Analysis and Utilization of Online Community Information for Technology Planning

Abstract

In the era of disruptive innovation, a core facet of technology planning entails exploring and analyzing information from a variety of external sources. With the development of information and communication technology, we now have means to both access and analyze a very effective source of such kind: online communities. The massive amount of information generated in online communities, where a diverse group of individuals voluntarily share opinions, ideas, and data, patently carries powerful potential in aiding technology planning. Despite their conceptual utility, previous studies have lacked empirical engagement on the role of online communities and leveraging of online community information into technology planning processes. The question, then, is how to distill the helpful information from swaths of data. Within this context, this thesis explores how to analyze and utilize the online community information to help in technology planning. It begins by distinguishing two types of online communities relevant for our purpose: tech-discourse community and tech-foresight community. First, the tech-discourse community, where tangible and foreseeable future is discussed among user innovators, can be a source for identifying potential needs and user ideas

as a representative case of tech-discourse community, the thesis discusses mobile App Store community. Second, the tech-foresight community, where conceptual and distant future is explored among large number of participants, can be a source for exploring potential disruptive signals and scenarios of future technology. This study especially coins the information from tech-foresight communities as futuristic data. The thesis consists of three research themes and six corresponding modules. The first theme aims to analyze the characteristics in tech-discourse and tech-foresight communities. Research module #1 analyzes the patterns of innovation in tech-discourse communities, choosing mobile application services from App Store as a representative case. In consequence, the module finds that innovations in tech-discourse communities are often based on divergence and convergence of existing products. Research module #2 analyzes the potential of futuristic data in tech-foresight communities. Since the concept of futuristic data is new in technology planning, futuristic data is defined and analyzed in terms of its utility in technology foresight. By comparing it to patent data in terms of future-orientation, information scope, and perspective, the module identifies futuristic data in tech-foresight communities as a potent source of foresight. The second theme aims to identify and take advantage of opportunities of innovation from tech-discourse communities. Research module #3 scans potential user needs from tech-discourse communities. To this end, the module introduces user-centric service map to discover new product opportunities in service vacuums, and makes suggestions toward those opportunities by using adjacent reference services. Research module #4 implements on user ideas based on existing products. The module focuses on more active and enthusiastic user innovators ideas, converting them into new product opportunities that are further developed by using reference services. To this end, text mining-based case-based reasoning approach is suggested. The last theme aims to take advantage of information from tech-foresight communities into technology planning. Research module #5 visually scans potential disruptive signals from futuristic data for technology roadmapping. In this module, potential disruptive signals are narrowed down through three successive keyword maps: self-organizing feature map-based keyword cluster map, treemap-based keyword intensity map, and network-based keyword relationship map. Research module #6 extracts rules to build scenarios from futuristic data. This module focuses on fuzzy cognitive map-based scenario building, and the modeling elements such as concept nodes and their causal edges are identified by text mining and fuzzy association rule mining. In whole, this thesis can help to provide concrete and systematic tools that facilitate technology planning by considering both user innovation and disruptive innovation. Ultimately, the tools can serve technology planners to proactively investigate innovation opportunities and drivers and candidates of future technology by tapping into the massive and ever-growing collective knowledge from a large number of voluntary participants.