Individual Knowledge Structuring for Smart Services Requirements Engineering

Purpose - Smart Services are now an emerging phenomena that is based on intelligent technologies which are widely used for better customer. Smart Service is supposed to be an IT-service based on rich intellectual capital, realtime tools, sophisticated analytics, and automation (definition based on Cisco approach from (Smart Services, 2011). Individual Knowledge Structuring (IKS) is a significant part of any design activity, as it forms together and within critical thinking an analytical preface to a synthesising process. We argue that the current palette of individual knowledge structuring techniques and their cognitive impact affect the effectiveness of requirement engineering procedures. Design/methodology/approach - The aim of this study is to show how the repertoire of IKS is affected by individual cognitive style (Witkin, Moore, Goodenough and Cox, 1977; Palmquist and Kim, 2000). We have done an experiment with 79 students to discover the impact of the cognitive style features on the knowledge structuring. From the plethora of cognitive style characteristics three parameters have been chosen: field dependence/field independence (FD/FID), impulsivity/reflectivity, and narrowness/width of the category. The point is that we received totally different knowledge models from respondents with different cognitive styles peculiarities. In this paper we aim to introduce our findings on this issue. Originality/value - The paper proposes new approach to using cognitive psychology for the knowledge analytical activity for requirement engineering. The proposed findings are based on wide survey and experiments of the authors. The paper contributes to a wider use of knowledge engineering methodologies for smart service design and development. Individual Knowledge Structuring helps to develop a holistic conceptual service model. In this paper we aim to overcome the limitations of traditional approach and to enrich the repertoire of the methods that can be used by service designers and analysts to broad the understanding of the customer needs and service capabilities issues. Practical implications - The essential attributes of Smart Services are that they deliver better insights and predictability through software enabled tools predictive analytics, and intelligent automation. Smart Services design and development needs thorough requirements engineering process (Ralph, 2013) that involves all the palette of knowledge engineering techniques and methods. The paper contributes to business service science and practice by introducing recommendations for service analysts at the crucial phase of the design and development process. Such approach merges the cognitive ergonomics issues with knowledge engineering.