Knowledge that Transforms

An exceedingly long-term analysis on the development of human society reveals an increasing capacity to process information. Indeed, it can be argued that this is the pre-eminent technology that humans have developed, since it is the foundation of all other technologies. Our ever-growing capacity to communicate with one another over time and space is at the heart of our transition from subsistence hunters and gatherers to a global society.

This paper examines the changes engendered when moving from a structured to an object-oriented systems development approach and reconciles the differing views concerning whether this represents an evolutionary or revolutionary change. Author co-citation analysis is used to elucidate the ideational and conceptual relationships between the two approaches. The difference in conceptual distance at the analysis and design level compared to that at the programming level is explained using Henderson’s framework for organizational change. The conceptual shift during analysis and design is considered architectural, whereas for programming it is deemed merely incremental. The managerial implications of these findings are discussed and suggestions for improving the likelihood of success in the adoption of object-oriented systems development methods are provided.

The extent of organizational innovation with information technology, an important construct in the IT innovation literature, has been measured in many different ways. Some measures have a narrow focus while others aggregate innovative behaviors across a set of innovations or stages in the assimilation lifecycle. There appear to be some significant tradeoffs involving aggregation: more aggregated measures can be more robust and generalizable and can promote stronger predictive validity, while less aggregated measures allow more context-specific investigations and can preserve clearer theoretical interpretations. This article begins with a conceptual analysis that identifies the circumstances when these tradeoffs are most likely to favor aggregated measures. It is found that aggregation should be favorable when: (1) the researcher's interest is in general innovation or a model that generalizes to a class of innovations, (2) antecedents have effects in the same direction in all assimilation stages, (3) characteristics of organizations can be treated as constant across the innovations in the study, (4) characteristics of innovations can not be treated as constant across organizations in the study, (5) the set of innovations being aggregated includes substitutes or moderate complements, and (6) sources of noise in the measurement of innovation may be present. The article then presents an empirical study using data on the adoption of software process technologies by 608 U.S. based corporations. This study—which had circumstances quite favorable to aggregation— found that aggregating across three innovations within a technology class more than doubled the variance explained compared to single innovation models. Aggregating across assimilation stages also had a slight positive effect on predictive validity. Taken together, these results provide initial confirmation of the conclusions from the conceptual analysis regarding the circumstances favoring aggregation.