Identification and classification of organizational level competencies for BI success

Business intelligence is a technology-oriented solution that businesses need to survive in today’s competitive and constantly changing market. To gain the benefits of BI systems, it is important to evaluate, assess, and improve factors that have an influence on BI success. Organizational competencies can provide answers to the question of how companies could gain more benefits from BI systems. While investment in BI systems is increasingly growing, measures to evaluate effective organizational competencies leading to BI success are gaining more importance. Therefore, this research identified a number of effective organizational competencies that contribute to BI success. Using the developed questionnaire for determining the effect of organizational level success on BI success, the research data was gathered for the study. A chi-square test confirmed the effectiveness of all nineteen identified competencies. Then, an exploratory factor analysis (EFA) was carried out on the data in order to identify the underlying dimensions. In addition, competencies were grouped into six categories, namely data management, information system/information technology (IS/IT) development, financial resources, relationship management, IS strategy and human capital policies. As a result, these competencies can be used as a measure to evaluate an organization’s status in holding some of the effective factors for BI success.


BI success
Business intelligence (BI) is a modern information technology that helps organizations to collect, manage and analyze structural or non-structural data (Lin, Tsai, Shiang, Kuo, & Tsai, 2009) (Nyblom, Behrami, Nikkilä, & Solberg Søilen, 2012). BI has a fast growing market (Abzaltynova & Williams, 2013) that continuously introduces new trends such as cloud BI, social BI, and mobile BI and in the future "customized" BI (Wang, 2015). Nowadays, business environments are constantly changing (Hoppe, 2013), highly competitive, and increasingly uncertain (Banerjee & Mishra, 2015) that organizations' solutions for avoiding bankruptcy depend on successful BI (Ranjan, 2008). In addition, organizations that utilize BI successfully can gain competitive advantages.
In successful BI, information technology and the business process and strategies must be aligned together, so enterprises can manage and benefit from their investments in BI by allocating BI resources, prioritizing projects, and minimizing the risk associated with BI implementations (Ranjan, 2008). Successful business intelligence can help organizations to make the best decision at the best time through integrating and analyzing data with decision support systems (Muntean, Gabriel Cabau , &Rinciog, 2014). Furthermore, successful BI provides the right information to the right people throughout the organization to improve strategic and tactical decisions (Li, Shue, & Lee, 2008). Company return from IT investment is an important part of successful BI. In other words, when a BI system is successful, the company gains tangible benefits from their investments in it.
The opposite side of successful BI implementation is BI failure. Reports of BI failures can highlight the importance of successful BI. About 50%-80% of business intelligence applications fail due to technological, organizational, cultural and infrastructural issues (Adamala & Cidrin, 2011). In addition, they report that most BI failures happened because of a number of issues, such as ignoring BI as a cross organizational business initiative, lack of management and sponsor commitments, lack participation of the business side and representatives, unavailable skilled staff, ignoring business analysis activities, lack of appreciation of the impact of dirty data on business profitability and lack of understanding of the necessity for and the use of meta-data (Chuah & Wong, 2013). All the failure reasons show that a number of organization and staff characteristics, which are called 'competencies,' play a crucial role for BI success. Competencies are related characteristics that prepare an organization to attain certain objectives. These characteristics can be categorized in two levels: organizational and individual. The effect of organizational level competencies on BI success is the topic of our study. As Worley et al. (2005) mentioned "Competencies can be analyzed at the level of an individual, gathering all the techniques allowing to facilitate the emergence, maintenance and development of personal competencies, but also at a collective level or even at an organizational level" . Individual competencies are human resource capabilities that lead to better achievement of the predetermined objectives such as human resource skills, motivations, and behaviors that influence their performance and at least productivity.
Although individual competencies are also very important in applying BI systems we limited our research scope to organizational level competencies. In general, organizational competencies are an organization's ability for optimizing use of available resources, setting short-and longrange goals, and developing the strategies and policies to achieve such goals. The concept of competencies first found its way into IS/IT studies in an attempt for supporting organizational IT/IS goals. Competencies have been found to have the potential to impact organizational success and to be relative to BI in particular. Specifically, it has been related to an organization's ability to derive benefits from their investment in IS (Chasalow, 2009).
The aim of this research is to determine the organizational level competencies that are necessary for BI to be applied successfully according to the BI success factors. Since the major reason that a large number of BI projects are considered to be failures is related to ignorance of organizational characteristics, the emergent competences identified in this research can help organizations understand the competencies that they need to build in order to benefit from their BI investments (Chasalow, 2009). Therefore, this research is directed towards developing a theoretical model for BI success. Although BI success is the positive value that an organization obtains from its BI investment, its definition differs from one organization to another. It depends on what benefits that organization expects (Sabanovic & Solberg Søilen, 2012) from its BI initiative. Benefits that are gained from improved profitability, reduced costs, and improved efficiency can be defined as BI success in an organization. For the purpose of this research, BI success is defined as the positive benefits of BI, which the organization may achieve as a result of implementing BI competencies as important elements in the success of information systems, and appear to have the potential to be of particular value in explaining achieving benefits from BI. This research will therefore seek to develop a model to help explain the organizational level competencies that would support the attainment of business value from BI. The developed model can be used as an instrument to improve the likelihood of an organization achieving benefits from their BI investments.

LITERATURE REVIEW
There are few studies about competencies that affect BI success. In this section, we study organizational level competencies related to IS/IT in addition to BI related competencies. First, competencies are described and then competency related research studies are introduced.
The literature review is summarized in Table1 and Appendix A.

Competencies
"Competencies have been studied from two different perspectives: (i) As assets, skills, or resources belonging to the company that allow an activity to be performed systematically (ii) As the activities themselves, that is, the operations that the firm is able to carry out by integrating a series of assets, emphasizing what the company does as opposed to what the company has" (En Escrig-Tena & Bou-Llusar, 2005).
Competencies are usually divided into two groups: organizational level competencies and individual competencies. Organizational competency is a term that has been used in the world of performance management for many years. It is routinely used by human resource professionals and by organizational change consultants to refer to the variety of employee skills (Nienabera & Sewdassb, 2016) that the company must have in order to achieve their plans (Coates & Associates, 2008). The current research focuses on non-individual competencies (organizational level competencies) studied in the IS/IT field.

BI related competencies
Competencies within the sphere of BI first appeared in the BI practitioner literature beginning with the Business Intelligence Competency Center (BICC). BICC encompasses a lot of issues: better use of BI across the organization, greater alignment and collaboration between business units, a BI strategy that supports the corporate strategy, standardized BI processes and initiatives, consistency of definitions, processes, and methodologies, and higher ROI from BI (Miller, Bräutigam, & Stefani, 2006). Miller and et al. (2006) introduced comprehensive competencies modeled in three dimensions: business skills, analytical skills, IT skills to support the development and support of BI in an enterprise. But, these competencies are primarily technical in nature and their focus is not on organizational level competencies (Miller, Bräutigam, & Stefani, 2006). Furthermore, Chasalow (2009) presented five competency factors on the organizational level: learning organization, participative leadership style, clearly defined business goals, technological resource availability, and financial resource availability. He argues that these five factors have an impact on business intelligence success (Chasalow, 2009). As Chasalow mentioned in his dissertation, his work is one of the few studies that have been done on organizational factor effects on IS systems and also these studies are still in an initial stage. Also, his study did not attended to some factors like relationship management that have been introduced in this research and are one of a company's challenges for implementing information systems in some organizations.
In addition to that, Ghazanfari (2011) presented an expert tool to evaluate the BI competencies of Iranian enterprises and identified six factors for his evaluation model: analytical and intelligent decision-support, access to related experimentation and integration with environmental information, optimization and recommended model, reasoning, enhanced decision-making tools, and finally, stakeholder satisfaction . Their view of BI competencies is limited to BI specification. Their study is not about organizational level competencies, but they mention some competencies like stockholder's satisfaction that we recognize as organizational level competencies.
Furthermore, Isık et al. (2013) studied the effect of the decision environment on business intelligence capabilities for achieving BI success. According to their study, technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success (Isık, Jones, & Sidorova, 2013). Although their study focused on technical capabilities, some of the capabilities, like data quality, are grouped into organizational level competencies in other studies like Chasalow's study.

Studies on organizational level competencies in the IS/IT field
Because there are few research studies in the field of BI-related competencies, organizational level competencies in the IS/IT field have been studied too. Since BI is an IS system, not only studies about competencies in the IS/IT field have been studied in our research, but they can make our literature review more inclusive. Competencies related to an IS facilitate the relationships between organizational processes and structures for beneficial use of IS resources in order to accomplish organizational tasks and obtain organizational goals (Tarafdar & Gordon, 2007). One of the most cited articles about IS related capabilities is by Feeny and Willcocks (1998) in which they offer a competency model (Feeny & Willcocks, 1998). Their model, which was revised in 2006, suggested four tasks and nine capabilities that grouped into three categories: business and IT vision, delivery of IT services, and design of IT architecture that can help a company benefit from the technology (Willcocks, Feeny, & Olson, 2006). Furthermore, there are other studies that have addressed the problem of value creation from IS investments in an organization as opposed to an IS functional perspective. Peppard, Lambert & Edwards (2000) developed a framework for mapping macro competencies and identified their related micro competencies. Four years later, Peppard and Ward (2004) (Doherty & Terry, 2009). As such, Wade and Hulland (2004) defined three IS resources and capabilities that can be used for gaining market opportunities. They also proved that IS resources rarely have a direct effect on sustained competitive advantage (SCA), but they can indirectly lead to sustained performance (Wade & Hulland, 2004). In another study, Doherty & Terry (2009) examined the impact of IS capabilities on competitive positioning at the process level. Also, Ravichandran (2007) presented how IS capabilities can offer digital options that lead to firm agility by investing in IT. Similarly, Tarafdar and Gordon (2007) illustrate how six IS competencies could affect the conception, development and implementation of process innovations. On the other hand, some studies addressed IT competencies as components of other concepts. For example, Ngai, Chau and Chan (2010) defined IT competencies (IT integration and flexibility) as supply chain competencies. Also, the theory of competency rallying (TCR) was presented for the first time by Katzy and Crowston (2000). Crowston and Scozzi (2002) then introduced the TCR model and tested it in the context of OSS projects as a virtual organization (Ghapanchi, 2013).
While all the studies on IS/IT discussed above have adopted the resource-based view of IS/IT competencies, some other research studies have introduced different views. For example, Caldeira and Dhillon (2010) categorized organizational competencies into two groups: facilitating competencies and fundamental competencies that lead to information technology advantages within organizations (Caldeira, Mário;Dhillon, Gurpreet, 2010). Additionally, Chen & Wu (2011) developed a model of IT management capability of CIOs and found that information technology competencies affect IT management activity.
Although these IS related studies did not consider some competencies that are more important for BI like data quality or metadata that are mentioned in BI related competencies, they mentioned important competencies that are necessary for BI implementation as an IS system. IS related studies are summarized in this research, because considering IS related studies beside BI related competencies can show their similarities and differences.
A review of the related literature is summarized in Table 1.

RESEARCH METHODOLOGY
To answer the research question of "what are organizational level competencies for BI success?", first we identified organizational level competencies from the literature review.
Then a questionnaire was designed to answer the question "Are these identified competencies effective in BI success?" In order to test whether the designed questionnaire was valid and reliable, and effective for answering the research question, we performed a validity test like EFA that classified constructs. The research steps as are follows in Figure 1: (1) Specifying the domain of the construct, (2) identifying the competencies by literature review and making the Semi-structured interviews, (3) constructing an initial framework, (4) designing the questionnaire, (5) Collecting data (6) testing the hypotheses, (7) Assessing construct validity and reliability of the measures. In the following sections, each step is elaborated in more details and some of the steps are explained in Section 4: data analysis and results.

Specifying the domain of the construct
According to what is described in the literature review, there are different competency-derived approaches. Moreover, competency-based studies on BI are in their infancy and limited. However, there are more research studies on IS/IT related competencies in the literature. Therefore, additional competencies were extracted from other competency-based studies including both BI and IS/IT, which use a more resource-based approach to competency indices. The literature identifies two levels of competencies: individual level and organizational level. The present paper addresses the organizational level.

Identification of the competencies from the literature review and interviews
The first step is to identify the competencies. This can be done through adopting either a qualitative or quantitative approach. In our case, the competencies were developed through reviewing the literature on IS/IT and BIrelated competencies. Initially, 35 IS/ITrelated competencies at the organizational level were identified. The next step was to examine the competencies identified for content validity. Content validity is whether or not the elements in a given construct represent the underlying concept to be measured. In our case, we used two methods for determining content validity: 1) Conducting interviews to investigate if variables are transparent enough, appropriate and relative. Some variables like knowledge management, project management, and change management that are more reflective than formative were eliminated.
As a result, 19 competencies were extracted from a total of 35 by eliminating or merging the elements. Appendix A outlines these 19 competencies and provides their related sources.
2) Developing an initial theoretical framework by grouping competencies in relevant constructs by an inductive reasoning method and via the help of experts who reviewed the elements in each group that are explained in the following sections.

Constructing an initial framework for determining the importance of competencies in BI success
Concepts comprise categories which in turn create the basis for the formation of a theory (Allan, 2003). The aim of categorizing competencies is indirectly to determine the importance of competencies in BI success, that is, how these 19 competencies lead to BI success.
The competencies were grouped into three BI related categories: IT infrastructure, IT governance, and resources. These categories and their variables are shown in Appendix A. a) IT Infrastructure group: Miller et al. (2006) argue that "infrastructure refers to the hardware, software, networking tools, and technologies that create, manage, store, disseminate, and apply information".

Figure 1
The research steps A business intelligence infrastructure has to be responsive to various needs of a business on demand and in real time. Also, well-defined infrastructure ensures data quality and availability. The V1 to V7 group of variables was assigned to the IT infrastructure category as critical IT assets. It is crucially important to build and expand the necessary data and analytic infrastructure that is agile, stable, scalable, and integrated. Data quality and stewardship especially are important for developing metadata (Miller, Bräutigam, & Stefani, 2006). b) BI Governance: this is a new term that a few references mentioned it. Turban et al. (2010) used BI governance for prioritizing BI projects and appropriate planning and forming an alignment with the business strategy as a factor for BI success. Beth (2006) also developed a BI governance framework and application portfolio that deals with the funding process, exceptions process, BI development process, tracking and measurements, and communications plan as governance mechanisms. The V8 to V 15 group of variables were assigned to the BI governance category, emphasizing the importance of strategy thinking to both sides of IS and business alignment to ensure BI success. It is evident that IS strategy is critical, however, it would be a waste of resources for both sides to overlook the business needs, and the alignment of business and IS strategy. c) Facilitating Resource: this is critical for determining the relative success or failure of IT adoption. In fact, resource facilitation supports fundamental competencies (Caldeira, Mário;Dhillon, Gurpreet, 2010). Chasalow (2009)  Resources. The implementation of BI systems does not just occur on one day and end there; they rather take place gradually over time and through data collection, hence there is the need for more financial support and budget allocation. On the other hand, even the best systems without utilizing skilled users could not amount to much, as a study asserts that inadequate education and training and lack of employees' morale and motivation cause the failure of ERP projects (Amid, Moalagh, & Zare Ravasan, 2012).

Questionnaire design
In the third step, a questionnaire was designed with three main sections. The first section of the questionnaire consisted of questions about the characteristics of the interviewees. The content of the second section entailed the description of BI success as described in the literature review. And the third section of the questionnaire included questions about the effect of the 19 competencies on BI success using a five-point-Likert scale ranging from (5) "highly effective" to (1) "highly ineffective", and additionally an "uncertain" option. The third section of the questionnaire was designed to measure the effect of the 19 organizational competencies on BI success in the organization.

Sample size and data collection
Using purposive sampling, the target population of this study was determined to include consultants and IT department members of the Ministry of Industries Mines and Trade. This study was conducted in Iran, because the environment in which Iranian organizations operate today is becoming more and more complex. Moreover, organizations and departments that are situated inside organizations face problems such as reduced budgets and amplified pressure from top managers to increase performance and profit and also from markets and consumers to lower the prices. In this kind of environment, managers must respond quickly, innovate, and be agile. Both private and public organizations are cognizant of today's business environment and pressures (Turban, Sharda, Delen, & King, 2010). In October 2011, the ministry approved a sizable budget for BI implementation that came into effect. The sample size was a major limitation in our study in terms of the available time. Additionally, some experts were not interested (e.g. due to lack of familiarity with the research subject) in cooperating with the research, especially, with the electronic form of the questionnaire. Consequently, the data was collected from questionnaires which were distributed among the minimum sample size of 80 individuals after removing none approved samples. There are different ideas about the minimum sample size in factor analysis. According to Lawley & Maxwell (1971), 51 more cases than the number of variables are enough.
Although the subject-to-variables (STV) ratio of the sample size is 4.2 (that is under 5), expletory factor analysis was conducted because the KMO is 0.62, which is above the ''average'' threshold of 0.5 (Amid, Moalagh, & Zare Ravasan, 2012;Kaiser, 1974), and the Bartlett test p-value is less than 0.05, which suggests a good correlation. Demographically, 5.8% of the respondents had a PhD degree, 46.37% had an M.E. degree, and 47.83% had a B.E. degree. Of these, 4.48% of the respondents were classified as university professors, while 41.79% were executives/managers, and 53.73% were IT department employees that they had work experience in the area of BI tools.

DATA ANALYSIS AND RESULTS
In this stage, the collected survey data from the questionnaire was used for testing the research hypothesis. It was necessary to determine the statistical distribution of the collected data from the third part of the questionnaire. Subsequently, based on the distribution of data, either a parametric or non-parametric test was performed to prove the hypothesis: H1: Do V (i=1 to 19) competencies have effects on BI success? The next step in the development of this type of measurement was to test the construct validity and reliability. Construct validity exists if the items accurately represent the underlying concepts that are being measured (Boudreau, Gefen, & Straub, 2001).
Therefore, some tests were performed on the data collected from the third part of the questionnaire.

Hypothesis test
In order to evaluate the effectiveness of 19 competencies on BI success, the results should support the hypothesis. As previously mentioned, these 19 items were included in the third part of the survey questionnaire constituting the hypothesis: H1. Do V (i=1 to 19) competencies have effects on BI success? One of the most accepted ways to identify the distribution of the data, statistically, is the one-sample Kolmogorov-Smirnov test. The Kolmogorov-Smirnov test compares the observed cumulative distribution function for a variable with a specified theoretical distribution, which may be normal, uniform, Poisson or exponential (Lilliefors, 1967). Many statistical parametric tests require normally distributed variables. The one-sample Kolmogorov-Smirnov test can be used to test whether or not a variable is normally distributed (Hollander & Wolfe, 1973). According to our test results, the p-value of all 19 items was less than 0.05, which shows that their distribution was not normal; hence there was a need for a statistical non-parametric test to prove H1. Therefore, a chi-square test was used to determine whether the frequencies of the upper categories of Likert questionnaire, (5) "highly effective" and (4) are higher than other categories (i.e. 1, 2, and 3). That is, the residual (R 2 ) values of categories (5) and (4) of the Likert scale are to be higher than categories (3), (2), and (1).
The chi-square test procedure (Cochran, 1954) tabulates a variable into categories and computes a chi-square statistic. This goodnessof-fit test compares the observed and expected frequencies in each category to test whether all categories contain the same proportion of values or test that each category contains a user-specified proportion of values.
A significance level below 0.05 for all the 19 items indicates that the observed frequencies differ from expected frequencies in each category and the average rate of frequencies do not significantly differ by category. On the other hand, the residual (R 2 ) of each category of items, which is equal to the observed frequency minus the expected value, shows that differences between observed frequencies (nonparametric tests, chi-square test) in (4) and (5) are a lot more than the expected frequencies and are completely positive. Thus, based on the significance level and residual test for all items, it can be concluded that all of the 19 competencies are highly effective for BI success in an organization.

Exploratory factor analysis
In this study, we use an exploratory factor analysis (EFA) as a statistical approach to determine the correlation among the variables in a dataset. This type of analysis provides a factor structure (a grouping of variables based on strong correlations). EFA is good for detecting "misfit" variables. In general, an EFA prepares the variables to be used for cleaner structural equation modeling. An EFA should always be conducted for new datasets (Statwiki, 2012). An EFA was used to examine the dimensions evidenced in the data and the loading of the items on the empirically specified dimensions of effective organizational competencies for success.
Principal component analysis was used to extract the factors with the Varimax rotation method to simplify the interpretation of the factors. The Guttman-Kaiser rule was applied to determine the number of capability factors. At this point, only factors with Eigen values of one or more were retained. A Kaiser-Meyer-Olkin (KMO) and Bartlett's test were conducted prior to the EFA. In addition, the KMO (Kaiser, 1958) examines whether the partial correlations among variables are small (Momeni & Mehrafzoon, 2013). Bartlett's test determines (Bartlett, 1950) whether the correlation matrix is an identity matrix, which would indicate that the factor model is inappropriate. The KMO is 0.62, which is above the ''average'' threshold of 0.5 (Amid, Moalagh, & Zare Ravasan, 2012;Kaiser, 1974), and the Bartlett test p-value is less than 0.05 which suggests good correlation. According to Hair et al. (1998), factor loadings over 0.3 meet the minimal level, over 0.4 are considered more important, and 0.5 and greater are practically significant. It is also suggested that the loadings over 0.71 are excellent, over 0.55 good, and over 0.45 fair (Amid, Moalagh, & Zare Ravasan, 2012). The factor analyses conducted in this study are assessed according to these criteria and because the chi-square test proved the effectiveness of the factors before the EFA, factor loadings over 0.45 are considered suitable for EFA.
The 19 variables were grouped into six categories of factors which had an Eigen value greater than one and factor loading greater than 0.45, and the interpretation variable was 70.8. Moreover, the extraction variances of the 19 variables were greater than 0.61. Table 1 summarizes the results of factor loading.

Factor denominations
The factors were named based on the meaning and functionalities of the competencies that were related to each factor (Momeni & Mehrafzoon, 2013). The names and content of the six factors are shown in Table 2. The following section offers an elaboration of each of the factors, which are based on explanations or model dimensions of their criteria-related resources.
Factor (1) Data management: This refers to capturing, storing and maintaining a large volume of data to support BI analysis (Chasalow 2009). Qualitative data is the most important part of an analysis. Capturing and storing metadata helps to create various reports from various dimensions. Here, data management is defined as how data can be integrated and validated in a proper way to be more profitable.
Factor (2) IS/IT development: This refers to the competencies that allow an organization to develop or experiment with new technologies. So, infrastructure must be flexible and IS architecture has to be designed in a way that allows development (Wade & Hulland, 2004).
Factor (3) Financial resources: First described by Chasalow (2009), financial resources deal with the availability of financial resources to support the collection and maintenance of BI tools. Many IS implementation projects failed because of a lack of financial resources. Although availability of the resources facilitates BI success, financial resources are an important competency that determines success and failures of these projects.
Factor (4) Relationship management: The aim of relationship management is to increase the connectivity with consumers, suppliers and other trading partners. One of the IS systems' (like SCM, CRM) tasks is facilitating relationships of organizations with their partners (Aziza, Oubrich, & Solberg Søilen, 2015). So well defined management systems can lead to IS systems like BI. Schaarschmidt, Walsh, Kortzfleisch (2015) mentioned interacting with external parties on a macro level of governance, which we considered a relationship management factor in IT governance groups.
Factor (5) IS strategy: This is defining organizational strategies in a way that integrates IS with business (Peppard & Ward, 2004). For BI success in an organization, organizational strategies must be well defined in a way that information systems meet the business needs. Besides, business strategy must consider IS needs.
Factor (6) Human capital policies: This is a very well defined system that can benefit an organization without well trained users. The human resources importance for IS success, especially in BI, is clear as described before and is considered to be individual competencies. But, human capital policies are permanent and continuing policies and the processes of an organization for selecting, evaluating and training IT and business staffs in a way that helps BI implementation and usage. Table 2 illustrates which competency (Vi) has been grouped into which factor (j). On the other hand, Appendix A illustrates relationships of the initial theoretical framework with competencies (Vi) and factors (j). As described earlier, the research theoretical framework groups competencies into three categories. The framework was then revised by EFA, so competencies which were assigned to the IT infrastructure category were divided into factor (1) and factor (2); the BI governance category was divided into factor (4) and factor (5); the facilitating resources category was divided into factor (3) and factor (6). V15 (Sourcing strategy) which was primarily grouped as one of the IT governance category, by EFA has been grouped into factor (3). Figure 2 also shows the factors and the initial framework relationships.

Reliability
Reliability is another aspect of the measurement scale to be evaluated in this step. This concept refers to the extent to which repeated use of the measurement scale would give the same results (Straub, 1989). The analysis of reliability is reported in Table 2 as composite reliability, and was entirely consistent with the factor analysis. Table 1 outlines Cronbach's alpha based on standardized items where values above the minimum of 0.6 for F4, F5, and F6 are unacceptable, and above the minimum of 0.7 for F1, F2, and F3 are considered acceptable.
For the reliability of the questionnaire, the Cronbach's alpha was estimated to be 0.86 (greater than 0.7), which implies good reliability of the instrument (Amid, Moalagh, & Zare Ravasan, 2012;Nunnally, 1978).

Discriminant validity
Discriminant validity refers to the extent to which factors are distinct and uncorrelated.
The rule is that variables should relate more strongly to their own factor than to another factor. Two primary methods exist for determining discriminant validity during an EFA. The first method is to examine the pattern matrix. In order to have discriminant validity, variables should load significantly only on one factor. The second method is to examine the factor correlation matrix, as shown in Table 2. Correlations between factors should not exceed 0.7. A correlation greater than 0.7 indicates a majority of shared variance (0.7 * 0.7 =49% shared variance) (Statwiki, 2012). As can be seen from the factor correlation matrix in Table  3, correlations between all factors are under 0.7 which supports the discriminant validity.

DISCUSSION
This paper presented a competency model as illustrated in Figure 2. Interpretations of factors and practical usages of this model are discussed in the following sections.

Interpretation of factors
According to our findings, there is no similar research that has presented effective competencies for BI success by studying previous research in IS fields. One of the differences between research studies about IS and BI related competencies is the emphasis of BI related research studies on data management and its factors that also were shown in EFA results. EFA shows that the data management factor has the highest variance, among other factors. This is due to the fact that a BI system's goal is analyzing data for exploring useful information for decision makers and it makes data management a critical factor for BI success. The importance of data management is highlighted in many sources and articles, such as Işık (2010) who defined data sources, data types, and data reliability as BI capability; or Cox (2010) who identified information availability, information quality, and information quantity as effective elements that improve decision-making speed and quality. Factor (2), IS/IT development, is an organization's ability to develop applications, architecture and infrastructure of IS without which data cannot be gathered and managed perfectly. Therefore, this factor is considered to be a base or infrastructure for data * indicate that the coefficient is significant at p ≤ 0.05 ** indicate that the coefficient is significant at p ≤ 0.01 *** indicate that the coefficient is significant at p ≤ 0.001 Figure 2 The model of organizational level competencies effects on BI success management. Most of the articles in which Factor (2) is referred to have mentioned application development capability as a competency and they have ignored the importance of architecture and system flexibility for implementing new information systems or developing new features for existing systems. In this article, IS/IT development refers to both soft (application) and hard (infrastructure) abilities and their flexibility of an organization for BI success.
Financial resource is not considered to be an IS ability, however, it provides the ground for other capabilities and because of their importance in IS implementation, we cannot overlook them, especially in BI implementation which is a time-bound development process. The importance of financial resources ignored in most articles related to IS competencies except Chasalow's study that emphasized its importance. As an initial classification, the sourcing strategy is classified into IT governance groups and funding for acquiring BI tools funding for building and maintaining an analytical data environment, classified into resource facilitation groups. Sourcing strategy that is classified into financial resources refers to both sides of the funding strategies of sourcing and selection of supplies. By grouping the sourcing strategy competency into financial resources factors, the first side of the competency (funding strategies of sourcing) was highlighted.
Both internal and external data gathered from suppliers and stakeholders are important to determine BI success. On the other hand, continued relationships with IT vendors (Solberg Søilen & Hasslinger, 2012) and consultants are necessary for having a better understanding of an organization's IT needs. Therefore, relationship management is another important ability, as well. It is one of the top and long-running concerns of the senior management that the organizational strategies are in alignment with the business strategy as well as the IS strategies. Research studies show that businesses rely on IT to execute the company strategy and the top priority is building the foundation for execution, which is the IT infrastructure and digitized business processes that automate the core capabilities of the enterprise.
Businesses should have strategic directions about IS investments that lead to alignment between IT strategy and business processes (Peppard, Lambert, & Edwards, 2000). The requirement for alignment of the organization's IS/IT strategy with the business's underlying goals and objectives was apparent. In IS strategy definition, IS/IT governance imply an important role for integrating the IT effort with business strategy and processes (Willcocks, Feeny, & Olson, 2006). In a similar way, BI governance responsible for arranging strategies, structures, processes, and activities of BI for a business is an important factor for BI success. Factor (5), IS strategy, is a strategy part of BI governance that refers to IS and business strategy and their alignment. This factor is the most referred to, directly and indirectly, among other factors that suggests the importance of IS and business strategies and their alignment.
Human resources determine how BI has been used in the organization. Skill, knowledge, and motivation of users (both business and IT users), such as IT skills, statistical and analytical knowledge, creativity and market knowledge are critical for working with BI systems, which are achieved through selecting, evaluating, and managing staff and ongoing IT training. Although human resource abilities refer to individual competencies, management of individual competencies and an organization's policies for directing them refer to organizational level competencies. Some articles like Peppard & Ward (2004) or Chasalow's study have mentioned human resource strategy and development importance. There is more need for specific studies about its importance in information systems; the gap is obvious among research studies in this subject area.

Practical usages
The results of the factor analysis indicate that the organizational competencies for BI success can be evaluated based on six main factors. To measure the maturity of these factors, an organization should be evaluated by nineteen criteria through questions about organizational competencies. Using the extracted loads of each criterion within its factor, the maturity of the organizational competencies can be measured and depicted on a chart (for the six factors). By comparing the "as is" situation of these six factors with the "to be" situation the probability of BI success can increase as revealed through interviews with the experts of the studied organizations.
Since BI success criteria may differ from one organization to another, in addition, the criteria defined for BI success have influence on the importance of defined competencies; BI critical success criteria must be defined in the organization first. Also, defining BI success criteria helps the organizations that is going to implement BI to measure the fulfillment of these criteria. Periodic evaluation of success criteria and their relative competencies can lead to continuous system performance improvement and better utilization of the information system.
The present research introduced a new measurement instrument by using a competency-based approach to BI, which helps companies achieve BI success. It should be noted that the authors utilized a case study to propose a valid measurement model. Nevertheless, it is believed that it can be generalized to apply to similar organizations, which plan to implement BI. The authors believe that the results of this research can help organizations make better decisions with regard to implementing BI, and shed light on effective organizational competencies according to critical success factors (CSFs) of BI implementation.

CONCLUSION AND FUTURE RESEARCH
The purpose of the study was to introduce new competency measurements on the organizational level for BI success. In this way, first we reviewed related literature about competencies and BI success. After we specified our research domain to the organizational level and IS/IT or BI related competencies, competencies of presented models in this domain were extracted and decreased to 19 competencies by combining and interviewing. Then, the questionnaire was developed that asked about the 19 competencies effect on BI success in its Part 3, which contains an explanation of the BI success definition in Part 2 (Part 1 was assigned to the respondent profile). All 19 of the competencies effects on BI success was approved by a chi-square test. An EFA, conducted to test the validity, grouped the 19 competencies into six factors that are grouped in the initial framework (IT infrastructure, BI governance, and Facilitating resources). The six factors are named and described completely in this article.
BI systems are new to Iranian companies and there are only limited numbers of companies that are familiar with BI systems. That was a limitation for this study. On the one hand, the number of experts who were qualified enough for participating in the study was limited. Nonetheless, some experts declined to participate and answer the questionnaire.
Indeed, this study is not comprehensive in relation to organizational competencies for BI success. This is because the scope of the study is limited due to the elimination of some competency constructs: knowledge management competencies (Alpar, Engler, & Schulz, 2015) that incude the capturing, filing and categorization of the information (Oubrich, 2011), business process competencies, project management competencies, and learning organization competencies (which were among the 40 competencies explored). Since these competency constructs can be in turn defined as independent study projects for future research, we found them to be beyond the boundaries of a single study.

APPENDIX A
The organizational competency descriptions and related sources.

ID Competency
The ability Related sources

IS architecture framework
The type of IS architecture framework determines the development and maintenance ability of the system (Caldeira, & Dhillon, 2010) (Chasalow 2009) (Feeny, & Willcocks, 1998)

V5
Well-defined data environment including stewardship and metadata To manage and maintain metadata and to administer technical metadata and ensure its adjustment with business metadata (stewardship) (Chen & Wu, 2011) (Miller, Bräutigam, & Stefani, 2006)

Metadata tools availability
To have and use metadata tools regularly across the organization (Chen & Wu, 2011) BI Governance

Stakeholder planning and management
To identify key business, human resources, and technical stakeholders to clarify the benefits of the change; and planning and managing their expectations (Caldeira, & Dhillon, 2010) (Miller, Bräutigam, & Stefani, 2006) (Peppard & Ward, 2004) V12

Sourcing strategy
To stablish criteria and processes to evaluate the cost-benefit of supply options and contracts with suppliers, to outsourcing IT services, and custom designed applications ( Caldeira, & Dhillon, 2010) (Feeny, & Willcocks, 1998) (Peppard, Lambert, & Edwards, 2000)