Measures of knowledge and their relevance to technology transfer

Summary form only given. Knowledge creation and management have been seen as a means for enduring a sustainable competitive advantage. However, there is little known about knowledge or how it can be measured. Also, knowledge, when used in research, is rarely explicitly defined. Technology transfer research includes an existing theoretical framework. The context of technology transfer suggests a "good setup" for theory building since knowledge must be transferred between differing units. As a first step for theory development, the case study approach is used. The primary focus of this case involves both the transfer and mapping of knowledge. The context of this case is the installation of an artificial intelligence technology at a major bank in Ohio for the purpose of reducing credit card fraud. A small software firm, specializing in fraud prevention AI systems for the financial services industry, developed the product. Knowledge had to be shared between the players while implementing the new AI product and utilizing an existing information system infrastructure. Each party's level of knowledge is discussed and used as supporting evidence for the model that maps knowledge into business, technical, cognitive and emotional dimensions. We were able to validate those aspects, while utilizing subjective (and comparative) measures for the levels of knowledge as a means for mapping. The paper then proposes a possible theoretical framework of knowledge, key measures and, based on our research and existing literature, we propose definitions of knowledge and knowledge management.