This is a comprehensive description of the Enterprise Ontology, a collection of terms and definitions relevant to business enterprises. We state its intended purposes, describe how we went about building it, define all the terms and describe our experiences in converting these into formal definitions. We then describe how we used the Enterprise Ontology and give an evaluation which compares the actual uses with original purposes. We conclude by summarising what we have learned. The Enterprise Ontology was developed within the Enterprise Project, a collaborative effort to provide a framework for enterprise modelling. The ontology was built to serve as a basis for this framework which includes methods and a computer tool set for enterprise modelling. We give an overview of the Enterprise Project, elaborate on the intended use of the ontology, and give a brief overview of the process we went through to build it. The scope of the Enterprise Ontology covers those core concepts required for the project, which will appeal to a wider audience. We present natural language definitions for all the terms, starting with the foundational concepts (e.g. entity, relationship, actor). These are used to define the main body of terms, which are divided into the following subject areas: activities, organisation, strategy and marketing. We review some of the things learned during the formalisation process of converting the natural language definitions into Ontolingua. We identify and propose solutions for what may be general problems occurring in the development of a wide range of ontologies in other domains. We then characterise in general terms the sorts of issues that will be faced when converting an informal ontology into a formal one. Finally, we describe our experiences in using the Enterprise Ontology. We compare these with the intended uses, noting our successes and failures. We conclude with an overall evaluation and summary of what we have learned.
PurposeThis study seeks to examine how the quantitative semantics of the learning curve phenomenon can be employed in order to derive monetary information for team learning observed within knowledge‐intensive production environments.Design/methodology/approachSoftware development is selected as an identical example of a team‐based, knowledge‐intensive production environment. The interaction of learning rate of the developer teams and the improvements on their average solving time (i.e. productivity) is modelled as a Lotka‐Volterra predator‐prey interacting populations system establishing a causal relationship between the human capital (HC) of organizational teams and the observed learning curve effects on their performance. In addition, empirical evidence illustrates that the estimated learning rates capture the entire range of team learning effects on performance fluctuations caused by the HC.FindingsThe fluctuations on the learning rates can be interpreted as a result of the HC variability across the population of developer teams. Hence, the cost implications of the HC within knowledge‐intensive production environments can be rationalised using the quantitative semantics of the learning curve phenomenonResearch limitations/implicationsThe learning curve is associated with the cost side of the organizational income‐generating process limiting its potential valuation applications for team learning observed within the context of the production environments.Originality/valueThe study offers a theoretical justification, supported by empirical evidence, for employing the mathematical expression of the learning curve paradigm to rationalize the financial consequences of team learning observed within production environments.
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