December 14, 2002Diane M. Strong, WPI1 A Unified Model of IT Use Choices: Contributions from TAM,...

December 14, 2002 Diane M. Strong, WPI 1

A Unified Model of IT Use Choices: Contributions from

TAM, TTF, and CSE

Diane M. Strong*

Worcester Polytechnic InstituteInvited Presentation

First Annual Workshop on HCI Research in MIS

Barcelona, Spain 2002

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


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


Task-Technology Fit Models

Individual Abilities

Technology Characteristics

FIT

Technology Utilization

Performance

Task Characteristics

Technology includes HW, SW, and data

*individual perf. *organizational perf.


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)


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)


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)


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


TTF-TAM Combined Model Intention

to Use Tool

Attitude Towards

Use

Perceived Ease of

Use Perceived Usefulness

Actual Tool Use

Task- Technology

Fit Tool

Experience

Task Characteristics

Tool Functionality

TAM

TTF


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)


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


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


Adding CSE to TTF/TAM

Perceived Ease of

Use Perceived Usefulness

Task- Technology

Fit

Computer Self-Efficacy

TAM

TTF


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


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


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


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


Task

.56mode4

.45kbe3

.53plane2

.70

diage1

Tech.76

transe7

.41

analysise6 .64

.48

construct e8

.20check e9

.66

utilization

.44

.56

.55

.31

Fit

-.16

-.48

e10

e12

.38

.75

.84

.73

.69.70

.87

.67

General TTF Model

•Chi Sq. 26.77, d.f. 17, p=0.061

•AGFI = 0.89, GFI = 0.95


Task

.56

mode4

.75

.45

kbe3

.67

.53

plane2 .73

.70

diage1.84

Tech.80

transe7

.39

anylse6.62

.52

const e8.18

check e9

.82

Utilization

.72

.42

.41

.26

.35

Fit

-.44

-.38

.53

e10

e12

.37

self eff

.46

.46

.36

.41

csee11

.64

.89

.43

General TTF Model with CSE

•Chi Sq. 27.24, d.f. 22, p=0.202

•AGFI = 0.91, GFI = 0.96


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


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


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


References to the StudiesStudy 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.

December 14, 2002Diane M. Strong, WPI1 A Unified Model of IT Use Choices: Contributions from TAM,...

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