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Linking Competitor Intelligence with Strategic Decision-Making (SDM)
By Sandra Schlick
Abstract
This research investigates the link between Competitor Intelligence and Strategic Decision-
Making (SDM). To this aim, a CIP is introduced to provide a structured approach to analyse
the information processes from a broad scope of internal and external sources, and a wide
range from raw data until processed intelligence feeding into SDM. To understand this
process the CIP was employed to review the literature and theory. The aim is to demonstrate
whether an increase in efficiency and effectiveness of the management of the CIP improves
SDM. A systems view is adopted to describe the CIP. A mixed methodology approach is
adopted, using interviews, questionnaire using Likert scales and two-valued response options,
quantitative static, and dynamic models to analyse primary and secondary data.
Key words: Competitive Intelligence, Critical Intelligence Portals, Decision Support System,
Strategic Decision-Making, Mathematical Modelling System.
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Introduction
This research investigates the link between Competitor Intelligence and Strategic Decision-
Making (SDM). To this aim, a CIP is used to provide a structured approach to analyse the
information processes from a broad scope of internal and external sources, and a wide range
from raw data until processed intelligence feeding into SDM.
Therefore, this research investigates in how far the CIP approach supports SDM. It further
investigates, if such an approach affects the work of the Competitive Intelligence
practitioners, and it aims to show that such an approach improves firm performance.
The relevance of the systems is checked within a case of the telecom sector. This is further
generalised for the census of the telecoms in one country.
The first section gives an overview of the research describing the stages of the research. The
following four sections examine each of the stages of the research. It examines literature
using the CIP approach, discusses an application within one telecom case, outlines future
work to investigate the census of the telecoms, and sketches future work to give
recommendations to firms. The last section evaluates shortly the internal and external validity
per stage of the research.
Overview of Research
A mixed method approach fulfilled the need of analysing qualitative and quantitative data
(Creswell 2003), or just using different methods in the coarse of a study (Trochim 2001).
The research design followed a sequential procedure path.
The research was carried out in 4 Stages as shown in Table 1.
Table 1: Overview of Research stages
Stage 1
Stage 2
Stage 3
Stage 4
Issue
Connection of
Competitive
Intelligence with SDM
Effect of
competitor actions
through time
Application of
CIP
Recommendat
ions for CIP
Strategy
Literature review,
Case study
Secondary data
analysis
Primary data
analysis
Synthesis and
optimization.
Data
sources
Interview
Data bases,
Internet,
Data request
Questionnaire
Results from
Stages 1 to 3
Within stage 1 published sources were examined using the CIP approach, as reports from
Telecom enterprises, professional reports, academic journals, statistical computing literature
and procedures, and the scope for a secondary data collection was evaluated.
Within stage 2 a case study was chosen to find out the effect of competitor actions. A
secondary data analysis and a model construction followed. Through data analysis, the
dependencies and correlation of variables (profit, new entrants, patents) were identified.
Within stage 3 of future work a primary data collection will be undertaken and restricted to
the MNE telecom branch to receive results of a comparable technical standard, to find out to
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the efficiency of the CIP within the firms. Within stage 4 of future work recommendations
for the CIP will be elaborated.
Research Stage 1: Literature structured in Critical Intelligence Portals (CIP)
The Critical Intelligence Portals (CIP) approach to competitive intelligence management was
developed by Wright (2005) as a means of illustrating and identifying the flow of intelligence
within an organisation. This was later integrated with the work of Schlick (2006), and the
resultant structure was adopted, further enhanced and elaborated as a framework with which
to organise the literature review for this study. The integrated CIP framework is shown in
Figure 1 and it can be seen that it consists of building blocks. Gathered information is
processed and structured within the information systems or management blocks. The
processed intelligence is then analysed within the decision support block, with the aim to
prepare recommendations for strategic decision-making.
In this research CI was used as a generic term to cover the activities undertaken to achieve the
objectives as set out in the definition suggested by (Jaworski and Wee 1992, p. 23):
“identifying the intelligence requirements of a company, systematically collecting relevant
information on competitors, and processing data into actionable knowledge about
competitors’ strategic capabilities, position, performance, and intentions”.
Figure 1: Critical Intelligence Portal (CIP) Approach
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CIP Role
For this research the system view embedded within the CIP approach was adopted focusing at
the interaction, links, and overlaps of the building blocks. This stood in contrast to the module
view of CI (planning and focus, collection, analysis, communication, and decision) (Prescott
2003), and (Dishman and Calof 2008).
Purpose of the CIP building blocks
The CIP consists of a gathering block as databases, Internet, file folders, etc accessible to the
information systems. The information systems are: Competitor Information System (CIS),
Management Information System (MIS), Mathematical Modelling System (MMS), and
Knowledge Management (KM). These building blocks feed information into Decision
Support System (DSS) processing information into intelligence for SDM through analytical
activities.
CIS supplies, and transforms data (Fleisher and Bensoussan 2007) similar to other
information systems for other management information purposes such as Strategic
Information Systems (SIS), and Executive Information Systems (EIS), and manages data of
competitors (Rajaniemi 2004). CIS identifies competitor strategies and tactics (Fletcher and
Donaghy 1993), (Sauter and Free 2005). It feeds the DSS with expected strategies and tactics
of competitors.
MIS monitors internal information; the users are managers and executives. The output
consists of predefined periodic reports, and the operations are to summarise information or
existing data. Within the example of a sales report for an airline, a MIS can provide frequently
updated ticket sales information enabling executives to make management decisions faster
(Watson et al. 2006). It feeds the DSS with summarised internal existing and planned
capability reports.
MMS provides computations to transform the acquired data. It feeds the DSS with outputs
ready for interpretations and choices. Some examples illustrate this further. MMS computes
upper and lower bonds within scenario analysis (Fleisher and Bensoussan 2007), identifies
optimal strategic movements within a game theory board (Nash 1951), and analyses dynamic
sets of internal and/or external data within time series models (Lütkepohl 2005).
KM organises existing internal information companywide. It is principally concerned with the
interrogation of known material in the firm. Swan (2001) claimed that the ability to organise
knowledge within KM enables firms to encourage their innovations. It feeds the DSS with
organised internal data.
DSS analyses and links the information and if necessary feeds back into one of the building
blocks. This can be for example the request to gather additional information or to transform
data. When accomplished, the DSS feeds the processed and analysed intelligence together
with resulting recommendations to decision-makers ready for SDM.
The DSS evolves within a knowledge generic engineering cycle as identified by Carson et al.
(1998, p. 80). This cycle consisted of “human expert, knowledge acquisition, knowledge
representation,” feeding into the “evolving DSS,” which closed the cycle when feeding back
to the “human expert”. Additionally a “control (inference mechanism)” was identified. This
corresponded to one of the basic assumptions of this research claiming the evolution of a DSS
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being core and that the DSS would not have to be a technologically sophisticated system, but
have a feedback from management to the experts and within the building blocks.
SDM is the process of finding managerial decisions. It concerns risky issues, and involves
various entrepreneurial functions within different academic disciplines (Papadakis and
Barwise 1997).
Research Stage 2: Case Study
A Swiss MNE telecom firm, Swisscom Solutions (SL) was investigated. Swisscom identified
the following analytical methods and sources for information about competitors and
innovation: market research and market information, strategic analysis of competitors,
coordination of information providers, observation of technological trends, customer data,
customer satisfaction analysis of potential, general information about competitors,
development of market, product specific observation of competitors (products, and prices),
portfolio management, information about competition from customers, and information about
competitors based on bids. These sources were partly embedded in the Internet, for fee
databases, software – supported systems, paper file folders within the business departments,
and through communication between and within the departments. Swisscom had several
partly independent CIS in various Business-Units. The structure of the various CIS was not
standardized. This was the reason they were not used for inter-unit purposes. There was a lack
of communication between units, which could be ameliorated with a new standardized CIS.
The process of gaining and operating information was not standardized. Communication
between Business-Units was not structured (not regular and not observing principles). The
aim of the SL CIS project was to exchange information more efficient and effectively
between Business-Units with standardised structures of information and to use one CIS also
for inter-unit purposes after a phase of realising, and learning (Schlick 2006).
Based on the results from the investigation of SL, and as a result from Stage 1 of this
research, a secondary data analysis was conducted to find out if competitor actions had an
effect on the results of enterprises.
The secondary data analysis was carried out with the profit data from Swisscom, patent data
from Switzerland, and number of new entrants in Switzerland with annual data during the
years 1990 to 2006. The results of a times series analysis using a VAR Model showed
instantaneous causality coming from the variables “Patents” and “New Entrants” to the
variable “Profit” of Swisscom, which means that there is a correlation between “Patents”,
“New Entrants” and “Profit” of Swisscom. A tendency of Granger causality of the “Patents”
to “Profit” was identified, which means that “Patents” are one variable to cause the profit of
Swisscom.
Research Stage 3 Future Work: Primary Data Analysis
The results from stage 2 demonstrated that there is a correlation between the analysed
variables. Therefore it makes sense to investigate if this can be generalised. A primary data
analysis further makes sense because no study exists, which has elaborated on the issue to
investigate the efficiency of the use of the CIP within firms. Based on the indications from
Stage 1 and the results from Stage 2 it is suspected that the standards of the CIP of the firms
vary with their performance.
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Sample Frame Selection and Justification
The census of the UK MNE telecom firms was identified. The population size was 162 MNEs
in June 2008. The expected response rate is about 20-30% from postal questionnaires. It is
estimated that profit and the efficiency of the CIP have a significant positive correlation (r >
0.25). The questionnaire will use forced choice and Likert scales, both apt for coding into
dummy variables. Statistical techniques will be employed to analyse the data. Suitable
contacts and addresses have been identified. Anonymity will be offered to respondents. The
results will provide the efficiency of the CIP of the UK telecoms.
Research Stage 4 Future Work: Recommendations to firms
Recommendations will be given for a holistic DSS and a CIP, if the findings from stage 3 of
this research confirm the findings from stages 1 and 2.
Internal and External Validity per Stage of Research
The internal and external validity was evaluated using the approach of (Trochim 2001) and
evaluating validity based on the different approaches per stage (Wright and Calof 2006).
Internal Validity per stage
Stage 1 was accurate as indepth investigation is undertaken.
Stage 2 was limited as just 16 years (16 data points) could be received.
Stage 3 was accurate as the status of the systems and their interaction of the telecom will be
investigated.
External Validity per stage
Stage 1 was only limited as one case was studied. Cases differ as they depend on
technological, administrative, and personal traits (Wright and Calof 2006). As an MNE was
investigated with all the required elements on a qualitative basis it was considered as
representative for the census in question consisting of MNEs.
Stage 2 was accurate considering the dependencies and the model (patent data hold true for
other places as European patents were chosen, new entrant data would change with each
country, Swisscom is considered as representative for telecom MNEs). If the dependencies
were internal of high validity, it would hold true also for other sectors. This holds true only on
a limited basis due to the traits (for example technological, personal, and cultural). Regarding
other times, the external validity is doubtable, as technological progress growth direction is
positive and accelerating, not regular, and not predictable for the far future. Therefore after
stage 2 the results are still limited but indicative to existing tendencies.
Stage 3 was accurate, as the whole census of the telecom sector will be investigated. It hold
true for other telecom sectors (Canada, Switzerland, USA) with variants of the traits.
Therefore after stage 3 the results are accurate.
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References
Brody, R. (2008), "Issues in Defining Competitive Intelligence: An Exploration", Journal of
Competitive Intelligence and Management, 4 (3), 3-15
Carson, E.R., Hovorka, R., Roudsari, A.V. and Summers, R. (1998), "Modelling and Decision
Support in Physiology and Medicine: A Methodological Framework with
Illustrations", Mathematical & Computer Modelling of Dynamical Systems, 4 (1), 73-
99
Creswell, J.W. (2003), Research Design: Qualitative, Quantitative, and Mixed Methods
Approaches, 2/E, Sage, London
Dishman, P.L. and Calof, J.L. (2008), "Competitive Intelligence: A Multiphasic Precedent to
Marketing Strategy", European Journal of Marketing, 42 (7/8), 766-785
Fleisher, C.S. and Bensoussan, B.E. (2007), Business and Competitive Analysis, FT Press, UK
Fletcher, K. and Donaghy, M. (1993), "The Role of Competitor Information Systems",
Marketing Intelligence & Planning, 11 (7), 4-18
Jaworski, B. and Wee, L.C. (1992), "Competitive Intelligence and Bottom-Line
Performance", Competitive Intelligence Review, 3 (3-4), 23-27
Lütkepohl, H. (2005), "New Introduction to Multiple Time Series Analysis." 2/E, Springer,
Berlin, Heidelberg
Nash, J.F. (1951), "Non-Cooperative Games." The Annals of Mathematics, 54 (2), 286-295
Papadakis, V. and Barwise, P. (Eds) (1997), Strategic Decisions, Kluwer, Norwell, MA
Prescott, J.E. (2003), "CI Teacher", in Carr, M.M. (2003) Super searchers on Competitive
Intelligence – The Online and Offline Secrets of Top CI Researchers, CyberAge
Books, NJ
Rajaniemi, K. (2004), "Literature Research Approach on Research Topic: Scanning
Competitive Environment", Proceedings of Frontiers of E-Business Research
Conference (EBRF), Tampere University of Technology, Finland, 428-443
Sauter, V.L. and Free, D. (2005), "Competitive Intelligence Systems: Qualitative DSS for
Strategic Decision Making", ACM SIGMIS Database, New York, 36 (2), 43-57
Schlick, S. (2006), "Report Swisscom Solutions", Swisscom Solutions, Zurich, Switzerland
Swan, J. (2001), "Knowledge, Purpose and Process: Linking Knowledge Management and
Innovation", Proceedings of the 34th Annual Hawaii international Conference on
System Sciences, IEEE Computer Society Washington, DC
Trochim, W.M.K. (2001), The Research Methods Knowledge Base, 2/E, Atomic Dog,
Cincinnati, OH
Watson, H.J,, Wixom, B.H., Hoffer, J.A., Anderson-Lehman, R. and Reynolds, A.R. (2006),
"Real-Time Business Intelligence: Best Practices at Continental Airlines", Information
Systems Management, 23 (1), 7-18
Wright, S. (2005), "Decision Support and CI for Competitive Advantage", Royal United
Services Institute for Defence and Security Studies Annual Conference, Whitehall,
London, UK
Wright, S. and Calof, J.L. (2006), "The Quest for Competitive, Business and Marketing
Intelligence: A Country Comparison of Current Practices ", European Journal of
Marketing, 40 (5/6), 435-456
