Use IT again? Dynamic roles of habit, intention and their interaction on continued system use by individuals in utilitarian, volitional contexts.

This paper employs a longitudinal perspective to examine continued system use (CSU) by individuals in utilitarian, volitional contexts when alternative systems are present . We focus on two key behavioural antecedents of CSU - habit and continuance intention - and theorise how the relationships between CSU and these antecedents evolve over time. In addition, we hypothesise how the interaction effect of habit and intention on CSU evolves temporally. Our theorising differs from extant literature in two important respects: 1) In contrast to the widespread acceptance of the diminishing effect of continuance intention on CSU in the information systems (IS) literature, we hypothesise that in our context, its impact increases with time; and 2) In contrast to the negative moderation effect of habit on the relationship between intention and CSU proposed in the literature, we posit a positive interaction effect. We collect longitudinal survey data on the use of a higher education IS from students in a European university. Our results suggest that the impact of continuance intention on CSU as well as the interaction effect between habit and intention are increasing over time. We further introduce a methodological innovation - the permutation approach to conduct the multi-group analysis with repeated measures - to the literature.

Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note.

Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy. In special cases where the ratio of loading to residual variance is the same for all items included in each parcel, factor indeterminacy is unaffected by parceling. Otherwise, parceling worsens factor indeterminacy. While factor indeterminacy does not affect the parameter estimates, standard errors, or fit indices associated with a factor model, it does create uncertainty, which endangers valid inference.

Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement.

Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty - distinct from both random error (but encompassing it) and systematic error - is a crucial characteristic of any measurement. This research argues that factor indeterminacy is uncertainty. Factor indeterminacy fundamentally threatens the validity of psychometric measurement, because it blurs the linkage between a common factor and the conceptual variable that the factor represents. Acknowledging and quantifying factor indeterminacy is important for progress in reducing this component of uncertainty in measurement, and thus improving psychological measurement over time. Based on our elaborations, we offer a range of recommendations toward achieving this goal.