A data mining approach for training evaluation in simulation-based training

a b s t r a c t With the significant evolution of computer technologies, simulation has become a more realistic and effective experiential learning tool to assist in organizational training. Although simulation-based training can improve the effectiveness of training for company employees, there are still many management challenges that need to be overcome. This paper develops a hybrid framework that integrates data mining techniques with the simulation-based training to improve the effectiveness of training evaluation. The concept of confidence-based learning is applied to assess trainees' learning outcomes from the two dimensions of knowledge/skill level and confidence level. Data mining techniques are used to analyze trainees' profiles and data generated from simulation-based training for evaluating trainees' performance and their learning behaviors. The proposed methodology is illustrated with an example of a real case of simulation-based infantry marksmanship training in Taiwan. The results show that the proposed methodology can accurately evaluate trainees' performance and their learning behaviors and can discover latent knowledge for improving trainees' learning outcomes

A novelty detection patent mining approach for analyzing technological opportunities

Early opportunity identification is critical for technology-based firms seeking to develop technology or product strategies for competitive advantage in the future. This research develops a patent mining approach based on the novelty detection statistical technique to identify unusual patents that may provide a fresh idea for potential opportunities. A natural language processing technique, latent semantic analysis, is applied to extract hidden relations between words in patent documents for alleviating the vocabulary mismatch problem and reducing the cumbersome efforts of keyword selection by experts. The angle-based outlier detection method, a novelty detection statistical technique, is used to determine outlier patents that are distinct from the majority of collected patent documents in a high-dimensional data space. Finally, visualization tools are developed to analyze the identified outlier patents for exploring potential technological opportunities. The developed methodology is applied in the telehealth industry and research findings can help telehealth firms formulate their technology strategies

Exploring innovation diffusion of two-sided mobile payment platforms: A system dynamics approach

Mobile payment (m-payment) can be considered as a two-sided platform that delivers payment services for mobile devices. Its penetration still faces high barriers in some countries, such as existing consumer payment behavior on traditional payment methods and fragmented merchant acceptance. This research aims to develop an empirical data-driven simulation methodology that integrates system dynamics (SD) and choice-based conjoint (CBC) analysis for analyzing diffusion dynamics of several competing m-payment platforms. Choice-based conjoint analysis is used to collect the multi-attribute preference data on different platform features from each side of user groups. A SD simulation model with the empirical user preference data is developed to evaluate how platform design strategies affect diffusion dynamics of various competing platforms under different scenarios. The methodology is applied to Taiwan's m-payment market. The research findings can help firms understand potential evolution of the m-payment market and formulate appropriate platform design strategies proactively to improve competitive advantages

A fuzzy set approach for R&D portfolio selection using a real options valuation model

Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero-one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.Management of science/technology Portfolio selection Project management Fuzzy sets Optimization

Flexibility planning for managing R&D projects under risk

Incorporating managerial flexibility in an innovative R&D project is important, because managers face greater uncertainty in today’s competitive and dynamic changing environment. It is essential to bring managerial flexibility into R&D project planning to decrease technical and market risks, while increasing potential market value. The objective of this paper is to develop a flexibility planning methodology based on real option analysis to improve managerial flexibility for R&D projects. The proposed methodology identifies potential risks that may occur during every R&D stage. It also recognizes a cascading option structure to resolve the identified risks, and evaluates and selects adequate options that maximize the potential value of the project. Instead of using a traditional option pricing method, a dynamic programming model that considers multidimensional product performance and market payoff is used to evaluate the R&D project value. Using the proposed methodology, managers can identify future scenarios as a function of their management actions. The proposed flexibility planning methodology can help managers improve managerial flexibility of R&D project and increase the success rate of product launch. A drug development project is used to illustrate the proposed methodology