Large and expensive IS/IT investments often lead to disruptions in businesses and could spell disaster for
organizations. It is reported that failure rates for IS/IT investments remain persistently high as still close to half of
organizations’ IS/IT spending is devoted to underperforming projects [1-3]. Hence, evaluation of these projects is
utmost importance to: (1) provide indication of the projects’ progress and likely success; (2) appraise worthiness of
continuing the projects, and; (3) allow intervention to projects which deviate from their plan [4-6]. There are several
courses of action ranging from continuation to termination. Keil [7] argued that “one of the most difficult
management issues that can arise in connection with IT projects is deciding whether to abandon or continue a
project that is in trouble” (p. 422).
Drawn from Bowen’s decision dilemma theory, this paper focuses on equivocality affecting the continuation
decision of IS/IT projects. Earlier studies have shown the importance of equivocality in affecting continuation
decisions [8]. Yet, the causes of equivocality are not well recognized. Equivocal situations raise potential problems
of unwarranted continuation and premature termination in decision-making and hinder organizations to purposefully
decide the projects next course of action [9, 10]. Grounded in prior literature review and exploratory studies, this
paper endeavors to investigate the equivocal situations by means of constructing the model and the measurement
explaining the causes of such situations. It is opted to ascertain causes that are more salient in determining equivocal
situations.
This paper presents the results of a pilot study of IS/IT project evaluation. By developing and testing the
measurement model, the construction of a valid survey instrument will be used in further studies. The remainder is
organized as follows: the next part presents the development of a model of equivocality through prior literature
review and exploratory studies. Then, the third part presents the development of the model and the measurements,
the questionnaire design as well as the data collection. The fourth part presents testing of the measurement model
using Partial Least Squares (PLS). Finally, a discussion of the findings and conclusions are presented.