Review of Limayem, M., S. G. Hirt, and C. M. K. Cheung (2007), “How Habits Limits the Predictive Power of Intention: The Case of Information Systems Continuance,” MIS Quarterly, Vol. 31, No. 4, 705-737.

1. 3 JUNE 2011
LITERATURE REVIEW
On
QUANTITATIVE RESEARCH METHODS
Limayem, M., S. G. Hirt, and C. M. K. Cheung (2007), “How Habits Limits the
Predictive Power of Intention: The Case of Information Systems
Continuance,” MIS Quarterly, Vol. 31, No. 4, 705-737.
Prepared by
Michael Ling
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INTRODUCTION
Past research in continued usage of IS was limited to the study of initial IS
adoption, which was under the assumption that it was primarily driven by intention.
The authors recognized that this assumption had ignored the effect of frequently
performed behaviours on IS continuance.
This paper contributed to IS research by exploring the roles that IS Habit took
in the context of continued IS usage. It proposed that IS Habit had a moderating
effect on IS Continuance Intention to the extent that its effect on IS Continuance
Usage would diminish as the usage behaviour became more habitual.
Drawing from the habit literature, the IS Habit construct and its four
antecedents were developed: frequency of prior behaviour, satisfaction, stable
context and comprehensiveness of usage. PLS was employed as the research
method where three competing models were compared for the effect of IS habit on IS
Continuance Usage. The moderator model was found to possess the best
explanatory power.
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SUMMARY
Data collection was divided into three rounds over a 4-week period to measure
university students’ usage of WWW. A total of 553 respondents answered the first
questionnaire, and 227 respondents participated in all three rounds. The first round
was to collect data for Perceived Usefulness, Confirmation, Satisfaction and IS
Continuance Intention; the second and third rounds were to measure IS Continuance
Usage. In particular, IS Continuance usage was measured by two items – frequency
of WWW usage (how often?) and intensity of usage (how many hours?).
The authors developed a six-item IS Habit scale. However, only the best
three items, which had composite reliability of 0.88, were used.
The data were analysed using PLS-Graph, which was selected for the
following reasons: (i) the formative nature of some of the measures and the non-
normality of the data; (ii) it was better suited to test moderation effects; (iii) it allowed
for small to medium-sized samples.
Regarding convergent validity, all reflective items had significant path loadings
at the 0.01 level, and acceptable levels of composite reliability (at 0.773 or above)
and average variance extracted (at 0.630 or above). The two formative items of IS
Continuance Usage had weights of 0.67 (t = 7.6) and 0.500 (t = 4.924).
Regarding discriminant validity, each construct shared greater variance with
its own block of measures than with other constructs that represented a different
block. The reflective measures fulfilled the criteria of cross-loadings.
A relatively large correlation (r = 0.751) was found between IS Continuance
Intention and IS Habit, which suggested that the measurements might have drawn
from the same construct. Nevertheless, the authors defended this point on
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theoretical grounds and by citing similar empirical results from Towler and Shepherd
(1991-1992) and Trafimow (2000).
Regarding common method bias, LISREL were conducted on six indicators
(three from each of the IS Continuance Intention and IS Habit measures) and two
latent variables (IS Habit and IS Continuance Intention) and a method factor. The
findings showed the fit of the model did not improve significantly.
Regarding non-response bias, the demographics of respondents in the first
round, but not in the last, were compared to those who participated in all three
rounds. No significant differences were found.
Three models were tested to determine which one provided the best
explanatory power for IS Continuance Usage. A baseline model without
incorporating the IS habit construct (R2 = 0.180), a second model that modelled IS
habit as having a direct effect (R2 = 0.211) and a third model that modelled habit as a
moderator (R2 = 0.261). All path coefficients were reported significant at the 0.01
level. The hierarchical difference test showed that the interactions effect had an
effect size f of 0.063 which, according to the authors, represented a medium effect.
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CRITIQUE
SEM was appropriate in this research as it allowed the specifications of the
relationships among the constructs and the measures underlying the constructs
concurrently, so that the measures of the construct and the hypothesized model
could be analysed simultaneously.
The selection of PLS-Graph, a component-based partial least squares
methodology, was appropriate compared to other covariance-based SEM (such as
LISREL) because PLS-Graph was better for theory development and predictive
applications.
The authors developed the antecedents of IS Habit: satisfaction, frequency of
past behavior, comprehensiveness of usage and stability of context. However,
stability of context was not used since “data are collected in only one context and we
therefore control for its impacts.” Nevertheless, the authors characterized stability of
context as “the presence of similar situational cues and goals across more or less
regularly occurring situations.” It was arguable that variations existed in universities,
just like any other social institutions, such as availability of facilities and examination
periods were likely to influence students’ usage of the WWW. As the research was
conducted over a period of four weeks, the probability that the respondents
experienced such unstable events could not be overlooked. The inclusion of the
stability construct might have increased the explanatory power of the model.
The authors defended the high correlation (r = 0.751) between IS Usage
Intention and IS Habit by citing references from theory and by making reference to
similar high correlation results previously found. Nevertheless, the high correlation
was a concern. The IS Habit measure was a new scale which, for all intents and
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purposes, would be different from other habit measures previously used. Thus, it
was not convincing to support their correlation results with previous habit scales.
The authors could have run the model unconstrained and also constraining the
correlation between constructs to 1.0. If the two models differed significantly on a
chi-square difference test, then the two constructs would be different.
Common method variance was a type of spurious internal consistency which
occurred when the apparent correlations among indicators were due to a common
source. Since the data was based on self-reports, the correlation might be due to the
propensity of the subjects to answer similarly to multiple items even when there was
no true correlation of constructs. LISREL test concluded that common method
variance was not an issue.
Convergent validity could be assessed in several ways: (i) the correlations
among items which made up the scale – internal consistency validity; (ii) the
correlations of the given scale with measures of the same construct using scales
proposed by other researchers and, preferably, already accepted in the field –
criterion validity; (iii) the correlations of relationships involving the given scale across
samples or across methods. The results of Cronbach’s alpha and the average
variance explained (AVE) provided evidence for internal consistency construct
validity. The authors demonstrated criterion validity for Perceived Usefulness,
Confirmation, Satisfaction and IS Continuance Intention by referring to scales that
had been validated in prior research. The authors developed a six-item habit scale
and used the best three items in this research but fell short of providing detail for the
decision. It would be helpful if the new habit scale were to be compared against
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previously developed habit scales. All the constructs were not tested for convergent
validity using cross samples or methods.
Discriminant validity referred to testing statistically whether two constructs
were different. Evidence was provided for discriminant validity, as below: (i) the item
loadings were higher for their corresponding constructs than for others; (ii) the square
root of the AVE for a given construct was greater than the correlations between it and
all other constructs.
The authors did not provide any reference to content or face validity. It was a
concern that whether the items measure the full domain implied by their label. The
indicators might exhibit construct validity, yet the label attached to the concept might
be inappropriate. Use of surveys or panels of content experts or focus groups were
methods in which content validity might be established.
Internal validity had not been adequately addressed by the authors. The
number of respondents participated in the three rounds was different – 553 in the first
round and 227 in all three rounds. It was not clear what sample size was used in the
model testing. The authors did not address the issue of mortality bias, which was an
obviously important issue here. For example, was there an attrition bias?
Another internal validity issue that had not been addressed was compensatory
rivalry. As the data collection took three weeks, the students might have promoted
competitive attitudes that could have biased the results.
The latent constructs that were associated with reflective measurement items
were Confirmation, Habit, IS Continuance Intention, Perceived Usefulness and
Satisfaction. The loadings of the reflective items were reported significant.
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Overall Assessment
The choice of PLS-Graph was appropriate. The derivation of a new scale for
IS Habit was a significant contribution to IS research. The authors obtained the
highest R2 in the IS Habit moderated model against the baseline and the direct effect
models. Though the R2 value (0.261) of the moderating model was low, the
conclusion that the moderating model had the best explanatory power was correct.
The exclusion of the stability context antecedents in the IS Habit construct might
have reduced the variance explained by the model. The high correlation between IS
Usage Intention and IS Habit was a potential concern. Convergent validity,
discriminant validity and common method bias were largely in order. Content validity
and internal validity were not adequately addressed. On balance, there were more
strengths than weaknesses in the paper.
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CONCLUSION
The key contribution of the paper rested on the scale development of the IS
Habit construct and the finding that there was moderating effect of IS Habit on IS
Continuance Intention and IS Continuance Usage.
The choice of the component-based PLS model, PLS-Graph, was appropriate
for the analysis. Three competing models were compared and the moderating model
was found to have the highest explanatory power. All loadings and weights of the
indicators were acceptable.
The research could have improved by addressing the concerns raised here.
In particular, further developed measurement scale of IS Habit; the inclusion of
stability in the model; consideration of interactions effect between Satisfaction and
Comprehensiveness of Usage and Frequency of Behavior.
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