| Summary: | 碩士 === 國立臺中科技大學 === 流通管理系碩士班 === 100 === Purpose: The aim of this study is to develop constraints inference model applying neural network and to assist leisure operators infer what leisure constraints people experienced through family life cycles and demographic variables. Besides, this study also explores the willingness of constraints negotiation of the respondents.
Design/methodology/approach: This paper focuses on the barriers to visiting The National Science Museum in Taiwan, and collects the samples by questionnaire survey method. First, we use K-means to classify the respondents into various groups. And then, we applying neural network to explore the relations of family life cycle and demographic variables to leisure constraints. Furthermore, we attempt to understand the influence of each variable on inferring constraint groups with sensitivity analysis. Finally, we observe the adoption intention with negotiation strategy of each constraint group.
Findings: There are three groups we clustered from respondents, namely time constraints, external constraints, and no constraints. Factually, this model can effectively infer the constraint group to which an individual belongs to base on his or her identification. The influence of gender, education level and housewife conform to the literature, but the influence of age and income are low. “reducing costs” and “searching for the ways to museum” are with higher adoption intention in time constraints. “Persuading friends to participate together” and “reducing the time from other activities” are with higher adoption intention in external constraints.
Originality/value: Museum operators can use this model and the results as an aid to develop effective negotiation strategies to induce participation of potential visitors.
Keywords: Leisure constraints, constraint groups, neural network, constraint negotiation
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