Analysing the Impact of a Business Intelligence System and New Conceptualizations of System Use

Purpose – In this study, three models were empirically compared, the DeLone and McLean model, the Seddon model and the Modified Seddon model, by measuring the impact of a business intelligence system (BIS) in companies in Peru. After that, the mediators and dependent constructs were analysed to determine if they were behaving properly (a good level of variance explanation and significant relations with others constructs). The study used a sample of 104 users of the BIS, from companies in several important economic sectors, in a quasi-voluntary context and with six constructs: information quality, system quality, service quality, system dependence (system use), user satisfaction and perceived usefulness (individual impact).<br><br>Design/methodology/approach – To interpret the results, the authors used structural equations. The idea was to look for the best fit and explanations for the outcomes. The main difference in these models is that the DeLone and McLean model considers system dependence (system use) as a part of information system success, but in the Seddonmodel, it is a consequence of it.<br><br>Findings – The Seddon model seems to show the best fit and explanation for the outcomes. After that, a review of the system use construct was realised, because of its limited variance explained and the few significant relations with other constructs, to improve its explanation power in future research.<br><br>Research limitations/implications – It is estimated that the sample includes more than 15 per cent of all the companies that use a BISs in Peru, so the size of the sample is adequate, but it is not entirely random and therefore limits the generalizability of outcomes. Besides that, a sample size that is bigger could be better for the sake of making a more detailed analysis, permitting the use of some items with less power, or the use of another statistical procedure for structural equations such as the Asymptotical Distribution Free, permitting a more detailed analysis (Hair et al., 2006).<br><br>Originality/value – Business intelligence (BI), one of the most important components of information systems (IS), is playing a very relevant role in business in this time of high competition, high amounts of data and new technology. Currently, companies feel pressured to respond quickly to change and complicated conditions in the market, needing to make the correct tactical, operational and strategic decisions (Chugh and Grandhi, 2013). BI is one of the most important drivers of the decade (Gartner, 2013). Big companies of IS are creating special units specialised in BI, helping companies become more efficient and effective in daily operations.


Introduction
Business intelligence (BI), one of the most important components of information systems (IS), is playing a very relevant role in business in this time of high competition, high amounts of data and new technology. Currently, companies feel pressured to respond quickly to change and complicated conditions in the market, needing to make the correct tactical, operational and strategic decisions (Chugh and Grandhi, 2013).
BI is one of the most important drivers of the decade (Gartner, 2013). Big companies of IS are creating special units specialised in BI, helping companies become more efficient and effective in daily operations. The field of BI is evolving at a fast speed, to become more innovative and obtaining knowledge of the data stream in a way never before done. Today innovative programmes of BI in all industries are being implemented (Chen et al., 2012;Sharda et al., 2014). A company that uses a business intelligence system (BIS) can be more effective and efficient and can disseminate knowledge inside the company, with business partners, improving the decision-making process and making the enterprise more competitive (Parzinger and Frolick, 2001). Measuring the impact of BIS is very important to get the best outcomes and increase the investment return rate.
From these models, the more relevant has been the McLean (1992/2003) and its contrasting model, the Seddon model of 1997. We have used the operationalisation of Rai et al. (2002) to compare both models (the name of the construct Use is changed by System Dependence, and for the construct, Individual Impact is changed by Perceived Usefulness). In this case, we are comparing the two models and a Modified version of Seddon, in a BIS, used by "real" professionals of IT in "real" situation of several Peruvian companies using that system.
An additional point to review is the construct System Use (System Dependence in this paper), that is a construct that has not been performing well in these Information System Success models.
The objective of this study, as previously mentioned, is to compare the DeLone and McLean (2003) model and the Seddon (1997) model (with one additional variation), applied to a Business Intelligent System. The study was accomplished in Peru, a developing country in South America, by using a sample of 104 users for the system in 13 enterprises, having a quasi-volitional IS use context. After this analysis, the mediators and dependent constructs were reviewed to determine if they were behaving properly (a good level of variance explanation and significant relations with other constructs). Delone and McLean (1992) established a model that tried to measure the impact of the information system, considering six constructs. After ten years, DeLone and McLean (2003) reviewed the model, weighing several studies that used partially or completely their model. They said that the model had fulfilled the main objective established: to obtain the information system success, through multidimensional and interdependent constructs. They modified the model considering the next constructs: information quality, system quality, service quality, system use or intention to use, user satisfaction and net benefits. The model can be observed in Figure 1.

Literature review 2.1 The Delone and McLean information systems success model
One of the independent constructs is Information Quality, and the variables related to it are accuracy, precision, output timeliness, reliability, completeness, relevance and currency. The second independent construct is System Quality, which recommends the consideration of variables such as performance of the system, trustworthiness of the computational system, on time and on-line response, and the ease of use of terminals (Swanson, 1974).
The third independent construct is Service Quality, which can be evaluated through technical competence of the IS staff, their attitude, their ability to complete the development of products and services on time, the span of time required to develop the systems. Marketing measuring tools such as SERVQUAL used to measure the dimensions of tangibles, responsiveness, assurance, reliability, and empathy (Chen et al., 2000).
In past years, several evaluations of the DeLone and McLean model have been made during studies that have been used partially or completely (Petter et al., 2008), corroborating most of the relations between constructs.

The Seddon information systems success model
The Seddon model (1997) tries to improve the DeLone and McLean (D&M) model from 1992. According to Seddon, the model was derived from the combination of a process models with another of variance. This model maintains a great part of the D&M model but is divided into two variance models, eliminating the process model. The first variance sub-model is the Partial Behavioural Model of IS Use. The second sub-model is the IS Success Model, a great part of the D&M model. Both models of variance are united, first from the Partial Behavioural model of IS Use, through the Individual, organisational, and Societal Consequences of IS Use, after that, from the IS Success model through the Partial Behavioural model of IS Use, from the User Satisfaction construct to the Expectations about the net benefits of future IS Use.   Figure 2. Seddon indicates that IS Use must be after impact and benefits because it does not cause them. It is affirmed by Seddon that IS Use is a behaviour that expresses a belief of goodness from using an information system. The Seddon model labels IS Use as behaviour caused by IS success. IS Use being a consequence of IS success. In relation to the construct System Use, this model was developed for volitional and non-volitional usage, in contrast to the DeLone and McLean model that solely assumes volitional use (Rai et al., 2002).
Several recent studies have used the Seddon model because it explains adequately the impact of an Information System (Brown and Jayakody, 2009;Kulkarny et al., 2006;and Sabherwal et al., 2006). Rai et al. (2002) used the DeLone andMcLean model (1992) and the Seddon model (1997) to estimate the validity of both. It was found that both models exhibited a reasonable fit. They considered a third alternative, modifying the Seddon model. It was estimated that the perceived usefulness was related to individual impacts, considering that DeLone and McLean (1992) connected several constructs to individual impacts.

IS Success Model
Source: Seddon (1997) In this way, Rai et al. (2002) established a model of five constructs: system quality, information quality, perceived usefulness (individual impact), user satisfaction and system use. Besides that, they represent system use in terms of system dependence. The Seddon model was modified, including a correlational path between system use (system dependence) and perceived usefulness, so the best fit and variance explanation would be obtained. The model can be observed in Figure 3. The more relevant studies that analysed the impact of Information System and Business Intelligence, using the DeLone and McLean, and Seddon models could be observed in Appendix 1.

Methods
The models used are quantitative, in which the individual User of the BIS, in a company that employs the system, is the unit of analysis. A Pilot Test was employed to test the tools, the questionnaire, and the model. Structural Equations were used for the analysis. The model is analysed with the DeLone and McLean model, the Seddon model, and the Modified Seddon model.
The study sample includes the most important companies in the Peruvian economy from different economic sectors: banking, food industry, consumer marketing products, pension funds, government, beauty products, market research, and credit cards. The Use of BISs in those companies is not mandatory and users have other channels providing the information, but in general, it is more cumbersome and perhaps the data is not as precise for the analysis, so the BIS is assumed as quasi-volitional or quasi-mandatory.

Quantitative analysis
A previous analysis of the data was realised to check the main characteristics of them that could be observed in Appendix 2 and 3. Reviewing the correlation table we find that the correlations are between medium and high, and that is because the variables are related to business, and we are going to find high correlations between items of the same construct, but using structural equations of covariance (SEM) that is not a problem (Hair et al., 2006).  Source: Rai et al. (2002) Impact of a business intelligence system Several tests were implemented to check the all the requirement for a multivariate analysis: normal distribution, completeness of the data, outliers, homoscedasticity, and linearity between dependent variables and independent variable. The Kolmogorov-Smirnov test was applied to all variables to verify the normality (Appendix 4). The multivariate normal distribution was verified using the EQS programme for SEM, eliminating the variables that could not satisfy this requirement. The data was complete for each one of the 29 items for the analysis. There were only a few descriptive variables that were incomplete. The homoscedasticity was checked through a homogeneity test of variance between the dependent variables and the independent variables and mediator variables, using the Barlett and Levene test. We found only two variables with problems: IIDU and IIWU and were corrected through a mathematics factor of elevating the variable to the cubic power and then divided by 7. To verify the linearity between dependent variables and independent variables, a regression was run for each combination, and then check a graphic representation of the residuals to verify the random distribution, with favourable outcomes.
We compare the DeLone and McLean model, the Seddon model and a modified Seddon¨s model, in a sample of companies that use BISs. The initial sample was of 110 surveys, but after eliminating some outliers, the final sample consisted of 104 (Hair et al., 2006). The measurement of the constructs was made using a seven-point scale (semantic differential, Likert, ordinal and ratio: Iivari, 2005;Hong et al., 2006;Chen et al., 2000;McKinney et al., 2002). The questionnaire has 29 statements for the six constructs. The questionnaire was obtained from several sources, and it was translated three times. From English to Spanish, then from Spanish to English and then from English to Spanish, through different translators to fulfil the correct procedure in research.
The validity of the constructs was verified through face validity, convergent validity, discriminant validity, and nomological validity. All construct¨s validity statistics were considered satisfactory. The general reliability coefficients in the CFA and Structural model were satisfactory: Cronbach¨s alpha of 0.954 and Rho of 0.974. A pilot test was conducted with 68 observations to verify the questionnaire and apply the Exploratory Factor Analysis through Principal Components and Varimax rotation to verify that each item pertained to only one construct. The main results of the analysis could be observed in Appendix 5.

Sample analysis
A standard procedure was performed, starting with the Confirmatory Factor Analysis (CFA), and thereafter the Measurement model was established. The estimated method used in structural equations was Maximum Likelihood Estimation, with the complementary method of Robust from the EQS programme.
The CFA was initially established, using all the observable variables. The fit of the model was modified, working with X 2 , CFI, RMSEA, multivariate normal distribution adjustment, and the average variance extracted (AVE) (Byrne, 2006). Thereafter, the final Confirmatory Factor Analysis (CFA) was obtained with 22 items derived from 104 observations. The software used for the statistical analysis was Minitab, while the structural equations used EQS version 6.1. See Table I for statistics from the Confirmatory Factor Analysis.

Results
After completing the Confirmatory Factor Analysis, the Structural Model was established. Figure 4 presents the structural model found with the DeLone and McLean model, including the relations between constructs and the variance, explained for each dependent construct through R 2 .
In this case, a variance explanation of 65.2 per cent for Perceived Usefulness (Individual Impact), 77.3 per cent for User Satisfaction and 12.3 per cent for System Dependence (System Use) was obtained, and three significant relations were found (alpha 0.05). The independent constructs Information Quality and Service Quality have significant relations with the mediator construct User Satisfaction. Likewise, User Satisfaction has a significant relation

Source: Own elaboration
Impact of a business intelligence system to the dependent construct, Perceived Usefulness (Individual Impact). In contrast, the independent construct System Quality does not have any significant relation to the mediator constructs. The System Dependence (System Use) construct shows no significant relation to the independent constructs or dependent construct. The dependent construct, Perceived Usefulness (Individual Impact), is explained in 65.2 per cent (R 2 ). This model does not find any significant relationship between the System Dependence (System Use) and other constructs of the model, and it is worth considering that, as an indicator of the success of the system, it makes sense if it is voluntary or discretional, and not when the system has captive users, who do not have an alternative system to process information (Lucas, 1978).
For the Seddon model, the next results were found (including the relations between constructs and the variance explained for each dependent construct through R 2 ), that can be observed in Figure 5.
In this second model, a variance explanation of 12.2 per cent for System Dependence (System Use), 80.3 per cent for User Satisfaction and 68.7 per cent for Perceived Usefulness (Individual Impact) and find five significant relations (alpha 0.05) are presented. The independent constructs Information Quality and Service Quality have significant relations with the mediator construct User Satisfaction. Likewise, User Satisfaction has a significant relationship with the dependent construct, System Dependence (System Use). The independent construct System Quality has a significant relationship with the mediator construct Perceived Usefulness (Individual Impact) and the Perceived Usefulness (Individual Impact) has a significant relation with the User Satisfaction construct. The dependent construct, System Dependence (System Use), is explained in 12.2 per cent (R 2 ).

Source: Own elaboration
For the modified Seddon model, the next results were found (including the relations between constructs, and the variance explained for each dependent construct through R 2 ), that can be observed in Figure 6. In this third model, a variance explanation of 11.7 per cent for System Dependence (System Use), 80.3 per cent for User Satisfaction and 68.7 per cent for Perceived Usefulness (Individual Impact), and six significant relations are presented (alpha 0.05). The independent constructs Information Quality and Service Quality have significant relations with the mediator construct User Satisfaction. Likewise, User Satisfaction has a significant relationship with the dependent construct, System Dependence (System Use). The independent constructs Information Quality and System Quality have a significant relationship with the mediator construct Perceived Usefulness (Individual Impact) and the Perceived Usefulness (Individual Impact) has a significant relation with the User Satisfaction construct. The dependent construct, System Dependence (System Use), is explained in 11,7 per cent (R 2 ).
We can compare the three models in the next Table II (2012), in which they found a significative relation (**) between User Satisfaction and BI Use (System Dependence in this study). In this study we found the same relation in two of the three models: there were no relation in the DeLone and McLean model, but there was a relation in the Seddon model (*) and the Modified Seddon model (*). Wieder et al. use a variation of the DeLone and McLean model, using PLS, that is less demanding that EQS (structured equations of covariance). 6. The system use construct (system dependence) There are several studies related to system usage, user satisfaction and the individual impact that had controversial outcomes. Some authors indicate that there is a direct relationship between system use and individual performance (Goodhue and Thompson, 1995); others did not find any relation between those constructs (Lucas and Spitler, 1999). There is a direct relation between System Use and User Satisfaction (Iivari, 2005;Halawi et al., 2007Halawi et al., -2008Bokhari, 2005;D´Ambra and Rice, 2000), and other authors indicate that there is not that relation (Baroudi et al., 1986). Other authors find a direct relation between System Use and the Individual Impact (Halawi et al., 2007(Halawi et al., -2008Rai et al., 2002;Yuthas and Young, 1998;Guimaraes and Igbaria, 1997). Other authors did not find that relation (Gelderman, 1998;Roldan and Millan, 2000). Norzaidi et al. (2008) examined the impact of user resistance on Intranet usage and its relation to performance in middle managers in an organisational context. They examined too, the cause and effect of usage and user resistance in a mandatory and in a voluntary usage. The study demonstrated that usage significantly explain the percentage of variance on the performance of managers. The outcomes of low resistance found in the study imply that the situation where managers are coerced to use Intranet because they do not have other alternatives to complete their jobs. Usage has been observed to have a strong effect on Source: Own elaboration manager performance, and that is one of the success¨factors that influence individual performance. Eom et al. (2012) realised a study about the role of information technology in e-learning system success in a mandatory context, using PLS to analyse the results, and found a significant relationship between Use and Individual Impact. They compared this study with the research of Rai et al. (2002) that worked in a voluntary context and analysed the data with LISREL and found significant relationships between both constructs. Besides that, compared with the research of Iivari (2005) that worked in a mandatory context analysed the data with PLS, and found no significant relations between both constructs. He and King (2008), investigated the role of user participation in IS through a metaanalysis and found that the construct Usage was initially thought to be a relevant in Voluntary contexts. As established initially by some researchers like DeLone and McLean (1992), but after that found that some researchers indicated that users still have power over the level of use, grounded on their personal ability (attitude and intention), and after that the variability of their usage qualifies the system use construct as a relevant one (Hartwick and Barki, 1994). Hennington et al. (2009) studied the usage in an electronic medical record system in a mandatory context. They found that understanding the correspondence between key technological acceptance constructs and usage, needed a multidimensional abstraction of the use construct (time spent using the system, timing of use, and mode of use). Although they agreed with Burton-Jones and Straub (2006) for using, context-specific measures for the use construct, instead of using lean measures (time spent using the system); they found that lean measures might be sometimes appropriate for specific conditions.

Voluntary and mandatory contexts
As was mentioned previously, Petter and McLean (2009) realised a meta-analysis of more than 50 studies that utilised the DeLone and McLean model to determine the validity and explanatory power of the Use (System Use) construct. They indicated that this construct needs to be improved, to establish significant relations with other constructs, and explain the impact of an IS model. Dishaw and Strong (1998) tried to explain the System Use construct with a model with a mediator construct, Intention to Use, and the independent constructs related with the Task-Technology Fit: Intrinsic Fit, Contextual Fit, Representation Fit and Accessibility Fit; and with the Behavioural Control construct. They could explain 16 per cent of the variance of the System Use construct and 70 per cent of the Intention to Use construct.

New conceptualizations of the system use construct
Burton-Jones and Straub (2006) re-conceptualize the system usage construct in specific nomological circumstances, working in two phases, definition and selection. This scheme permits researchers to establish precise measures of system usage for a particular context. The first phase necessitates a definition of the system use and determines basic assumptions. In the selection phase, the system usage needs to be designed according to its structure and function.
To explain in a better way the System Use construct (duration, frequency, and intensity), Venkatesh et al. In the last years, the DeLone and McLean model, which is one of the more used models to estimate the impact of an IS, has been evaluated through several studies that have used it partially or completely. Most of the relations between constructs have been confirmed (Petter et al., 2008). The construct System Use, as previously mentioned, could be a good indicator of a successful system when it is voluntary or discretional, and not when the system has captive users, who do not have an alternative system to process information (Lucas, 1978). Besides that, Petter and McLean (2009) performed a meta-analysis on the DeLone and McLean Success of IS model, considering 52 studies. They concluded that the User construct needed to be improved. Given that there no more consistent or confident measures exist, it would be difficult to find relations between this construct and the others of the model. Wieder et al. (2012) performed research about the impact of BI tools on Performance working with the DeLone and McLean model of IS Success. They did not find a significant relation between User Satisfaction and BI Use but found a weak relation between BI Use and Performance Indicators. They indicated that it is possible to find several particularities in BISs: first, the most advanced users of the system use the system more, in its full capacity, find errors, create difficult questions about the system, become less satisfied with the system and possibly use it less. On the other hand, the less experimented users look for the simple ways of using the system, find everything they need, are happy with the system, and would use it more.
Second, the BIS are configured to elaborate reports for easy usage in a fully-automated way. Users who want to get the most from the system need to have advanced technical skills and need to know a little more about the basic configuration of the system. This is related to the frustration of the user, indicating a lack of friendliness and adequate technical characteristics of the system. Finally, there would be an inadequate System Use because of a shortage of mental and cultural awareness of BI, and the BIS would lack performance. Because of those reasons, the construction of the System Dependence (System Use), according to Rai et al., 2002, is not working well in the DeLone and McLean model, it does not get a good fit, nor does it have a good level of explanation. The Modified Seddon model considers an additional restriction compared with the Seddon model. It indicates that there is a correlation between the Perceived Usefulness (Individual Impact) and the System Dependence (System Use) constructs. In this way, a better explanation and fit will exist. The supposition here is that the users of the Information System only have one viable choice for getting and analysing information, increasing the perceived benefit of the Perceived Usefulness (Individual Impact), so there is a correlation between Perceived Usefulness (Individual Impact) and System Dependence (System Use), with no causal relation between them.
The System Use (System Dependence) construct seems to work well in this research, using the Seddon IS Success model, nevertheless that variance explanation is not so high (12.2 per cent), and showing no relation with the Individual Impact (Perceived Usefulness) (observed when applying the modified Seddon IS Success model). Therefore, it could be interesting to make future research comparing these three models but working with a modified System Use (System Dependence) construct, considering the predictors of use, as proposed by Venkatesh et al. (2008) and Lallmahomed et al. (2013). The System Use construct could be mediated by a Behavioural Intention construct that works with the next independent constructs: Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. The System Use construct would have the next variables or components: User aspect (cognitive absorption), System Aspect (volume, frequency, intensity), and Task aspect (deep structure use).
As a way of attesting the practical and theoretical implications, and insinuate some conclusions, we can say that the Seddon model seems to explain better what happens with a BIS and will be preferable to use this model when making a research, nevertheless that it would be relevant to repeat this analysis to confirm the results. In the case of the Use Construct (System Dependence), it seems necessary when using it as a part of an Information System Success model, like the Delone and McLean model or Seddon model, that it has to be established with more detail, considering some complementarities like the mentioned by Burton-Jones and Straub (2006)

Limitations and recommendations for future studies
It is estimated that the sample includes more than 15 per cent of all the companies that use a BISs in Peru, so the size of the sample is adequate, but it is not entirely random and therefore limits the generalizability of outcomes. Besides that, a sample size that is bigger could be better for the sake of making a more detailed analysis, permitting the use of some items with less power, or the use of another statistical procedure for structural equations such as the Asymptotical Distribution Free, permitting a more detailed analysis (Hair et al., 2006).
As mentioned previously in the Discussion section, it would be interesting to continue the research of this subject, comparing the same three models, but conceptualising the System Use construct considering the predictors of use, to obtain a better explanation (variance) and getting more significant relations with other constructs. Another point would be to try to be more rigorous would be equally better, allowing the utilisation of some objective measures, not only perceptual measures for several of the constructs. Impact of a business intelligence system Swanson, B.E. (1974), "Management information systems: appreciation and involvement", Management Science, Vol. 21 No. 2, pp. 178-188. Venkatesh, V., Brown, S.A., Maruping, L.M. and Bala, H. (2008), "Predicting different