Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
An Assessment Model for Human Performance
Measurement - Theoretical Model and Distance based
Implementation
Satyakama Paul1,Tshilidzi Marwala2 and Fernando Buarque de Lima
Neto3
Abstract: Human resources are of critical importance in
organizations that aim to attain and sustain their competitive
advantage over rivals. Such sustained competitive advantage is
most likely achieved by organizations that have policies and
procedures directed towards transformation of human resources into
assets. While such policies and procedures have found place in a
number of Quality Assessment models. However the models are of
limited use as they are limited in the provision of decision
functionalities and assessment tools. In this context, this paper
presents a novel theoretical approach of measuring human
performance in a knowledge-based organization as a combination of
three separate factors – Human, Customer, and Organization
capability. In addition, a fictitious example is provided to show the
use of the proposed approach as a decision making tool in
promotion decisions.
Field of research: Management – Managing People and Organization.
1. Introduction
In today’s business world of high competition, every firm wants to achieve business
success and sustain it. Such success is enjoyed by the firm when it secures a
competitive advantage over its rivals, and also maintains it in the long run. (Hamel
1994) remarks that a firm achieves sustained competitive advantage (SCA) by
acquiring unique characteristics that distinguished it from its competitors. Such
characteristics often include strategies as optimal utilization of critical resources,
processes to ensure low conversion costs, etc. As discussed in the subsequent
section, one such critical process required to achieve SCA is the adoption of policies
and practices that are directed to transforming human resource into “assets”. In the
present research work, the authors develop a generic assessment model (primarily
for measurement and analysis) of performance of such assets in a knowledge based
1
Department of Mechanical Engineering Science, FEBE B2 Lab 210, Auckland Park Kingsway
Campus, University of Johannesburg, Johannesburg, 2006, South Africa. Tel.: +27-074-377-5422.
E mail: psatyakama@student.uj.ac.za
2
Office of the Deputy Vice Chancellor (Research, Innovation and Advancement), Auckland Park
Kingsway Campus, Corner University and Kingsway,Auckland Park, University of Johannesburg,
Johannesburg,2006, South Africa.Tel.: +11-559-4814/4815. Fax: +11-559-4816.
E-mail:tmarwala@uj.ac.za
3
University of Pernambuco, POLI (Computing Engineering Program), RuaBenfica, 455 – Madalena,
50.720-001, Recife/ Pernambuco, Brazil. Tel.: +55(0)81 3184-7242. FAX: +55(0)81 3184-7548.
E-mail: fbln@ecomp.poli.br
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
economy. The primary aim of the model is to provide support for decisions such as
promotions. Subsequently an example of implementation of the model (using
distance-based measurement) is provided for better understanding and application
by the practitioners.
2. Literature review
Currently the knowledge economy has become an integral part of a nation’s
economic development. In 1998, the World Development Report noted that “For
countries in the vanguard of the world economy, the balance between knowledge
and resources has shifted so far towards the former that knowledge has become
perhaps the most important factor determining the standard of living –more than
land, than tools, than labor” (The World Bank, 1998). With the growth of the
knowledge economy, the demand for its products and services has at times
exceeded its supply. Thus, the organizations engaged in the production of the
knowledge intensive products and/or services face a number of problems, one of the
major ones being the problem of management of its talents. In order to resolve such
problems, business organizations have increasingly felt the need for creating
workforces where professionals can independently work as centers of intelligent
actions (i.e. “assets”) coordinated towards SCA, rather than employees who can
merely execute orders (i.e. “resources”). In this regard, borrowing from Barney’s
Resource Based View of the firm, we define an asset as a resource that satisfies the
four special characteristics of value, rareness, imperfect imitability, and nonsubstitutability (Barney, 1991), (Galbreath, 2005). In contrast, resources are “inputs
into the production process” (Grant, 1991).
A number of researchers have shown that workforce practices that are targeted
towards transformation of human resources into assets have benefited the
organization. Here we review a couple of the important ones (Mohapatra, Ray, &
Sarkar, 2008).(Kravetz, 1988) and (Hansen & Wernerfelt, 1989) have shown that
firms with the best workforce practices aimed at transforming human resources into
assets outperform other firms in terms of growth of profit, sales, and dividends.
Similar work by the U.S Department of Labor (Labor, 1993), (Kling, 1995), (Delaney
& Huselid, 1996), (Becker & Huselid, 1998), and (Appleby & Mavin, 2000) have
shown that integrated human resource strategies of companies which are aimed at
transforming human resources into assets have helped them to achieve world-class
status. (Welbourne & Andrews, 1996), while studying 136 non-financial organizations
showed that organizations that placed a high value on their human resources had a
higher probability of survival (0.79), compared to organizations that placed lower
values on their human resources (0.60). (Pfeffer, 1998) made a significant
contribution by identifying seven principles of workforce management that
differentiated the companies with the largest percentage of stock market returns
from their competitors, in the past quarter century. They are: (i) employment security,
(ii) selective hiring of new personnel, (iii) self-managed teams and decentralization of
decision making, (iv) comparatively high compensation contingent on organizational
performance, (v) extensive training, (vi) reduced status distinctions and barriers, and
(vii) extensive sharing of financial and performance information.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Such workforce practices have gained ground and have found place in various
Quality Assessment models, such as the Malcolm Baldridge National Quality Award,
the European Foundation for Quality Management, etc. as the essential criteria for
quality management. However, they have not been adequately applied or their
applications led to limited success. While there are a number of reasons attributed to
the non-application of these models or their applications leading to limited success,
one important reason is that they do not provide decision models and analysis tools
that can support an organization in its own performance assessment (Chin, Pun, &
Lau, 2003). It is in this context that the proposed research endeavor assumes
importance. The intended research work is aimed at the development and
formulation of a generic assessment model for human performance measurement
and analysis (APM).
Any attempt directed towards the improvement of the quality of the workforce should
be synchronized with the maturity of the organization, and its customers. In this
perspective, APM tries to present an approach in which the transformation of human
resources into assets takes place in a framework comprising of three factors:
Human, Customer, and Organizational Capability4 factors.
3. Development of the theoretical model - the framework of the
three factors
a) The Human capability factor
In the recent times, Adult Career Development has been a major area of research in
Human Resource Management (HRM). A number of models provide insights into the
career path of professionals in a knowledge economy and the main ideas underlying
three major ones are briefly summarized. In the Career Stage Model (Dalton,
Thompson, & Price, 1977), the career path of a professional in a knowledge
economy is divided into four stages: Entry, Colleague, Counselor, and Advisor. At
the Entry stage, the individual works under the direction of his superior and learning
takes place through the apprenticeship method. At the Colleague stage, the
individual acquires competence as an independent technical contributor. During the
Counselor stage, the individual acts as a leader or mentor for others; and lastly in the
Advisor stage, he becomes competent enough to provide long range direction to the
organization. Career Success Map (Derr, 1986) identifies an individual's five career
motives to be: (a) Getting ahead, (b) Getting secure, (c) Getting free, (d) Getting
high, and (e) Getting balanced. Schein’s Career Anchor Model (Schein, 1978),
(Schein, 1985)defines an anchor as “a pattern of self- perceived talents, motives and
values that serve to guide, constrain, stabilize and integrate individual careers.”. His
career anchors are: (a) Technical/Functional Competence, (b) General Managerial
Competence, (c) Autonomy/Independence, (d) Security/Stability, (e) Entrepreneurial
Creativity, (f) Service/Dedication to a cause, (g) Pure Challenge, and (h) Lifestyle.
4
In business management literature, the term capability has been defined in a number of ways. The
more general ones define Capability as the: (i) ability to execute a specified course of action, and (ii)
ability to perform designated activities and to achieve results, which fulfill specific requirements.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
APM utilizes the Career Stages Model to create the transformational path of
generating assets from human resources. In addition, it exploits the various anchors
and motivators of Schein and Derr to chart the growth path of a professional. In the
initial stages of a professional’s career, Technical/Functional Competence and
Security/Stability are the most important career anchors. However, as the individual
employee ascends to the higher stages, General Managerial Competence,
Autonomy/Independence, and Pure Challenge become more relevant to him than
the previous anchors. Lastly, as he ascends to the highest stage of his career,
Entrepreneurial Creativity and Service/Dedication act as the most important career
anchors. A similar comparison with Derr’s Career Success Map Model shows that
Getting secure and Getting ahead are the most important career motives at the initial
stages of one’s career, Getting free at the higher stages, and Getting high and
Getting balanced at the highest stage.
b) The Customer capability factor
Customer’s expectation changes with time and Customer centricity of an
organization revolves round the same. Although(Duffy, 2001) model looks at
customer centricity from the perspective of the organization, yet a reversal of the
outlook i.e. to view it from the customer’s side, provides an idea of the how the
customer’s demand towards the organization matures. At the first stage
(Acquisition), the customer has a weak, temporary and transactional relationship with
the organization. At the second stage (Retention), the customer having become
familiar with the organization, shares information about ones' taste and preference,
purchasing power, etc. At the third stage (Loyalty), the customer expects the
organization to be supportive of his /her operations. And, in the last stage
(Collaboration), the customer wants the organization to create value for it.
An insight into the above two factors, namely, the Human capability and the
Customer capability factor mutually supporteach other. Accordingly, as a
professional ascends in his growth path from Entry to Advisor stage, the customer’s
expectation from the professional matures from the provision of low value-added
products and/or services (at the Acquisition stage) to those of the high value added
ones (at the Collaboration stage). Thus, while a junior executive of a company is
primarily concerned with selling a product to the customer; his seniors are concerned
with building strategies of how to customize the organization’s products to better
serve the customer.
The proposed model can be used in determining the attributes and parameters for
each level of Customer centricity. The starting point of such an investigation may be
SERVQUAL tool (Parasuraman, Berry, & Zeithaml, 1991), (Parasuraman, Zeithaml,
& Berry, 1988). A comparison between Customer centricity and the dimensions of
SERVQUAL may show that in the initial stages of theCustomer centricity, Assurance
and Responsibility characterize the customer expectations. However, as the
customer’s expectations mature, Empathy, Reliability, etc. better characterize the
customer’s needs.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
c) The Organizational Capability Factor
(Hansen & Wernerfelt, 1989) refers to Hamel’s description of capability as a bundle
of assets or resources required to perform a business process that consists of
individual activities. As an example, the product development process of any
organization can be its capability that consists of the individual activities such as
conceptualization of the products, its designing, testing, etc., which are carried out by
the organization’s resources.
It can be noted that as an individual in a knowledge economy progresses along the
various stages of his career path, the organizational resources should be more
specifically aligned to development of his competencies, required at that particular
stage. At the Entry stage, the organizational resources are more focused at providing
him the technical skills through Training and Development, instilling in him a
Participatory Culture, etc. However, at the higher stages, the organizational
resources are more focused at development of his General Managerial and Team
building skills, Mentoring skills, Innovation skills, etc.
Since this performance of the organizational resources (aimed at developing the
quality of the work force) is manifested by the Organizational Capability Factor, so its
use in the model is justified. We suggest that one way to measure the
Organizational Capability can be to study the expenses incurred by an organization
to impart various trainings to its professionals.
d) Devising formulae for human performance measurement
With the basic theoretical framework of APM being outlined above, the next step is to
obtain a formula for measurement of human performance. We utilize the concept of
Intellectual capital. Intellectual capital has been defined in a number of ways. Some
of them are as follows: (i) Intellectual capital is formed through the interaction
existing among the human, customer and organizational capital (Bukowitz & Petrash,
1997), (ii) Intellectual capital is the sum of human capital, innovation capital, process
capital, and relationship capital (Joia, 2000). In addition, a review (Mohapatra, Ray,
& Sarkar, 2008) of the three well known models on Intellectual capital - the Scandia
Navigator (Skandia, 1994), Sveiby’s Intangible Assets Monitor Model (Sveiby, 1997),
and the Intellectual Capital Index Model (Roos & Roos, 1997) has been carried out.
The Scandia Navigator is a collection of intangible measurement methods, and
allows a comprehensive view of the performance of intellectual assets from five
perspectives: human focus, customer focus, process focus, renewal and
development focus, and financial focus. The model views a firm’s capital as
composed of its financial capital and Intellectual capital, which, in turn, is composed
of human capital and structural capital. Human capital encompasses the
organization’s people and their skills and measures people’s competence, their
attitude, and intellectual agility. Structural capital is made up of customer focus,
process focus, and renewal and development focus (such as patents, proprietary
processes, procedures, databases, etc.). In his model of Intangible Assets Monitor,
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Sveiby identifies three classes of intangible assets: competence of people, internal
structure, and external structure. Lastly, in their Intellectual Capital Index Model,
Roos et al. remarked that Intellectual capital is composed of human capital and
structural capital. They also noted that human capital consists of the individual
employee’s competence, attitude, etc.; and structural capital is made up of
relationship capital, organizational capital, and renewal and development value.
Based upon such an evaluation of the existing literature, we use the following
equality for the measurement of human performance:
Asset capital  1  Human capital   2  Relational capital   3 Structural capital 
Eq. [1]
Where: Human capital comprises the formalized knowledge and experience,
competencies, etc.; Relational capital comprises the business relationships (such as
those with the customers, suppliers and other stake holders, the reputation, image,
customer loyalty, etc.); and Structural capital relates to the processes as employed
to the organizations systems (such as internal processes, procedures, and
administrative systems), organizational values, innovation and technology resources,
R&D expenditure, etc. ω1, ω2, and ω3 are the respective weights/influences of the
three capitals on Asset capital.
Thus we can see that the three factors of Human, Customer, and Organizational
capability can be individually measured by Human capital, Relational capital, and
Structural capital, respectively. And a summation of the three capitals provides a
formula for performance measurement (of a professional in a knowledge economy)
at various stages of his career (Fig. 1, Fig. 2).
Fig. 1: A schematic diagram of the Input Output Model of APM
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Fig. 2: A schematic diagram of the use of APM at various stages of a
professional’s career
This model may be used to formulate a knowledge-based/expert system (Fig. 3).
Intelligent heuristics in the Knowledge Repository and correct logical conclusions in
the Inference Engine can help the expert system to become Valuable, Rare,
Imperfect imitable and non substitutable and hence help the organization in acquiring
SCA. Wide access of the Explanatory Facility (by the employees regarding the rules
of performance evaluation, promotion, etc.) through the User Interface can help the
organization in creating greater employee satisfaction.
Fig. 3: Proposed structure of the knowledge-based/expert system of APM
4. An example of implementation of the proposed model
The objective of this section is to provide a fictitious example of how the above
theoretical model can be used in practice. Specifically we intend to demonstrate the
utilization of the model in promotion decisions. Here we assume that an organization
has to take a decision regarding promotion of a professional (among three possible
candidates namely c1, c2 and c3) from the Entry stage to the Colleague stage. We
assume that the various attributes that characterize the Entry stage5 are given in
Table 1.
5
For each stage of career, the identification of the various attributes for each of the three factors can
be done through questionnaire survey, structured and unstructured interviews, expect opinion, etc.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Capitals
Human
Relational
Organizational
Table 1: Characterization of the Entry stage
Dimensions
Attributes
Denoted
by
Human
Technical competency
A1
capability
Team work competency
A2
Customer
capability
Organizational
Capability
Expertise in presenting
practical solutions to the
customers
Adherence to project
deadline
Quality
of
technical
training provided by the
organization
to
the
professional
Quality
of
team
development
training
provided
by
the
organization
Weights
assigned
ω1
ω1
A3
ω2
A4
ω2
A5
ω3
A6
ω3
We also assume that the importance6 of the three weights ω1, ω2 and ω3 are 1, 0.8,
and 0.5, respectively. Thus the summation of their valuation is 2.3, and the relative
importance’s of the weights are ω1= (1/2.3 = 0.434), ω2= (0.8/2.3 = 0.347), and ω3 =
(0.5/2.3 =0.217).
In the second step, we use a semantic scale in the hendecagonal system to grade
the attributes. The semantic scale is shown in Table 2.
Table 2: Grading of the attributes on an eleven point semantic scale
Grading the attributes Corresponding
values
Excellent
1.0
Very very good
0.9
Very good
0.8
Good
0.7
Quite Good
0.6
Not so good
0.5
Quite bad
0.4
Bad
0.3
Very bad
0.2
Very very bad
0.1
Terrible
0
Expect opinion is then sought to find the desired values (c0) of the attributes with
respect to promotion. The closer a candidate is to the desired values, the better
6
The value of the weights can be assigned through expect opinions.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
chance is for him/her to be promoted. Thevector below describes the desired value
of the six attributes:
c0
A1
0.7
A2
0.6
A3
0.9
A4
1.0
A5
0.8
A6
0.7
Also an evaluation of the three professionals for the six attributes gives the
corresponding actual vectors for each of the three candidates. In usual practice,
such evaluations are done by the manager/supervisor of a department.
c1
A1
0.8
A2
0.3
A3
0.7
A4
0.4
A5
0.9
A6
0.8
c2
A1
0.6
A2
0.7
A3
0.5
A4
0.2
A5
0.9
A6
0.8
c3
A1
1.0
A2
0.5
A3
0.6
A4
0.6
A5
0.9
A6
0.8
It is to be noted here that the above values of A5 and A6 are same for all the three
candidates. This is because we assume that the same training is imparted to all of
them.From equation [1], we calculate their asset capital as:
c1 :  0.434  *  0.8  0.3 
0.347  * 0.7  0.4   0.217  * 0.9  0.8 
 1.228
c2 :  0.434  *  0.6  0.7  
0.347  * 0.5  0.2 

0.217  * 0.9  0.8
 1.176
c3 :  0.434  * 1.0  0.5 
0.347  * 0.6  0.6 

0.217  * 0.9  0.8
 1.436
From the above calculations it can be seen that the asset capital of candidate c3 is
the highest and c2is the lowest. Hence the human performance of the three
candidates (based on the calculation of Asset capital) would suggest that c3 should
be given the highest preference for promotion, followed by c1 and c2.
In addition, we introduce further authenticity in the analysis by classifying the
attributes into two different categories. In the first category (containing A1), values of
the attributes both above and below the desired level is penalized. Thus it is argued
that if the technical competency of a professional is less (than the desired level) –
he/she would produce poor quality of goods/services. However the more of technical
competency (above the desired level) would compel him/her to raise questions
regarding the quality standards of the goods/services (which at times might be
undesirable considering the fact that such quality standards are mostly set after
rigorous cost-benefit analysis over considerable time period, market demands,
availability of technology, etc.), interfere with the work of colleagues, etc. In the
second category (containingA2, A3…,A6), values of the attributes only below the
desired level is penalized, and is indifferent to values above the desired level.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
In the third step, we aim to rank the candidates by comparing their individual actual
vectors against the desired vector. One method of comparison is the utilization of the
mathematical concept of distance measurement. In the first case (for A1) we penalize
for values both above and below the desired level.Thus for the attribute A1, the
distances of the three candidates from the desired levelis calculated by the
expression:
 µ[{ *  C }
d
, m
– { *  Ca ci }]
Exp. [1]
Where:
 : Property by which actual level above and below the desired level is penalized.
Mathematically this can be achieved by the modulus operation
m : Refer to the attribute
 : Weight of attribute m
Cd :Desired level of the attribute m required for promotion
Ca ci : Actual level of the attribute m possessed by candidate ci
c1 : 0.434 * {0.7  0.8}  0.0434
c2 : 0.434 * {0.7  0.6}  0.0434
c3 : 0.434 * {0.7  1.0}  0.1302
In the second case (for A2 to A6), we penalize values of the attributes that are below
the desired level, and are indifferent to the values above them. Thus the distances of
the three candidates from the desired level are calculated by:
[v{ *  C }
d
– { *  Ca ci }]
v, n
Exp. [2]
Where:
v : Property by which actual level that is below the desired level is penalized
n : Refer to the attribute n
 : Weight of attribute n
Cd : Desired level of the attribute n required for promotion
Ca ci : Actual level of the attribute n possessed by candidate ci
c1 : [ 0.434  *{v  0.6  0.3}   0.347  *{v  0.9  0.7 }   0.347  *{v 1.0  0.4 }] 
 0.217  *{v  0.8  0.9 }  0.217  *{v  0.7  0.8 }]  0.4078
c2 : [ 0.434  *{v  0.6  0.7 }    0.347  *{v  0.9  0.5 }     0.347  *{v 1.0  0.2 }]
[ 0.217  *{v  0.8  0.9 }     0.217  *{v  0.7  0.8 }]  0.4164
c3 : [ 0.434  *{v  0.6  0.5 }    0.347  *{v  0.9  0.6 }     0.347  *{v 1.0  0.6 }]
[ 0.217  *{v  0.8  0.9 }     0.217  *{v  0.7  0.8 } ]  0.2863


Combining the values from expression [1] and [2], we calculate the total distance (D)
as:
D   µ[{ *  Cd } – { *  Ca ci }]  [v{ *  Cd } – { *  Ca ci }]
, m
v,n
Eq. [2]
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
Thus the values of D for the three candidates are:
c1 : 0.0434  0.4078  0.4512
c2 : 0.0434  0.4164  0.4598
c3 : 0.1302  0.2863  0.4165
D can be interpreted as the distance between the desired profile and the actual
profile – the smaller this distance, the closer a candidate is to the desired profile.
Thus from the above calculations, since c3 has the smallest distances among the
three candidates it should have the highest preference for promotion, followed by c 1
and c2.It is to be noted that this result is the same as that obtained from the
calculation of human performance based on Asset capital.
5. Conclusion
The novelty of the above developed model is its holistic approach in integration of
the three factors of Human, Customer and Organizational capability to find an index
for human performance. Also it can be used as a decision tool to facilitate such
organizational tasks as promotion decisions, analysis of the problem areas with
regard to the three factors etc. In the future, we intend to concentrate on the
development of the Inference Engine. We envisage that the selection of weights
(associated with the evaluation aspects) can be done through implementation of
intelligent heuristic methods originating from computational intelligence (specifically
artificial neural networks.
Acknowledgement
The authors would like to acknowledge the insights into the study on Adult Career
Development and Asset Excellence, namely, Prof. P.K.J Mohapatra, Prof P.K Ray,
and Dr. Chinmoy Sarkar at the Indian Institute of Technology, Kharagpur; and also
the funding authority -Tata Consultancy Services, India.
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Proceedings of 8th Annual London Business Research Conference
Imperial College, London, UK, 8 - 9 July, 2013, ISBN: 978-1-922069-28-3
References:
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