Business modeling is the managerial equivalent of the scientific method – you start with a hypothesis, which you then test in action and review when necessary.
Joan Magretta (Quoted from Magretta, J., Why Business Models Matter, Harvard Business Review, May, 86-92, 2002.)
One of very important understandings on business model is that “business model is an institution”.
When you develop a business model, you should behave as you deal with a new institution.
Therefore, the study on institution so far contributes to the theory on business models.
By Kyoung Jun Lee (Quoted from his lecture)
Kyoung Jun Lee and Federico Casalegno, An Explorative Study for Business Models for Sustainability, In Proceedings of the 14th Pacific Asia Conference on Information Systems, July 9-12, Taipei, Taiwan, 2010. pdf
Sustainability now becomes one of the key issues in innovating existing environments, where we live, and behaviours of people, how we live. There have been a lot of new attempts and initiatives for promoting the sustainability by government, industries, and communities. However, for the survival and successful adoption of the innovative efforts to real world, they need to be institutionalized or established as stable formal/informal institutions or business models. Especially, the efforts in private sectors, incumbents or entrepreneurs, should develop and find out, even through trial and errors, a viable business model for the sustainability. This paper reviews the various initiatives from the business model perspective, analyze the characteristics of the sustainability business models and suggest key dimensions to design new business models for sustainability.
Business Model, Sustainability, Green Business, Green IT.
Shin, K. Lee, K., Bankruptcy Prediction Modeling Using Multiple Neural Network Models, Lecture Notes in Artificial Intelligence 3214:668–674, September, 2004. – SCIE, ISSN:0302-9743
The primary goal of this paper is to get over the limitations of single neural network models through model integration so as to increase the accuracy of bankruptcy prediction. We take the closeness of the output value to either 0 or 1 as the models confidence in its prediction as to whether or not a company is going to bankrupt. In case where multiple models yield conflicting prediction results, our integrated model takes the output value of the highest confidence as the final output. The output of the confidence-based integration approach significantly increases the prediction performance. The results of composite prediction suggest that the proposed approach will offer improved performance in business classification problems by integrating case-specific knowledge with the confidence information and general knowledge with the multi-layer perceptrons generalization capability.
Shin, K. Lee, K., Neuro-genetic Approach for Bankruptcy Prediction Modeling, Lecture Notes in Artificial Intelligence 3214:646–652, September, 2004. – SCIE, ISSN:0302-9743.
Artificial neural network (ANN) modeling has become the dominant modeling paradigm for bankruptcy prediction. To further improve the neural networks prediction capability, the integration of the ANN models and the hybridization of ANN with relevant paradigms such as evolutionary computing has been demanded. This paper first attempted to apply neurogenetic approach to bankruptcy prediction problem for finding optimal weights and confirmed that the approach can be a good methodology though it currently could not outperform the backpropagation learning algorithm. The result of this paper shows a possibility of neurogenetic approach to bankruptcy prediction problem since the simple neurogenetic approach produced a meaningful performance.
Shin, K., Lee, K., Kim, H., Support Vector Machines Approach to Pattern Detection in Bankruptcy Prediction and its Contingency, Lecture Notes in Computer Science 3316:1254-1259, November, 2004. – SCIE ISSN:0302-9743. pdf
This study investigates the effectiveness of support vector machines (SVM) approach in detecting the underlying data pattern for the corporate failure prediction tasks. Back-propagation neural network (BPN) has some limitations in that it needs a modeling art to find an appropriate structure and optimal solution and also large training set enough to search the weights of the network. SVM extracts the optimal solution with the small training set by capturing geometric characteristics of feature space without deriving weights of networks from the training data. In this study, we show the advantage of SVM approach over BPN to the problem of corporate bankruptcy prediction. SVM shows the highest level of accuracies and better generalization performance than BPN especially when the training set size is smaller.
Lee, S. K., Lee, J. K. K. J. Lee, “Customized Purchase Supporting Expert System: UNIK-SES”, Expert Systems with Applications, vol. 11, no. 4, pp.431-441, 1996.pdf
An expert system that assists the customized men’s wear purchasing process is developed. In the past, configuration systems have adopted either a rule- or a constraint-based representation. To overcome the limitation of the representation, an integrated representation of the Constraint and Rule Satisfaction Problems (CRSP) is adopted. Since the general purpose tool UNIK-CRSP provides concurrent, integrated and interactive reasoning, UNIK-CRSP is very suitable for assisting the personalized customer product configuration process in a natural manner. Using UNIK-CRSP, the domain-specific tool UNIK-SES is developed with the additional features of candidate products selection and user interfaces. This approach should be useful for customer support in electronic marketing on the information superhighway.
Lee, K. J. Chang, Y. S., Time-Bounded Negotiation Framework for Multi-Agent Coordination, Lecture Notes in Computer Science, vol. 1599, pp. 61-75, 1999.pdf
For the efficient and informative coordination of multiple agents, a time-bounded agent negotiation framework is proposed utilizing time-based commitment scheme. By attaching the commitment duration to agent messages, the traditional Contract Net Protocol is extended to a time-bounded environment, thereby giving rise to a Time-Bounded Negotiation Framework (TBNF). The proposed negotiation framework has a new message type to agree upon the extension of a commitment duration, and a novel commitment concept in the form of Negative Commitment. We interpret the semantics of the messages with the commitment duration, and then formally define and compare the three typical negotiation protocols – nothing-guaranteed protocol, acceptance-guaranteed protocol, and finite-time guarantee protocol – which can be incorporated into TBNF. The Time-Bounded Negotiation Framework should provide a background for efficient and effective agent coordination while accommodating each agent’s adaptive negotiation strategy.
Lee, K. J., Chang, Y. S., Lee, J. K., “Time-Bounded Negotiation Framework for Electronic Commerce Agents”, Decision Support Systems, vol. 28, no.4, pp. 319-331, June, 2000.pdf
For efficient and informative coordination of agents especially in electronic commerce environment, a time-bound agent negotiation framework is proposed utilizing a time-based commitment scheme. By attaching commitment duration to agent messages, the traditional contract net protocol is extended to a time-bound negotiation framework TBNF. The proposed negotiation framework has a new message type which allows for parties to agree upon the extension of commitment duration, and a novel commitment concept in the form of negative commitment. The semantics of the messages with the commitment duration are interpreted, and then the three typical negotiation protocols are formally defined and compared — nothing-guaranteed protocol, acceptance-guaranteed protocol, and finite-time guarantee protocol — which can be incorporated into TBNF. The TBNF should provide a background for efficient and effective electronic commerce negotiation while accommodating each agent’s adaptive negotiation strategy.
Communication protocols; Multi-agent negotiation; Electronic commerce; Contract Net Protocol; Multi-agent coordination
Jin, D. Lee, K, “Impacts and Limitations of Intelligent Agents to Internet Commerce,” Lecture Notes in Computer Science, vol. 2105, pp. 33-48, July, 2001.pdf
Agent-based economy or agent-based electronic commerce is the term for describing one of possible next steps of electronic commerce. The systematic understanding of the agent-based economy is important for researchers to develop practical intelligent agent systems, and for current electronic commerce industries to cope with the challenges of the intelligent agents. With these purposes, we conduct a comprehensive review of ongoing and future impacts of intelligent agents to electronic commerce from business model perspective. We classify intelligent agents by their functions and roles in electronic commerce and analyze the business model change by intelligent agents, based on Timmers’s definition of business model. Changes in architecture of flows, responses of players, influences to revenue model and participant’s benefits, and funding source are discussed with real world business examples and related researches. We also discuss the limitations of intelligent agents in electronic commerce.