p-ISSN 2980-4868 |
e-ISSN 2980-4841
https://ajesh.ph/index.php/gp
The Role of Competitive
Intelligence in Strategic Decision-Making:
A Literature
Review
Muhammad
Alfi Fadhlurrahman1*, Stanislaus Riyanta2, Abdul Rivai
Ras3
Universitas
Indonesia, Indonesia
Emails
: muhammadalfi99@gmail.com1,
stanislaus@ui.ac.id2, rivai_ras@yahoo.com3
ABSTRACT
Competitive Intelligence (CI) is an important
element in a company's strategic decision-making in the midst of increasingly
fierce business competition. In business literature, CI has been recognized as
a vital tool for understanding a company's external environment and analyzing
competitors' strategies. However, there is still debate regarding the role and
effectiveness of CI in strategic decision-making. This study aims to explore
the role of CI in strategic decision-making through a comprehensive literature
review. The research method used is a qualitative method by analyzing various
articles and journals related to CI and strategic decision making. The
discussion in this study includes the importance of CI in providing relevant
information, the methods used in CI implementation, and its impact on the
company's strategic decisions. The results show that a deeper understanding of
the role of CI in the context of strategic decision making, as well as
providing recommendations to improve the effectiveness of CI in supporting
corporate strategic decisions. The implication of this research is to provide
valuable insights for business practitioners and researchers in understanding
the contribution of CI to the strategic decision-making process. This research
is also expected to be a reference for companies in optimizing CI
implementation to strengthen competitive advantage in the market.
Keywords: Competitive Intelligence,
Strategic Decision Making, CI Ethics, CI cycle, Big Data.
INTRODUCTION
Competitive Intelligence (Competitive Intelligence) emerges as a
vital component in the strategic planning and management process within the
organization (Sassi et al., 2022). CI is becoming an interesting thing to discuss because the
contribution of CI has increased over the past two decades and has become a
challenge for business professionals and is beginning to be studied and
implemented in large and small companies, in the private and public sectors, as
well as in various industry contexts. Interest in CI was also observed among
academic researchers. The existing literature reveals that CI is a
multidisciplinary concept studied by researchers with different areas of expertise
(management, marketing, information technology, decision support, and knowledge
management) and discussed from different perspectives (as concepts, products,
processes, practices/disciplines, methods, and as systems). While there is
great diversity in the pool of knowledge related to CI, there have been no
empirical studies that touch on the practical aspects of the problem by
developing complete CI solutions that can be delivered to decision-makers in
anticipation of competitor reactions. The ever-evolving competitive environment
is characterized by increased competition, rapid market changes, and
globalization. Companies must increase their competitive advantage to survive.
In order to gain an information advantage over competitors (knowing their capabilities,
weaknesses, intentions, and potential moves), companies need to continuously
monitor and process information related to the competitive environment of
competitors. Therefore, Competitive Intelligence (CI) is emerging as a vital
component of the strategic planning and management process (Sassi et al., 2022).
CI sits at the
crossroads of art and science, involving the careful collection and analysis of
data on competitor activities and strategies, it is an ongoing process that is
integrated with all aspects of strategic planning, from go-to-market to product
development. By leveraging the power of CI, businesses can predict market
shifts, know competitor movements, make informed decisions, bridge the path to
sustainable growth and success. CI is not only about competitive intelligence
but also uses it strategically to formulate a strong competitive intelligence
program. Competitive intelligence, often referred to as competitor intelligence
or corporate intelligence, is the foundation of a comprehensive competitive
intelligence strategy. CI is often associated with short-term profits, the real
power of which lies in shaping the long-term strategic planning for a company's
business. CI in an organization functions to improve information quality,
accelerate decision-making, improve organizational business processes, increase
effectiveness, reduce unnecessary costs, increase organizational awareness,
increase information flow and dissemination, identify opportunities and
threats, and save time (Oraee et al., 2020). CI is not just a tool for internal improvement and sales
strategy; also plays an important role in improving operational efficiency with
automated CI processes, such as those offered by various tools, which provide
timely insights for organized operations. Consistent and regular data
collection is the backbone of CI. Competitive intelligence is also referred to
as enterprise intelligence, it is essential for businesses seeking insights to
inform strategic decisions. A competitive intelligence practitioner is
essential to understand the importance of systematically gathering competitive
intelligence information, which goes beyond the capabilities of competitors to
include a broader understanding of market dynamics, corporate espionage, and
business competitor strategies. In a rapidly changing business landscape,
operational efficiency is critical. Automated CI processes play a crucial role
in providing timely insights that not only streamline operations but also
improve strategic decision-making. Competitive intelligence is a tool that can
be used by decision-making managers to improve their ability to deal with
uncertain situations and increase profitability. It would be more beneficial to
input business performance as a prediction factor, and environmental
uncertainty as an additional CI factor (Wu et al., 2023).
CI analysis can make the
business it runs is able to adapt proactively. A competitive intelligence
analyst who is experienced in different types of competitive intelligence plays
a crucial role in breaking down the information gathered through continuous
monitoring. This proactive approach ensures businesses maintain a competitive
edge and can respond quickly to shifts in the competitive landscape.
Competitive intelligence refers to the systematic collection and analysis of
data about competitors' activities and strategies. This continuous process is
integral part of a comprehensive business strategy. By understanding the
competitive landscape, companies gain insights that go beyond conventional
market analysis. It's not just about knowing what competitors are doing; It's
about understanding their movements and predicting market shifts. In the arena
of battle, understanding your business opponents is just as important as
understanding your own abilities. CI is key to unlocking deep insights into
competitors' strengths and weaknesses, allowing businesses to strategically
position themselves for profit. Collecting data regularly and continuously
allows businesses to adapt proactively. A competitive intelligence analyst who
is experienced in different types of competitive intelligence plays a crucial
role in breaking down the information gathered through continuous monitoring.
This proactive approach ensures businesses maintain a competitive edge and can
respond quickly to shifts in the competitive landscape.
The purpose of
Competitive Intelligence (CI) is to support managers in making strategic
decisions. CI encompasses all activities of gathering, analyzing, and
disseminating intelligence information about a company's products, customers,
competitors, or other aspects related to the company's environment. However, it
is important to underline that CI is different from business espionage; CI is
ethical, legal, and legitimate. Competitive intelligence is an important
foundation for companies in the process of developing competitive strategies (Lin et al., 2023). Another goal of CI is to create knowledge that is useful in
supporting internal business and reducing risk based on the information
obtained (Ranjan & Foropon, 2021). Competitive intelligence gathering methods such as surveys and
expert assessments cannot provide a quick response to business user
perceptions. A company's competitive strategy is a key component in strategic
business management. By developing a good competitive strategy, companies can
establish certain positions in the market and maintain those positions, thus
helping companies to earn sufficient profits in a competitive market.
Competitive intelligence intelligence is the first step in creating a competitive
strategy. Competitive intelligence mining can help companies determine the
difference between user needs and user perceptions of competitors' products and
enterprise products, which is one of the core problems that companies must
solve in creating competitive strategies.
Competitive Intelligence
(CI) is a systematic process for collecting, analyzing, and interpreting
information about the external business environment, competitors, and market
trends with the aim of supporting strategic decision-making within an organization.
Competitive intelligence is more focused on the external of the company,
inversely proportional to Business Intelligence which focuses on the internal
of the company/organization. Business Intelligence (BI) focuses on collecting
and analyzing internal company data such as customer data, supplier data, etc.
While Competitive Intelligence (CI)
focuses on competitor data (Ranjan & Foropon, 2021). The main goal of CI is to provide in-depth insight into the
company's business and competitive environment, thereby enabling companies to
identify opportunities and threats, as well as develop effective strategies to
gain a competitive advantage.
Based on the background
description above, the purpose of this study is to analyze the application of
Competitive Intelligence (CI) in companies, especially in supporting the
strategic decision-making process that focuses on the company's external environment,
competitors, and market trends. This research aims to identify how companies
can utilize CI to increase competitive advantage, as well as understand the
role of CI in developing effective business strategies in the face of
increasingly fierce competition. The benefit of this research is to provide
insight for companies in applying Competitive Intelligence as a strategic tool
to improve the effectiveness of managerial decisions, accelerate adaptation to
market changes, and minimize business risks. This research will also contribute
to the development of a more comprehensive CI practice, which focuses not only
on collecting competitor data, but also on a thorough analysis of opportunities
and threats arising from the external environment. The results of this study
are expected to provide guidance for companies in designing and implementing CI
programs that are more integrated and responsive to market dynamics.
RESEARCH METHOD
The method that will be used by the researcher is to
use qualitative research. Qualitative research is a research approach to
understand the characteristics of individuals or groups of people to social
problems. This research process uses questions and procedures, collecting data
in the environment of the object to be researched and making an interpretation
of the meaning of the data obtained (Creswell
& Poth, 2016). Qualitative research is carried out by
collecting a variety of empirical materials, such as case studies, personal
experiences, curriculum vitae, interviews, observational, and historical
history (Crozier
et al., 2018).
Qualitative research involves four main functions,
namely contextual functions, explanatory, evaluative, and generative functions.
In this study, the researcher's focus is on the explanatory function, which
aims to explain how competitive intelligence can influence and contribute to
the strategic decision-making process within an organization or company.
Includes an explanation of how competitive intelligence is collected, analyzed,
and used in the context of strategic decision-making, as well as its impact on
the organization's performance and competitive position.
Definition
of Competitive Intelligence
CI (Competitive Intelligence) is a process that includes elements of
collecting, processing, analyzing, and disseminating information into
intelligence that can be used as a basis for decision-making, thus providing a
competitive advantage for the company (Köseoglu
et al., 2021). Competitive intelligence, from an
organizational point of view, can be defined as the collection, analysis,
interpretation, and dissemination of strategic information at the right time to
be used in the decision-making process (Ranjan
& Foropon, 2021). Only organizations with competitive
features can gain a competitive advantage in improving customer experience,
maintaining employee loyalty, and achieving better performance than ever
before. There are many sources for business managers to obtain information
about competitive intelligence, ranging from internal and external sources,
questionnaires, expert assessments, and social network analysis (Lin et
al., 2023). Getting accurate and timely
competitive intelligence can help companies develop competitive strategies that
match their targets. This puts the company in an advantageous position in terms
of competition with competitors (Lin et
al., 2023). According to Strategic and Competitive
Intelligence Professionals (SCIP) defines CI as a systematic and ethical
program for collecting, analyzing, and managing external information that can
influence a company's plans, decisions, and operations (Sassi et
al., 2022). The purpose of CI is to support
managers in the company's strategic decisions. CI consists of all the
activities of gathering, analyzing, and disseminating intelligence about the
company's products, customers, competitors, or any other aspects related to the
company's environment. However, this should not be mistaken for business
espionage; because CI is ethical, legal, and legitimate (Sassi et
al., 2022).
The concept of CI has evolved gradually since the 1970s. Interest in CI
arises with the collection of CI itself, and the practice of skill development
in information acquisition. Then, in the 1980s, CI became increasingly
important and business professionals began to implement CI processes in
organizations. In the 1990s, interest was more focused on strategic
decision-making, which provided direct input to bottom line, the role of
information technology, CI technology, supply and demand, and counter-intelligence.
In the last two decades, the issue of CI has become a major issue that has core
capabilities, such as parallel process management, intelligence infrastructure
for multinational companies, CI as a learning material, and network analysis.
Today, the concept of CI is studied and implemented in large and small
companies in the private and public sectors, and in different industry contexts
(Sassi et
al., 2022). CI has become more well-known and
considered very important by business professionals who recognize its benefits
for organizations/companies, as well as have a positive impact on company
performance. CI (Competitive Intelligence) provides a foundation for better
decision-making, which in turn leads to the achievement of the set business
goals. Companies that take a proactive stance (anticipating what will happen
instead of just reacting) can better understand external influences, make
informed and timely decisions, and achieve a better market position (Sassi et
al., 2022). Interest in CI is not only among
business professionals but also among academic researchers who aim to
understand the concept, explain it, and develop methods, approaches, systems,
and solutions to deal with it. A thorough literature review shows that most
research studies have understood the concept of CI, its process, goals, and
benefits. The existing works are less than experiments and practical studies
that focus on developing CI solutions in terms of anticipating competitor
reactions.
Competitive
Intelligence Process
Simply put, Competitive Intelligence is the process of collecting,
analyzing, and disseminating information about competitors and the business
environment to gain a competitive advantage. The CI process includes monitoring
competitors with the aim of providing intelligence information that can be used
as a basis for taking action for the organization (Ranjan
& Foropon, 2021). The CI cycle consists of obtaining
data (acquiring), collection (Gathering), evaluate (Evaluating), and analyze
(Analyzing) unformatted and raw business data, and turn it into ready-to-use
intelligence for policymakers. The goal is to understand the strengths and
weaknesses of competitors, identify opportunities and threats in the market,
and develop more effective strategies. The benefits of competitive intelligence
include increased market share, profitability, and more successful new product
launches. The sources of information are diverse, such as internal, external
data, questionnaires, and data analysis. The competitive intelligence process
includes planning, data collection, data analysis, information dissemination,
and follow-up. The challenges include collecting accurate data, analyzing data
effectively, protecting confidential information, and ensuring the ethical use
of competitive intelligence. Organizations that succeed in competitive
intelligence have a culture that supports the collection and use of
information, trained teams, access to a variety of information sources, and
clear processes for analyzing and disseminating information. Competitive
intelligence is an essential tool for companies to gain a competitive advantage
in a competitive market.
When reviewing the literature related to CI, researchers found that CI is
a multi-disciplinary concept that is studied by researchers with a variety of
different expertise and viewed from different perspectives, such as From a
management perspective to explore and describe its concepts, related variables,
processes, and activities (García-Madurga
& Esteban-Navarro, 2020). As a product with an emphasis on the
development of methods and techniques to produce the expected results (Kula
& Naktiyok, 2021). As a CI practitioner or profession
based on a code of ethics (Köseoglu
et al., 2020). As a tool or system in which the
objective is to develop intelligence systems and support decisions for
companies (García-Madurga
& Esteban-Navarro, 2020).
Competitive
Intelligence Cycle
The intelligence cycle is the process of developing raw information into
intelligence products for use by policymakers who have power in
decision-making. There are several opinions regarding the intelligence cycle
cycle, some consist of four steps, five steps, and some have a six-step cycle.
Some models of the intelligence cycle are shaped like clockwise in the shape of
a circle, and some are in opposite directions. According to the Director of
National Intelligence (DNI), the intelligence cycle consists of 6 (six)
processes (Jensen
III et al., 2022) that is:
1.
Planning and direction, setting consumer
intelligence requirements and planning intelligence activities accordingly. The
planning and direction steps determine the stages for the intelligence cycle.
2.
Data
collection. Gathering the raw data needed to produce the finished
product. Data collection is carried out to collect raw data related to five
basic intelligence sources such as Basic Intelligence (Geospatial Intelligence
/ GEOINT), Human Intelligence (HUMINT), Open Source Intelligence (OSINT), and
Signal Intelligence (SIGINT).
3.
Processing and exploitation, converting raw data
into a comprehensive format that can be used for the production of finished
products in the form of reports. This step of processing and exploration
involves the use of highly trained and specialized personnel and high-tech
equipment to transform raw data into usable and understandable information.
4.
Analysis and production. Integrate, evaluate,
analyze, and prepare processed information for inclusion in finished products.
The analysis and production steps also require highly trained and specialized
personnel (analysts) to give meaning to the processed information and
prioritize it against known requirements.
5.
Dissemination. Sending product reports to the
company/organization's stakeholders. Policymakers who request reports in
finished products, usually via electronic message. The dissemination of
information is usually done through means such as websites, emails, and
hardcopy distribution.
6.
Evaluation, continuously getting feedback throughout
the intelligence cycle and evaluating that feedback to improve each individual
step and the intelligence cycle as a whole. Evaluation and feedback from
consumers are essential for those involved in this intelligence cycle. The
intelligence cycle will later adjust and improve activities and analysis in
more detail to meet the changing needs of consumers.
Figure 1. Intelligence
cycle according to the Director of National Intelligence (DNI)
Competitive
Intelligence Collection Methods
In the context of Competitive Intelligence, data collection methods have
a crucial role in supporting the company's strategic decision-making.
Traditional methods such as observations, questionnaires, mass media, images,
official reports, interviews, and Benchmarking has long been an important
instrument for understanding the market and competitors (Grigorescu,
2020). Observation provides first-hand
insight into competitor activity, while questionnaires and interviews allow
companies to gain insights from customers and other stakeholders. On the other
hand, modern methods such as big data analysis and semantic data warehouse (Casarotto
et al., 2021), social media analysis, creating brief
summaries of some relevant text documents, the use of the internet as an open
source CI (Calof
& Sewdass, 2020); (Maune,
2021), and artificial intelligence /
Artificial Intelligence (AI) offers the ability to collect and analyze large
volumes of data quickly and efficiently.
Figure 2. Conceptual framework for the use
of big data methods for CI processes
Source:
(Ranjan & Foropon, 2021)
Big data analysis serves
to identify previously undetected patterns (see Figure 2), while social media
analytics can provide insights into market sentiment realtime. In the
beginning, the use of technology Internet of Things (IoT), big data, and cloud
computing (Cloud Storage) has created good value for customers and the company.
Using big data methods can increase the impact of CI in gathering information
from various big and diverse data sources to gain useful insights, trends,
patterns, and knowledge to predict, analyze, and understand competitor
scenarios (Ranjan & Foropon, 2021). Recently, Competitive Intelligence (CI) has been attracting more
attention because of the amount of data available in the open source through
mobile phones, social media, blogs, text messages, emails, and other digital
communications; data from these sources is critical in building CI (Ranjan & Foropon, 2021). The use of artificial intelligence (AI) is able to automate
processes and increase efficiency in data collection and analysis. The
combination of traditional and modern methods can provide comprehensive data on
the business environment, companies are able to develop appropriate strategies
and are responsive to market and competitive changes.
The biggest
misconception mentioned by organizations in the use of Big Data analytics is
the lack of experience, awareness, and knowledge about Big Data in CI among
existing CI staff. Senior managers are worried about developing, monitoring,
and implementing counter-intelligence tactics and dashboards to improve CI.
These findings were reflected in all interviews. First, 21 participants (age
44, female) stated: We are recruiting IIM [India Institute of Management]
graduates specifically to collect CI through Big Data. But they weren't
equipped with the right analytics approach, and we ended up terminating their
contract. The employee is quite expensive, but the insights generated are not
there. Second, another participant (age 58, male) observed: "How can we
trust the internet? The quality of the data is highly questionable, and making
decisions based on those insights is skeptical." Third, another
participant (age 46, female) stated, "We use basic Big Data such as
analytics, competitor analysis, SWOT, segmentation analysis, 5 strengths
analysis; However, we haven't used this in real-time on larger data. We want to
use advanced text mining, natural language processing, etc., all of which are
related to Big Data.
Table 1. Methods used in CI.
|
CI Methods Used |
Percentage |
Rank |
|
Competitor analysis |
58.8% |
1 |
|
Customer segmentation |
52.9% |
2 |
|
SWOT analysis |
47.1% |
3 |
|
Industry/5 forces |
35.3% |
4 |
|
Financial analysis |
29.4% |
5 |
|
Win/loss analysis |
23.5% |
6 |
|
Benchmarking |
17.6% |
7 |
|
Others |
17.6% |
7 |
|
Scenario analysis |
11.8% |
9 |
|
Data Dissemination Points |
Percentage |
Rank |
|
Presentations/staff briefings |
82.4% |
1 |
|
Printed alerts/reports |
52.9% |
2 |
|
Newsletters |
41.2% |
3 |
|
Company intranet |
41.2% |
3 |
|
Central database |
29.4% |
5 |
|
Threat/Opportunity |
Percentage |
Rank |
|
New customer/target audiences |
76% |
1 |
|
New competitors |
52.9% |
2 |
|
Customers' DEMANDS |
41% |
3 |
|
Industry competitors |
23% |
4 |
|
Potential suppliers |
17% |
5 |
|
Staffing Options |
Percentage |
|
|
Project team in house/external |
64% |
|
|
Project team/employees |
35% |
|
|
External consultants |
5% |
|
|
Sources of CI in Firms |
Percentage |
Rank |
|
Commercial databases |
64.7% |
1 |
|
Industry experts |
64.7% |
1 |
|
Customers |
58.8% |
3 |
|
Publications (print/online) |
52.9% |
4 |
|
Social media |
17.6% |
5 |
|
Internal data |
11.8% |
6 |
|
Company employees |
5.9% |
7 |
|
Criteria for CI Effectiveness |
Percentage |
Rank |
|
New or increased revenue |
35% |
1 |
|
New products or services deployed |
34% |
2 |
|
Cost savings/avoidance |
23% |
3 |
|
No measure used |
20% |
4 |
|
ROI calculation |
17% |
5 |
|
Big Data Tools/Software Tools Used |
Percentage |
|
|
Yes |
64.7% |
|
|
No |
11.8% |
|
|
Challenges in Adopting Big Data Applications in CI |
Percentage |
Rank |
|
Developing, monitoring, and implementing counterintelligence tactics |
52.9% |
1 |
|
Capturing the competitive information held by the firm's employees |
41.2% |
2 |
|
Developing an integrated competitive insights dashboard |
29.4% |
3 |
Source: (Ranjan
& Foropon, 2021)
From the data presented (see Table 1),
it can be concluded that organizations use a variety of methods for competitive
intelligence (CI), including competitor analysis, customer segmentation, SWOT
analysis, and industry analysis. The main sources of information for CI include
commercial databases, industry experts, customers, and internal company data.
There are challenges in adopting Big Data applications in CI, with most
respondents having difficulty coping with them. However, there are still
efforts to develop, monitor, and implement counter-intelligence tactics, as
well as capture competitive information possessed by company employees. A
holistic and diverse approach is needed to ensure the effectiveness of CI and
make optimal use of the potential of big data.
Figure 3. Approach to using Big Data in CI
Source: (Ranjan & Foropon, 2021)
Based on Figure 3, it can be seen that the CI method carried out without
a big data approach involves steps such as data mapping, the use of data that
has been mapped, data collection and analysis from various sources, and the use
of publicly available data sources. On the other hand, the CI approach with a
big data approach leverages technology to analyze data networks, collect online
social data in real-time from various industry players, use real-time analysis
through big data, and synthesize and build complex data structures to support
more effective decision-making. With this approach, companies can optimize the
use of data in generating deeper insights and be responsive to changes in the
business environment.
Several methods are applied to translate large volumes of information
into valuable knowledge that can be implemented in CI, depending on the nature
of the data (structured, semi-structured, or unstructured), including:
a)
Data
Mining and Text Mining: Using a data warehouse system to store structured and
semi-structured data from a variety of sources, then applying data mining
techniques to identify useful patterns and trends (Casarotto et al., 2021).
b)
Web
Scraping: Uses automated tools to extract information from websites and online
databases. This can help in the extensive and continuous collection of data
from public sources such as forums, news sites, or social media platforms.
c)
Competitor
Analysis Tools: Use specialized software to analyze competitors, including data
about products, marketing strategies, or brand reputation. This allows for a
direct comparison between the performance of a company and its competitors.
d)
Sentiment
Analysis: Analyzing the opinions and sentiments contained in the text, both
from internal and external sources, to gain an understanding of the market's
perception and response to a company or its products.
e)
Machine
Learning Algorithms: Using machine learning algorithms to analyze and
understand patterns in data, both for prediction and for data grouping. This
allows for the identification of complex patterns and more accurate
predictions.
Despite the diverse knowledge from previous research
work related to CI, there have been no studies that concern the practical
aspects of the problem by developing solutions that can be conveyed to decision
makers. Previous research has only contributed theoretically by presenting:
surveys (Calof
& Sewdass, 2020), a model, conceptual framework
(Conceptual framework), the relationship between CI and other concepts such as
innovation and strategic thinking (Strategic Thinking), as well as CI levels
according to the level. To address this gap, it is critical to develop a
practical approach that can be implemented in a tangible way by decision-makers
(Stakeholders), can be achieved by integrating CI into strategic
decision-making processes, identifying market opportunities and threats, and
evaluating the potential impact of strategic decisions. This approach allows
organizations to leverage information competitively to gain a competitive
advantage and position their business for long-term success. For example, CI
can be used to identify market gaps and opportunities, by understanding what
competitors are doing (both good and bad), organizations can identify areas
where they can differentiate themselves in the market. CI can also help assess
the strengths and weaknesses of competitors, so that if a competitor has difficulty
providing services to customers, the company can prioritize improving its
customer service to be different from other competitors. Additionally, CI can
be used to assess the potential impact of strategic decisions, so that if an
organization is considering entering a new market, companies can use CI to
understand the competitive landscape and potential barriers. Create a feedback
cycle (feedback) is essential to continuously incorporate new information into
the company's decision-making. The feedback cycle includes regular updates to
the CI strategy, iterating on competitive landscape analysis, and gathering
more information from primary and secondary sources.
By using CI effectively, organizations can gain a competitive advantage
in their industry and set their business up for long-term success. Since CI can
be used by various parties with different needs within a company, CI has a
broad concept and the activities associated with it depend on the goals
achieved. For example, when implementing CI in a company, the objectives
consist of: (i) identification and detection of market trends, opportunities,
strengths, risks, and threats (ii) anticipating competitor actions (iii)
studying competitor successes and failures (iv) learning new technologies,
products, and processes; and (v) monitor political, legislative, and regulatory
changes in areas affecting the business (Sassi et
al., 2022). Getting accurate and precise CI
information can help companies develop competitive strategies that suit the
company's targets. However, this method is not able to provide a quick response
in providing perception and feedback users related to the company's competitive
situation and conditions. With the speed of product updates, even in large
manufacturing industries such as the automotive industry, the product update
cycle has been shortened from 3–5 years to 1–2 years, and some automakers even
launch several new products in the same year. Companies need information
collection and processing systems that provide quick feedback on user
perceptions of the company and its competitors. This system can provide
decision support for companies to develop competitive strategies.
Information collection is the most important basic thing. In competitive
intelligence, the quality of the information collected directly affects the
quality of competitive intelligence products (Grigorescu,
2020). The intelligence community
(Intelligence Community) uses the suffix "INT" which means
"Intelligence" to describe various methods of intelligence gathering.
There are several categories of intelligence gathering that are only owned by
the Government and the State Intelligence Agency, but not by private
companies/organizations, namely:
Human Intelligence (HUMINT).
Humint is the oldest method of collecting intelligence data, a term used in the
world of intelligence and the world of security to refer to one of the methods
of collecting information that involves humans as a source of information.
HUMINT involves the use of intelligence agents who directly interact with other
people or entities to obtain important information, such as information about
the intentions, activities, and plans of a particular opponent or target. The
use of humint is not only from secret sources, open source can be very valuable
information for intelligence agents in obtaining competitive intelligence
information.
a)
SIGINT (Signal Intelligence) is a branch of
intelligence that focuses on collecting, analyzing, and interpreting
information from electronic signals and communications. It includes the
monitoring and collection of electronic signals such as telephone conversations,
text messages, internet data, and other radio transmissions that can provide
insight into the activities and intentions of a particular adversary,
organization, or individual. SIGINT activities involve the use of advanced
equipment and technology to detect, monitor, and analyze electronic signals.
This can include intercepting communications, tracking radio signals, cracking
codes, and other techniques to obtain relevant information.
b)
GEOINT (Geospatial Intelligence) is a type of
intelligence that focuses on collecting, analyzing, interpreting, and
disseminating geographic and spatial information. It combines geographic data,
such as maps, satellite imagery, geodetic data, as well as other geographic
information, to gain insights into the physical environment and human
activities in different geographic regions.
c)
MASINT (Measurement and Signature Intelligence), one
of the disciplines in the world of intelligence that focuses on collecting,
analyzing, and interpreting data resulting from measurements and signatures
that are different from an object or phenomenon. MASINT differs from other
branches of intelligence such as SIGINT (Signal Intelligence) or HUMINT (Human
Intelligence), as it focuses more on physical characteristics and measurements
than human communication or information. In contrast to other methods of intelligence
gathering, MASINT developed in the 20th century when technology was evolving to
be more sophisticated.
d)
OSINT (Open Source Intelligence) is a type of
intelligence that focuses on collecting, analyzing, and interpreting
information that is openly and publicly accessible. The information in OSINT
comes from open sources such as newspapers, websites, social media, books,
government publications, and other public sources. There is a view that only
classified information can be categorized as good or useful information. A lot
of very useful information is unclassified and publicly available. Especially
today, in a world where we are flooded with information. There are thousands of
newspapers, television stations, and radio networks in operation. Plus all the
information is available on the Internet. The difficulty for the intelligence
community is often not due to a lack of information, but to having to sort out
really valuable information from useless.
Intelligence
Ethics in the Perspective of Competitive Intelligence
The term ethics has 2 perceptions, namely ethics as an examination and
analysis of concepts, theories, and moral principles that guide human behavior
and decision-making. Then, ethics becomes a value assessment that assesses the
truth or untruthfulness of a certain action. Ethics and Intelligence are
essentially oxymoronic (as opposed to each other). Intelligence activities are
often controversial, such as spying, confidential information gathering, and
possibly actions that violate human rights or certain ethical norms. Often when
intelligence ops are outside the boundaries of accepted norms of ethics. On the
other hand, ethics is very important in the world of intelligence. Ethics is
considered to provide guidelines and limits for intelligence actions, help
prevent abuse of power, and ensure that the operations carried out are in
accordance with moral and legal values. When intelligence engages in activities
of "lying, deceiving, manipulating and sometimes committing much worse
acts". Are intelligence and ethics compatible?. A former British official
said he "felt there was a problem with ethics" and considered ethics
to be an "obstacle in the intelligence system".
Figure 4. Intelligence Cycle
a)
Planning and direction, at this stage
decision-makers / stakeholders must have a role in determining the priority of
intelligence activities, determining an activity / task that is appropriate and
legally valid in justifying the existence of intelligence activities, and
recruiting agents to carry out their duties. Stakeholders must also consider
the safety and security of their citizens. Then, authorized policymakers have
an obligation not to put excessive pressure on intelligence agents to obtain
intelligence at all costs, as it may ignore ethics and/or violate the law. In addition, planning and direction also
includes recruitment and training process activities. Intelligence agencies
must have good ethical standards in the recruitment process. Drexel Godfrey
recommends that ethics tests should be part of the recruitment process at an
intelligence agency. Stephen Marrin and Jonathan Clemente emphasized the need
for ethical licensing or certification processes in the intelligence community,
such as procedures used in other professions such as doctors.
b)
Data collection, information collection activities
are divided into several ways: IMINT, SIGNINT, HUMINT, and OSINT. Especially
HUMINT, sometimes in the collection of intelligence information, the use of
aggressive interrogation techniques and the existence of torture, this problem
is justified in the effort to collect very important information, especially
the collection of information of a terrorist nature. There are differences in
pros and cons related to the use of violence ethics in information collection.
According to Alex Danchev and Alex Bellamy, violence is however unjustified and
violates human rights provisions. On the other hand, according to Dershowitz,
the method of violence can be ethically justified if a target has information
that can save the lives of many people.
c)
Analysis and Production, at this stage the data and
information collected are processed to produce intelligence products. Turning
the collected pieces of information into something that can be used by
policymakers. The results of this analysis can be in the form of written
reports, oral submissions, and brief memorandums. The ethical obligations that
must be considered are (a) avoiding the politicization of intelligence (b) not
justifying illegal and inappropriate changes in the processed intelligence information.
d)
Intelligence Dissemination, is the process of
disseminating intelligence information that has been collected to the
authorities or who need it for national security purposes. The purpose of
intelligence dissemination is to provide a broader understanding to
decision-makers and responsible parties, so that they can make better decisions
and take effective actions based on existing intelligence information. There
are 4 (four) ethics that must be met when disseminating intelligence
information, namely: (a) The truth of intelligence information to the
authorities (b) Sharing intelligence information with the outside world (c)
Sharing intelligence with domestic institutions (d) Prevention of the
dissemination of intelligence information without permission.
The challenge in this area is to find a
balance between the demands of national security and the ethical values that
underlie society. At the planning and direction stage, policymakers must be
able to set priorities for intelligence activities, determine an appropriate
and legally valid activity/task in justifying intelligence activities, recruit
agents to carry out duties, recruitment and training process activities, and
intelligence institutions must have good ethical standards in the recruitment
process. At the data collection stage, information collection activities are
divided into several ways: IMINT, SIGNINT, HUMINT, and OSINT. Especially
HUMINT. Sometimes in the collection of intelligence information of a state
nature, the use of aggressive interrogation techniques and the existence of
torture, this problem is justified in the efforts to collect very important
information, especially information collection in the field. In addition, a
field intelligence agent must be able to comply with all applicable laws, both
domestic and international laws, provide accurate and relevant information,
avoid conflicts of interest between intelligence agencies, provide
recommendations and conclusions honestly, correctly and realistically in
carrying out their duties, and faithfully comply with and submit to the
organization's policies and objectives.
The conclusion
in this study is the role of Competitive Intelligence (CI) in the context of
strategic decision making, it can be concluded that CI makes a significant
contribution in helping companies face the challenges of increasingly complex
business competition. CI plays a role in providing relevant and timely
information about the company's external environment, including competitor
analysis, understanding market trends, and industry changes that can affect
business strategy. With this information, companies can make better and
informed strategic decisions, and be able to respond to market changes more
quickly and effectively. The
contribution of this research is to provide a deeper insight into the
importance of developing an effective and integrated CI strategy. This research
also highlights the importance of utilizing advanced information technology and
data analysis tools to improve the quality and accuracy of information obtained
through CI. The role of CI is expected to continue to grow and become
increasingly important in the company's business strategy in the future. Along
with the development of technology, companies are expected to be able to
collect, analyze, and utilize data more efficiently through CI, which in turn
will improve their understanding of the market and competitors, and support
smarter and more timely strategic decision-making. Therefore, investment in the
development of CI capabilities and the use of the right technology can provide
significant benefits for the company's long-term growth and success.
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Muhammad Alfi Fadhlurrahman, Stanislaus Riyanta, Abdul Rivai Ras
(2024) |
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First publication right: Asian
Journal of Engineering, Social and Health (AJESH) |
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