Previous studies surrounding the DeLone and McLean model of information systems (IS) success have called for future research and further examination of its measure in different contexts. We draw from the literature on strategic IS planning and organizational culture to contextualize the DeLone and McLean model. There is some evidence that a high-quality information technology (IT) plan leads to system success; therefore, we empirically examine the inclusion of the IT plan quality construct as an antecedent to IS success. We also empirically examine the relationships among constructs in the model of IS success in the context of different corporate cultural types' entrepreneurial and formal. The results provide strong support for the research model and suggest that variations in IS success are explained by the quality of the IT plan and the corporate culture exhibited by a firm. We discuss implications related to our finding that IT plan quality has a greater impact on IS success in organizations that exhibit an entrepreneurial corporate culture than in those that exhibit a formal corporate culture. Furthermore, we discuss how the relationships in the DeLone and McLean model of IS success differ in diverse corporate cultural types and the meaning of these differences.
Researchers and practitioners alike have taken note of the potential value of an organization's IT infrastructure. IT infrastructure expenditures account for over 58 percent of an organization's IT budget and the percentage is growing at 11 percent a year. Some even have called IT infrastructure the new competitive weapon and see it as being crucial in developing a sustained competitive advantage. Unique characteristics of an IT infrastructure determine the value of that infrastructure to an organization. One characteristic, IT infrastructure flexibility, has captured the attention of researchers and practitioners. In fact, in most recent surveys featuring issues of most importance to IT executives, the development of a flexible and responsive IT infrastructure and related topics are always at or near the top of the responses. Although the importance of IT infrastructure flexibility has been established, the development of a valid, reliable instrument to measure this construct has not been reported in the literature. The purpose of this paper is to better define the IT infrastructure flexibility construct and to develop a valid, reliable measurement instrument for this construct. In addition to the definition and operationalization of the IT infrastructure flexibility construct, this study explores the instrument's predictive validity with possible antecedent and consequent variables.
Requirements analysis (RA) involves end users and systems analysts interacting in an effort to recognize and specify the data and information needed to develop an information system. In the design of expert systems, a similar process of eliciting information, in this case human knowledge, has been studied under the banner of knowledge acquisition (KA). When examined closely, many entities and processes involved in RA and KA are almost identical. However, researchers in each area are seemingly unaware of the developments in the other area. In order to facilitate a merged awareness of both research streams, this article compares representative RA and KA techniques, which are grouped, according to elicitation mode, on three dimensions: communication obstacles, a technique's locus of control, and the nature of the understanding gained from using the technique. This comparison demonstrates that these two research streams have many things in common and that researchers in one area can benefit from developments in the other area. Additionally, this analysis leads to several suggested research areas: (1) rigorous examinations of these techniques as they are used to overcome communication obstacles and enrich understanding; (2) investigations into the seeming match between certain elicitation types and problem domain categories; (3) examinations into synergetic effects of elicitation techniques; (4) development of more techniques for eliciting information requirements to serve emerging needs; and (5) comparisons of the relative advantage of generalized versus specialized elicitation techniques.