隨著台灣地區近年經濟發展快速,生活水平大幅提昇,消費者對健身與運動益漸重視,健身俱樂部產業亦蓬勃發展,本研究主要針對大台北地區健身俱樂部會員為樣本,進行問卷調查,瞭解其加入健身俱樂部的主要動機與考量因素,將問卷結果藉由SPSS統計軟體進行敘述性統計與交叉分析,瞭解不同背景之會員消費行為模式,作為健身俱樂部業者擬定公司策略及行銷之決策支援,協助業者創造關鍵競爭優勢。 針對台北市區俱樂部會員,計發出二次問卷調查,第一次發出70份,回收66份;第二次發出70份,回收68份,除據以實施敘述性統計分析與交叉分析外。另透過邏輯斯迴歸分析(logistic regression analysis),得到受訪者基本資料對於考量因素與參加動機的迴歸公式。研究結果對於健身俱樂部會員加入之主要動機與考量因素,經邏輯斯迴歸分析得出其預測公式。未來當擁有一組樣本基本資料時,即可知道此樣本對於考量因素與參加動機問題選項的勾選機率,據此作為業者之決策參考。 關鍵字:健身俱樂部、公司策略、競爭優勢、邏輯斯迴歸
As Taiwan's rapid economic development in recent years, and living standards increase, consumers new pay more attantion on fitness and exercise. Because of that the businesss of fitness club is also booming. This study use the members of the fitness clubs in Greater Taipei Area samples, conducting a questionnaire survey, to understand the main motive and consideration of those people joining fitness clubs. The survey results will go to thru the SPSS software to conduct narrative statistics and cross analysis, to learn more about the different backgrounds of consumers’behavior patterns, so the fitness club operators can draw up the company policy and marketing decision support. Finally it can assist the company operators to create the key competitive advantages. Point at the members of the Taipei City Clubs, Gage issued second questionnaires. first issue 70, recycling 66 copies; the second issue 70, recycling 68 copies, basis for implement narrative statistical analysis and crosstab.Through Logistic Regression Analysis, we get the regression formula of those respondents’basic data for consideration and the motive of joining fitnesss club. Study results for the motive of those members joining fitnesss club and their consideration, through the Logistic Regression Equation, the prediction formula can be got. And when basic data of the sample is got, then the selecting probability between “the considerations and the motivefo participate”and “the sample”will also be able to know. So that can be a reference resource of decision making for the firm owner. Keywords: Fitness Club, company policy, competitive advantage Logistic Regression