The goal of any modeling activity is a complete and accurate understanding of the real-world domain, within the bounds of the problem at hand and keeping in mind the goals of the stakeholders involved. High-quality representations are critical to that understanding. This paper proposes a comprehensive Conceptual Modeling Quality Framework, bringing together two well-known quality frameworks: the framework of Lindland, Sindre, and Sølvberg (LSS) and that of Wand and Weber based on Bunge's ontology (BWW). This framework builds upon the strengths of the LSS and BWW frameworks, bringing together and organizing the various quality cornerstones and then defining the many quality dimensions that connect one to another. It presents a unified view of conceptual modeling quality that can benefit both researchers and practitioners.

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The quality of conceptual models directly affects the quality of the understanding of the application domain and the quality of the final software products that are ultimately based on them. This paper describes a systematic literature review (SLR) of peer-reviewed conference and journal articles published from 1997 through 2009 on the quality of conceptual models written in UML, undertaken to understand the state-of-the-art, and then identify any gaps in current research. Six digital libraries were searched, and 266 papers dealing specifically with the quality of UML models were identified and classified into five dimensions: type of model quality, type of evidence, type of research result, type of diagram, and research goal. The results indicate that most research focuses on semantic quality, with relatively little on semantic completeness; as such, this research examines new modeling methods vs. quality frameworks and metrics, as well as quality assurance vs. understanding quality issues. The results also indicate that more empirical research is needed to develop a theoretical understanding of conceptual model quality. The classification scheme developed in this paper can serve as a guide for both researchers and practitioners.

This research investigates technology flexibility, which is the technology characteristic that allows or enables adjustments and other changes to the business process. Technology flexibility has two dimensions, structural and process flexibility, encompassing both the actual technology application and the people and processes that support it. The flexibility of technology that supports business processes can great& influence the Organization's capacity for change. Existing technology can present opportunities for or bamers to business process flexibility through structural characteristics such as language, plavom, and design. Technology can also indirectly affect flexibility through the relationship between the technology maintenance organization and the business process owners, change request processing, and other response characteristics. These indirect eflects rejlect a more organizational perspective offlexibility. This paper a s h the question, "what makes technology flexible?" This question is addressed by developing and validating a measurement model of technology flexibility. Constructs and dejnitions of technology flexibility are developed by examining the concept offlexibility in other disciplines, and the demands imposed on technology by business processes.The purpose of building a measurement model is to show validity for the constructs of technology flexibility. This paper discusses the theory of technology flexibility, develops constructs and determinants of this phenomenon, and proposes a methodology for the validation and stu& of the flexibility of emerging technologies.