A learning curve explanatory theory for team learning valuation
| Date | 10 April 2009 |
| Published date | 10 April 2009 |
| Pages | 20-39 |
| DOI | https://doi.org/10.1108/03055720910962425 |
| Author | Yannis Zorgios,Orestes Vlismas,George Venieris |
| Subject Matter | Information & knowledge management |
A learning curve explanatory
theory for team learning valuation
Yannis Zorgios
CLMS (UK) Limited, Croydon, UK, and
Orestes Vlismas and George Venieris
Department of Accounting and Finance,
Athens University of Economics and Business, Athens, Greece
Abstract
Purpose – This study seeks to examine how the quantitative semantics of the learning curve
phenomenon can be employed in order to derive monetary information for team learning observed
within knowledge-intensive production environments.
Design/methodology/approach – Software development is selected as an identical example of a
team-based, knowledge-intensive production environment. The interaction of learning rate of the
developer teams and the improvements on their average solving time (i.e. productivity) is modelled as
a Lotka-Volterra predator-prey interacting populations system establishing a causal relationship
between the human capital (HC) of organizational teams and the observed learning curve effects on
their performance. In addition, empirical evidence illustrates that the estimated learning rates capture
the entire range of team learning effects on performance fluctuations caused by the HC.
Findings – The fluctuations on the learning rates can be interpreted as a result of the HC variability
across the population of developer teams. Hence, the cost implications of the HC within
knowledge-intensive production environments can be rationalised using the quantitative semantics of
the learning curve phenomenon
Research limitations/implications – The learning curve is associated with the cost side of the
organizational income-generating process limiting its potential valuation applications for team
learning observed within the context of the production environments.
Originality/value – The study offers a theoretical justification, supported by empirical evidence, for
employing the mathematical expression of the learning curve paradigm to rationalize the financial
consequences of team learning observed within production environments.
Keywords Team learning,Learning curves, Human capital, Assetvaluation
Paper type Research paper
1. Introduction
Intellectual capital reporting frameworks (ICRFs) aim to satisfy the requirements of
various stakeholders for valid intel lectual capital (IC) information abou t the
contribution of the organizational learning on value creation (Mouritsen and Larsen,
2005). Argyris and Scho
¨n (1978, p. 29) used the term organizational learning as a
metaphor for processes in which “... members of the organization act as learning
agents for the organization by detecting and correcting errors in organizational
theory-in-use and embedding the results of their inquiry in private images and shared
maps of the organization”. Defining the IC as “... the sum of the knowledge of its
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0305-5728.htm
The authors wish to acknowledge and thank the Alexander Onassis Public Foundation
Institution for supporting the doctoral research of Orestes Vlismas, part of which was carried out
within the scope of this study.
VINE
39,1
20
VINE: The journal of information and
knowledge management systems
Vol. 39 No. 1, 2009
pp. 20-39
qEmerald Group Publishing Limited
0305-5728
DOI 10.1108/03055720910962425
members and the practical translation of this knowledge” (Roos et al., 1997) and
perceiving learning as a problem solving process; imply that the IC is a source of
organizational value because it represents the problem solving knowledge
(incorporated to organizational structures, intellectual assets, human assets, etc.) and
the collective ability to translate such knowledge to action by leveraging
organizational learning processes (Reinhardt et al., 2001). Human capital (HC) is a
key dimension of ICRFs. According to Dess and Picken (1999), HC is “... individual’s
capabilities, knowledge, skills and experience of the company’s employees and
managers, as they are relevant to the task at hand, as well as the capacity to add to this
reservoir of knowledge, skills, and experience through individual learning”.
Empirical evidence in various production environments (i.e. Argote and Epple,
1990; Benkard, 2000; Rapping, 1965) advocates that learning curve models are capable
of reflecting the observed improvements in the input-output productivity ratios as a
result of learning. Despite the importance of the implications of learning curve
phenomenon in various production environments existing IC research approaches
seem to ignore the potential benefit of using the quantitative semantics of the learning
curve paradigm for the purpose of developing financial reporting approaches for
learning observed within the team based production environments of knowledge
organizations. Two important reasons can be identified though why this has not been
achieved as yet. First, the lack of a rigorous learning curve explanatory framework
(theory), which proves the causal relationship between the human capital (HC) of
organizational teams and the learning curve effects on their performance. Second, the
absence of relevant empirical evidence supporting the above argument within the
context of production processes in knowledge organizations.
This study addresses the above research problems by studying how the
productivity behaviour is correlated to learning curve effects in the knowledge
intensive processes of software development. Initially, a learning curve explanatory
framework is formulated by modelling the interaction of learning rate of the developer
teams and the improvements on their average solving time as a predator – prey
interacting populations system described by a type of Lotka-Volterra differential
equations. The formulation of the learning curve explanatory framework is based on
the idiosyncratic productivity measurement methodology of software industry, which
measures the produced volume by defining the components problems of a software
project and, thus, it integrates the cognitive and the physical dimension of task
performance. Taking a systems perspective, the decision making process in many
knowledge intensive industries can be viewed as a problem solving process, namely, as
a modelling activity that results in specific mental structures.
We then obtained further empirical evidence based on data retrieved from
International Software Benchmarking Standards Group (ISBSG, 2007), a world-class
benchmarking database in the software industry, indicating that the learning curve
captures the whole spectrum of learning effects on performance caused by different
sources of organizational learning, such as those described by the typologies of
organizational learning literature (i.e. single-loop, double-loop and deutero learning).
This study offers a theoretical justification, supported by sound empirical evidence,
for employing the mathematical expression of the learning curve paradigm to
rationalize the learning outcome and, potentially, derive IC monetary information
within production environments. However, learning curve emphasizes the cost savings
Team learning
valuation
21
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