Analysing the impact of a business intelligence system and new conceptualizations of system use.
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.
In the last 40years, there have been developed several models to measure the impact of IS. We could mention Theory of reasoned action (1975-1980), Theory of planned behaviour (1985), Technology Acceptance model (1986), User involvement (1984), Delone and McLean (1992), Seddon model (1997), Soh and Markus (1995), the modified model of Delone and McLean (2003), and others (Gonzales, 2008).
From these models, the more relevant has been the Delone and 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).
2. Literature review
2.1 The Delone and McLean information systems success model
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.
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.
2.2 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.
The Partial Behavioural model of IS Use is composed of expectations about the net benefits of future IS Use construct, that is directly related to the IS Use construct (behaviour). The IS Success model is composed by three bodies. The first one is Measures of Information and System Quality, with System Quality and Information Quality constructs. The second body is General Perceptual Measures of Net Benefits of IS Use, with the Perceived Usefulness, and User Satisfaction constructs. The second body is Other Measures of Net Benefits of IS Use, with the net benefits for Individuals, Organisations, and Society. The constructs of the first and third body influence the constructs of the second body. Besides that, the Perceived Usefulness of the second body is directly related to the User Satisfaction construct. Finally, the User Satisfaction Construct offers feedback with construct Expectations about benefits for future IS Use, of the Partial Behavioural model of IS Use. The Seddon model can be observed in 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).
2.3 The modified Seddon information systems success model
Rai et al. (2002) used the DeLone and McLean 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.
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.
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.
4. 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).
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.
4.1 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.sup.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.
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.sup.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 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.sup.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.sup.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.sup.2]).
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.sup.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.sup.2]).
We can compare the three models in the next Table II. The best model is the Seddon model; the second-best model is the Modified Seddon model, which is quite similar to the first model; and thirdly the DeLone and McLean model. The Seddon model has the best for CFI (0.958 against 0.957 and 0.948), RMSEA (0.068 against 0.068 and 0.076), [R.sup.2] of Perceived Usefulness (0.687 against 0.687 and 0.652), [R.sup.2] of User Satisfaction (0.803 against 0.803 and 0.791). The Seddon model explains the significance of the System Dependence construct in relation to other constructs of the model (like the Modified Seddon model), but the DeLone and McLean does not. The DeLone and McLean better explain System Dependence ([R.sup.2] of 0.123, against 0.122 and 0.117 for the other models). In addition, for the total number of significant relations between constructs, the Modified Seddon model explained six relations, against five for the Seddon model and three for the DeLone and McLean model.
Some additional comparisons that we can make are with the study of Rai et al. (2002), in which there are various differences, especially with the Delone and McLean model, but less with two models of Seddon. The main differences could be explained by the fact that the Rai et al. (2002) study was realised with students of only one university, and this study was realised with executives of the IT department of several companies (Appendix 6).
The other comparison is with Wieder et al. (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., 2007-2008; Bokhari, 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., 2007-2008; Rai et al., 2002; Yuthas and Young, 1998; Guimaraes and Igbaria, 1997). Other authors did not find that relation (Gelderman, 1998; Roldan and Millan, 2000).
6.1 Voluntary and mandatory contexts
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 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.
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.
6.2 New conceptualizations of the system use construct
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 TaskTechnology 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.
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. (2008), use a mediator construct, Behavioural Expectations, and two independent constructs, Behavioural Intentions and Facilitating Conditions, in a longitudinal field study, and explained between 60 per cent and 65 per cent of the variance of the dependent construct, System Use.
Continuing research to further specify the System Use Construct, Lallmahomed et al. (2013) utilised a model with the mediator construct Behavioural Intention, the independent constructs Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Hedonic Performance Expectancy, and System Use as dependent construct (Cognitive Absorption; volume, frequency and intensity; and Deep Structure Use), and achieved a variance explanation of 71 per cent of the dependent construct.
As could be observed in the previous lines, there are contradictory results of the relation of the System Use construct (System Dependence construct) with the Perceived Usefulness construct (Individual Impact construct), and other constructs as User Satisfaction. It is not completely clear too when specifying the voluntary or mandatory context, if the System Use construct has a direct and significant relationship with other constructs. Furthermore, some authors as Venkatesh et al. (2008) and Lallmahomed et al. (2013) has tried new conceptualisation of this construct, working with predictors of use, and have obtained good explanations of the System Use construct (between 60 and 65 and 71 per cent of the variance explained).
The results received from this research indicates that the Seddon model performs and explains better what is happening with the BIS, compared to the DeLone and McLean model and with the Modified Seddon model. The fit is better than in the DeLone and McLean model (CFI of 0.958 vs 0.948; RMSEA 0.068 vs 0.076), the same goes for [R.sup.2] Average explained (0.537 vs 0.522). The Seddon model has a significant relationship between the System Dependence (System Use) and the other constructs, whereas the DeLone and McLean do not. The Seddon model has five significant relations between the constructs, while the DeLone and McLean has only three. The Modified Seddon model obtains an almost as good result, as the Seddon model, with the only advantage that explains one additional significant relation between constructs (six in total).
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), Venkatesh et al. (2008), or Lallmahomed et al. (2013).
8. 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.
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Table AI. Main Studies of the Impact of IS and BI Authors Description/Objective of the Study 1 Iivari (2005) Test the IS Success of DeLone and McLean in the adoption of a new IS 2 Rai, Lang &Welker (2002) Assess the validity of IS models 3 Lee, Hong & Katerattanakul Impact of DW in the performance of (2004) retail companies 4 Hong, Katerattanakul, Hong Use and perceptual impact of the DW in &Cao(2006) financial companies in Korea 5 Park (2006) Effect of DW as support for performance improvement with the use of DSS 6 Wixom & Watson (2001) Success factors in the implementation of a DW 7 Nelson, Todd & Wixom Antecedents of the quality in the DW (2005) (System quality and Information quality) 8 Shin (2003) Success Factors of the DW 9 Brown & Jayakody (2009) IS Success model applied to B2C e-Commerce 10 Chen, Soliman, Mao, & Satisfaction in DW Exploratory study Frolick (2000) Type of Study Measures 1 Quantitative System quality, Information quality, Actual use, User Satisfaction and Individual Impact 2 Quantitative Ease of Use, Information Quality, System Dependence, User Satisfaction & Perceived Usefulness 3 Quantitative Financial performance: improvement in Sales by employee, ROS, growth of Sales Not financial performance: promotional performance by customer, seller, and marketing segmentation 4 Quantitative Individual Impact, Use of the System, Perceived Ease of Use, Percieved Use, Data Quality, Reply time accessibility, Support t and training 5 Quantitative Effect in decision process (precision) and income maximization 6 Quantitative Perceived net benefits, Data quality, System quality, administrative support, user's involvement, development techn. 7 Quantitative System quality and Information quality 8 Quantitative & Data quality, ability to locate data, Qualitative access authorisation, ease of use, user training, system performance & information utility 9 Quantitative Continuance Intention, User Satisfaction, Perceived Usefulness, Service quality, System quality, Information quality, and Trust 10 Quantitative Exploratory 21 items related with the satisfaction of the use of a DW Research Strategy 1 Sample through questionnaire 2 Sample through questionnaire 3 Sample through questionnaire 4 Sample through questionnaire 5 Laboratory experiment 6 Field study Primary data 7 Field study Primary data 8 Sample through questionnaire 9 Sample through questionnaire 10 Sample through questionnaire Authors Description/Objective of the Study 1 Benefits DeLone & McLean (1992) 2 Benefits DeLone & McLean (1992) & Seddon(1997) 3 Benefits DeLone & McLean (1992), Pitt, Watson & Kavan (1995) Myers, Kappelman & Prybutok (1998) 4 Benefits and DeLone & McLean (1992) TAM outcomes of the system 5 Benefits and DeLone & McLean (1992,2003) Decisions Seddon(1997) Laboratory Experiment 6 Benefits DeLone & Mc Lean (1992) Seddon(1997) 7 Benefits Seddon (1997) & (1994) DeLone & McLean (1992) 8 User Satisfaction De Lone & McLean (1992,2003) 9 Contin uance DeLone & McLean (1992,2003) Intention Seddon (1997) Seddon & Kiev (1996) 10 21 items in a five DeLone & McLean (1992) Bailey & points Likert scale Person (1983) Doll & Torkzadeh (1988) Type of Study Measures 1 Indivitual Impact - 0.35 Actual Use - User Satisfaction - Indivitual 0.18 User Satisfaction = 0.58 Impact (**) Actual Use - Individual Impact - No Actual Use - User Satisfaction - No 2 Usefulness - 0.60 System dependence - System dependence- 0.30 User Satisfaction = 0.51 Usefulness (**) User satisfaction- Usefulness (**) User satisfaction- System dependence (**) 3 Anova analysing impact of DW on Promotional Effects (*) Vendor organizational outcomes Effects (*) Customer Effects (**) Market Segmentation Effects (*) 4 Ease of Use = 0.523 Usefulness = 0.637 Ease of use-Usefulness (*) Individual Impact - 0.699 System Use - Usefulness-System use (*) System 0.342 use-Individual Impact (*) 5 Anova Analysis Dependent variable: Two hypothesis related with the decision performance. Independent: advantage of the DW were Tradicional vs DW supported, and three hypothesis rejected 6 Net beneficts - 0.369 Data quality - Data quality-Net benefits (*) 0.016 System quality = 0.128 System quality-Net benefits (***) 7 Infor mation quality - 0.761 System System quality-Information quality = 0.759 Satisfact. (**) System quality- System Satisfaction (***) 8 User Satisfaction - 0.70 Data quality-User satisfaction (*) Ability to locate data-User satisfact. (*) System throughput- User satisfaction (*) 9 Usefulness-User Satisfaction (*) Usefulness-Continuance Intention (***) User Satisfaction- Continuance Intention (*) 10 Factor analysis: found 3 factors: Support End-user satisfaction is related provided to end-users, Accuracy, with the support provided by Fulfilment of end-user needs Information centres Notes: Elaborated by Gonzales (2008) and the authors. Significance (p-values): (*) (<0.05), (**) (<0.01), (***) (<0.001)
Table AII. Descriptive statistics Variable Abreviated Variable Business Sector BUSS-SECT Activity user of the system ACT-USU Years of experience YEARS Information quality- Data relevance IQ-DR Information quality- Detail level IQ-DL Information quality- Data accuracy IQ-DA Information quality- Data currently IQ-DC Information quality- Data understanding IQ-DU Information quality- Data completeness IQ-COM System quality- Data retrieveness SQ-DR System quality- System access SQ-SA System quality- Access tools SQ-AT System quality- Waiting time SQ-WT System quality- Flexibility SQ-F Service quality- User training SEQ-UT Service quality- Rapid repply SEQ-RR Service quality- Effective solution SEQ-ES Service quality- Management Encouragement SEQ-ME Service quality- Management support SEQ-MS Use- Common use USE-C Use- Average use USE-A Use- Average time USE-AT User Satisfaction- After using the system US-AU User Satisfaction- After using the system-2 US-AU2 User Satisfaction- When using the system US-WU User Satisfaction- After using the system US-AUS User Satisfaction- Suggesting the system to other company US-SOC Individual Impact- Swiftness doing my tasks II-SDT Individual Impact- Job performance improvement II-JPI Individual Impact- Productivity improvement II-PI Individual Impact- Decisions upgrade II-DU Individual Impact- Work usefulness II-WU Variable N N lost Business Sector 110 0 Activity user of the system 109 1 Years of experience 86 24 Information quality- Data relevance 110 0 Information quality- Detail level 110 0 Information quality- Data accuracy 110 0 Information quality- Data currently 110 0 Information quality- Data understanding 110 0 Information quality- Data completeness 110 0 System quality- Data retrieveness 110 0 System quality- System access 110 0 System quality- Access tools 110 0 System quality- Waiting time 110 0 System quality- Flexibility 110 0 Service quality- User training 110 0 Service quality- Rapid repply 110 0 Service quality- Effective solution 110 0 Service quality- Management Encouragement 110 0 Service quality- Management support 110 0 Use- Common use 110 0 Use- Average use 110 0 Use- Average time 110 0 User Satisfaction- After using the system 110 0 User Satisfaction- After using the system-2 110 0 User Satisfaction- When using the system 110 0 User Satisfaction- After using the system 110 0 User Satisfaction- Suggesting the system to other company 110 0 Individual Impact- Swiftness doing my tasks 110 0 Individual Impact- Job performance improvement 110 0 Individual Impact- Productivity improvement 110 0 Individual Impact- Decisions upgrade 110 0 Individual Impact- Work usefulness 110 0 Variable Averg SD Business Sector 2.790 2.279 Activity user of the system 3.991 2.602 Years of experience 5.372 3.767 Information quality- Data relevance 5.909 1.080 Information quality- Detail level 5.473 1.029 Information quality- Data accuracy 5.273 1.188 Information quality- Data currently 5.582 1.252 Information quality- Data understanding 5.536 1.147 Information quality- Data completeness 5.091 1.138 System quality- Data retrieveness 5.291 1.251 System quality- System access 5.491 1.319 System quality- Access tools 5.264 1.155 System quality- Waiting time 4.873 1.421 System quality- Flexibility 4.455 1.542 Service quality- User training 5.309 1.457 Service quality- Rapid repply 5.027 1.371 Service quality- Effective solution 5.145 1.107 Service quality- Management Encouragement 5.373 1.439 Service quality- Management support 5.336 1.221 Use- Common use 5.364 1.276 Use- Average use 4.936 1.486 Use- Average time 4.273 1.670 User Satisfaction- After using the system 5.191 1.153 User Satisfaction- After using the system-2 5.164 1.146 User Satisfaction- When using the system 5.036 1.234 User Satisfaction- After using the system 5.055 1.074 User Satisfaction- Suggesting the system to other company 5.091 1.282 Individual Impact- Swiftness doing my tasks 5.409 1.069 Individual Impact- Job performance improvement 5.482 1.090 Individual Impact- Productivity improvement 5.382 1.211 Individual Impact- Decisions upgrade 5.709 1.120 Individual Impact- Work usefulness 5.700 1.208 Variable Variant Coeff Business Sector 82% Activity user of the system 65% Years of experience 70% Information quality- Data relevance 18% Information quality- Detail level 19% Information quality- Data accuracy 23% Information quality- Data currently 22% Information quality- Data understanding 21% Information quality- Data completeness 22% System quality- Data retrieveness 24% System quality- System access 24% System quality- Access tools 22% System quality- Waiting time 29% System quality- Flexibility 35% Service quality- User training 27% Service quality- Rapid repply 27% Service quality- Effective solution 22% Service quality- Management Encouragement 27% Service quality- Management support 23% Use- Common use 24% Use- Average use 30% Use- Average time 39% User Satisfaction- After using the system 22% User Satisfaction- After using the system-2 22% User Satisfaction- When using the system 25% User Satisfaction- After using the system 21% User Satisfaction- Suggesting the system to other company 25% Individual Impact- Swiftness doing my tasks 20% Individual Impact- Job performance improvement 20% Individual Impact- Productivity improvement 23% Individual Impact- Decisions upgrade 20% Individual Impact- Work usefulness 21% Variable Min Max Business Sector 1 9 Activity user of the system 1 7 Years of experience 1 20 Information quality- Data relevance 2 7 Information quality- Detail level 3 7 Information quality- Data accuracy 2 7 Information quality- Data currently 1 7 Information quality- Data understanding 2 7 Information quality- Data completeness 2 7 System quality- Data retrieveness 2 7 System quality- System access 1 7 System quality- Access tools 2 7 System quality- Waiting time 1 7 System quality- Flexibility 1 7 Service quality- User training 1 7 Service quality- Rapid repply 1 7 Service quality- Effective solution 1 7 Service quality- Management Encouragement 1 7 Service quality- Management support 1 7 Use- Common use 2 7 Use- Average use 2 7 Use- Average time 2 7 User Satisfaction- After using the system 2 7 User Satisfaction- After using the system-2 2 7 User Satisfaction- When using the system 1 7 User Satisfaction- After using the system 2 7 User Satisfaction- Suggesting the system to other company 1 7 Individual Impact- Swiftness doing my tasks 3 7 Individual Impact- Job performance improvement 3 7 Individual Impact- Productivity improvement 2 7 Individual Impact- Decisions upgrade 3 7 Individual Impact- Work usefulness 2 7 Source: Own elaboration
Table AIII. Kolmogorov-smirnov normality TEST Variable (item) Average SD N D Aprox. P-Value IQ-DR 5.90 1.079 110 0.061 >0.15 IQ-DL 5.47 1.029 110 0.032 >0.15 IQ-DA 5.27 1.187 110 0.034 >0.15 IQ-DC 5.58 1.251 110 0.063 >0.15 IQ-DU 5.53 1.146 110 0.052 >0.15 IQ-COM 5.09 1.137 110 0.033 >0.15 SQ-DR 5.29 1.251 110 0.055 >0.15 SQ-SA 5.49 1.318 110 0.059 >0.15 SQ-AT 5.26 1.154 110 0.034 >0.15 SQ-WT 4.87 1.421 110 0.054 >0.15 SQ-F 4.45 1.542 110 0.047 >0.15 SEQ-UT 5.30 1.457 110 0.060 >0.15 SEQ-RR 5.02 1.371 110 0.060 >0.15 SEQ-ES 5.14 1.107 110 0.052 >0.15 SEQ-ME 5.37 1.439 110 0.064 >0.15 SEQ-MS 5.33 1.221 110 0.060 >0.15 USE-C 5.36 1.275 110 0.050 >0.15 USE-A 4.93 1.485 110 0.040 >0.15 USE-AT 4.27 1.669 110 0.059 >0.15 US-AU 5.19 1.153 110 0.039 >0.15 US-AU2 5.16 1.145 110 0.031 >0.15 US-WU 5.03 1.233 110 0.038 >0.15 US-AUS 5.05 1.073 110 0.024 >0.15 US-SOC 5.09 1.281 110 0.049 >0.15 II-SDT 5.40 1.069 110 0.037 >0.15 II-JPI 5.48 1.089 110 0.046 >0.15 II-PI 5.38 1.211 110 0.054 >0.15 II-DU 5.70 1.119 110 0.057 >0.15 II-WU 5.70 1.208 110 0.066 >0.15 Source: Own elaboration
Table AIV. Confirmatory factor analysis Construct Variables Information quality IQDA IQDC IQCOM System quality SQDR SQSA SQAT Service quality SEQRR SEQES SEQME SEQMS Eliminated observations (6): 34, 39, 47, 56, 76, 107 Multivariate kurtosis: 2.86 Maximum likelihood Chi-square 290 CFI 0.956 RMSEA 0.070 Ave by construct IQ 39.99% SQ 54.41% SEQ 53.36% UES 51.97% US 70.74% II 61.86% Reliability coefficients Cronbach's alpha Reliability coefficient RHO Construct Construct Information quality USE (system dependence) System quality User satisfaction Service quality Individual impact (Perceived usefulness) Eliminated observations (6): 34, 39, 47, 56, 76, 107 Multivariate kurtosis: 2.86 Maximum likelihood Robust Chi-square CHI-SQUARE CFI CFI RMSEA RMSEA Ave by construct Construct reliability IQ IQ SQ SQ SEQ SEQ UES UES US US II II Reliability coefficients Cronbach's alpha 0.954 Reliability coefficient RHO 0.974 Construct Variables Information quality USEC USEA USEAT System quality USAU USAU2 USWU USAUS Service quality IISDT IIJPI IIPI IIDU IIWU Eliminated observations (6): 34, 39, 47, 56, 76, 107 Multivariate kurtosis: 2.86 Maximum likelihood Chi-square 281 CFI 0.946 RMSEA 0.067 Ave by construct IQ 0.66 SQ 0.78 SEQ 0.82 UES 0.75 US 0.91 II 0.89 Reliability coefficients Cronbach's alpha Reliability coefficient RHO Source: Own elaboration
Table AV. Comparison with the study of rai, Lang and Welker (2002) (Significance and Variance Extracted ([R.sup.2])) Rai et al. DeLone & McLean model Easy of use-System dependence (*) Easy of use-User Satisfaction (**) Information Quality-System (**) dependence Information Quality-User (**) Satisfaction User Satisfaction-System (**) dependence System dependence-Perceived (**) Usefulness User Satisfaction-Perceived (**) Usefulness [R.sup.2] System Dependence 30% User Satisfaction 51% Perceived Usefulness 60% Seddon model Easy of use-Perceived usefulness (**) Easy of use-User Satisfaction (**) Information quality-Perceived (**) Usefulness Information quality-User (**) Satisfaction User Satisfaction-Perceived (**) Usefulness User Satisfaction-System (**) dependence [R.sup.2] Perceived Usefulness 41% User Satisfaction 55% System Dependence 27% Modified Seddon model Easy of use-Perceived usefulness (**) Easy of use-User Satisfaction (**) Information quality-Perceived (**) Usefulness Information quality-User (**) Satisfaction User Satisfaction-Perceived (**) Usefulness User Satisfaction-System (**) dependence Perceived Usefulness-System (**) dependence [R.sup.2] Perceived Usefulness 41% User Satisfaction 55% System Dependence 53% Main characteristics of the study Structural equations Lisrel Year of the study 2002 Unit of analysis University students of one university Information System Computarized student information System (Significance and Variance Extracted ([R.sup.2])) This study DeLone & McLean model Easy of use-System dependence - Easy of use-User Satisfaction - Information Quality-System - dependence Information Quality-User (*) Satisfaction User Satisfaction-System - dependence System dependence-Perceived - Usefulness User Satisfaction-Perceived (*) Usefulness [R.sup.2] System Dependence 12% User Satisfaction 77% Perceived Usefulness 65% Seddon model Easy of use-Perceived usefulness (*) Easy of use-User Satisfaction - Information quality-Perceived - Usefulness Information quality-User (*) Satisfaction User Satisfaction-Perceived (*) Usefulness User Satisfaction-System (*) dependence [R.sup.2] Perceived Usefulness 69% User Satisfaction 80% System Dependence 12% Modified Seddon model Easy of use-Perceived usefulness (*) Easy of use-User Satisfaction - Information quality-Perceived (*) Usefulness Information quality-User (*) Satisfaction User Satisfaction-Perceived (*) Usefulness User Satisfaction-System (*) dependence Perceived Usefulness-System - dependence [R.sup.2] Perceived Usefulness 69% User Satisfaction 80% System Dependence 12% Main characteristics of the study Structural equations EQS Year of the study 2014 Unit of analysis Executives of IT department of thirteen companies Information System BIS Note: Significance (p values): (*) (<0.05), (**) (<0.01) Source: Own elaboration
Rolando Gonzales can be contacted at: firstname.lastname@example.org
Tecnologias de Informacion e Investigacion Operativa, Universidad ESAN, Lima, Peru, and
Information Technology, ESADEBusiness School-Campus Sant Cugat, Sant Cugat del Valles, Spain
[C] Rolando Gonzales and Jonathan Wareham. Published in Journal of Economics, Finance and Administrative Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licenses/by/4.0/legalcode
Received 22 May 2018
Revised 30 November 2018
Accepted 1 December 2018
Table I. Main statistics of the confirmatory factor analysis Statistics - confirmatory factor analysis Number of observations 104 Multivariate Kurtois 2.86 Method ML Chi-squared 290 RMSEA 0.070 Method robust Chi-squared 281 CFI 0.946 RMSEA 0.067 Average variance extracted Information Quality 39.99% System Quality 54.41% Service Quality 53.36% System Dependence 51.97% User Satisfaction 70.74% Perceived Usefulness 61.86% Source: Own elaboration Table II. Comparison of the three models Statistics DeLone and McLean [chi square] 311,71 Degrees of Freedom 196 Comparative Fit Index (CFI) 0.948 RMSEA 0.076 [R.sup.2] Perceived Usefulness (Individual Impact) 0.652 [R.sup.2] User Satisfaction 0.791 [R.sup.2] System Dependence (System Use) 0.123 [R.sup.2] Average explained 0.522 Significant relation of the System Dependence No (System Use) construct with other constructs Total significant relations between constructs 3 Models Statistics Seddon [chi square] 290,67 Degrees of Freedom 197 Comparative Fit Index (CFI) 0.958 RMSEA 0.068 [R.sup.2] Perceived Usefulness (Individual Impact) 0.687 [R.sup.2] User Satisfaction 0.803 [R.sup.2] System Dependence (System Use) 0.122 [R.sup.2] Average explained 0.537 Significant relation of the System Dependence Yes (System Use) construct with other constructs Total significant relations between constructs 5 Statistics Modified Seddon [chi square] 290,40 Degrees of Freedom 196 Comparative Fit Index (CFI) 0.957 RMSEA 0.068 [R.sup.2] Perceived Usefulness (Individual Impact) 0.687 [R.sup.2] User Satisfaction 0.803 [R.sup.2] System Dependence (System Use) 0.117 [R.sup.2] Average explained 0.536 Significant relation of the System Dependence Yes (System Use) construct with other constructs Total significant relations between constructs 6 Source: Own elaboration
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|Author:||Gonzales, Rolando; Wareham, Jonathan|
|Publication:||Journal of Economics, Finance and Administrative Science|
|Date:||Jul 1, 2019|
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