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Using decision tree modelling to support Peircian abduction in IS research: a systematic approach for generating and evaluating hypotheses for systematic theory development
Author(s) -
OseiBryson KwekuMuata,
Ngwenyama Ojelanki
Publication year - 2011
Publication title -
information systems journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.635
H-Index - 89
eISSN - 1365-2575
pISSN - 1350-1917
DOI - 10.1111/j.1365-2575.2010.00368.x
Subject(s) - computer science , data science , management science , development (topology) , empirical research , decision tree , development theory , tree (set theory) , knowledge management , data mining , epistemology , mathematics , engineering , mathematical analysis , philosophy , economics , market economy
Since their early development, computers have had a profound impact on how we conduct modern scientific research. The disciplines of mathematics and operations research are perhaps the earliest to be dramatically transformed by information technology. However, over the years, computing technologies have provided many new opportunities for information processing, problem solving and knowledge creation. In this paper, we explore the potential of data mining technology for providing support for systematic theory testing based on Peirce's theory of abduction. We propose a data mining approach to abducting and evaluating hypotheses based on Peirce's scientific method. We believe that this approach could assist scientist to more efficiently explore alternative hypotheses for existing theories. We demonstrate our approach with empirical observations collected using instruments from the well known user performance area of information systems research.