iChance: A Web-Based Innovation Support System for Business Intelligence

iChance: A Web-Based Innovation Support System for Business Intelligence

Hao Wang (The University of Tokyo, Japan) and Yukio Ohsawa (The University of Tokyo, Japan)
DOI: 10.4018/ijoci.2011100104
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Abstract

In the dynamic and competitive market, enterprises have to timely launch new as well as creative products and services to fulfill the consumers’ demands for occupying much more market share. Therefore, they all regard innovation as their strategic slogan. This paper presents a novel Web-based innovation support system (ISS) named iChance for collaborative innovation, such as idea generation and knowledge creation, based on Idea Discovery Model (IDM). iChance includes four main functional modules: (1) scenario-based innovation module, (2) requirement module, (3) communication module, and (4) toolbar module. The whole operating procedure of iChance is in accordance with one version of Innovators’ Market Game (IMG) which was invented by Ohsawa as a tool for aiding humans’ innovative thoughts and communication. Finally, case studies demonstrate all the principles of IMG are well realized in iChance system when applied in the early phase of product or service development.
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Introduction

The first decades of the 21st century is referred to as “age of innovation” (Prahalad & Krishnan, 2008). Innovation is a widely used buzzword in current business environment. Many famous enterprises in Japan directly and indirectly regard innovation as their significant strategic slogan, such as Leading Innovation (TOSHIBA), Empowered by Innovation (NEC), Ideas for Life (PANASONIC) and Inspire the Next (HITACHI). Thus people in companies spent much time in group activities, such as collaboration, communication, argument, negotiation, and agreement for problem solving, so that creative ideas could be generated by interaction and simulation between them. As a result, how to rapidly create new solutions and products has become a core competence in today’s business.

Many relevant techniques have been proposed to foster creativity of individuals or groups, such as brainstorming, mind mapping and morphological analysis. Increasingly interest has focused on computer support for creative problem solving (Huber et al., 2006; Farooq, Carroll, & Ganoe, 2005).

Research on creativity has two directions: analyzing definition and nature of creativity itself and building computerized support creativity tools – creativity support system (CSS) (Liu & Tang, 2006). Intuitive methods and logical methods are usually used to study CSS. A new theory called Computational Creativity Dynamics (CCD) is proposed by integrating aforementioned methods to build a CSS for assisting human creative activities through patents (Wen et al., 2006).

However, research shows brainstorming and other creativity techniques supported by a computer system are more effective (Bostrom & Nagasundaram, 1998). Nowadays users may access to the Web 2.0 applications easily to get together, share ideas, spread and share their knowledge in various ways (Barradas & Ferreira, 2009). Foster (2009) built a creativity support system (CSS) - IdeaStream which supported various creativity techniques on the Web.

Tang and Liu (2004) proposed Group Argumentation Environment (GAE) as a man-machine cooperative CSS for idea generation, knowledge creation and wisdom emergence. Based on meta-synthesis approach (MSA), Tang (2009) presented two technologies, CorMap and iView, to conduct exploratory analysis toward those topics or ideas created bottom-up and to facilitate human-machine interaction by visualization of the analytical process based on different algorithms.

In the last few years, chance discovery, proposed by Ohsawa in 2000, has been widely applied in various research areas, especially in business (Wang, Ohsawa, & Nishihara, 2011a). Chance discovery is a human-computer interaction process to detect rare but important chances for decision making. A chance means to understand an unnoticed event or situation which might be uncertain but significant for a decision (Ohsawa & McBurney, 2003). A core visualization tool called KeyGraph can generate scenario map to aid human’s value cognition in the process of chance discovery. KeyGraph is a novel keyword extraction algorithm that applies to single document without a corpus (Ohsawa, Benson, & Yachida, 1998). A document is represented as a graph where each node corresponds to a term and each edge means the co-occurrence between terms. Based on the segmentation of a graph into clusters, Keygraph extracts keywords by selecting the terms which co-occurs with clusters.

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