An Empirical Analysis of Information Systems Changes

The return on investivent for Information Systems Development projects takes place during the period of stability following the system's implementation. This return on investment can be leveraged by extending the period of stability or reducing the ongoing costs of maintenance and enhancement. Since the usefulness of information systems is dependent on their alignment with the organization's goals and objectives. the abilityofa sy'stem to absorb change directly affects its life span. Given also the risk of failure associated with systems development projects (Gladden 1982; Turner 1982; Hammer 1990, Lyytinen 1990), risk averse organizations are keen to delay such risky expenditure by extending an information system's life span. This rescatch develops a theoreticallybased model ofinformation systems change, after Olle et al. (1991; see also Lederer and Prasad 1992; Marche 1993) and isbased on the premise thatstable information systems can react to changes in business requirements. Three types of adaptive maintenance activities are identified (Swanson and Beath 1989) which affect the surface structure. the physical structure or the dccp structure of the information system (after Bung¢1979; Chomsky 1965; Weber 1987; Wand and Weber 1990). Theory is used to justify changcs in the activity mix during themaintenancephase. A bathtub shaped curve is derived which predicts that more effort will be spent on deep structure changes at the beginning and toward the end of a system's useful life. These are the periods when an information system is generally the most unstable. After implementation, an information system is generally unstable due to the need to address business requirements overlooked during s>stem design or those thathave subsequently arisen dudng the cons uction phase. Toward theend of asystem's life it can also become unstable duc to tic inability of the system to respond to business requirements efficiently and effectively. A system will bc replaced if it is unable to restoreits stability. Monitoring asystem's stabilityduring the maintenance phase can provide useful input to decisions relating to the undertaking of significant enhancement projects or system replacement. A wayto measuresystem stability is developed which iS then subjected to empirical testing. A research method, incorporating a time series analysis, is designed to determine whether there is support for the shape of the bathtub curve. Some 4,000 user change requests havc bccn collected and are currently being analyzed. Results from the analysis of this data will be presented. Thc effects of covariates, identified from work done by Delleva ( 1992), Willcocks (1992), and Willcocks and Lester (1992), such as the stability of organizational managemet organizational considerations, system class and complexity, methodology used, semantic relativism (e.g., RDBMS systems), and semiotic representation (ag., CASE) on the shape and length of the curve are subjected to empirical testing. These are factors that affect both the level of system maintenance required during the maintenance phase as well as the life span of a system. A cross sectional analysis of systems exhibiting different characteristics is currentlybeing performed to determine whether the covariates affect the stability Intasure of a system. Logistic regression is used to test the effects of the covariates and the results from this cross sectional analysis will also be presented. BIBLIOGRAPHY Bunge,M. Treatise on Basic Philosophy: Volume 4: Ontology 11: A World of Systems. Boston: Re\de\, 1979. Chomsky, N. Aspects of ihe 77:eory ofsynfax. Boston: MIT Press. 1965.