Critical Factors of Competitive Intelligence in the Power Plant Industry : The Case Study of MAPNA Group

This paper aims to discuss the critical factors of competitive intelligence that influences the Iran’s power plant industry (MAPNA Group). Design/methodology/ approach: The paper has identified critical factors of competitive intelligence through Iran’s power plant industry based on a comprehensive review of recent literature. For this purpose, a questionnaire was designed, applied and analyzed by the use of statistical methods. The results discuss various perspectives from a competitive intelligence point of view, and provide critical factors and a regression model for showing essential issues on the subject. Findings: The statistical analysis determines seven factors as critical issues in this case study. These factors are “Proportion of company’s structure and goal”, “Company’s competitive conditions”, “International Policies about foreign trade”, and “Economics and Politics condition of country”. Research limitation/ implications: The extracted factors can act as a guideline to design a strategic plan. This helps to ensure that the essential issues are covered during design and implementation of the plan. For academics, it provides a common language to discuss the factors crucial for competitive intelligence in this industry. Originality/ Value: The paper may represent high value to researchers in the competitive intelligence and strategic management fields. This study further provides an integrated perspective of critical issues for competitive intelligence in the power plant industry. It gives valuable information and guidelines that can help leaders consider the important issues during strategic planning.


INTRODUCTION
Today's firms operate within a rapidly changing business climate created by advances in technologies (Aaby and Discenza, 1995;Raymond, 2003), economic and social changes (Wheelen and Hunger, 1998), and fast-shortening product life cycles, which lead to "hypercompetition" (Chakravarthy, 1997).Such complex and unstable environment necessitates a need for timely, first-rate business information and knowledge (Hannula and Pirttimaki, 2003).Thus, companies must devote a greater proportion of their resources to knowledge and innovation (Raymond, 2003;Guimaraes, 2000).Hannula and Pirttimaki (2003) argue that a competitive edge is gained through the ability to anticipate information, turn it into knowledge, craft it into intelligence relevant to the business environment, and actually use the knowledge gained from it.In planning their strategies, companies need to analyze carefully the business environment, especially the pressures and challenges caused by it, in order to thrive in the global digital economy (Hannula and Pirttimaki, 2003).Thus, enterprises should view the strategic plan as a reaction to external stimuli rather than a long-term, unchangeable course of action (Persidis, 2003).Groom and David (2001) point out that corporate planning in the 1960s and 1970s consisted simply of new product development to meet the growing affluence of consumers, especially in the USA.Nowadays, the world economy is experiencing a downturn as global growth has slowed, intensifying competition, and changing customer needs.Also, the macroeconomy continuously challenges businesses, requiring them to evaluate and change their strategic goals (Groom and David, 2001) and strategic plans (Persidis, 2003) constantly, in order to gain efficiency and a competitive advantage.Persidis (2003) points out that, a few years ago, business managers talked in terms of 5-8 year strategic plans, whereas today they talk more of 2-3 year plans, and many firms are discovering that the only way to grow is by taking market share from the competition and introduce new products (Groom and David ).
CI is generally a new research area at the international level, the vast majority of the research being concentrated in US firms (Wright et al., 2002).The focus of this paper reflects the fact that Iran has undergone significant competitive economic changes over the last few years and plays a key role in the economy of the Middle East.Its market is attractive and open, although regulations and government operations may seem bureaucratic and complex.Yet, there is a scarcity of research on CI in Iran.The findings of this study could be of value to both marketing practitioners and academics, because of the challenges faced in operating in a speedily changing globalized business environment.
Its aim is to explore how familiar Iranian companies work with CI and to what extent they make use of it.Specific objectives are to:  investigate the key factors of CI;  identify the key factors in Iranian companies  establish a model from key factors The remainder of this paper is organized as follows: in next section a brief overview of the literature on competitive intelligence is presented; then we focus on the methodology followed and the empirical analysis of the data; finally, in the last section, conclusions are reached and recommendations made.

LITERATURE REVIEW
Competitive intelligence (CI) is a business tool that can make a significant contribution to the strategic management process in modern business organizations, driving business performance and change by increasing knowledge, internal relationships and the quality of strategic plans (Bernhardt, 1993).CI is formally defined by the Society of Competitive Intelligence Professionals as "a systematic and ethical program for gathering, analyzing and managing external information that can affect your company's plans, decisions and operations" (www.scip.org).According to Myburgh (2004), the objectives of CI are to manage and reduce risk, make knowledge profitable, avoid information overload, ensure privacy and security of information, and use corporate information strategically.In essence, CI helps strategists to understand the forces that influence the business environment and, more importantly, to develop appropriate plans to compete successfully (McGonagle and Vella, 2002).Because of this critical impact on business decisions and on shaping company strategy, CI should be an important responsibility of top management (Wee Tan Tsu, 2001).Further, Guimaraes (2000) argues that a company can improve its competitive edge and its overall performance by applying an effective CI program, and thereby satisfy two vital goals for its survival.
The literature of CI is limited (Wright et al., 2002).It appeared as a "marketing child" in the 1960s (Walle, 1999) and has developed slowly, but regularly since the mid-1970s due to expansion of companies into foreign countries, globalization of markets, and the varying needs of consumers (Prescott, 1995).Indeed, all of these have influenced the life and actions of companies and have led management to a continuous search for new theories and techniques to help them face the competition (Fuld, 1995).Executives in small and medium sized enterprises normally focus mainly on strategic initiatives that will yield direct profits (Wright et al., 1999).They are cautious about actions that could damage the company economically, and thus prefer to invest in a plan that will deliver profit in the short-term rather than one that obliges them to wait for results in the medium or long run.This attitude militates against adoption of CI for, even though it can yield direct profits, the medium or long-term outcomes are what render it priceless (Wright et al., 1999;Prescott, 1995;White, 1998).
CI is both a product and a process.The product is information on the competitors in the market, which is used as the basis for specific action.The process is the systematic acquisition, analysis and evaluation of information for competitive advantage over known and potential competitors (Myburgh, 2004).Information assists decision makers to understand their competitors and to make sound strategic decisions (Wee Tan Tsu, 2001; Hewitt-Dundas et al., 1997; Simkin and Cheng, 1997).
It is a common mistake to confuse CI with market research, but the gathering and analysis of information takes a quite different form (Wright et al., 1999;Prescott, 1995;White, 1998;Attaway, 1998;Walle, 1999;Vedder andGuynes, 2000/2001).Threats in the market do not emanate only from the large competitors, and planners should, therefore, find ways to monitor the whole market, in order to stay ahead of competition.Guimaraes (2000) provides a summary of the benefits of CI practice in strategic planning: bringing to light business opportunities and problems that will enable proactive strategies; providing the basis for continuous improvement; shedding light on competitor strategies; improving speed to market and thereby supporting rapid globalization; improving the likelihood of company survival; increasing business volume; providing better customer assessment; and improving understanding of external influences.
Although it seems obvious that CI is becoming more and more vital to a firm's survival in today's dynamic markets (McGonagle and Vella, 2004), a large number of companies still have no formal CI department.This is typically the result of cost cutting and competition from abroad (Attaway, 1998), but another reason might be the lack of formal education in CI (Fleisher, 2004).However, there is evidence in the USA that more companies are beginning to recognize CI as a critical component of the best strategic and tactical decisions (Heath, 1996), and thus have organized formal CI units.Typically, these are the major players: Shermach (1995) names GE, Xerox, Motorola, Microsoft, H-P, IBM, AT&T as cases in point.Persidis (2003) suggests that a larger number of smaller companies are also recognizing CI as an important part of their operations, and do practice it, possibly without realizing they are doing so.Previous studies have verified these trends.In 1998, research by the Futures Group in 103 large, small and medium enterprises in the USA found that exactly three quarters had a formal CI department.Interestingly, exactly half said they did not believe that their competitors watched them (Groom and David, 2001;Dishman and Calof, 2008).
The concept of intelligence as a process has long been proposed as an effort to improve the firm's competitiveness and its strategic planning process (Guyton, 1962; Montgomery and Urban, 1970; Pearce, 1971Pearce, , 1976;;Montgomery and Weinberg, 1979;Porter, 1980).Already in 1966 William Fair proposed the creation of a corporate "Central Intelligence Agency" within the firm whose function it would be to "collect, screen, collate, organize, record, retrieve and disseminate information" (Fair, 1966, p. 489).Since that time, this proposition has grown to become an emerging business function with delineated job functions directly responsible for intelligence collection, analysis, and dissemination (Kahaner, 1996).Competitive intelligence's goal is to provide "actionable intelligence" (Fahey, 1999;Fuld, 1995Fuld, , 2000;;Nolan, 1999), namely, information that has been synthesized, analyzed, evaluated and contextualized.Competitive intelligence presents part of the strategic information management process that is aligned with an organization's strategy (Bergeron, 1996;Kennedy, 1996;Moon, 2000).

RESEARCH METHODOLOGY
Based on literature review, the points discussed above, the authors' recent researches on CI and applying some statistical methods, the research structure of this study has been developed in five main stages as shown in Figure 1.In this way, at the first stage, a questionnaire was designed with some questions that evaluate CI effects on the company.The content of second section is based on critical dimensions of competitive intelligence listed in Table 1 which are the important factors; and finally the third section of the questionnaire including questions about the characteristics of the interviewees.

Figure 1: Research methodology
It is important to say that a hypothesis test must be designed to evaluate positive CI effects on organizational success and considering this hypothesis proof at second stage, the research can be continued.
At the second stage, the survey is run to collect data from interviewees and based on the collected data; a reliability analysis can be performed.Reliability analysis allows you to study the properties of questionnaire and the items that make them up.The reliability analysis procedure calculates a number of commonly used measures of scale reliability and also provides information about the relationships between individual items in the measurement scale (Hair et al., 1998).
The main purpose of third stage is to confirm the mentioned hypothesis in stage one.In this way, it is necessary to determine the statistical distribution of collected data at the first part of the questionnaire.Subsequently, based on distribution of data, one of parametric or non-parametric tests can be performed for hypothesis proof.The fourth stage of research framework is based on "factor analysis" and is concentrated on extraction and identification of the critical factors affecting the CI in the Iranian Companies.
Factor analysis is also known as a generic name given to a class of multivariate statistical methods whose primary purpose is to define the underlying structure in a data matrix.Broadly speaking, it addresses the problem of analyzing the structure of the interrelationships (correlations) among a large number of variables (e.g.test scores, test items, questionnaire responses) by defining a set of common under-lying category, known as factors.With factor analysis, the researcher can first identify the separate factors of the structure and then determine the extent to which each variable is explained by each factor.Once these factors and the explanation of each variable are determined, the two primary uses for factor analysis-summarization and data reduction-can be achieved.In summarizing the data, factor analysis derives underlying factors that, when interpreted and understood, describe the data in a much smaller number of concepts than the original individual variables.Data reduction can be achieved by calculating scores for each underlying factors and substituting them for the original variables (Hair et al., 1998).Evaluating the suitability of collected data, performing factor analysis and naming the extracted factors are different steps of this stage.Finally, the most important factors and their effect become clear through multiple regression analysis at stage five.The linear regression model assumes that there is a linear or straight line relationship between the dependent variable and each predictor.Linear regression estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable (Hair et al., 1998).

Data collection
The research targets were members of MAPNA Group including managers, senior experts and effective staff in decision making.MAPNA Group has already posted seminars on competitive intelligence and organizational success.Therefore, most of the members are aware of the importance of CI.
In order to understand the viewpoints on CI from all sectors of the MAPNA central office and different factories, questionnaires were sent to different departments including information, research and development, academic and human resource departments.The number of questionnaires sent out was 600; the number returned was 390, which showed a return rate of 65 percent.

Reliability analysis
With reliability analysis, you can get an overall index of the repeatability or internal consistency of the measurement scale as a whole, and you can identify problem items that should be excluded from the scale.The Cronbach's is a model of internal consistency, based on the average interitem correlation.The Cronbach's a (Likert, 1974) calculated from the 32 variables of this research was 0.894 (89 percent), which showed high reliability for the designed measurement scale.

Demographic profiles of interviewees
The demographic profile of employees who participate in the survey has been summarized in Table 2.The results showed that 54.36 percent of the interviewees are from central office and the others are from factories.The subjects of this study were members of the MAPNA Group, who are specialized in Power plant projects design and development.All of the members had Bachelor of Science (BS) or higher education, as shown in Table 2.For the job title point of view, 73 percent of the participants were experts, 18 percent were supervisors and the others were managers at different levels.

Identification of critical factors
The main technique of this stage is based on "Factor analysis".Factor analysis is a technique particularly suitable for analyzing the patterns of complex, multidimensional relationships encountered by researchers.It defines and explains in broad, conceptual terms the fundamental aspects of factor analytic techniques.Factor analysis can be utilized to examine the underlying patterns or relationships for a large number of variables and to determine whether the information can be condensed or summarized in a smaller set of factors or components.To further clarify the methodological concepts, basic guidelines for presenting and interpreting the results of these techniques are also included.Factor analysis provides direct insight into the interrelationships among variables or respondents and empirical support for addressing conceptual issues relating to the underlying structure of the data.It also plays an important complementary role with other multivariate techniques through both data summarization and data reduction (Hair et al., 1998).
An important tool in interpreting factors is factor rotation.The term rotation means exactly what it implies.Specifically, the reference axes of the factors are turned about the origin until some other position has been reached.The un-rotated factor solutions extract factors in the order of their importance.The first factor tends to be a general factor with almost every variable loading significantly, and it accounts for the largest amount of variance.The second and subsequent factors are then based on the residual amount of variance.Each accounts for successively smaller portions of variance.The ultimate effect of rotating the factor matrix is to redistribute the variance from earlier factors to later ones to achieve a simpler, theoretically more meaningful factor pattern.The simplest case of rotation is an orthogonal rotation, in which the axes are maintained at 908 (Hair et al., 1998).
In order to determine whether the partial correlation of the variables is small, the authors used the Kaiser-Meyer-Olkin measure of sampling adequacy (Kaiser, 1958) and Bartlett's x 2 test of sphericity (Bartlett, 1950) before starting the factor analysis.The result was a KMO of 0.692 and less than 0.05 for Bartlett test, which showed good correlation as depicted in Table 3. Factor loading of each variable on the resulted seven factors is depicted in Table 5.Each variable should have significant factor loading (greater than 0.5) only on one factor.Therefore, Factors 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 had 5, 5, 2, 1, 4, 3, 3, 2, 1 and 1 variable.In this way, V4, V5, V6, V7, V8 are significant for Factor 1; V18, V19, V20, V21, V23 are significant for Factor 2; V22, V29 are significant for Factor 3; V30 is significant for factor 4; V11, V24, V25, V26, are significant for Factor 5; V15, V16, V17, V28 are significant for Factor 6; V3, V9, V10 are significant for Factor 7; V13, V14 are significant for Factor 8 ;V31 is significant for Factor 9 and V12 is significant for Factor 10.Because of factor loading less than 0.5, the variables V1, V2, V27 and V32 can be omitted.The variable V12, V30, V31 because of grouping just in 1 factor, can be eliminated as well.The content of each factor can be seen in Table 5.So the interpretation variable is 76.747 percent.

Factors naming
The authors attempted to name the factors compendiously without losing contents of factors.
In this way, the names and content of the seven factors are shown in Table 6."Proportion of company's structure and goal", "Company's competitive conditions", "Environmental conservation union", "International Policies about foreign trade", "Human Resource", "Economics and Politics condition of country" and finally "Social milieu" are the names which have been allocated to the extracted factors.Economics and Politics condition of country

V3
Economic terms V9 International Politics V10 Governmental Politics

SUMMARY
This study attempts to detect critical CI factors in the power planet industry in Iran.We use a "Likert Scale" to measure affected factors on the power plant industry.From a comprehensive literature review 32 critical variables of competitive intelligence were distinguished and embedded in the second part of the research.The interviews selected more important dimensions from these 32 variables by assigning ranks to them.
After factor analyzing the variables were reduced to 10 groups.Three groups only have 1 factor, so they were eliminated.The remaining groups included 25 variables, so 7 variables were reduced.After that, we used regression coefficients to predict the effect of independent variables on dependent variables.The results showed that "Proportion of company's structure and goal", "Company's competitive conditions", "International Policies about foreign trade", and "Economics and Politics condition of country" are effective in the regression model.

Recommendations
The authors believe that after this research, power plant industry management can decide in a better way how to establish a competitive intelligence system using the 7 factors defined here in their strategies.
Further research is needed.One area is influence of each factor on power plant industry's profitability.Other research directions can include studying the effects of the work environment on CI.

Table 2
also shows the seniority of the participants.

Table 2 :
Demographic profile of the interviewees

Table 3 :
KMO and Bartlett test resultsThe 32 variables were grouped into ten factors.The results can be seen in Table4.Ten factors have an Eigen value greater than one and the interpretation variable is 91.943 percent.The factors are rotated according to Varimax.

Table 4 :
Factor analysis results

Table 6 :
The name and content of critical factors

Table 7 :
Summary of multiple regression analysis