Obviously, research that compares the relative impact of graphical versus tabular format remains inconclusive (Vessey, 1991). One explanation for these disparate results is that user characteristics are ignored. Therefore, in an attempt to resolve the controversy, a considerable number of studies have suggested looking at individual differences amongst the users of information (Amer, 1991; Chandra and Krovi, 1999; Ganzach, 1993).
Several factors are believed to influence the success experienced by organisations regarding their development of management information systems (MIS). These include, organisational characteristics, environmental characteristics, task characteristics, personal characteristics, interpersonal characteristics, MIS staff characteristics, and MIS policies. A few researchers however agree that the largest amount of research activity have involved the influence of individual differences on MIS design, implementation and usage.
Firstly the term “individual differences” could be used most generally to be suggestive of any dissimilarities across people, including differences in perceptions and behaviour (Agarwal, 1999). More specifically, it refers to user factors that such as personality and demographic variables, as well as situational variables that account for
differences attributable to circumstances such as experience and training (Harrison and Rainer, 1992; Alavi and Joachimsthaler, 1992).
Furthermore, other studies have found that individual differences are significant factors in both end-user computing (Harrison and Rainer, 1992) and décision-support systems (Alavi and Joachimsthaler, 1992).The theory of planned behaviour also suggests that individual differences could directly affect usage over and above PU and PEOU through their effect on users’ perceived behavioural control—their perceptions of constraints on their behaviour. According to Ajzen (1985; 1991), when behaviour is not completely volitional, perceived behavioural control directly affects intentions and behaviour, over and above attitudes and subjective norms.
Zmud (1979) presented a model which is represented in Figure 3.4 illustrating that the manner in which individual differences are believed to impact MIS success can be characterised as cognitive and attitudinal influences. According to the model, two distinct paths were conceptualised. Firstly, an upper path which finds individual differences amplifying or dampening limitations in human information processing and decision behaviour, thereby imposing or suggesting MIS design alternatives directed toward motivating or facilitating MIS usage. A second path which is the lower one depicts the impact of individual differences upon the attitudes held by potential MIS users and upon the tendencies for MIS users to involve themselves in the MIS development effort. Zmud, 1979 suggested that these paths represent the cognitive and attitudinal influences of individual differences upon MIS success. This study falls within the domain of the former, as it makes use of the knowledge of cognitive behaviour in the design of visual information. Notably, a decade after the review by Zmud (1979), Davis (1989) put forward
the Technology Acceptance Model (TAM) which was to lead a string of related research along the attitudinal path. In particular, Agarwal (1999) extended the technology acceptance model by specifying the role of individual differences in the model and presented an example of how the process through which such variables influence IT acceptance may be explicated. In particular, it was suggested that beliefs or perceptions as represented in TAM “intervene” between individual difference variables and IT acceptance.
Figure 3.4: Impact o f individual differences upon MIS success (Zmud, 1979).
MIS researchers believe that individual differences most relevant to MIS success can be grouped into three classes: cognitive style (Zmud, 1979) personality and demographic/situational variables (Harrison and Rainer, 1992; Alavi and Joachimsthaler, 1992; Agarwal, 1999). Whilst cognitive styles represent characteristic modes of functioning shown by individuals in their perceptual and thinking behaviour, are dependent on task and situational elements and generally agreed to be multidimensional (Zmud, 1979), personality refers to both the cognitive and affective structures maintained by individuals to facilitate their adjustments to the events, people and situations encountered
in life (Gough, 1976). Some personality variables believed to strongly impact MIS success include; locus of control, dogmatism, ambiguity tolerance, extroversion/introversion, need for achievement, risk taking propensity, evaluative defensiveness, and anxiety level (Klauss and Jewett, 1974).
Lastly, demographic/situational variables are believed to cover a broad spectrum of personal characteristics. In particular, demographic variables such as sex, age, experience, education, professional orientation and organisational level are believed to influence MIS usage, as have attributes such as general intellectual abilities and knowledge of specific content areas. Similarly, Davis (1989) suggested familiarity or experience as other variables that could be examined in the Technology Acceptance Model (TAM) to determined MIS usage.
Of the large number of studies that have addressed the impact of individual differences upon information processing and decision behaviour, the strongest associations have been observed with regard to the personal characteristics that directly relate to individual perception and structuring of environmental stimuli (Zmud, 1979).
For example, some researchers have tested the effect of subject variations or situational factors such as specific knowledge on decision making accuracy and performance (Cardinaels, 2007) and task complexity (Bonner, 1994). According to Zmud (1979), results have shown that subjects with higher general intelligence have been observed to process information faster, select information more effectively, retain information better, make decisions faster, and to better organize information in their minds (Hunt and Lansman, 1975). Subjects with higher quantitative abilities make more use of short-term memory but less use of long-term memory than do subjects with lower
quantitative abilities (Hunt and Lansman, 1975), and subjects with greater verbal abilities possess enhanced short-term memory when compared with subjects with lesser verbal abilities Hunt et al. (1973). Experienced decision makers were shown to select information more effectively but to integrate it less effectively and to be more flexible but less confident (Taylor and Dunnette, 1974). Zmud (1979) however noted that cognitive behaviours are also dependent on contextual i.e. task and environmental, factors in addition to individual differences.
More recently Cardinaels (2007), contributed to the on-going debate on graphs versus tables by demonstrating that based on the theory of representational congruence, a graphical ABC format is likely to reduce cognitive burden of less knowledgeable decision makers by providing a fit to analogue graphical representations that are stored in memory (Morarity, 1979; Stock and Watson, 1984; Wright, 1995). Thus, this fit should facilitate data retrieval and in turn their performance should improve. Knowledge can influence the internal memory representations of a decision maker and could therefore be linked to the external presentation format.