Journal of Knowledge Management Practice, Vol. 9, No. 3, September 2008

Task-Technology Fit and Information Systems Effectiveness

Michael L. Irick, Indiana University-Purdue University of Indianapolis


Task-technology fit is a key but often overlooked construct in understanding the impact of technology on individual performance.  This article examines task-technology fit as a way to measure the effectiveness of information systems.  Several different task-technology fit models are evaluated and compared.  Research methods, such as user evaluations, for measuring task-technology fit are examined.  The impacts of cultural differences as they pertain to task-technology fit are also explored. 

Keywords: Task-technology fit, Information systems, User evaluations

1.         Introduction

Information systems are designed to help users perform tasks more effectively and efficiently.  Organizations spend millions of dollars on information systems to improve organizational or individual performance (Goodhue, 1995).  A critical concern in information systems research has been to better understand the linkage between information systems and individual performance.   Task-technology fit is a key but often overlooked construct in understanding the impact of technology on individual performance. 

Goodhue defines task-technology fit (TTF) as the degree to which a technology assists an individual in performing his or her tasks.  More specifically, it is the fit among task requirements, individual abilities, and the functionality and interface of the technology (Goodhue, 1997).  In the context of information systems research, technology refers to computer systems (such as hardware, software, and data) and user support services (such as training and help lines).  Technologies are viewed as tools used by individuals in carrying out their tasks.  Tasks are defined as “the actions carried out by individuals in turning inputs into outputs” (Goodhue, 1995; pp. 1828).

In order for an information system to have a positive impact on individual performance, 1) the technology must be utilized, and 2) there must be a good fit with the tasks the technology supports (Goodhue & Thompson, 1995).  If either the task-technology fit of the technology or its utilization is lacking, the technology will not improve performance.  This theory has been formally recognized in studies such as those by Pentland (1989), who found that IRS auditors had positive attitudes toward PCs and utilized them extensively, but that the PCs had little positive impact on their performance, or even negative impacts.  According to Pentland, the PCs were being utilized for inappropriate tasks, that is, tasks where the technology was not a good fit with task needs (Pentland, 1989).

A more recent study by Keil (1995) found that task-technology fit is more important than the user interface of an information system.  In this study, a computer company implemented an expert support system for its sales representatives, but found that usage was low.  Feedback indicated that the system was difficult to use, so the company’s developers performed a major rewrite of the user interface.  After deploying the new and improved tool, they surveyed the users and found that there was no significant increase in use.  In addition, the users still perceived the software to be cumbersome.  Based on user comments, it was determined that the deficiencies were a function of the mismatch between the tasks the system supported and those that the users needed to perform (Keil et al, 1995).  Here, task-technology fit was completely overlooked; rendering a system that was of no value to the users.

2.         Task-Technology Fit Models

There are two major models or research streams linking technology to performance.  The first (and most common) is the utilization model.  Utilization research is based on theories of user attitudes, beliefs, and behaviors.  The implication of this model is that increased utilization will lead to positive performance impacts.  The second is the task-technology fit (TTF) model.  The TTF model indicates that performance will be increased when a technology provides features and support that fit the requirements of the task (Goodhue & Thompson, 1995).

Researchers have pointed out limitations of using either of these models alone.  Considering only the utilization model ignores the fact that not all utilization is voluntary.  For example, a system may be used simply because it is all that is available, and the user has no choice but to use that technology.  To the extent that utilization is not voluntary, performance impacts will depend increasingly upon task-technology fit rather than utilization (Goodhue & Thompson, 1995).  In addition, there is the possibility that increased utilization of a system will not necessarily lead to higher performance, as in Pentland’s study of the IRS auditors.  There are also limitations with relying strictly on the TTF model.  Models focusing on fit alone do not give adequate attention to the fact that systems must be utilized before they can have any impact on performance. 

Due to the limitations of these models, Goodhue and Thompson have proposed a model that combines both utilization and task-technology fit.  This model, called technology-to-performance chain (TPC), utilizes both lines of research and recognizes that technologies must be utilized and fit the task they support in order to have a performance impact.  The TPC model (Figure 1) gives a more accurate picture of the way in which technologies, user tasks, and utilization relate to changes in performance (Goodhue & Thompson, 1995).



Figure 1:  Technology-to-Performance Chain Model


Other researchers offer their own variations on integrated models.  Dishaw and Strong (1999) combine the technology acceptance (TAM) model with the task-technology fit (TTF) model.  The TAM model is adapted from the theory of reasoned action, which states that a behavior is determined by an intention to perform the behavior.  The TAM model focuses on attitudes toward using a particular information system or technology, which users develop based on their perceived usefulness and ease of use of that system or technology.  The weakness of TAM for understanding utilization and performance is the lack of task focus.  Dishaw and Strong argue that constructs in the TTF model determine, in part, three variables in the TAM model:  utilization, perceived usefulness, and perceived ease of use.  According to the authors, user beliefs about usefulness and ease of use are likely to be developed from rational assessments of the characteristics of the technology and the tasks for which it could be used (Dishaw & Strong, 1999).  Dishaw and Strong’s model is similar to the Goodhue and Thompson model, both integrating theories of user attitudes, beliefs, and behaviors (utilization) with tasks and technology (fit).    

3.         Measuring Task-Technology Fit

Performance impacts from information systems are difficult to measure directly.  For this reason, many information systems researchers and practitioners rely on surrogate measures of IS success, such as user evaluations.  A user evaluation is an assessment made by a user, typically consisting of a survey containing a series of questions in which users are asked to respond along some continuum from positive to negative, about certain qualities of information systems.  However, these measures have been criticized for their lack of strong theoretical and empirical evidence (Goodhue, 1995). 

According to Goodhue, what is needed for user evaluations to be an effective measure of IS success is the identification of some specific user evaluation construct, defined within a theoretical perspective that can usefully link underlying systems to their relevant impacts.  Goodhue proposes that user evaluations can be based upon the concept of task-technology fit.  In his study, user evaluations were found to be influenced directly by system, task, and individual characteristics.  These results give support to two key assertions necessary for the use of user evaluations of TTF as measures of IS success.  First, the value of a technology does appear to depend upon the tasks of the user.  Secondly, users appear to be capable of evaluating the task-technology fit of their technologies (Goodhue, 1995).

Goodhue and Thompson (1995) utilized user evaluations to measure task-technology fit.  Their instrument was based on TTF dimensions such as quality, locatability, authorization, compatibility, ease of use/training, production timeliness, systems reliability, and relationship with users.  Their results found supportive evidence that user evaluations of TTF are a function of both systems characteristics and task characteristics, and strong evidence that in order to predict performance both TTF and utilization must be included (Goodhue & Thompson, 1995).

In a separate case study by Goodhue (1997), the TTF model was used as a conceptual basis for assessing the impacts of the Integrated Information Center (IIC) on end-users.  In this study, two methods were utilized to measure task-technology fit.  The first method was a survey of the target population.  The second method was a set of in-depth interviews of a smaller, carefully selected group.  In addition, both data collection efforts were carried out longitudinally, the first before the IIC was operational, and the second after the IIC had been operational for about 21 months.  This allowed a before and after picture of the impacts of the IIC, making it possible to better track changes in the target population and their task processes (Goodhue, 1997).

4.         Cultural Perceptions Of Task-Technology Fit

Cultural differences also have important implications for TTF.  In an article written by Massey et al (2001), two global organizations were studied to determine how technology facilitates communication tasks.  In an experiment involving 150 participants located in the United States, Japan, and Europe, significant cultural differences were found in perceptions of task-technology fit. 

The experiment involved using groupware technology to convey information and make decisions.  Participants of U.S. origin perceived less difficulty in conveying opinions than did the Asian or European participants.  However, those of Asian origin perceived groupware to be a better fit for explaining themselves.  Results also indicated that Asian and European participants viewed groupware to be a better fit for convergence-oriented communications than the participants of U.S. origin.  Cultural differences became evident when the team disagreed and conflict emerged.  Collectivist, high context, and higher uncertainty avoidance cultures (Asian) tend to prefer reaching decisions through indirect communication.  Conversely, individualist, low-context, and lower uncertainty avoidance cultures (U.S.) tend to prefer more exacting communication that values confrontation and debate (Massey et al, 2001).  

Overall, the technology both enabled and hindered certain culturally driven communication behaviors (Massey et al, 2001).  It is therefore important to recognize that technology can evoke different reactions among individuals with different cultural orientations.  These cultural perceptions of fit are an important aspect of task-technology fit and information systems evaluation.

5.         Conclusion

In conclusion, task-technology fit plays a key role in affecting individual impact and performance in the use of information systems.  An information system must be both utilized and fit the task that is supported in order to have a positive impact on performance.  Performance impacts can be difficult to measure, so surrogates such as user evaluations are commonly used.  User evaluations based on task-technology fit have been effective measures of information systems success.  While researchers, such as Goodhue, have carried out several studies on this subject, there is still room for further research in assessing task-technology fit and in how to best measure information systems effectiveness.    

6.         References

Dishaw, M.T., Strong, D.M. (1999), Extending the Technology Acceptance Model with Task-Technology Fit Constructs, Information & Management 36.1, 9-21.

Goodhue, D. (1997), The Model Underlying the Measurement of the Impacts of the IIC on the End-Users, Journal of the American Society for Information Science 48.5, 449-453.

Goodhue, D.L. (1995), Understanding User Evaluations of Information Systems, Management Science 41.12, 1827-1844.

Goodhue, D.L., Thompson, R.L. (1995), Task-Technology Fit and Individual Performance, MIS Quarterly 19.2, 213-236.

Keil, M., Beranek, P.M., Konsynski, B.R. (1995), Usefulness and Ease of Use: Field Study Evidence Regarding Task Considerations, Decision Support Systems 13, 75-91.

Massey, A.P., Montoya-Weiss, M., Hung, C., Ramesh, V. (2001), Cultural Perceptions of Task-Technology Fit, Communications of the ACM 44.12, 83-84.

Pentland, B. T. (1989), Use and Productivity in Personal Computers: An Empirical Test, in DeGross, J., Henderson, J. and Konsynski, B. (Eds.), Proceedings of the Tenth International Conference on Information Systems, Boston, 211–222.

About the Author:

Michael L. Irick is an Adjunct Professor in the Computer and Information Technology department at Indiana University-Purdue University of Indianapolis.  He can be contacted at: 799 West Michigan Street ET 301, Indianapolis, IN 46202-5132; Email: