Developing a General Method to Assess Task-Technology Fit

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A Unified Model of IT Use Choices: Contributions from TAM, TTF, and CSE 

Diane M. Strong*

Worcester Polytechnic Institute

Invited Presentation

First Annual Workshop on HCI Research in MIS

Barcelona, Spain 2002

*This is joint work with Mark T. Dishaw, University of Wisconsin Oshkosh

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General Research Objective 

• Understand the software utilization choices of end users, by using and extending existing models
• Task-technology Fit (TTF) models
• Technology Acceptance Model (TAM)
• Individual Abilities Constructs, e.g., Experience, Computer Self-efficacy
• Conduct a series of studies testing the models and combinations of them

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Task-Technology Fit Models

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1. TTF Model Study 

Operationalize the TTF model in the software maintenance context

• Task Model - Vessey's debugging model (planning, knowledge building, diagnosis, modification activities) plus coordination
• Technology Model - Henderson & Cooprider Functional Case Technology Model (Production and Coordination functionality)

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Dimensions of Fit  

• Fit along two dimensions
• Production Fit: how well the tool��s production functions support software maintenance activities
• Coordination Fit: how well the tool��s coordination functions support maintenance coordination activities
• Compute Fit using an interaction approach (Venkatramen, 1989)

(Dishaw & Strong, 1998)

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2. Add Experience to TTF  

Operationalize Individual Abilities as:

• experience with the task
• experience with the technology

Tool experience and its interaction with tool characteristics is significant

Task experience not significant

Adjusted R² of 0.63

(Dishaw & Strong, Forthcoming)

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3. Combined TAM and TTF 

• TAM: beliefs about the technology, i.e., perceived usefulness and perceived ease of use
• TTF: matching of the technology to the needs of the task to deliver benefits
• TAM + TTF: addresses both technology beliefs and rationally computed fit to task
• Tool experience as an individual ability
• Path model, rather than regression
• Fit as latent variable, rather than computed as interaction

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TTF-TAM Combined Model

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Combined TAM / TTF Results 

Better results than either TAM or TTF alone

Utilization variance explained:

• 36% with TAM
• 41% with TTF
• 51% with TAM/TTF

(Dishaw and Strong, 1999)

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4. Add Computer Self-efficacy 
(Work-in-progress) 

• CSE may be a better predictor of individual ability for new tools than is tool experience
• Generalize TTF assessment beyond software maintenance tasks and tools
• Develop an instrument for assessing problem-solving tasks, and the support of such tasks with software
• Test previous TTF and TAM/TTF models with a new dataset

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Computer Self-Efficacy 

• Derived from the Social Cognition literature, and is based on Bandura��s work on self-efficacy
• A specialized definition of Self-efficacy, i.e., a person��s belief in their ability to accomplish a specific task
• A judgment of one��s ability to use a computer

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Adding CSE to TTF/TAM  

 

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Model Operationalization 

• Software maintenance TTF is generalized by changing the questionnaire items since
• Task model is well grounded in the problem solving and cognitive science literature
• Technology model is grounded in the literature on information technology support functionality
• Add Compeau & Higgins (1995) 10-item, single factor measure of CSE

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Item and Scale Testing 

• Item Testing using a panel of faculty, advanced students, and professionals
• Pilot Study using a small number students and professionals in the university

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Data Collection 

• Use revised instrument
• Subjects are students in several classes after the completion of an ordinary assignment
• Currently, have 136 data points from:
• Operations Management simulation class doing modeling
• Programming class doing 3 GL program maintenance
• Programming class doing OO program maintenance
• Business analysis class doing statistical modeling

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Data Analysis  

Using Amos 4.0, test the models

1. TTF
2. TTF plus CSE
3. Combined TAM/TTF
4. Combined TAM/TTF plus CSE

Have results for Models 1 and 2

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General TTF Model 

  

• Chi Sq. 26.77, d.f. 17, p=0.061
• AGFI = 0.89, GFI = 0.95

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General TTF Model with CSE 

• Chi Sq. 27.24, d.f. 22, p=0.202
• AGFI = 0.91, GFI = 0.96

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Lessons for a Unified Model: 
Importance of Task 

• Traditional HCI focuses on Usability, with little or no Task emphasis
• TAM adds Usefulness, which implicitly includes Task
• TTF has explicit Task focus, which adds to the explanatory power

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Lessons for a Unified Model: 
The Fit Construct 

• Beyond production and coordination Fit to additional dimensions of Fit
• Beyond a point estimate of Fit to a process of Fitting over time (as in implementation)
• Beyond individual level models (TTF, TAM) to organizational level models, e.g., for Enterprise systems

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Lessons for a Unified Model: 
Experience and CSE 

• Measure Experience and Self-efficacy for both Task and Technology
• Self-efficacy theory: As Experience increases, Experience dominates abilities as measured by Self-efficacy
• Need to better understand relationship between Experience and Self-efficacy

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References to the Studies 

Study 1: Dishaw, M. T. and D. M. Strong, "Supporting Software Maintenance with Software Engineering Tools: A Computed Task-Technology Fit Analysis", Journal of Systems and Software, Vol. 44, No. 2, December 1998, pp. 107-120.

Study 2: Dishaw, M. T. and D. M. Strong, "The Effect of Task and Tool Experience on Maintenance CASE Tool Usage", Information Resources Management Journal, Forthcoming.

Study 3: Dishaw, M. T. and D. M. Strong, "Extending the Technology Acceptance Model with Task-Technology Fit Constructs", Information & Management, Vol. 36, No. 1, July 1999, pp. 9-21.

Study 4 (in-progress): Dishaw, M. T., D. M. Strong, and D. B. Bandy, ��Extending the Task-Technology Fit Model with Self-Efficacy Constructs��, Proceedings of the Americas Conference on Information Systems, August 9-11, 2002, Dallas, TX, pp. 1021-1027.