線上消費者再購行為之探討：機率分配和迴歸因子模型方法的比較

Revisiting Online Repeat Purchase Behavior: Deterministic Versus Stochastic Behavioral Modeling

10.6338/JDA.201410_9(5).0002

張順全(Shun-Chuan Chang)；唐瓔璋(Ying-Chan Tang)；吳全益(Jung-Kuei Chu)

再購行為 ； 機率分配模型 ； 實徵研究 ； Repurchase Behavior ； Stochastic Behavioral Modeling ； Empirical Research

Journal of Data Analysis

9卷5期（2014 / 10 / 01）

23 - 46

繁體中文

本論文為一實徵研究方法論的綜合論述，主要探討機率分配和迴歸因子模型在電子商務線上消費者再購行為實徵研究的適用情境。文章架構在2003年經濟學克拉克獎得主，對人類個體行為有深刻理解的實證經濟學者史蒂芬．李維特（Steven D. Levitt）一系列實證經濟學論述中，所揭櫫如何做好實徵研究的重要各面向。本篇論文除指陳若干使用「決定論」即傳統迴歸因子模型的線上消費者再購行為的實徵文章可能存在的問題，而再整理國外過去以「機率論」研究線上消費者再購行為的行銷科學相關文獻，同時以國內實際的電子商務資料驗證，並透過便於產學界應用的MS-EXCEL試算表實作計算。藉由實證分析線上消費者再購行為發現，應用機率分配模型確實可以作好預測未來銷售狀況，並可據以解釋再購行為的內隱機制。由於電子商務網站的客戶大多屬於非訂定合約（Non-contractual）型客戶，本論文亦討論以機率論為主體出發，加入迴歸因子為輔，對一般電子商務網站的客戶再購行為研究的可能性，這逆向思考將有別於一般以迴歸因子模型處理線上消費者再購行為議題的研究。

The aim of this paper is to elucidate two intrinsically different methodologies, the stochastic versus deterministic behavioral modeling, to contribute to a better theoretical knowledge about consumer online repeat purchase behavior. It is based on Steven Levitt's series of pioneering work on empirical research in microeconomics, which engrave him to win 2003 John Bates Clark Medal, by applying natural variation incidences to answer important human behavior questions. Deterministic E-commerce studies about online purchase behavior are hard to perform and justify, and the generated panel data are often fortuitous in interpreting corresponding relationships. This revisiting research is characterized by investigating the drivers and the effects of consumer repeat purchase behavior from empirical evidences to test the behavioral postulates for predicting consumer's stochastic choices. We present the MS-EXCEL spreadsheet of a stochastic modeling process based on a real online transaction database. This calibrated data set comprises non-contractual customers who made purchases on particular CDs without certainties on consumer's repeat purchase. Results indicate the proposed stochastic model is robust in predicting online repurchase outcomes. Insights for convergence and validation of empirical relations in incorporating deterministic element into stochastic scenarios are also discussed.

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