The proposed framework of this research integrates TAM, the updated IS success model, self-efficacy theory, perceived risk theory and value theory. This was based on the current literature and the findings from the exploratory study as part of this research. The proposed framework is used to inform the establishment of a research hypothesis. The following sections discuss the relevant theories/models and highlight their strengths.
6.3.1 The Updated DeLone and McLean IS Success Model
In this research, the proposed framework has adapted the DeLone and Mclean (2003) IS success model with six dimensions as shown in Figure 6.1. DeLone and MacLean’s original model was proposed in 1992 based on their in-depth insights into and comprehensive review of IS success literature (Wu and Wang, 2006; DeLone and McLean, 2003). DeLone and MacLean’s (1992) original model was a crucial milestone in research measuring IS success since it was introduced based on the critical analysis of 180 research articles relevant to the field (Hu et al., 2005). Also, it has been validated, tested and cited by many researchers.
According to DeLone and McLean (1992), “in searching for IS success measures, rather than finding none, there are nearly as many measures as there are studies”. Sedera and Gable (2004), cited in (Petter et al., 2008), tested different success models including the DeLone and McLean and Seddon models, finding that the DeLone and McLean model is the best model to measure the success of enterprise systems. The main purpose of the DeLone and McLean (1992) review was to synthesise IS research into coherent knowledge. Also, the previous attempts to address IS success were not properly addressed (Petter et al., 2008). This was due to the complexity, interdependency, and multidimensionality of the IS success problem (Petter et al., 2008).
According to DeLone and McLean (2003), their model has been cited by many researchers in their studies. The validation and the use of the model in different applications of IS are strong indicators of the strength of this model (Petter et al., 2008;
DeLone and McLean, 2004). Also, the proposed model by DeLone and McLean can be applied and used for both the individual and at organisational level (Petter et al., 2008).
Actually, it fits well with this study because individuals’ level is deemed appropriate for the analysis.
Figure 6.1: Updated DeLone and Mclean IS success model (DeLone and McLean, 2003)
6.3.2 TAM
Acceptance of technology by users has become an important subject in the field of IS over the last three decades. Many studies attempted to propose models that can interpret and predict system use. TAM is among those models that were widely used and it remains well known amongst IS researchers. Thus, it becomes essential in this study to consider TAM when intending to understand the acceptance of e-government technology by users. The first theory that was proposed in the context of understanding human behaviours that influence IT adoption was the Theory of Reasoned Action (TRA)
(Compeau and Higgins, 1995; Arrivals et al., 2007). This theory was introduced by Fishbein and Ajzen (1975) and it gained attention of researchers in this field (Compeau and Higgins, 1995). Figure 6.2 shows TAM as proposed by Davis (1989).
Figure 6.2: TAM as proposed by Davis (1989)
TAM was proposed by Fred Davis in 1985 with the main purpose of investigating the mediating role of perceived usefulness and perceived ease of use and their relation to other external variables and the extent to which they affect system use (Legris et al., 2003). Recently, Davis has suggested a new version of TAM –naming it TAM2– with a new construct: ‘subjective norms’ (Legris et al., 2003).
6.3.3 Self-efficacy Theory
Bandura (1986) defined self-efficacy as: “People’s judgments of their capabilities to organize and execute courses of actions required to attain designated types of performances. It is concerned not with the skills one has but with judgments of what one can do with whatever skills one possesses”. The term ‘self-efficacy’ originated from psychology. In the context of computing, computer self-efficacy is defined as: “a judgement of one’s capability to use a computer” (Compeau and Higgins, 1995).
Self-efficacy has become commonly used by researchers in the field of IT to understand individual behaviours towards IT (e.g. (Kim et al., 2010; Reid, 2009; Li et al., 2012)).
Thus, it has been decided to include it in the theoretical framework of this research and it has been emphasised by some of the interviewees in the exploratory study (see Chapter 5).
Furthermore, it is based on a call by (Bandura, 1986; Compeau and Higgins, 1995) to tailor the measurements of self-efficacy to the specific domain which is undergoing testing to increase prediction accuracy. This study considered computer self-efficacy and adapted the measures proposed by Compeau and Higgins (1995) with some modifications to make it applicable to the context of e-government.
6.3.4 Perceived Risk
Featherman and Pavlou (2003) argued that past research on technology adoption has primarily focused on the positive utility gains which can be attributed to technology adoption. Perceived risk is considered as negative utility or potential losses that can be attributed to e-services adoption (Featherman and Pavlou, 2003). They call it “Perceived Risk Theory” in their study, integrate it with TAM, and empirically test it which results in a proposed model for e-services adoption.
Perceived risk is interpreted as to feel uncertain regarding potential negative consequences/results of utilizing a service or a product (Featherman and Pavlou, 2003).
It is defined in the marketing discipline as: “the expectation of losses associated with purchase and acts as inhibitor to purchase behaviour” (Peter and Ryan, 1976).
In the world of online services (e.g. e-commerce), consumers have demonstrated reluctance to accomplish purchase in the form of simple on-line transaction (Hoffman et al., 1999). The reason which makes them reluctant to interact with online services is:
“consumers simply do not trust most Web providers enough to engage in relationship exchange involving money and personal information with them” (Hoffman et al., 1999).
According to Lee (2009), modelling perceived risk as a singular variable construct in previous e-banking research led to failure in reflecting the real characteristics of perceived risk and telling why users resist using online services. In this research, the perceived risk is first modelled as a single variable within the proposed framework, and then decomposed into its multi-facets. This is in line with Featherman and Pavlou (2003) and Lee (2009).
To deeply understand the role of perceived risk in e-government portals' success, this study carried out a more in-depth research of what the sub-facets of perceived risk are.
Thus, perceived risk has been divided into six categories: performance risk, financial risk, social risk, time risk as theorized by Featherman and Pavlou (2003), security, and privacy as theorized by Featherman and Pavlou (2003) and Fu et al. (2006).
6.3.5 Personal Values
Values were defined by Rokeach (1973) and Schwartz (1992) as cognitive representations of desirable and abstract goals. Personal values can influence the behaviour of individuals in various aspects of life. The ten basic values identified by Schwartz (1992) have the strength of including all the core values that are widely
recognized in various cultures in the world (Schwartz, 2009). Table 1 lists the ten value types identified by Schwartz (1992).
Table 6.1: The value types and their definitions Value type Definition
Power Social status and prestige, control or dominance over people and resources
Achievement Personal success through demonstrating competence according to social standards
Hedonism Pleasure or sensuous gratification for oneself Stimulation Excitement, novelty, and challenge in life
Self-direction Independent thought and action—choosing, creating, exploring
Universalism Understanding, appreciation, tolerance, and protection for the welfare of all people and for nature
Benevolence Preservation and enhancement of the welfare of people with whom one is in frequent personal contact
Tradition Respect, commitment, and acceptance of the customs and ideas that traditional culture or religion impose on the self
Conformity Restraint of actions, inclinations, and impulses likely to upset or harm others and violate social expectations or norms
Security Safety, harmony, and stability of society, of relationships, and of self
Schwartz (1992) justifies the identification and classification of human values in that study: “identification of a universal structure would permit the derivation of basic value dimensions that could be used for the purpose of comparisons”. This will help future researchers who include personal values in their frameworks/models to know what values are most related to their phenomenon and what values have no impacts.
Rokeach (1973) states the importance of personal values inclusion in all sciences and when it is vital to study human behaviours: “The value concept, more than any other, should occupy a central position ... able to unify the apparently diverse interests of all the sciences concerned with human behaviours”. Schwartz (1992) commented on these words and stated that these words proclaim the centrality of personal values.
To know which of the ten personal values are most relevant to e-government portals, a Delphi study was conducted with a panel of experts. The aim of this Delphi study was to investigate which value types are particularly relevant to e-government portals’ success or have a significant impact in the context of e-government portals; those values which are chosen as the result of this Delphi study are used later in this research to examine to what extent and how those identified value types affect e-government portals’ success.