A dynamic manpower forecasting model for the information security industry

Pages368-384
DOIhttps://doi.org/10.1108/02635570810858778
Publication Date21 Mar 2008
AuthorSang‐Hyun Park,Sang M. Lee,Seong No Yoon,Seung‐Jun Yeon
SubjectEconomics,Information & knowledge management,Management science & operations
A dynamic manpower forecasting
model for the information
security industry
Sang-Hyun Park
National Information Society Agency, Seoul, South Korea
Sang M. Lee and Seong No Yoon
College of Business Administration, University of Nebraska – Lincoln, Lincoln,
Nebraska, USA, and
Seung-Jun Yeon
Electronics and Telecommunications Research Institute, Daejun, South Korea
Abstract
Purpose – The purpose of this paper is to develop an integrated model for manpower forecasting for
the information security (IS) industry, one of the fastest growing IT-related industries. The proposed
model incorporates three critical factors (feedback structure, time lags, and a flexible saturation point)
in a system dynamics (SD) simulation frame.
Design/methodology/approach A simulation model using SD is developed for a dynamic
manpower forecasting by decomposing complex processes of manpower planning into a set of
feedback loops with a causal-loop diagram. Data gathered from a Korean Government agency were
utilized in the simulation for forecasting the manpower demand and supply in the context of the IS
industry.
Findings – The simulation results showed an overall IS manpower shortage in the IS industry.
Policy alternatives were proposed based on the simulation results. The simulation model was rerun to
reflect the various alternatives to achieve a stable manpower balance between demand and supply.
Originality/value – The research provides insights into the development of effective manpower
planning at the industry level (macro level), and policies to increase its efficiency and effectiveness.
The research model was developed and verified using SD.
Keywords Manpower planning,Systems analysis, Simulation,Information systems, South Korea
Paper type Research paper
1. Introduction
“Computing is becoming ubiquitous” (Yoo and Lyytinen, 2005). This statement is
about the emerging integration of computing and the physical space by embedding
computers into objects and places (Weiser, 1991, 1993). In this age of embedded and
invisible computing, computers operate silently and autonomously, requiring no or
very little human intervention or complicated manipulations (Mattern, 2001; Sa ha and
Mukherjee, 2003; Wong and Whitten, 2006). Ubiquitous computing, based on networks
made up of massive quantities of chip sensors that are interlinked through wireless
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
This research was supported by the IT Scholarship Program supervised by Institute for
Information Technology Advancement & Ministry of Information and Communication of
South Korea.
IMDS
108,3
368
Received 21 September 2007
Revised 27 November 2007
Accepted 10 December 2007
Industrial Management & Data
Systems
Vol. 108 No. 3, 2008
pp. 368-384
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570810858778
connections and accessible through mobile devices from virtually anywhere and at any
time, is expected to bring major changes to our lifestyle from e-commerce to ubiquitous
life (Gershman and Fano, 2005; Jessup and Robey, 2002).
This paradigm change in information and communication technology (ICT) is also
expected to result in major changes in the information security (IS) environment. Below
are some of the most important ramifications of ubiquitous networking for IS: first, a
computing environment centered on portable devices would reveal various previously
unknown IS vulnerabilities. Also, due to the reliance on batteries as the power source,
mobile devices and sensor devices have more moderate CPU processing capabilities
than traditional PCs, which undercut the performance of hardware-based encryption
in these devices (Perrig et al., 2004). Second, it is more difficult to guarantee
confidentiality and integrity of data within a wireless network environment
(Cam-Winget et al., 2003; Housley and Arbaugh, 2003). Data communicated over a
wireless network are more vulnerable to interception (Smith, 2007). Therefore, policy
alternatives should be prepared for resolving vulnerability and privacy issues and
improving users’ trust in ubiquitous computing, especially u-commerce. Third,
advances in digital technologies and growing capacities of data transmission, coupled
with the wide penetration of mobile devices make it possible to digitize various types of
analog information such as text, audio, and video. Such digitization may include
contextual information on a real time basis, and increase the risk of confidential
information disclosure vulnerability and invasion of privacy over the network (Lahlou
et al., 2005).
In addition, the effect of these changes in ICT on the IS environment is expected to
create new demand for IS manpower. Not only are technological developments
important, but effective forecasting and planning for the manpower supply and
demand also are necessary to prepare for major changes in ubiquitous computing.
Manpower forecasting is an important practice for the government as well as business
organizations because effective forecasting techniques can lead to better business or
governmental strategies (Flores et al., 2007). Therefore, this study focuses on the
development of a dynamic manpower forecasting model for IS industry with the
system dynamics (SD) methodology.
Various modeling and forecasting techniques have been developed for either
demand or supply needs of manpower (O’Brien-Pallas et al., 2001). Much of the
literature on demand analysis is devoted to manpower forecasting at the corporate
level (micro level), yet manpower forecasting at the industry level (macro level) is
equally important, especially for economic development of a country (Kao and Lee,
1998). In addition, many factors have been identified and incorporated into the
manpower forecasting models so as to improve their accuracy involving time lags (e.g.
hiring lead time, delays in training manpower, etc.) (Grinold, 1976; Kwak et al., 1977)
and fixed or variable saturation points (Osaki et al., 2001; Sharif and Ramanathan,
1981).
Considering the dynamic nature of demand and supply, where demand triggers
supply and vice versa, feedback is also an important factor for manpower forecasting.
Therefore, the purpose of this study is to develop an integrated model for the
IS industry, which focuses on the combination of the demand and supply of manpower
based on SD. The proposed manpower forecasting model incorporates three factors
(the feedback structure, time lags, and a flexible saturation point) for the IS industry.
Information
security industry
369

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