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This study develops an instrument that may be used as an information systems (IS) functional scorecard (ISFS). It is based on a theoretical input-output model of the IS function's role in supporting business process effectiveness and organizational performance. The research model consists of three system output dimensions-systems performance, information effectiveness, and service performance. The "updated paradigm" for instrument development was followed to develop and validate the ISFS instrument. Construct validation of the instrument was conducted using responses from 346 systems users in 149 organizations by a combination of exploratory factor analysis and structural equation modeling using LISREL. The process resulted in an instrument that measures 18 unidimensional factors within the three ISFS dimensions. Moreover, a sample of 120 matched-paired responses of separate CIO and user responses was used for nomological validation. The results showed that the ISFS measure reflected by the instrument was positively related to improvements in business processes effectiveness and organizational performance. Consequently, the instrument may be used for assessing IS performance, for guiding information technology investment and sourcing decisions, and as a basis for further research and instrument development.WORDS AND PHRASES: functional scorecard, information systems performance measurement, instrument development, structural equation modeling. Downloaded by [Selcuk Universitesi] at 03:33 09 February 2015 86 JERRY CHA-JAN CHANG AND WILLIAM R. KING ASSESSING THE INFORMATION SYSTEM (IS) function's performance has long been an important issue to IS executives. This interest is evident from the prominence of this issue in the various IS "issue" studies [12,13,34,49,72] as well as the popularity of annual publications such as ComputerWorld Premier 100 and InformationWeek 500, which involve the use of surrogate metrics to assess overall IS functional performance (ISFP). Executives routinely seek evidence of returns on information technology (IT) investments and sourcing decisions-both types of choices that have become more substantial and a competitive necessity. As the unit that has major responsibilities for these decisions, the IS function is usually believed to be an integral part of achieving organizational success. Yet the overall performance of the IS function has proved to be difficult to conceptualize and to measure.As the outsourcing of IS subfunctional areas such as data centers and "help desks" has grown into the outsourcing of the entire IS function, there is an ever-growing need for formal performance assessment [61]. This will permit the establishment of baseline measures to use in judging outsourcing success. So, the issue of an overall IS functional metric, which is, and has been, high on IS executives' priorities, is becoming even more important.Although there has been a good deal of research on IS efficiency, effectiveness, and success at various levels of analysis, overall functional-level performanc...

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The use of causal indicators to formatively measure latent constructs appears to be on the rise, despite what appears to be a troubling lack of consistency in their application. Scholars in any discipline are responsible not only for advancing theoretical knowledge in their domain of study but also for addressing methodological issues that threaten that advance. In that spirit, the current study traces causal indicators from their origins in causal modeling to their use in structural equation modeling today. Conclusions from this review suggest that unlike effect (reflective) indicators, whose application is based on classical test theory, today's application of causal (formative) indicators is based on research demonstrating their practical application rather than on psychometric theory supporting their use. The authors suggest that this lack of theory has contributed to the confusion surrounding their implementation. Recent research has questioned the generalizability of formatively measured latent constructs. In the current study, the authors discuss how the use of fixedweight composites may be one way to employ causal indicators so that they may be generalized to additional contexts. More specifically, they suggest the use of meta-analysis principles for identifying optimum causal indicator weights that can be used to generate fixed-weight composites. Finally, the authors explain how these fixed-weight composites can be implemented in both components-based and covariance-based statistical packages. Implications for the use of causal indicators in academic research are used to focus these discussions.