CHAPTER 2 LITERATURE REVIEW
2.8 The Research Model
2.8.1 Information Quality
The researcher uses the IS success theory discussed in the previous section as a lens to explore the success of BI systems in South Africa. The information quality construct from the DeLone and McLean model (2003) was used to explore the influence of information quality on BI systems success in South Africa. The information for a BI system typically resides in a DW. Quality information is an essential asset for any organisation (Wang, Storey & Firth, 1995). Numerous studies (Haley, 1997;
Thomann & Wells, 1999; Rudra & Yeo, 2000; Wixom & Watson, 2001; Shin, 2003; Nelson, Todd
& Wixom, 2005; Hwang & Xu, 2008; Holsapple & Lee-Post, 2006; Lin, 2007; Chiu et al., 2007;
Leclercq, 2007; Wu & Wang, 2006; Almutairi & Subramanian, 2005) suggest that information quality is essential for success.
Information quality refers to the desirable features of the information produced by the system.
Measures of information quality include features such as timeliness, accuracy and relevance (Petter et al., 2008; Hwang & Xu, 2008). Information quality consists of four dimensions: accuracy, completeness, currency, and format (Nelson, et al., 2005). On the other hand, DeLone and McLean (1992) points out that information quality refers to the quality of the information the system produces
(DeLone & McLean, 1992). Shin (2003) proposed that BI implementation should include a process to increase the quality of the data.
Organisations need to focus on the quality of data that is captured by the front-end systems. This is to ensure that the information used by decision makers is accurate and able to increase productivity and efficiency. DeLone and McLean (1992) identified the following information quality dimensions in Table 2.12.
Table 2.12 : Information Quality Measures (DeLone & McLean, 1992) Information Quality Measures
Accuracy, Precision, Currency, Timeliness, Reliability, Completeness, Conciseness, Format, Relevance
Bailey and Pearson (1983)
Perceived usefulness of specific report items
Blaylock and Rees (1984) Perceived importance of each information
item
Jones and McLeod (1986) Currency, Sufficiency, Understandability,
Freedom from bias, Timeliness, Reliability, Relevance to decisions, Comparability, Quantitativeness
King and Epstein (1983)
Report accuracy, Report timeliness Mahmood (1987)
Report usefulness Mahmood and Medewitz (1985)
Completeness of information, Accuracy of information, Relevance of reports,
Timeliness of reports
Miller and Doyle (1987)
Usefulness of information Rivard and Huff (1984) Report accuracy, Report relevance,
Understand ability, Report timeliness.
Srinivasan (1985)
This study uses the following seven measures of information quality: accuracy, usefulness, timeliness, completeness, relevance, understandability[sic] and trustworthiness. Information relevance refers to how much the information provided by the BI system is required by the end users. If the information provided by the BI system is not needed by the end user, then the BI system can be viewed as not useful to the end user (Huizingh, 2000). Usefulness refers to the assessment by the end user that the information from the BI system will increase their ability to perform tasks (Gehrke & Turban, 1999).
The usefulness of information have been associated to the success of many IS (Davids, 1989).
Understandability of information can be described as how clear and how good the information from the BI system is. The BI system should provide information which is easy to interpret and easy to
understand. If the end users are unable interpret and understand the information provided by the BI system, they won’t be able to extract any value by using the system (Huang, Lee and Wang, 1999).
On the other hand, a BI system that provides accurate information is one that provides information that is correct, has no errors and is relevant to the end users of the information (Matsumura, 1996).
Completeness of information refers to the delivery of complete information from the BI system (Ozkon, 20081). The BI system should present the end user with a complete picture and should not leave room for the end user to guess.
Timeliness refers to the ability of the BI system to provide the end user with the required information on time (Ozkon, 2008). It is important that the end users of the BI system receive the information in a timely manner in order to perform tasks or make decisions.
Trustworthiness refers to the ability of the BI system to provide information that is valid and credible (Kim, 1999). A BI system that delivers reliable information is one that delivers dependable and consistent information (Daft & Lengel, 1986).
The purpose of this study is to identify factors and sub factors that contribute to the success of BI systems in South Africa. The above section is relevant because it illustrates sub factors of information quality identified in available literature for other IS. Factors and sub factors chosen depend on the application being evaluated (Petter et al., 2008).
The relationship between information quality and user satisfaction has strong support in the literature (Iivari, 2005; Wu & Wang, 2006; Bharati, 2002; Leclercq, 2007). Some researchers have found a significant positive relationship between information quality and user satisfaction at the individual unit of analysis (Holsapple & Lee-Post, 2006; Lin, 2007; Chiu et al.,2007; Halawi, McCarthy &
Aronson (2007); Leclercq, 2007; Kulkarni, Ravindran & Freeze, 2006.; Wu & Wang, 2006; Almutairi
& Subramanian, 2005; Hunton & Flowers, 1997). Thus, the following hypothesis is proposed in this study:
H10: Information quality is not related to user satisfaction in a BI system.
H1A: Information quality is related to user satisfaction in a BI system.
The association between information quality and individual impact has moderate support in the literature (Petter et al., 2008). Some researchers have found a significant positive relationship between information quality and individual impact at the individual unit of analysis (Seddon & Kiew, 1994;Santos, Takaoka & de Souza, 2010). Thus, the following hypothesis is proposed in this study:
H60: Information quality is not related to individual impact in a BI system.
H6A: Information quality is related to individual impact in a BI system.