Ranking of Human Capital Indicators Using Analytic Hierarchy Process

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Procedia - Social and Behavioral Sciences 107 ( 2013 ) 22 – 28

Selection and peer-review under responsibility of Universiti Malaysia Kelantan, Malaysia doi: 10.1016/j.sbspro.2013.12.394

ScienceDirect

Evaluation of Learning for Performance Improvement International Conference

Malaysia, 25 – 26 February, 2013

Ranking of Human Capital Indicators using Analytic Hierarchy

Process

Lazim Abdullah

a

, Sunadia Jaafar

b

and

Imran Taib

c

*

a,b,c*_{Department of Mathematics, Faculty of Science and Technology, University Malaysia Terengganu, 21030 Kuala Terengganu, Malaysia}

Abstract

Emphasis on human capital development and a knowledge based economy becomes increasingly important especially in developing countries. This paper aims to propose an analytical pair-wise comparison approach to ranking indicators of human capital in Malaysia. The Analytic Hierarchy Process is employed to integrate the multi-facets preferences of the five criteria of human capital to determine the importance of the four identified indicators. A case study of human capital measurement is presented and the proposed model is applied to facilitate the decision making process. Interviews with three decision makers were administered to collect data over the comparative judgement of human capital measures in Malaysia. The results show that creating result by using knowledge is the most important measurement indicators and employee’s skill index is the least important measurement indicator. The overall ranking reflects the importance of measurement indicators in steering Malaysia to become a worthy human capital investment.

Keywords: Decision making; human capital; Analytic Hierarchy Process; indicators

1. Introduction

It has been widely accepted that the taxonomy of Intellectual Capital (IC) can be recognised into human capital (HC), structural capital and customer or relationship capital (Saint-Onge, 1996; Edvinsson & Malone, 1997; Sveiby, 1997; Stewart, 1999). Out of the three taxonomies, HC is the most recognisable category in the dynamic and competitive world. The formal concept of HC was developed in the 1960 by a group of economist associated with the University of Chicago (Becker, 1964). Nearly twenty years later Becker (1993) defines HC as expenditures on education, training, medical care to produce human. Husz (1998) forwards a definition of HC as a function of time, experience, knowledge and abilities of an individual household or a generation, which can be used in the production process. Other authors, for example Schultz (1992) strictly defines human capital investment as enrolment rates multiplied by the cost of education for one individual. Lucas (1998) measures human capital probably by expenditures on education and external human capital, which he believes to be able to measure by calculating the returns to land. It seems that there are no perfect and comprehensive definitions of HC despite its important contribution in human development in an enterprise. Bozbura, Beskese, and Kahraman, (2007) placed HC as the most

*Lazim Abdullah. Tel: +609-668-3335 ; fax: +609-668-4660 E-mail address: lazim m@umt.edu.my.

Selection and peer-review under responsibility of Universiti Malaysia Kelantan, Malaysia Open access under CC BY-NC-ND license.

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important asset of the IC, since it is the source of creativity in the organization. HC is important because it is believed to be a source of innovation and strategic renewal.

It is known the fact that one of the key factors in the success of human management is evaluation and measurement. It turns out not to ignore the importance of evaluation in HC. No one in the work study field would deny that evaluation is an important part of the management process. Indeed evaluation is vital for sustaining success of an organisation. Evaluation is a common activity in management flowcharts and in fact, it is the fundamental basics of any management systems. Dagum and Slottje (2000) made a breakthrough contribution to HC evaluation. The authors combine its microeconomic estimation as a standardized latent variable with the macroeconomic estimation of its average value in the population. The standardized latent variable is obtained by applying the partial least squares method after transforming the qualitative indicators considered as investments in human capital and called formative indicators. Average of variables and partial least squares methods are among the common analysis in statistical approaches. The method however received an improvement by Vittadini and Lovagtio (2007). They introduce an improved statistical method of household HC estimation as standardised latent variable.

The present study takes a different perspective. Rather than use typical statistical methods, the approach advocates here uses a comparative evaluation model of decision making approach. The Analytic Hierarchy Process (AHP) is a decision making technique uses a process of pair-wise comparison to determine the relative importance or priority of alternatives in evaluation. Based on mathematics and human psychology, AHP firstly proposed by Saaty(1990) and has been widely used to solve multiple-criteria decision-making problems. AHP, since its invention, has been a tool at the hands of decision makers and researchers and it is one of the most widely used multiple criteria decision-making tools (Vaidya and Kumar, 2006)

2. Method of AHP

The AHP method is a systematic approach in alternative selection or ranking and justification problems by using the concepts of hierarchical structure analysis. Ozer (2007) proposed five steps to find the weight by using arithmetic mean and rank policies for group decision making in marine ecosystem. In this paper, we propose seven steps and used geometric mean to calculate the weight. A geometric mean tends to dampen the effect of extreme values, which might bias the mean if a straight average were calculated. Proof of the inequalities between these two means can be retrieved from Uchida (2008). The works of Saaty (1980), Spires (1991), Vaidya and Kumar (2006), Ozer (2007), and Abdullah et al., (2009; 2012) are conceptualised in the following seven-stepwise algorithm.

Step 1: Construct the hierarchical structure and obtain normalized matrix.

First, the criteria are compared with respect to the goal. A n x n matrix, denoted as A, is created using the pair wise comparisons with the elements a ij_{indicating the value of i}th_{criterion relative to j}th_{criterion, as shown in the} following formula. n n nn a n a n a n a n a a a a a a an a A u » » » » » » » » ¼ º « « « « « « « « ¬ ª 3 2 1 2 23 22 21 12 13 1 11 (1)

The values aij are obtained by the aii = 1, aij = 1/aji, where aij > 0, for all i. Therefore, if a number is assigned to element i when compared to element j, then j has the reciprocal value when compared with i. Second, its entries are normalized by dividing them by their sum. This is repeated for all columns to obtain the normalized matrix A (Anorm) as follows.

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n n n ' /a nn a 3 ' /a n3 a 2 ' /a n2 a 1 ' /a n1 a n ' /a 2n a 3 ' /a 23 a 2 ' /a 22 a 1 ' /a 21 a n ' /a 1n a 3 ' /a 13 a 2 ' /a 12 a 1 ' /a 11 a norm A u » » » » » » » ¼ º « « « « « « « ¬ ª (2)

aij in the above matrix is defined as the pair-wise comparisons of

i

th row relative to

i

thcolumn, and

a

'n is the sum of

the pairwise comparisons in the

i

th column. Step 2: Find the criteria weight.

¦ ¦ ¦ n i m j aij 1 1 n 1 i μi (3) and geometric means of n_μ_i respectively

Step 3: Find the eigenvector by normalised the pair-wise comparisons.

䌥 = = i w _n 1 i μi n i μ (4)

Step 4: Check the consistency ratio (CR).

The comparison matrix will be considered to be consistent if there exist CR < 0.1. Calculate the maximal latent root λmax

䌥 = n i n A max λ i w i w (5)

Calculate the coincidence indicators (CI)

1 n n) max (λ CI -= (6) Check the CR. The CR is consistent when it value is less than 0.1.

Thus CR =CI/RI (7)

where RI is the random index and depends on the number of element being compared, n and takes on the following values:

Step 5: Compare the alternatives pair wise with respect to each criterion. Since there are n criteria in a decision making problem, there will be n matrices of judgments for the alternatives. Each matrix contains the weights for each alternative and it’s determined in the same way as described above for determining the weights for the criteria.

n 2 3 4 5 6 7 8

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Step 6: Select the most preferred alternative among

m

alternatives.

If there are n criteria and m alternatives, then a matrix

A

_AHP_Scoreof size

n

u

m

is created. The

A

_AHP_Score matrix contains the weights results

a

_ij for the alternatives with respect to the criteria. According to the AHP, the best alternative (in the maximization case) is indicated by the following relationship.

¦ n j aijwj Score AHP A 1 ,

max for i=1,2,3,...,m. (8)

where

AAHP-Score= Overall relative rating

Aij = Average normalisation rating for j with respect to factor i.

i

w= Average normalisation weight for i.

Step 7: Ranking the alternatives.

Obviously ranking is generated according to decreasing value ofA_AHP_-6FRU .

In summary, the steps in AHP offer a hierarchy using ordinal scales through pair-wise comparison. The proposed method is then tested in HC evaluation.

3. An Empirical study

According to Becker, Huselid, and Ulrich, (2001) there are five main criteria to maximize HC in an organization. These criteria are talent (T), strategically integration (SI), Cultural Relevance (CR), Knowledge Management (KM) and leadership (L). In Becker et al., (2001) fifty three human resource efficiency indicators are defined. In another research, Abeysekera and Guthrie (2004) define HC with twenty five indicators. Bontis, Keow, and Richardson (2000) listed twenty indicators for HC. Too many indicators sometimes make things complicated and redundancy and possibly it is better grouped into a number of main indicators. The present research summarised indicators in Bontis et al., (2000) into four main indicators. The four main indicators are Creating Results by Using Knowledge (CRbUK), Employee’ Skill Index (USI), Sharing and Reporting Knowledge (SaRK), and Succession Rate of Training Program (SRoTP). This research focuses on ranking of four main indicators (alternatives) of HC based on the five criteria. The hierarchical structure of evaluation model, alternatives and criteria can be seen in Figure 1.

Figure 1: Hierarchical Structure of the Evaluation Model

In accordance with the purpose and the research framework, a group of three experts’ opinions were sought via interviews to elicit information on the preference of the selected criteria and alternatives. Two academicians from two public universities in Malaysia and one high-rank officer at Public Service Department of Malaysian Government were the experts that believed to be the right personnel to offer linguistic data in the evaluation. All the three experts are considered as the group decision makers in the multi-criteria decision making problems. AHP has particular application in group decision making and is used around the world in a wide variety of decision situations. The decision makers were asked to compare pairs of indicators (for example CrbUK versus USI) and to indicate whether

Ranking of HC indicators

CRbUK USI SaRK SRoTP

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they felt that one indicator was ‘equally important’ or ‘extremely important’ to another indicator. Another level of comparison was made to compare the relative importance of criteria toward indicators. The experts were asked to state their preferences on a nine-point scale of relative importance. The scales are similar to the one used in the original instrument (Saaty, 1980). According to the scale used in this study, 1 represented ‘equally important’, 2 represented ‘equally important to somewhat important’, 3 represented ‘somewhat more important’, 4 represented ‘somewhat important to moderately important’, 5 represented ‘moderately important’, 6 represented ‘moderately important to very important’, 7 represented ‘very important’, 8 represented ‘very important to extremely important’ and 9 represented ‘extremely important’.

The relationship between the criteria and indicators of HC are analysed using AHP. The details are presented as follows.

Equations (1) and (2) give the pair wise comparison of five criteria and its normalised matrix. The normalised matrix is presented in Table 1

Table 1: Normalised matrix for pair wise comparison of decision criteria with respect to the goal.

Criteria T SI CR KM L T 1 3 2 1/5 ¼ SI 1/3 1 2/3 1/15 1/12 CR 1/2 3/2 1 1/10 1/8 KM 5 15 10 1 5/4 L 4 12 8 4/5 1

Equation (3) gives criteria weight as , 7860 . 0 1 μ μ₂ 0.2620, μ₃ 0.3930,μ₄ 3.9300,μ₅ 3.1440

Eigenvector is obtained using equation (4), and can be written as

T

W [0.0923,0.0308,0.0462,0.4615,0.36925] Equations (5), (6), and (7) are utilised to check the

consistency ratio, 0.0002 12 . 1 000217 . 0 RI CI CR .

Since the Consistency Ratio(CI/CR)0.10, so the degree of consistency is satisfactory. The decision maker’s comparison is probably consistent enough to be useful. The next computation is to obtain weights for indicators and the results are tabulated in Table 2

Table 2: The weights of each indicators Criteria Criteria

weight CRbUk USI SaRK SroTP T 0.0923 0.4947 0.1995 0.0584 0.2474 SI 0.0308 0.1244 0.1291 0.4355 0.3111 CR 0.0462 0.2143 0.2500 0.4286 0.1071 KM 0.4615 0.1225 0.2041 0.4286 0.2449 L 0.3692 0.5848 0.1949 0.1033 0.1170

The final step is to select the most preferred indicator among the five indicators. The AAHP-Score matrix contains the weights for the indicators with respect to the criteria. According to the AHP, the best alternative (in the maximization case) is indicated by the relationship as Equation (8). Table 3 shows the results of the final step.

Table 3: Ranking the indicators of human capital Indicators

of Human Capital Weight of Indicator Ranking

CRbUK 0.3318 1

USI 0.2000 4

SaRK 0.2745 2

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Hence the results indicate that creating result by using knowledge (CRbK) is the first in ranking. It implies that the CRbK is the most important indicator in the HC management in Malaysia. Sharing and Reporting Knowledge (SaRK) ranked at second place followed by Succession Rate of Training Program (SRoTP). Employee Skill Index (ESI) is measured at the lowest among the four HC indicators.

4. Conclusion

HC is the most important sub dimension of IC in an enterprise or organisation. In ensuring a successful management of HC, its evaluation must be defined and its indicators must be prioritized. However, evaluation of HC is a straight forward process as it involves many criteria and alternatives. This paper has shown the feasibility to evaluate HC by offering a ranking for the indicators. The ranking was obtained using a pair-wise comparison approach of AHP. The ability of AHP to synthesize multi-criteria preferences and provide diagnostic information, which enables decision makers to better understand the behavioural process underlying choices, makes it an important decision tool. The subjective and intangible nature of the criteria used in the evaluation pave the way to AHP to execute the most appropriate method in the evaluation. AHP framework has successfully prioritized the indicators based on the weights of the five criteria. The results of the experiment indicate that creating result by using knowledge is the most important indicators for the HC in Malaysia. The evaluation of human capital indicators using the AHP not only contributed to the methods itself but more importantly reaffirmed the role of knowledge in producing meaningful results in intellectual capital investment.

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