CHAPTER 5 DISCUSSION
5.2 What are the factors that contribute to the success of BI systems in South
The purpose of this research study was to determine the post implementation factors that contribute to the success of BI systems success in South Africa. The figure below shows the significant paths validated in the previous chapter. H5 is included in the model for completeness.
Figure 5.1: BI systems success validated model H5
H7 H1
Information Quality
System
Quality Individual
Impact User
Satisfaction
User Quality
H4
In this study information quality was defined as the desirable characteristics of the information produced by the BI system (Petter et al., 2008; Hwang & Xu, 2008). Based on prior literature seven items were selected to measure information quality namely; accuracy, usefulness, timeliness, completeness, relevance, understandability[sic] and trustworthiness. The relationship between information quality and user satisfaction of a BI system in South Africa was examined by hypothesis one. This hypothesis was drawn from the updated DeLone and McLean (2003) model (as described in chapter 2). The results of the structural model show that there is a positive relationship between information quality and user satisfaction (β = .24, p =.000).
The results of this study are consistent with the results of prior studies (DeLone & McLean, 2003;
Holsapple & Lee-Post, 2006; Lin, 2007; Chiu et al., 2007; Halawi et al., 2007; Leclercq, 2007;
Kulkarni et al., 2006; Wu & Wang, 2006; Almutairi & Subramanian, 2005; Hunton & Flowers, 1997).
The results also support the findings of Wixom and Todd (2005), which found that when users believe that the quality of the information provided by the BI system is favourable, they are more likely to be satisfied with it. Therefore, this study further provides support for the theory that the higher the quality of the information of the BI system the more satisfied the users will be with the BI system.
The results suggests that BI system end users are more likely to be satisfied with the BI system if the information quality is high. This finding maybe valuable and might have important implications for other companies in the developing world intending to adopt a BI system. This finding was also supported in the interviews, where all three respondents indicated information quality is a vital factor in the success of BI systems.
Organisations wishing to adopt BI systems could focus on the quality of the information in order to improve user satisfaction levels. Good information quality improves the way information is provided thereby enabling a better decision-making environment. In order to achieve high levels of information quality,BI systems need to develop and promote strategies that emphasise accuracy, completeness, currency, relevant, format, and integrity of information.
The relationship between information quality and individual impact was tested by hypothesis six. The structural model showed that the relationship between information quality and individual impact is not statistically significant (β = .01, p =.902). This finding is inconsistent with previous studies where information quality is reported to have a positive significant influence on individual impact (Santos,
Takaoka & Souza, 2010). This result suggests that end users do not view information quality as affecting their job performance.
However, this study is not alone in providing evidence of a non-significant relationship between quality information and individual impact. In a study by Rudra and Yeo (2000) it was found that information quality does not directly influence IS success. Rudra and Yeo (2000) found mixed reactions to information quality by end users using DWH among large companies in Australia. In the study by Rudra and Yeo (2000) the majority of the end users were unsure of the quality of information produced by the systems and therefore did not consider it important.
In this study, system quality refers to the desirable characteristics of the BI system (Petter et al., 2008). The items used in this study to measure system quality were: ease of use, user friendliness, responsiveness, learnability, stability, security, and reliability and availability. Hypothesis 2 was used to find out if there is a relationship or not between system quality and user satisfaction.
H20: System quality is not related to user satisfaction in a BI system.
H2A: System quality is related to user satisfaction in a BI system.
The results show there is a no significant relationship between user satisfaction and system quality (β
= .06, p =.374). This result suggests that the end users’ perception of the quality of the system is not significantly related to the BI system’s satisfaction levels. The results of this study do not agree with the findings of many previous studies, which suggested that system quality and user satisfaction have a strong positive relationship (DeLone & McLean, 2003; Holsapple & Lee-Post, 2006; Lin, 2007;
Halawi et al.,2007; Kulkarni et al., 2006; Wu & Wang, 2006; Almutairi & Subramanian, 2005).
Nevertheless, the results are consistent with the results of Wang and Liao (2008), who found an insignificant relation between system quality and user satisfaction in the banking sector in Taiwan.
The lack of statistical support for the system quality and user satisfaction could also be attributed to the difference in applications. The different types of applications investigated and the different contexts can explain differences among the results of this study and previous studies. Thus, while the present study examined BI systems in South Africa, the DeLone and McLean (2003) examined e- commerce in developed economies.
Hypothesis 7 was used to explore the relationship between system quality and individual impact. The result shows that system quality has a positive influence on individual impact (β = .27, p =.000). This
finding is consistent with previous studies where systems quality is reported to have a positive influence on individual impact (Bharati & Chaudhury, 2006; Wixom & Todd, 2005).
This finding could be explained by the fact that the use of an easy to use, user friendly and responsive BI system could facilitate the improvement of the quality of information. Furthermore, higher levels of BI system quality may help provide easy-to-understand information outputs and timely reports, and the changed information needs can be met easily. Additionally, a poor BI system could place the business at a competitive disadvantage because of its inability to provide quality information, specifically in terms of accuracy and content.
User satisfaction was defined in the previous chapters as the perception of the end user towards the BI system in relation to what the end user expected upon first use of the BI system (Seddon, 1997).
The items that were used to investigate user satisfaction were: efficiency, effectiveness and overall satisfaction. Hypothesis 5 investigated the relationship between user satisfaction and individual impact namely;
H50: User satisfaction is not related to individual impact in a BI system.
H5A: User satisfaction is related to individual impact in a BI system.
The results show that there is no significant relationship between individual impact and user satisfaction (β = .05, p =.487). Therefore, the null hypothesis cannot be rejected. According to Petter et al. (2008), there is a strong support for association between user satisfaction and individual impact.
Prior studies have found user satisfaction to be positively related to: a user’s job performance (Yoon
& Guimaraes, 1995; Torkzadeh & Doll, 1999) increase in productivity and effectiveness (Rai et al., 2002; Halawi et al., 2007), improved decision making (Vlahos & Ferratt, 1995; Vlahos et al., 2004), enhanced job satisfaction (Ang & Soh, 1997).
The findings for this study are therefore inconsistent with the research studies of Yoon and Guimaraes (1995); Ang and Soh (1997). However, this research is not alone in providing evidence of a non- significant relationship between user satisfaction and individual impact. For example, Yuthas and Young (1998) found that user satisfaction was only weakly associated with decision-making performance. The result suggests that the satisfaction levels of end users has no impact on their job performance. End users within an organisation may have expectations about the BI system. If these expectations are unrealistic and cannot be met, this may cause user disappointment and dissatisfaction with the system.
User quality measures the impact of the end users’ capabilities on the BI system success. The items that were used to investigate user quality were: technical skills, business skills and analysis skills.
The influence of user quality on individual impact of BI systems in South Africa is examined by hypotheses nine, namely;
H90: User quality is not related to individual impact in a BI system.
H9A: User quality is related to individual impact in a BI system.
The results of the hypothesis test found there was no significant relationship between user quality and individual impact (β = .02, p =.779). This finding suggests that the level of the user’s skills in analysing the data provided by the BI system are not very important for the success of the BI system.
The results of this study do not agree with the findings of other previous studies (Wixom & Watson, 2001; Hwang et al., 2004). The reason of this finding could be that, the BI end users that possess the required skills such as technical, business, and analytical skills take for granted the importance of their skills in the better management of data and hence do not see these skills as impacting on their daily work tasks.
Hypothesis four examined the relationship between user quality and user satisfaction of a BI system namely:
H40: User quality is not related to user satisfaction in a BI system.
H4A: User quality is related to user satisfaction in a BI system.
The results of the study show that there is a positive relationship between user quality and user satisfaction (β = .30, p =.000). As the user quality levels increase, the user satisfaction levels of the BI system increases. This result is consistent with the study of Wixom and Watson (2001) which found that high levels of user skills is positively related to DWH success. A study by Hwang et al.
(2004) also found similar results when investigating the project team skills and DWH adoption. The result of the study suggests that as the levels of user skills increase so does the user satisfaction with the BI system.
The reason for this finding could be that, highly skilled BI end users do not have unrealistic expectations of their BI systems and hence are more easily to be satisfied by the BI systems.
Furthermore, knowledgeable BI end users are more likely to comprehend the business requirements and have the resolve to make action-based decisions which result in the improvement of the business.