# Economic Intelligence, Quésaco?

Leila Zemmouchi-Ghomari

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Leila Zemmouchi-Ghomari. Economic Intelligence, Quésaco?. 2023. hal-04078590v2

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# Economic Intelligence, Quésaco?

<figure><img src="image_1.png" /></figure>

Leila Zemmouchi-Ghomari *

leila.ghomari@ensta.edu.dz

Ecole Nationale Supérieure des
Technologies Avancées, ENSTA,
Algiers (Algeria)

**Abstract:** Industrial economic intelligence (EI) is a strategic information management discipline based on knowing and anticipating environmental changes, protecting and preserving strategic information against competitor attacks, and utilizing the knowledge produced to develop environmental improvements. As such, it facilitates the implementation of strategic actions by enabling companies to anticipate threats and opportunities and be reactive. Despite its importance, EI is often misunderstood, confused with business intelligence and knowledge management, and hence, underexploited. This paper aims to describe it to participate in its dissemination to a broader use than it is currently.

**Keywords:** Competitive Intelligence, Strategic Management, Technology Management, Influence, Protection, Watch

## Introduction :

Economic intelligence (EI) is a set of environmental monitoring actions to collect, process, analyze, and disseminate valuable information to economic actors. It integrates the protection of the information thus produced and its use in influence and lobbying actions. The objective of EI is to explain the relationship of organizations faced with the emergence of the information society and offer them tools to take advantage of these transformations (Pokrovskaja et al., 2016).

Global economic news points to the proliferation of risks of confrontation due to the progressive limitation of resources, growing tensions over the question of energy, and the desire for commercial conquest of new entrants (Esambert, 1996). This economic war requires the use of tools and techniques to allow the company to achieve a competitive advantage. Hence, the interest in using economic intelligence techniques such as the watch as a means of anticipation and monitoring, protection as a means of defending the information assets acquired in an increasingly competitive environment, and influence

<footnote>* Corresponding author</footnote>
# Economic Intelligence, Quésaco?

<figure><img src="image_2.png" /></figure>

Leila Zemmouchi-Ghomari *

leila.ghomari@ensta.edu.dz

Ecole Nationale Supérieure des
Technologies Avancées, ENSTA,
Algiers (Algeria)

**Abstract:** Industrial economic intelligence (EI) is a strategic information management discipline based on knowing and anticipating environmental changes, protecting and preserving strategic information against competitor attacks, and utilizing the knowledge produced to develop environmental improvements. As such, it facilitates the implementation of strategic actions by enabling companies to anticipate threats and opportunities and be reactive. Despite its importance, EI is often misunderstood, confused with business intelligence and knowledge management, and hence, underexploited. This paper aims to describe it to participate in its dissemination to a broader use than it is currently.

**Keywords:** Competitive Intelligence, Strategic Management, Technology Management, Influence, Protection, Watch

**Introduction :**

Economic intelligence (EI) is a set of environmental monitoring actions to collect, process, analyze, and disseminate valuable information to economic actors. It integrates the protection of the information thus produced and its use in influence and lobbying actions. The objective of EI is to explain the relationship of organizations faced with the emergence of the information society and offer them tools to take advantage of these transformations (Pokrovskaja et al., 2016).

Global economic news points to the proliferation of risks of confrontation due to the progressive limitation of resources, growing tensions over the question of energy, and the desire for commercial conquest of new entrants (Esambert, 1996). This economic war requires the use of tools and techniques to allow the company to achieve a competitive advantage. Hence, the interest in using economic intelligence techniques such as the watch as a means of anticipation and monitoring, protection as a means of defending the information assets acquired in an increasingly competitive environment, and influence

<footnote>* Corresponding author</footnote>
as a means of impacting its environment and imposing a point of view, a brand image, a reputation capable of enabling the organization to play an active role in the global economic sphere (Figure 1).

<figure><img src="image_2.png" /><figcaption>**Figure 1:** The Three Pillars of Economic Intelligence</figcaption></figure>

Source: Authors Contribution

As mentioned earlier, EI as strategic information management is based on three aspects: a) Knowing and anticipating changes in the environment; b) Protecting and preserving strategic information against competitor attacks; c) Using the information produced and the knowledge to develop actions of positive influence on the environment.

Two factors favored the emergence of economic intelligence, namely, the end of the Cold War, which led intelligence executives to become consultants in matters of strategy at the service of economic enterprises as well as the increase in global industrial production (technological development) and the concern of companies to differentiate themselves (Rochdane & Hamdani, 2018). Indeed, exploring the business environment, monitoring it, and interpreting it to innovate is imperative. The winner is the best informed (Comprendre & Entreprendre, 2016). Economic intelligence allows:

* Disseminate the correct information to the right actors

* Anticipate and be reactive
as a means of impacting its environment and imposing a point of view, a brand image, a reputation capable of enabling the organization to play an active role in the global economic sphere (Figure 1).

<figure><img src="image_3.png" /><figcaption>**Figure 1:** The Three Pillars of Economic Intelligence</figcaption></figure>

Source: Authors Contribution

As mentioned earlier, EI as strategic information management is based on three aspects: a) Knowing and anticipating changes in the environment; b) Protecting and preserving strategic information against competitor attacks; c) Using the information produced and the knowledge to develop actions of positive influence on the environment.

Two factors favored the emergence of economic intelligence, namely, the end of the Cold War, which led intelligence executives to become consultants in matters of strategy at the service of economic enterprises as well as the increase in global industrial production (technological development) and the concern of companies to differentiate themselves (Rochdane & Hamdani, 2018). Indeed, exploring the business environment, monitoring it, and interpreting it to innovate is imperative. The winner is the best informed (Comprendre & Entreprendre, 2016). Economic intelligence allows:

* Disseminate the correct information to the right actors

* Anticipate and be reactive
* Create new skills and stimulate innovation

* Understand the threats and opportunities of the environment that allow the implementation of strategic actions

* Help in decision-making

This paper aims to explain the ins and outs of the discipline of economic intelligence that we think is wrongly misunderstood and not exploited enough. Furthermore, we show through this paper the contributions of this discipline to achieve the competitive advantage of a company or an organization and how to apply it to participate in its dissemination to a broader use than it is currently.

The following section clarifies the differences between economic intelligence, business intelligence, and knowledge management. Next, section 3 describes the economic intelligence framework, and then usages of this discipline, as reported in the literature, are presented to illustrate how economic intelligence is perceived and often misunderstood. Finally, we conclude this paper by reminding you of the most essential aspects of EI and the current work perspectives.

## 2. Economic Intelligence vs. Knowledge Management vs. Business Intelligence:

Economic intelligence is often confused with other areas, such as Business Intelligence (BI) or Knowledge Management (KM). There is confusion since all these disciplines are intended to support strategic decision-making. KM focuses on the practical and theoretical know-how of groups of people within organizations (Harriet, 2014). KM provides a means to capitalize all the information likely to be helpful for the company (corporate memory) in solving the problems it faces. In (Harriet, 2014), the authors discuss how temporal properties can be exploited in EI projects. Through reasoning, new projects can make timely decisions based on reusable and available knowledge.
* Create new skills and stimulate innovation

* Understand the threats and opportunities of the environment that allow the implementation of strategic actions

* Help in decision-making

This paper aims to explain the ins and outs of the discipline of economic intelligence that we think is wrongly misunderstood and not exploited enough. Furthermore, we show through this paper the contributions of this discipline to achieve the competitive advantage of a company or an organization and how to apply it to participate in its dissemination to a broader use than it is currently.

The following section clarifies the differences between economic intelligence, business intelligence, and knowledge management. Next, section 3 describes the economic intelligence framework, and then usages of this discipline, as reported in the literature, are presented to illustrate how economic intelligence is perceived and often misunderstood. Finally, we conclude this paper by reminding you of the most essential aspects of EI and the current work perspectives.

## 2. Economic Intelligence vs. Knowledge Management vs. Business Intelligence:

Economic intelligence is often confused with other areas, such as Business Intelligence (BI) or Knowledge Management (KM). There is confusion since all these disciplines are intended to support strategic decision-making. KM focuses on the practical and theoretical know-how of groups of people within organizations (Harriet, 2014). KM provides a means to capitalize all the information likely to be helpful for the company (corporate memory) in solving the problems it faces. In (Harriet, 2014), the authors discuss how temporal properties can be exploited in EI projects. Through reasoning, new projects can make timely decisions based on reusable and available knowledge.
BI processes the company's information, allowing it to optimize its activities and costs. Table 1 shows the differences between them.

<table><caption>**Table 1:** Comparison between BI, KM, and EI</caption><thead><tr><th>Criterion</th><th>EI</th><th>KM</th><th>BI</th></tr></thead><tbody><tr><td>Information Source</td><td>EI is based on information external to the company.</td><td>KM is based on information internal to the company (produced by the company)</td><td>BI is based on information internal to the company (produced by the company)</td></tr><tr><td>Information Structuring</td><td>The practice of EI is based on unstructured information.</td><td>KM uses structured and unstructured information.</td><td>BI is based on structured information, managed in data warehouses,</td></tr><tr><td>Time Focus</td><td>EI focuses on the future</td><td>KM uses past and present information</td><td>BI mainly analyzes the past.</td></tr></tbody></table>

Source: Authors Contribution

The main difference between these disciplines (as illustrated in Table 1) is that EI focuses more on external sources. It is an anticipatory activity. On the contrary, KM and BI are more focused on historical information. Although the outcomes of KM and BI serve EI to set practical objectives to solve the problems faced by the corporation, some of these problems are identified by BI and KM, and some have not yet occurred because they are risks and potential dangers.

EI, KM, and BI are complementary (Eidizadeh et al., 2017). BI can serve economic intelligence by processing, analyzing, and interpreting data. EI and KM are part of the same process of transforming information into actionable knowledge to improve a company's competitiveness.

These three areas give the organization the capacity for innovation, the ability to achieve performance objectives, and the effectiveness necessary for a competitive advantage.
BI processes the company's information, allowing it to optimize its activities and costs. Table 1 shows the differences between them.

<table><caption>**Table 1:** Comparison between BI, KM, and EI</caption><thead><tr><th>Criterion</th><th>EI</th><th>KM</th><th>BI</th></tr></thead><tbody><tr><td>Information Source</td><td>EI is based on information external to the company.</td><td>KM is based on information internal to the company (produced by the company)</td><td>BI is based on information internal to the company (produced by the company)</td></tr><tr><td>Information Structuring</td><td>The practice of EI is based on unstructured information.</td><td>KM uses structured and unstructured information.</td><td>BI is based on structured information, managed in data warehouses,</td></tr><tr><td>Time Focus</td><td>EI focuses on the future</td><td>KM uses past and present information</td><td>BI mainly analyzes the past.</td></tr></tbody></table>

Source: Authors Contribution

The main difference between these disciplines (as illustrated in Table 1) is that EI focuses more on external sources. It is an anticipatory activity. On the contrary, KM and BI are more focused on historical information. Although the outcomes of KM and BI serve EI to set practical objectives to solve the problems faced by the corporation, some of these problems are identified by BI and KM, and some have not yet occurred because they are risks and potential dangers.

EI, KM, and BI are complementary (Eidizadeh et al., 2017). BI can serve economic intelligence by processing, analyzing, and interpreting data. EI and KM are part of the same process of transforming information into actionable knowledge to improve a company's competitiveness.

These three areas give the organization the capacity for innovation, the ability to achieve performance objectives, and the effectiveness necessary for a competitive advantage.
### 3. Economic Intelligence framework:

A framework is an analytical tool with several dimensions intended to illustrate an overall picture of a domain of study and organize its main concepts. From a practical point of view, a framework includes content, process (Figure 2), and people dimensions. For example, the EI process has been described in several papers and books, such as (Briciu et al., 2009; Macron & Moinet, 2011; Lafrem, 2020; Popovici & Mălăescu, 2015). The stages of this process can be defined as follows:

1. **Identify the problems to solve regarding threat, risk, and danger:** several reasons to trigger an EI process, examples: modernization, expansion, innovation, developing new export markets, or seeking to increase their market share. Others may feel threatened by fierce competition. Goals will change depending on industry structure; the Porter model can help in defining these goals (Porter, 1985);

<figure><img src="image_3.png" /><figcaption>**Figure 2: EI Process**</figcaption></figure>

Source: Adapted from (Briciu et al., 2009)
**3. Economic Intelligence framework:**

A framework is an analytical tool with several dimensions intended to illustrate an overall picture of a domain of study and organize its main concepts. From a practical point of view, a framework includes content, process (Figure 2), and people dimensions. For example, the EI process has been described in several papers and books, such as (Briciu et al., 2009; Macron & Moinet, 2011; Lafrem, 2020; Popovici & Mălăescu, 2015). The stages of this process can be defined as follows:

1. **Identify the problems to solve regarding threat, risk, and danger:** several reasons to trigger an EI process, examples: modernization, expansion, innovation, developing new export markets, or seeking to increase their market share. Others may feel threatened by fierce competition. Goals will change depending on industry structure; the Porter model can help in defining these goals (Porter, 1985);

<figure><img src="image_4.png" /><figcaption>**Figure 2: EI Process**</figcaption></figure>

Source: Adapted from (Briciu et al., 2009)
2. **Transformation of decision problem into information search problem:**
define relevant keywords related to the problem

3. **Identification of relevant information sources:** sources can be local, national, or global: universities, trade associations, government departments, databases, contact persons, interviews, meetings, telephone conversations, internet, newspapers, periodicals, reports (statistics), patents, surveys, opinions, rumors, etc.

4. **Validation of the information sources:** in terms of integrity and trust

5. **Collection and validation of information:** The risk is infobesity (information overload). Filter the information collected, keeping only the most essential and helpful information.

6. **Processing the collected information for calculating and interpreting the indicators:** objectively and statistically analyze data and general trends. Some experts suggest one-third of the EI process should be devoted to analyzing the collected information. Many potentially helpful analysis tools enhance information in competition, markets, and technology. Some techniques available include the Porter model, SWOT analysis, competitor profiling, patent analysis, and benchmarking techniques. For those dealing with technical information, the tools of Scientometrics may be of interest. These techniques use statistical, scientific, and technological information in databases, including patents (Google patents).

7. **Decision-making for the resolution of the problem:** Present information in an organized, transparent, concise way to decision-makers to help them in their missions

8. **Protection of informational patrimony throughout the entire process.**

The human dimension of a framework is related to the actors participating in the EI process. Usually, there are three actors:

1. **Decision maker:** the organization's individual who can identify and pose a problem to be solved in terms of stake, risk, or threat that weighs on the
2. **Transformation of decision problem into information search problem:**
define relevant keywords related to the problem

3. **Identification of relevant information sources:** sources can be local, national, or global: universities, trade associations, government departments, databases, contact persons, interviews, meetings, telephone conversations, internet, newspapers, periodicals, reports (statistics), patents, surveys, opinions, rumors, etc.

4. **Validation of the information sources:** in terms of integrity and trust

5. **Collection and validation of information:** The risk is infobesity (information overload). Filter the information collected, keeping only the most essential and helpful information.

6. **Processing the collected information for calculating and interpreting the indicators:** objectively and statistically analyze data and general trends. Some experts suggest one-third of the EI process should be devoted to analyzing the collected information. Many potentially helpful analysis tools enhance information in competition, markets, and technology. Some techniques available include the Porter model, SWOT analysis, competitor profiling, patent analysis, and benchmarking techniques. For those dealing with technical information, the tools of Scientometrics may be of interest. These techniques use statistical, scientific, and technological information in databases, including patents (Google patents).

7. **Decision-making for the resolution of the problem:** Present information in an organized, transparent, concise way to decision-makers to help them in their missions

8. **Protection of informational patrimony throughout the entire process.**

The human dimension of a framework is related to the actors participating in the EI process. Usually, there are three actors:

1. **Decision maker:** the organization's individual who can identify and pose a problem to be solved in terms of stake, risk, or threat that weighs on the
organization. In other words, he is expected to know the organization's needs, issues, possible risks, and threats.

2. **Watcher or collector of information:** This actor is the person within the organization who specializes in collecting and analyzing information. Its objective is to obtain indicators (from information) or the added value on which decision-makers depend for their decision-making processes. After receiving the problem to be solved as expressed by the decision maker, the watcher must translate it into information attributes (specific objectives) to be collected, which are used to calculate the indicators.

3. **Coordinator:** This actor was introduced based on the logic that questioned the adequacy of the existing actor responsible for the search operation in the possibility of being overloaded over time and having only the vision of an internal company, thereby reducing efficiency and the possible introduction of errors into its work. Although the terminology is officially known as info- intermediary, coined from "information and mediator," it was later changed to reflect a broader role in the process. Its mission is to bring insight to the consumer or client. The coordinator is, however, responsible in 2 main ways

* It can exist as a neutral entity, third party, or unbiased information provider. They are not necessarily hired by an organization/company but help consumers, in general, determine the suitability of their choices.

* It can also provide suppliers with consumer information, enabling sellers to develop/manufacture products tailored to consumer specifications. This type generally protects the interests of consumers.

Table 2 describes the EI framework's content, objectives, and scope.

<table><caption>**Table 2: EI Framework**</caption><thead><tr><td></td><td>Why (motivation)</td><td>Where</td><td>When</td><td>Who (actor)</td><td>How (Procedure)</td><td>What (information)</td></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
organization. In other words, he is expected to know the organization's needs, issues, possible risks, and threats.

2. **Watcher or collector of information:** This actor is the person within the organization who specializes in collecting and analyzing information. Its objective is to obtain indicators (from information) or the added value on which decision-makers depend for their decision-making processes. After receiving the problem to be solved as expressed by the decision maker, the watcher must translate it into information attributes (specific objectives) to be collected, which are used to calculate the indicators.

3. **Coordinator:** This actor was introduced based on the logic that questioned the adequacy of the existing actor responsible for the search operation in the possibility of being overloaded over time and having only the vision of an internal company, thereby reducing efficiency and the possible introduction of errors into its work. Although the terminology is officially known as info- intermediary, coined from "information and mediator," it was later changed to reflect a broader role in the process. Its mission is to bring insight to the consumer or client. The coordinator is, however, responsible in 2 main ways

* It can exist as a neutral entity, third party, or unbiased information provider. They are not necessarily hired by an organization/company but help consumers, in general, determine the suitability of their choices.

* It can also provide suppliers with consumer information, enabling sellers to develop/manufacture products tailored to consumer specifications. This type generally protects the interests of consumers.

Table 2 describes the EI framework's content, objectives, and scope.

<table><caption>**Table 2: EI Framework**</caption><thead><tr><th></th><th>Why<br>(motivation)</th><th>Where</th><th>When</th><th>Who<br>(actor)</th><th>How<br>(Procedure)</th><th>What<br>(information)</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th></th><th>(Decision level)</th><th>(Timing, frequency)</th><th></th><th></th></tr></thead><tbody><tr><td>General Objectives/ Scope</td><td>Anticipate threats and opportunities to gain or maintain a competitive advantage.</td><td>Corporate strategic and tactic level</td><td>Regularly and continuously</td><td>EI Actors</td><td>EI Process using the watch, protection, and influence tools and techniques</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Strategic, External, and condensed information</td></tr></tbody></table>

Source: Authors Contribution

## 4. Economic Intelligence usage, as reported in the literature

In (David & Thiery, 2002), the authors work on using Information Systems as an Economic Intelligent System component through the EQuA2te (Explore, Query, Analyze, and Annotate) architecture. Authors consider intelligent economic systems to be Information-based decision-making and user modeling. The case study in this paper concerns student modeling in computer-aided education, and they proposed a model based on the habits of a learner. In this system, the decision-maker and the watcher explore, interrogate, analyze, and annotate history to see if the university has faced a similar problem.

From our point of view, authors have used knowledge management bases to store problems and solutions and exploit this memory as needed, and this is more related to knowledge management than to the economic intelligence discipline, which can use history but is more oriented to collect information from external sources which are anticipative before they occur to gain a competitive advantage over concurrent.

In (Pokrovskia et al., 2016), the author poses the problem of integrating information systems in a university and the problems caused by the lack of interoperability. It proposes to use the process of economic intelligence to develop a global information system dedicated to decision support, mainly the search function and access to documentary information.
<table><thead><tr><th></th><th></th><th>(Decision level)</th><th>(Timing, frequency)</th><th></th><th></th></tr></thead><tbody><tr><td>General Objectives/ Scope</td><td>Anticipate threats and opportunities to gain or maintain a competitive advantage.</td><td>Corporate strategic and tactic level</td><td>Regularly and continuously</td><td>EI Actors</td><td>EI Process using the watch, protection, and influence tools and techniques</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Strategic, External, and condensed information</td></tr></tbody></table>

Source: Authors Contribution

## 4. Economic Intelligence usage, as reported in the literature

In (David & Thiery, 2002), the authors work on using Information Systems as an Economic Intelligent System component through the EQuA2te (Explore, Query, Analyze, and Annotate) architecture. Authors consider intelligent economic systems to be Information-based decision-making and user modeling. The case study in this paper concerns student modeling in computer-aided education, and they proposed a model based on the habits of a learner. In this system, the decision-maker and the watcher explore, interrogate, analyze, and annotate history to see if the university has faced a similar problem.

From our point of view, authors have used knowledge management bases to store problems and solutions and exploit this memory as needed, and this is more related to knowledge management than to the economic intelligence discipline, which can use history but is more oriented to collect information from external sources which are anticipative before they occur to gain a competitive advantage over concurrent.

In (Pokrovskaja et al., 2016), the author poses the problem of integrating information systems in a university and the problems caused by the lack of interoperability. It proposes to use the process of economic intelligence to develop a global information system dedicated to decision support, mainly the search function and access to documentary information.
The proposed solution is based on creating data warehouses for the type of user (students, teachers, administrators, managers) to meet the latter's needs best. However, the data warehouse being a multidimensional database (based on several criteria) that draws its sources from operational databases, this data transfer is carried out periodically to refresh the warehouse, and this artifact is mainly used in the field of BI and therefore not related to the discipline of EI.

This work falls within the framework of strategic decision support based on internal facts, not external trends and risks, as EI advocates.

The article (Peguiron, 2006) uses an energy company as a case study to demonstrate the impact of EI at the managerial level.

The author believes a comprehensive approach to EI requires carefully examining the extended scope, considering corporate culture and human relationships. Organizations are characterized by economic intelligence by default, and informal practices characterize formal implementation. Furthermore, it has been observed that some structures frequently practice economic intelligence functions naturally, sometimes relatively integrated. Marketing, communication, and innovation entities are among them. This service cannot position itself as a leader in project management because of these practices that operate in a vacuum.

Observing this raises the broader question of the profile of an economic intelligence practitioner, a role that requires considerable managerial skills, a transversal position within an organization that allows for a global perspective, and a relatively high hierarchical status that allows for the coordination of information flows.

A primary activity of EI is its watch (Oladejo et al., 2009, p. 15), which is continuous and iterative monitoring of the environment that is self-directed, voluntary, and based on interest. EI involves more than just collecting information; it filters, interprets, capitalizes, and disseminates it. A professional monitoring process complies with the constraints and needs associated with a specific request. It is shown in (Oladejo et al., 2009)
The proposed solution is based on creating data warehouses for the type of user (students, teachers, administrators, managers) to meet the latter's needs best. However, the data warehouse being a multidimensional database (based on several criteria) that draws its sources from operational databases, this data transfer is carried out periodically to refresh the warehouse, and this artifact is mainly used in the field of BI and therefore not related to the discipline of EI.

This work falls within the framework of strategic decision support based on internal facts, not external trends and risks, as EI advocates.

The article (Peguiron, 2006) uses an energy company as a case study to demonstrate the impact of EI at the managerial level.

The author believes a comprehensive approach to EI requires carefully examining the extended scope, considering corporate culture and human relationships. Organizations are characterized by economic intelligence by default, and informal practices characterize formal implementation. Furthermore, it has been observed that some structures frequently practice economic intelligence functions naturally, sometimes relatively integrated. Marketing, communication, and innovation entities are among them. This service cannot position itself as a leader in project management because of these practices that operate in a vacuum.

Observing this raises the broader question of the profile of an economic intelligence practitioner, a role that requires considerable managerial skills, a transversal position within an organization that allows for a global perspective, and a relatively high hierarchical status that allows for the coordination of information flows.

A primary activity of EI is its watch (Oladejo et al., 2009, p. 15), which is continuous and iterative monitoring of the environment that is self-directed, voluntary, and based on interest. EI involves more than just collecting information; it filters, interprets, capitalizes, and disseminates it. A professional monitoring process complies with the constraints and needs associated with a specific request. It is shown in (Oladejo et al., 2009)
that many Internet users achieve the watch on their own time to fulfill an informational
need. EI and its activities are intuitive and do not require explaining its benefits to
individuals.

As part of (Hamdani, 2018), the authors attempted to answer the question: How can economic intelligence contribute to business competitiveness? In a broader sense, how does EI contribute to developing a smart territory by carrying out projects, technological initiatives, and sustainable development plans? This study was intended to provide insight into this issue and propose a framework relating EI practices to business competitiveness and smart cities. According to them, informing means offering representations for solving problems, given that those representations need to be adapted to the context of use. The information enables one to gain knowledge, which changes how the world is viewed and reduces uncertainty. Quantitatively, qualitatively, and temporally, it is relevant. Whenever a piece of information is valuable and helps make decisions, it comes under economic intelligence.

(Rochdane & Hamdani, 2018; Peguiron, 2006, Oladejo et al., 2009; Oladejo et al., 2009) I have correctly assimilated the concepts behind EI and advocated that it is achieved intuitively and informally, arguing that it is naturally necessary for corporations.

**5. Conclusion:**

Economic intelligence brings together several good practices of strategic management, which deserve to be acclaimed for wide use at the enterprise level to achieve competitive advantage.

As we have seen, this discipline is often confused with BI and KM, which are complementary decision-support systems.

We plan to survey the case studies reported in the literature, which have described the adoption of competitive intelligence as a continuous and iterative practice of strategic management to identify the obstacles to its adoption and recommend adequate solutions.
that many Internet users achieve the watch on their own time to fulfill an informational
need. EI and its activities are intuitive and do not require explaining its benefits to
individuals.

As part of (Hamdani, 2018), the authors attempted to answer the question: How can economic intelligence contribute to business competitiveness? In a broader sense, how does EI contribute to developing a smart territory by carrying out projects, technological initiatives, and sustainable development plans? This study was intended to provide insight into this issue and propose a framework relating EI practices to business competitiveness and smart cities. According to them, informing means offering representations for solving problems, given that those representations need to be adapted to the context of use. The information enables one to gain knowledge, which changes how the world is viewed and reduces uncertainty. Quantitatively, qualitatively, and temporally, it is relevant. Whenever a piece of information is valuable and helps make decisions, it comes under economic intelligence.

(Rochdane & Hamdani, 2018; Peguiron, 2006, Oladejo et al., 2009; Oladejo et al., 2009) I have correctly assimilated the concepts behind EI and advocated that it is achieved intuitively and informally, arguing that it is naturally necessary for corporations.

**5. Conclusion:**

Economic intelligence brings together several good practices of strategic management, which deserve to be acclaimed for wide use at the enterprise level to achieve competitive advantage.

As we have seen, this discipline is often confused with BI and KM, which are complementary decision-support systems.

We plan to survey the case studies reported in the literature, which have described the adoption of competitive intelligence as a continuous and iterative practice of strategic management to identify the obstacles to its adoption and recommend adequate solutions.
## Referrals and References:

- Briciu, S., Vrîncianu, M., & Mihai, F. (2009). Towards a new approach of the economic intelligence process: Basic concepts, analysis methods, and informational tools. *Theoretical and Applied Economics*, 4(4), 21.

- Canet, F. (2017). La veille ordinaire en ligne: une pratique informationnelle en émergence. *I2D: information, données & documents*, (2), 70-79.

- Comprendre & Entreprendre, Avril 2016 – N°17 – 03. « L'intelligence territoriale au défi des smart cities : comment concilier efficacité 2.0 et démocratie ? ».

- David, A., & Thiery, O. (2002). Application of "EQuA2te" Architecture in Economic Intelligence. *Information and Communication Technologies Applied to Economic Intelligence-ICTEI*, 32.

- Eidizadeh, R., Salehzadeh, R. and Chitsaz Esfahani, A. (2017), "Analysing the role of business intelligence, knowledge sharing, and organizational innovation on gaining competitive advantage," *Journal of Workplace Learning*, Vol. 29 No. 4, pp. 250-267.

- Esambert, B. (1996). La guerre économique. *Defense Nationale-Paris-Etudes Politiques Strategiques Militaires Economiques Scientifiques*, 52, 78-87

- Harriet, L. (2014). L'intelligence économique à la lumière des concepts managériaux: l'étude de cas d'une entreprise du secteur énergétique (Doctoral dissertation, Université de Bordeaux).

- https://blog.ecole-managementnormandie.fr/wpcontent/uploads/2017/07/Comprendre_Entreprendre_n17_Cl_Rochet.pdf

- Lafrem, M. (2020). L'intelligence économique comme outil de prise de décision stratégique: une étude exploratoire qualitative dans les organismes publics marocains. *Revue internationale d'intelligence économique*, 12(1), 90-109.

- Macron, Ch. & Moinet, N. (2011). « L'intelligence économique ». Dunod, 2ème édition, pp. 62-63.

- Oladejo, B., Osofisan, A., & Odumuyiwa, V. (2009). Knowledge management in economic intelligence with reasoning on temporal attributes. *arXiv preprint arXiv:0906.1673*.
## Referrals and References:

- Briciu, S., Vrîncianu, M., & Mihai, F. (2009). Towards a new approach of the economic intelligence process: Basic concepts, analysis methods, and informational tools. *Theoretical and Applied Economics*, 4(4), 21.

- Canet, F. (2017). La veille ordinaire en ligne: une pratique informationnelle en émergence. *I2D: information, données & documents*, (2), 70-79.

- Comprendre & Entreprendre, Avril 2016 – N°17 – 03. « L'intelligence territoriale au défi des smart cities : comment concilier efficacité 2.0 et démocratie ? ».

- David, A., & Thiery, O. (2002). Application of "EQuA2te" Architecture in Economic Intelligence. *Information and Communication Technologies Applied to Economic Intelligence-ICTEI*, 32.

- Eidizadeh, R., Salehzadeh, R. and Chitsaz Esfahani, A. (2017), "Analysing the role of business intelligence, knowledge sharing, and organizational innovation on gaining competitive advantage," *Journal of Workplace Learning*, Vol. 29 No. 4, pp. 250-267.

- Esambert, B. (1996). La guerre économique. *Defense Nationale-Paris-Etudes Politiques Strategiques Militaires Economiques Scientifiques*, 52, 78-87

- Harriet, L. (2014). L'intelligence économique à la lumière des concepts managériaux: l'étude de cas d'une entreprise du secteur énergétique (Doctoral dissertation, Université de Bordeaux).

- https://blog.ecole-managementnormandie.fr/wpcontent/uploads/2017/07/Comprendre_Entreprendre_n17_Cl_Rochet.pdf

- Lafrem, M. (2020). L'intelligence économique comme outil de prise de décision stratégique: une étude exploratoire qualitative dans les organismes publics marocains. *Revue internationale d'intelligence économique*, 12(1), 90-109.

- Macron, Ch. & Moinet, N. (2011). « L'intelligence économique ». Dunod, 2ème édition, pp. 62-63.

- Oladejo, B., Osofisan, A., & Odumuyiwa, V. (2009). Knowledge management in economic intelligence with reasoning on temporal attributes. *arXiv preprint arXiv:0906.1673*.
- Peguiron, F. (2006). Application de l'Intelligence Economique dans un Système d'Information Stratégique universitaire: les apports de la modélisation des acteurs (Doctoral disser-tation, Université Nancy 2).

- Pokrovskaja, N., Snisarenko, S., & Golohvastov, D. (2016, April). Institutional confidence and economic intelligence are needed for performance at macro and micro-networks in the International Conference on Management, Leadership, and Governance (pp. 255-263).

- Popovici, V. I., & Mălăescu, P. S. A. M. (2015). Economic Intelligence Theoretical And Practical Aspects. Analele Universității Constantin Brâncuși Din Târgu Jiu: Seria Econo-mie, 1(6), 286-286.

- Porter, M. E. (1985). Technology and competitive advantage. Journal of business strategy.

- Rochdane, H., & Hamdani, S. (2018, April). Economic Intelligence: A Global Approach to Business Competitiveness and the Emergence of Smart Cities. In 2018 Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE) (pp. 1-5). IEEE.
- Peguiron, F. (2006). Application de l'Intelligence Economique dans un Système d'Information Stratégique universitaire: les apports de la modélisation des acteurs (Doctoral disser-tation, Université Nancy 2).

- Pokrovskaja, N., Snisarenko, S., & Golohvastov, D. (2016, April). Institutional confidence and economic intelligence are needed for performance at macro and micro-networks in the International Conference on Management, Leadership, and Governance (pp. 255-263).

- Popovici, V. I., & Mălăescu, P. S. A. M. (2015). Economic Intelligence Theoretical And Practical Aspects. Analele Universității Constantin Brâncuși Din Târgu Jiu: Seria Econo-mie, 1(6), 286-286.

- Porter, M. E. (1985). Technology and competitive advantage. Journal of business strategy.

- Rochdane, H., & Hamdani, S. (2018, April). Economic Intelligence: A Global Approach to Business Competitiveness and the Emergence of Smart Cities. In 2018 Renewable Energies, Power Systems & Green Inclusive Economy (REPS-GIE) (pp. 1-5). IEEE.