Several authors (e.g. DeLone & McLean 1992, 2003; Hayen, Rutashobya & Vetter 2007; Shin 2003) have found that system quality has an effect on the use of BI system and user satisfaction, and user satisfaction has an influence on individual and organisational impact, which together make up a firm’s ‘net benefits’. Studies indicate that in general service quality also has a positive influence on user satisfaction (Almutairi & Subramanian 2005; Coombs, Doherty & Loan-Clarke 2001; Gelderman 2002; Halawi, McCarthy & Aronson 2007; Livari 2005; McGill, Hobbs & Klobas 2003; Seddon & Kiew 1996; Wu & Wang 2006) and that when users are satisfied with a BI system it is hoped that they would derive positive benefits from using it (Gelderman 1998; Law & Ngai 2007).

Researchers (e.g. DeLone & McLean 1992; Doll & Torkzadeh 1988), by utilising, for example, the case study design, survey design or Delphi technique method have identified user satisfaction as one of the most extensively used single measure of BI success. Chiu et al. (2005) further explained that the user’s level of satisfaction is based on the level to which the application meets the users’ expectations. According to Smart (2009), individual impact affects the organisational impact of the BI system and they are both influenced by user satisfaction. This study adopted Seddon’s (1997:246) definition of user satisfaction as ‘the net feeling of pleasure or displeasure resulting from aggregating all the benefits that a person hopes to receive from interaction with the information system’. Perceived net benefits are the core dimension used to determine the diverse and multifaceted success of BI (Wixom & Watson 2001).

This study was based on three theoretical models, namely, technology acceptance model (TAM) (Davis 1989), task technology fit (TTF) (Goodhue & Thompson 1995) and social cognitive theory (SCT) (Bandura 1986). TAM posits that perceived usefulness of a BI system is directly impacted by perceived ease of its use and they both determine an individual’s intention to use it with intention to use serving as a mediator of actual system use. The model assumes that when people form an intention to act, they will be free to act without limitation, and in practice, constraints such as limited ability, time, environmental or organisational limits, and unconscious habits will limit the freedom to act. In this study, this model was extended by introducing six major factors of information systems success, namely: (1) system quality, (2) information quality, (3) use, (4) user satisfaction, (5) individual impact and (6) organisational impact factors from the DeLone and Mclean model (1992). The TTF theory holds that IT affects individual performance if its capabilities match the concerned tasks and quality, compatibility, ease of use/training, systems reliability and relationship with users, etc., and affect the task–technology fit which in turn determines the system’s effectiveness (Goodhue & Thompson 1995). SCT identifies human behaviour as an interaction of personal factors, behaviour, and the environment (Bandura 1986) (which involves the influences of a person’s thoughts and actions). The second interaction between the person and the environment involves human beliefs and cognitive competencies that are developed and modified by social influences and structures within the environment. The third interaction between the environment and behaviour involves a person’s behaviour determining the aspects of their environment (Jones 1989) and in turn their behaviour is modified by that environment.

This study hypothesised that in the BI system, user satisfaction moderates the effects of system quality, information quality and service quality on perceived net benefits and it mediates the relationship between these independent variables and net benefits. System quality as an essential factor in a successful data warehouse implementation (Seddon 1997) comprises five key dimensions: flexibility, reliability, response time, accessibility and integration (Nelson, Todd & Wixom 2005; Petter, DeLone & McLean 2008; Seddon, 1997). Information quality consists of four dimensions: accuracy, completeness, currency and format, which are, by implication, an essential asset for BI success for any organisation (Haley 1997; Hwang & Xu 2008; Nelson et al. 2005; Rudra & Yeo 1999; Shin 2003; Thomann & Wells 1999; Wixom & Watson 2001). Zhu and Kraemer (2005) identified four attributes with respect to information quality as follows: (1) origin has an effect on reliability of the data and the trust one can place in it referring to the origin relationship as data lineage, (2) correctness signifies that the data are free of errors, (3) completeness describes the coverage of the data and (4) objectivity refers to the lack of prejudice in the data. Service quality is defined as the quality of the support that BI system users receive from the IS department and IT support personnel (Pitt, Watson & Kavan 1995; Petter et al. 2008). Grover and Segars (1996), defined service quality based on the degree of disparity between what the customer expects in terms of service standard and perceived service performance (Culiberg & Rojsek, 2010; Parasuraman, Zeithaml & Berry 1988).

The conceptual framework diagram is shown in Figure 1.

Table 1 shows the items or concepts that were measured for a particular construct.

A total of 211 businesses (with more than 100 full-time employees) and a maximum response of two BI users (from different departments) were used in this study. An anonymous and confidential online semi-structured questionnaire was used to collect data.

In this study, correlational analysis and multiple regression were used to analyse the data. The dependent variable was perceived net benefits. System quality, information quality and service quality were the independent variables and user satisfaction was treated as a moderating or mediating variable. A moderator is a variable that alters the direction or strength of the relationship between an independent variable and a dependent variable. This implies that the effect of an independent variable on the dependent variable depends on the level of another variable referred to as a moderating variable, just like an interaction effect. A mediator helps to explain a relationship between an independent variable and a dependent variable. The multiple regression model used in this study was:

where y_i is the i^th observation on the net benefit variable, x^th is the i^th observation on the j^th independent variable, and b_i is the effect of the j^th independent variable on net benefit and e_i is the error of the i^th observation.

Statistical significance was checked by the F-test of the overall model fit and t tests of individual parameters. In order to test for the moderation effect of user satisfaction on the effect of the independent variables on the dependent variable, new variables were created by multiplying system quality, information quality and service quality by user satisfaction and including the interaction terms in the regression model. Two regression models were fitted; the first one included only the independent variables and user satisfaction. The second, apart from the independent variables and user satisfaction, also included the newly created interaction variables. Because in multiple regression, interaction variables are often highly correlated with the variables from which they are created, the independent variables and interaction variables were standardised to decrease the correlation.

In order to know whether adding the interactions led to a significant improvement in how well the regression performed, R² was examined to find out whether it increased. This increase was tested for significance using the F-test.

To test for mediation, again, two models were fitted. The first one was fitted with only system quality, information quality and service quality as independent variables, and the second one, apart from including these variables, also included user satisfaction. The mediation effect of user satisfaction was identified by examining whether the differences between the coefficients of the first model and those of the second model were statistically significant. The Stata Statistical Package was used to analyse the data.

The mean scores of the constructs were 4.34, 3.95, 3.49 and 4.36 for system quality, information quality, service quality and user satisfaction, respectively. This implies that in general, the respondents were satisfied with the BI system.

The Kendall rank correlation coefficient was used to measure the correlations between the constructs as the sample was relatively small (Field, Miles & Field 2012; Yamane 1967). Table 2 shows the results (with probabilities in the brackets).

The results indicate that there is a high positive correlation between system quality and user satisfaction (r = 0.34, p = 0.000 < 0.01), information quality and user satisfaction (r = 0.27, p = 0.001), information quality and perceived net benefits (r = 0.20, p = 0.006 < 0.01) and user satisfaction and perceived net benefits (r = 0.31, p = 0.000 < 0.01); and there is a high negative correlation between service quality and user satisfaction (r = -0.20, p = 0.019 < 0.05) and service quality and perceived net benefits (r = -0.21, p = 0.007 < 0.01). However, these correlations are close to zero, which implies that the relationships are weak.

In South Africa, system quality and user satisfaction should be enhanced and maintained in order to achieve perceived positive net benefits and this would make the BI system more effective and efficient. A research study focussing on a specific industry could be very helpful in facilitating a clearer understanding of the relationships among the variables.