the

potential to transform it into skilled action provides firms with competitive

advantage (Drucker 1999; Prahalad & Hamel 1998). Ulrich (1998) has proposed an

innovative formula based on Human Resource Management (HRM) principles:

intellectual capital = competence x commitment. Ulrich however, does not offer

theoretical or empirical causal links between the variables in the formula (Burr &

Girardi 2001), nor does he include situational influences that may have an impact

on intellectual capital.

Ferris, Hochwarter, Buckley, Harrell-Cook & Frink (1999) suggest that our

discipline has numerous under-developed or specified constructs, leading to the

criticism that the ‘black-box’ phenomenon prevails in our understanding of HRM

effectiveness. Ferris et al. (1999) suggest some new directions for HRM research,

which could heighten the impact of Ulrich’s formula in terms of both HRM

research and practice. First, they recommend that HRM research should be specific

about what HRM effectiveness criterion is being measured. There is very little in

the literature at present that clearly evaluates the impact of HR practices on the

firm’s holdings of intellectual capital. There is a need therefore for research that

specifically links HR practices from an organisational behaviour perspective with

the value of a firm’s intellectual capital. Ulrich’s formula provides a useful

conceptual basis for measuring intellectual capital as the outcome of effective HR

practices. It also avoids the use of accounting principles, which are currently used

to calculate intellectual capital (Flamholz 1999), but omit individual and

psychological factors.

Second, Ferris and his colleagues highlight the need for research that unpacks

the ‘black-box’ by specifying psychological mechanisms: the intermediate linkages

among these mechanisms and the boundary conditions that underpin HRM—firm

outcome linkages. Ulrich’s formula (1998) does indeed draw on psychologically

based, cognitive explanations for predicting a company’s intellectual capital.

However, it is not specific about the psychological mechanisms associated with

competence and commitment (both of which are multi-faceted), and their impact on

intellectual capital. Nor does it take into account boundary conditions that can

activate, appreciate or depreciate stocks of intellectual capital as a result of high

competence and commitment. In this paper, we extend Ulrich’s model based on

organisational behaviour research and theory to suggest that intellectual capital is

determined by factors in addition to specific facets of competence and commitment

and discuss implications for future research and practice.

2. Intellectual Capital Defined

Current definitions of intellectual capital are ambiguous. At present it is no one

particular entity, but a rather broad and vague concept that needs to be supported

by and composed of a variety of interrelated elements (Bukh, Larsen & Mouritsen

2001). A widely used definition describes intellectual capital as the knowledge,

information, intellectual property and experience that can be put to use to create

wealth (Stewart 1997). It is the future earning potential from a combination of

human capital (brains, skills, insights), and the potential of an organisation’s people

(Edvinsson 2000). Intellectual capital, which is a sub-set of an organisation’s

market capital, is generally categorised into two elements (see figure 1): human

capital and structural capital (Edvinsson 1997; Stewart 1997; Sveiby 1997). Human

capital has been described as being made up of four facets: ability, behaviour,

effort and time (Davenport 1999), all of which are owned and controlled by

workers. It is at the worker’s discretion to use personal initiative at work (Frese,

Kring, Soose & Zempel 1996) and decide when, what, where and how they will

use the skills they possess to add value to the firm’s operations. Structural capital

on the other hand has been described as ‘the backbone of the organisation’, and

includes not only intellectual property but also infrastructure consisting of an

organisation’s strategies, processes and policies (Dzinkowski 2000).

Ulrich’s definition of intellectual capital focuses solely on human capital and

does not take into account any of its structural dimensions. Edvinsson, Kitts and

Beding (2000) specifically state that intellectual capital is about a fit between

essential state variables (market and customer value) and free parameters (e.g.

competence and commitment in Ulrich’s formula and organisational processes,

systems and structures), which are adjustable variables that can be changed through

managerial intervention. Like Ulrich, Edvinsson, Kitts and Beding (2000) believe

that the systematic transformation of human capital into value requires structural

capital as a multiplier, to realize sustainable earnings potential for the organisation.

Converting knowledge into something that has value creates intellectual

capital (Drucker 1999; Dzinkowski 2000). This implies that knowledge is only

useful for what it does, how it is used and acted upon. In order to sustain the value

of knowledge as an internal good, it has to be put to use or activated through

opportunities provided by the work system and the individual’s willingness to

apply their abilities and skills (Roselander 2000). Therefore, value creation results

from the interaction of the human and structural components of intellectual capital.

The conclusions to be drawn from the definition of intellectual capital is that

it is a product of:

• Capacity which is the knowledge, skills, abilities, information and experience

of people;

• Willingness of people to apply capacity; and

• Opportunity provided by the work system to activate stocks of intellectual

capital.

Capacity reflects the competence component of the Ulrich (1998) formula, and

willingness mirrors commitment. The opportunity element however, is missing in

Ulrich’s conception of intellectual capital. Our aim is to extend the Ulrich model to

include this missing element. Our discussion proceeds in three steps. The first step

specifies psychological mechanisms and their intermediate linkages which

underpin the components of human capital covered in Ulrich’s model. The second

step identifies boundary conditions (or structural capital) in the form of job control

as an additional element in the model. The third step draws together a model that

presents intellectual capital as the outcome of the interaction of competence and

commitment with job control.

Figure 1

Components of Market Capital

Intellectual Property Intangible Assets

Innovation Capital Process Capital

Customer Capital Organisation Capital

Human Capital Structural Capital

Financial Capital Intellectual Capital

Market Capital

3. Unpacking Ulrich’s Formula

Competence is a multi-dimensional construct. The rationalist approach couches

competence in terms of the personal attributes of workers such as education level,

which is often used as an objective measure of intellectual capital (Dzinkowski

2000). This approach is fairly narrow. A broader and more common definition of

competence in organisational settings is that it includes an individual’s

demonstrated knowledge, skills and abilities (Ulrich, Brockbank, Yeung & Lake

1995).

Sandberg (2000) expressed concerns that the rationalist approach defines

competence in indirect terms, as these descriptions do not indicate whether the

worker uses these attributes. Sandberg advocates the use of an interpretative

approach to discover the workers’ definition and understanding of their jobs. In

Sandberg’s view, this interpretation determines the workers’ definition of job

competence and therefore the range of skills they utilise at work.

Bandura (1986) also suggested that knowledge and skills possessed are not

enough. One must also consider a worker’s efficacy beliefs about being able to

mobilise these skills for successful performance. Self-efficacy is described as the

‘beliefs in one’s capabilities to mobilise the motivation, cognitive resources and

courses of action to meet given situational demands’ (Bandura & Wood 1989,

p. 408). Efficacy beliefs are strongly linked to learning and organisational

performance (Stajkovic & Luthans 1998), through their motivational properties.

The conception of competence therefore needs to extend beyond capacity

defined as knowledge, skills and abilities (KSAs) to include more dynamic

elements such as skill utilisation and efficacy beliefs, which convert KSAs into true

intellectual capital. This leads to our first proposition:

Proposition 1: In valuing intellectual capital, competence needs to be measured as

a function of rationalist measures of capacity (KSAs),

interpretative measures (skill utilisation, determined by the

worker’s understanding of job requirements) and cognitions of

capability (efficacy beliefs).

Commitment is also a multi-faceted construct. It has been defined as a job attitude

or belief that reflects ‘the relative strength of an individual’s identification and

involvement in a particular organisation’ (Steers 1977, p. 46). A frequently used

operationalisation of organisational commitment is the three-factor model

developed by Meyer and Allen (1992). The factors are continuance commitment,

normative commitment, and affective commitment. Ulrich not only fails to

discriminate between these facets of commitment but also does not take into

consideration the differential impact of the three facets on intellectual capital as

discussed below.

Affective commitment is the most studied dimension (Dunham, Grube &

Castaneda 1994). Affective commitment is often described as loyalty to the

organisation, demonstrated by emotional attachment and identification with

organisational goals (Meyer & Allen 1984). This type of commitment therefore

reflects the willingness of people to provide discretionary effort. Continuance

commitment is attachment to the organisation induced by recognition of the costs

of leaving the firm. Continuance commitment is therefore essential for retention of

intellectual capital. The final component of organisational commitment is

normative commitment, which reflects the employees’ feelings of obligation to

remain with the organisation. These obligations are compiled through identification

with the organisation’s values and culture. This facet of commitment ties in with

elements of structural capital, which are the organisation-based sources of

intellectual capital such as organisational processes, systems, culture, values and

management philosophy (Dzinkowski 2000). This leads to our second proposition:

Proposition 2: Affective, continuance and normative commitment should all be

included when valuing intellectual capital.

Ulrich (1998) suggested that commitment is gained by engaging employees’

emotional energy, avoiding burnout and stress through high involvement work

practices based on high levels of employee autonomy, and self-regulation (job

control). Ulrich therefore acknowledges that structural variables have an impact on

commitment, but does not include them in his model. Similarly, there is a growing

body of research that highlights that competence can be influenced by structural

factors, specifically job control (Burr & Cordery 2001; Parker & Wall 1998). The

next part of the discussion therefore examines job control as a major boundary

condition that influences both the capacity and willingness elements of the Ulrich

formula.

4. Job Control as a Boundary Condition

Empirical and theoretical research supports the proposition that job design (a

structural capital variable) and in particular job control or work autonomy

(Hackman & Oldham 1976), has the potential to activate value-creating intellectual

capital mechanisms. Within the dominant job design paradigms, job control is

viewed as allowing individuals to act directly on the environment so as to produce

desired outcomes or avoid negative ones (behavioural control) and/or allowing a

choice among several possible actions, outcomes, or tasks (cognitive control)

(Wall, Corbett, Martin, Clegg & Jackson 1990).

A series of job redesign studies within advanced manufacturing systems by

Wall and colleagues (Jackson & Wall 1991; Wall et al. 1990; Wall, Jackson &

Davids 1992), has provided evidence that significant performance improvements

within high control job designs arose not due to employees working harder, but

rather as a result of the development of new knowledge, which enabled the

prevention of errors. These findings closely approximate the propositions of the

demand-control model of job design that mastery outcomes are engendered by

active, high control jobs (Karasek & Theorell 1990). Evidence substantiating this

‘active learning’ finding is emerging in other work environments. For example, job

control has been found to influence skill utilisation (Girardi 1999), job related

efficacy beliefs (Burr & Cordery 2001; Parker 1998; Speier & Frese 1997), and job

crafting (Wrzesniewski & Dutton 2001) in work settings as diverse as process

control, the knowledge work environment and in the service industry.

High Performance Work Systems (HPWS) (Huselid 1995; Lawler, Mohrman

& Ledford 1995), predicated on high control-based job design, have also been

shown to contribute to the development of intellectual capital. Emerging evidence

shows that HPWS are instrumental in creating committed, long-term employee

relationships, which have an impact on firm performance (see Lawler et al. 1995;

Pfeffer 1998). Broad justifications for these outcomes are based on principles of

worker empowerment ( Spreitzer 1995; Thomas & Velthouse 1990).

However, HPWS have been demonstrated to be effective only when three

pre-conditions exist (Macduffie 1995). First, employees must be competent and

possess knowledge and skills valued by the firm. Second, employees must be

willing and motivated to apply these skills through voluntary effort. Third,

employees must have the opportunity to contribute to the firm’s business or

production strategy through discretionary effort. It is evident therefore that an

interaction of individual competence, willingness (commitment) and opportunity

(via job control) is needed if positive outcomes are to be recognised from systems

that were designed to enhance intellectual capital (Huselid 1995). This leads to our

third proposition:

Proposition 3: Job control will moderate the impact of competence and

commitment on intellectual capital.

5. The Extended Model

The discussion so far has highlighted two issues. First, there is a need to

decompose the broad elements of Ulrich’s existing formula for valuing intellectual

capital to include specific psychological mechanisms. Second, the formula needs to

be expanded to include job control as a boundary condition. An expanded formula

for valuing intellectual capital is therefore proposed:

Proposition 4: Intellectual Capital = Competence x Commitment x Control

In which:

Competence = Rationalist measures of capacity (KSAs), interpretative measures

(skill utilisation) and cognitions of capability (efficacy beliefs);

Commitment = Affective, continuance and normative commitment; and

Control = Work autonomy.

There are a number of organisational behaviour models that support this three-way

interaction. For example, Amabile’s (1988) multiplicative componential model of

creativity and innovation in organisations includes organisational components

similar to control (resources, motivation to innovate, management practices) and

individual components similar to competence and commitment (skills in creative

thinking and the task domain, motivation). Another example is Wrzesniewski and

Dutton’s (2001) job crafting model that proposes that interactive relationships

between ability, motivation and opportunities provided by job control determines

job crafting or role redefinition in response to dynamic job requirements. Similarly,

Blumberg and Pringle (1982) proposed a simple model in which job performance is

the outcome of the moderated relationships between the willingness and capacity of

individuals and the opportunity provided by the organisation to perform.

6. Implications for Future Research and Practice

The debate about valuing intellectual capital has been dominated by practitioners to

date (Bukh et al. 2001; Larsen, Bukh & Mouritsen 1999). It is fitting therefore to

draw out the practical implications of the proposed model first and then the agenda

for future research.

The accounting profession has long been interested in assigning monetary

value for intellectual capital, in spite of its intangible nature. However, it is

recognised that existing assessments such as the difference between the firm’s

market and financial or book value, the Tobin’s q ratio, and the calculated

intangible value (CIV) measure are not useful indicators of intellectual capital

(Dzinkowski 2000; Larsen et al. 1999). It has been suggested as a result, that

intellectual capital has to be defined on its own terms (Larsen et al., 1999). In this

paper we have proposed an organisational behaviour theory-based formula to do

this.

While it may be difficult to assign financial value to intellectual capital using

the proposed formula, we believe that it adds structure to efforts towards the

development of intellectual capital statements (Bukh et al. 2001; Larsen et al.

1999) elsewhere. Such statements describe activities that management might apply

in order to mobilise intellectual capital and specify how it is drawn upon to produce

organisational benefits. In keeping with the proposed formula, intellectual capital

statements make connections between intellectual resources, the motivation

directed towards use of these resources, and activities that draw upon them (Bukh

et al. 2001).

HPWS are one set of management activities that can enable the utilisation of

capabilities based on employee commitment and empowerment (Tomer 2001). The

message therefore for organisations interested in increasing their intellectual

capital, is that they need to pay attention to all the different facets of competence,

commitment and control and put into place complementary ‘bundles’ of HRM

practices. In doing so, the visible consequences of how these three intellectual

capital elements interact can be observed and can collectively provide a clearer

definition of intellectual capital. This responds to Ferris et al.’s (1999) suggestion

that HRM research should be specific about what HRM effectiveness is being

measured for—in this case valuing intellectual capital.

The research agenda is determined by Ferris et al.’s second suggestion, which

highlights the need for research that unpacks the ‘black-box’ by specifying

psychological mechanisms, intermediate linkages between them and boundary

conditions that underpin HRM—firm outcome linkages. This paper has sought to

address this suggestion in the development of an expanded interactive model for

valuing intellectual capital, which serves as a framework for future research.

There is a need for empirical research, to test the intermediate linkages both

between and within the elements of the expanded intellectual capital formula. It is

expected that when levels of job control are high, and competence and commitment

are high, intellectual capital will be maximised. However, how do the various

facets of control, commitment and competence influence this maximisation? In

order to answer this question, two avenues need to be explored.

The first is to deal with the validation and/or development of measures for the

constituents of competence, commitment and control. Whilst psychometrically

sound measures of job control (e.g. Jackson & Wall 1991) and commitment (Meyer

& Allen 1992) are available, measures of competence need further refinement

(Sandberg 2000).

The second is to empirically test for an interaction effect. Although

methodological limitations regarding interaction analysis exist (Jaccard, Turrisi &

Wan 1990), and must be acknowledged, the theoretical and practical importance of

moderated relationships exceed these concerns (Baron & Kenny 1986; Karasek &

Theorell 1990). Some of the main methodological obstacles are: the issue of

multicollinearity between the formula variables; the impact of measurement error

which can result in biased estimates and lead to statistical power problems thereby

undermining significance tests; and that effects sizes reported in interaction studies

in industrial and organisational psychology tend to be small (Jaccard & Wan 1996).

However recent developments in testing interaction effects with structural equation

modelling (Schumacker & Marcoulides 1998) provide avenues to overcome some

of these difficulties.

A preliminary empirical test of Ulrich’s formula (Burr & Girardi 2001) has

found support for the two-way interaction between competence and commitment in

predicting intellectual capital. The agenda for future research to test the three-way

interaction includes the development of innovative methodologies in addition to

conventional statistical methods. One suggestion is to follow the methodology

adopted by the Danish Intellectual Capital Project (Bukh et al. 2001). This project

is testing the use of a combination of quantitative and qualitative measures such as

statistical information, internal ratios, measurement of effects and improvements,

knowledge narratives, stakeholder reports and gap analysis to identify optimum

stocks of intellectual capital and its firm-specific components of intellectual capital

and management challenges. The field of accounting for intangible assets (Lev

1997), and development of measures such as the intellectual capital multiplier

(Åberg & Edvinsson 2001), also provides opportunities for cross-disciplinary

research for quantifying the human and structural components of the intellectual

capital formula. Other approaches such as Mayo’s (2001) Human Capital Monitor

and Sveiby’s (1997) Intangible Assets Monitor measure intellectual capital as a

metric that incorporates human asset value, human resource costs and revenue.

Future research will benefit from using such objective measures of intellectual

capital as the dependent variable. Empirical tests of the predictive validity of our

expanded formula against each of these measures of intellectual capital will be

challenging. However it is an area worthy of research efforts if it highlights the

contribution of organisational behaviour and HRM in the valuation of a firm’s

intellectual capital.

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