In a bid to explain the linkage between information technology utilization and individual performance, Goodhue and Thompson (1995) developed a conceptual model of technology- to-performance chain, the Task Technology Fit theory (TTF) is seen as an important development in information system theory. According to Goodhue and Thompson (1995), TTF theory assumes that information technology is more likely to have a positive effect on individual performance and to be used if the capabilities of information technology match the task that the user must perform. This conceptual framework was based on two separate research streams: first, the utilization of information technology with its antecedent of attitude and behaviour, and second, the ‘fit focus’ evident in research investigating the performance of individual information technology user.
Venkatraman in Suryaningrum (2012: 113) discussed the concept of ‘fit’ assessment in strategy research comprehensively using six alternative perspectives and approach of fit; 1) Fit as moderation perspective; effect of fit as a moderating variable of an independent variable (predictor variable) on dependent variable (criterion variable); 2) Fit as mediation perspective; an existence of intervening (indirect) effects between an antecedent variable and its consequent (criterion) variable; 3) Fit as matching perspective; fit as a theoretically defined match between two related variable; 4) Fit as gestalts; gestalts could be defined as the degree of internal coherence among a set of theoretical attributes (fit as on the identification of different groups); 5) Fit as profile deviation; the degree of adherence to a specified profile; and 6) Fit as co-variation; a pattern of co-variation or internal consistency among a set of theoretically related variables (McGill & Hobbs 2006; Teo and Men 2008).
Arguably, Goodhue and Thompson (1995) use the concept of fit as moderating variable, as they proposed: ‘information system (systems, policies, staff of IS, among others) have a positive impact on performance only when there is a correspondence between their functionality and the task requirements of users.’ The study by Goodhue and Thompson (1995) found supportive evidence that TTF is a function of system characteristics and task characteristic, and also a strong evidence of performance where TTF and utilization must be included.
Even if TTF has some supporting evidence, some researchers have extended TTF with TAM in varying areas; conceptualization perspective (Dishaw et al. 2002), consumer of e- commerce (Klopping and McKinney 2004), education (Strong et al. 2006), e-Tourism (Usoro
et al. 2010), hotel industry (Schrier et al. 2010). They did that to obtain a more comprehensive explanation of human behaviour associated with the use of information systems. This new model of individual performance is trying to integrate TTF with DTPB, because even though TAM has proved a robust model, it is also a simple model, while DTPB assumed to provide a complete and more understanding of IT usage, but is complex as a result.
According to McGill and Hobbs (2006: 2), task-technology fit relates to the match between a user’s task requirements, their abilities, and the functionality of the technology to support the task, and has been identified as an important contributor to the success of an information systems as postulated by Goodhue and Thompson (1995) in McGill and Hobbs (2006: 2). Goodhue and Thompson (1995) proposed the technology-to performance chain model to help end users and organizations understand and make more effective use of information technology.
The technology-to-performance chain model combines insights from research on user attitudes as predictors of utilization and insights from research on task-technology fit as a predictor of performance thereby providing some insight into this study about the adoption of technology for learning and teaching in Online and Distance Learning programs in Africa Nazarene University. Figure 2 below illustrates the Task-Technology Fit Theory (TTF)with the components of the model. The model states that task characteristics, technology characteristics and individual characteristics determine task-technology fit. Task-technology fit in turn both directly influences performance, and indirectly influences utilization via precursors of utilization such as expected consequences of use, affect toward use, social norms, habit and facilitating conditions. It also proposes that utilization directly influences performance. Task characteristics Technology characteristics Individual characteristics Precursors of utilisation Utilisation Task- Technology Fit Performance Impacts
Figure 3.4: Adapted from The technology-to-performance chain (Goodhue & Thompson, 1995)
McGill and Hobbs (2006: 3) posit that there is some evidence that supports aspects of the technology-to-performance chain in various domains such as studies by Dishaw and Strong on the task-technology fit of computer-aided software engineering (CASE), Dishaw and Strong (2003) on tools and several groups like Lim and Benbasat (2000), Pendharkar, Rodger and Khosrow-Pour (2001) have also researched on technology to performance in the health care domain.
However, there has been little research on its application in the e-learning domain and no comparison of different types of users within the e-learning domain. It is possible, given the different roles of students and instructors in interacting with VLEs, that the level of task- technology fit and other precursors of task success may differ between the two types of users. Satisfaction with an information system is commonly measured as an indicator of information systems success (Hwang & Thorn 1999) and has been identified as a precursor of performance impacts in DeLone and McLean’s (1992) model of IS success. Despite not being included in the technology-to-performance chain it is relevant to research on e-learning and is probably the most often considered outcome variable in e-learning research.
Other precursors of IS success of interest in this study include expected consequences of use, attitude toward use, social norms, facilitating conditions and levels of use. Triandis (1971) introduced the role of expected consequences in influencing behaviour. Goodhue and Thompson (1995) argued that expected consequences of use should be influenced by the task- technology fit (that is, the better the task-technology fit the more positive anticipated consequences of use of a system) and that increased anticipated consequences of use should then lead to increased utilization of systems. Seddon (1997: 246) also included expectations about the consequences of future IS use in his test of DeLone and McLean’s model of information systems success defining it as ‘a valence-weighted sum of the decision-maker's expectations about the costs and benefit of future IS use’.
Attitude is defined as the amount of effect one feels for or against some object or behaviour (Fishbein & Ajzen 1975). Fishbein and Ajzen (1975) argue that attitudes towards objects do not strongly predict specific behaviour towards the objects, rather it is the attitude towards the specific behaviour that determines whether the behaviour is performed. In the technology-to- performance chain attitude towards use of the system it is proposed as a predictor of
utilization (Goodhue & Thompson 1995). Hence attitude of the users towards use of online and distance learning or VLEs is also of interest in this study. Social norm (also known as subjective norm) refers to the user’s beliefs as to whether other individuals want them to perform the behaviour. The role of social norm in IS success has been investigated with mixed results.
Staples and Seddon (2004) in McGill and Hobbs (2006: 4) found that social norms influenced utilization when use was mandatory, and Venkatesh and Davis (2000) found that social normsinfluenced user acceptance. However, Dishaw and Strong (1999) found that social norms did not influence intention to use. This confusion might be explained by Karahanna, Straub, and Chervany’s (1999) finding that social norm is important in determining initial adoption, but not in intention to continue. McGill and Hobbs (2006: 4) from another perspective argue that various capabilities or features of the technology used such as ease of access to the system, relationship of the user with support staff among others could also influence the use and performance. This is reflected in DeLone and McLean’s addition of service quality to their updated model of IS success (DeLone & McLean 2003).
Utilization has been defined as 'the behaviour of employing the technology in completing tasks' (Goodhue & Thompson 1995: 218). Utilization of information systems has been measured in various ways including measures of frequency of current and anticipated use and diversity of application use. The technology-to-performance chain predicts that task- technology fit will lead to increased utilization, but evidence has been mixed. For example, although Goodhue and Thompson (1995) found weak support for the relationship, Staples and Seddon (2004) found no relationship between utilization and performance.
Performance impact refers to the effect of the system on the behaviour of the user or the outcomes for the user. The impacts most commonly considered in information systems success research relate to management performance and decision-making (DeLone & McLean, 1992 in McGill and Hobbs 2006: 4), but in the e-learning domain, performance impact can relate to impacts on academic results or student perceptions of learning success, among others (Piccoli et al. 2001). To enhance the coherence of the two models, some studies have used the sociology theory of symbolic interactionism. The addition of the various factors that may also affect technology adoption such as system characteristics, provision of support to users brings an enriching dimension with reasonable contribution from these proponents of Task Technology Fit theory (TTF).
3.8 HUMAN-TASK-TECHNOLOGY INTERACTIONS AND PERFORMANCE