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With the increasing integration of information technologies (IT) into business, today's organizations tend to sanction the use of information systems applications over which their employees do not have the privilege of selection. For example, applications such as enterprise resource planning (ERP) systems are widely used and standardize the way software technologies are applied to business processes. Alternatively, an organization may install a standard array of desktop applications, each befitting certain tasks. To study system use in such mandatory environments, researchers often apply the Technology Acceptance Model (TAM) with two approaches: (a) using the TAM as is but adding voluntariness as a moderator, or (b) adding antecedents to perceived usefulness and perceived ease of use. Either way, the constructs of intention to use and use are central to the models, as they are in the TAM. We argue that both approaches are incognizant of the fact that, in mandatory environments, intention and use may not have enough variances to be meaningful criterion variables. Informing science considers systems at three levels of abstraction (E. Cohen, 1999; Gill & Bhattacherjee, 2007, 2009). From this perspective, the centrality on intention and use reflects a limiting view that construes mandatory use only at the first level, i.e., the user is informed via system use. Mandatory systems, however, often aim at goals achieved only when new instances of informing occur. For instance, governments are taking advantage of the ever-increasing capabilities of IT to make their citizenry better informed. However, employees' mandated use of IT systems is no guarantee that benefits made possible by IT are indeed realized. To inform the citizens, first the employees must be properly informed and be able to trust information output from the systems. Second, a new instance of informing takes place when employees use the information to inform citizens. The desired results are benefits to the governments, such as better relationships with their constituencies, cost effectiveness, and positive image of the governments. The purpose of this paper, therefore, is to develop a theoretical model to explain mandatory use of software technologies by employing multiple levels of abstraction in the spirit of informing science. Such a vantage point affords us an integrative view on prior studies. We adapted Wixom and Todd's (2005) model to explain a conceptual gap between system characteristics (specifically, information quality) and system use, which DeLone and McLean's (1992, 2003) milestone models of system success have not addressed in full. Wixom and Todd (2005) filled the gap by reasoning that users' evaluation of system characteristics ("object-based beliefs") impacts their affective feeling toward those characteristics ("object-based attitudes"), which, in turn, exerts influence on two TAM predictors of use - perceived usefulness and perceived ease of use ("behavioral beliefs"). Although Wixom and Todd (2005) did justice to user attitude toward system use ("behavioral attitude"), their model was not specifically designed for mandatory environments and thus treats intention as the ultimate criterion variable. We addressed this by introducing DeLone and McLean's (2003) constructs of user satisfaction and net benefits. The latter is the goal of informing at the second level of abstraction and the former is the connection between the first and second levels. Looking again at government systems, employees who are satisfied processors of information are the key to the successful creation of new instances of informing. This paper is organized as follows. First, we briefly review the informing science framework and the three levels of abstraction, which are the foundation of our theoretical approach. Then, we propose our model and introduce the relevant theories and models that we integrate by using the informing science approach. …

A Model for Mandatory Use of Software Technologies: An Integrative Approach by Applying Multiple Levels of Abstraction of Informing Science