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研究生:江曉嬋
研究生(外文):Hsiao Chan Chiang
論文名稱:多世代產品重覆購買與創新擴散之研究
論文名稱(外文):A Study of Multi-generation Diffusion Model with Repeated Purchase and Product Innovation
指導教授:張炳騰張炳騰引用關係彭泉彭泉引用關係
指導教授(外文):Ping Teng ChangChyuan Perng
學位類別:碩士
校院名稱:東海大學
系所名稱:工業工程與經營資訊學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:88
中文關鍵詞:擴散模型重覆購買創新產品生命週期多世代產品定價參數敏感度
外文關鍵詞:Diffusion ModelRepeated PurchaseInnovationProduct Life CycleMulti-generation Product PricingParameter Sensitivity
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Bass (1969)所提出之擴散模型只考量首次購買的部份,並假設購買者僅採用ㄧ單位,然而隨著產品科技越來越先進,重覆購買現象亦會隨著大眾媒體的傳播或口耳相傳而逐漸產生。故從1981年開始有學者將重覆購買與重置購買加入擴散模型進行探討,將重覆購買與重置購買應用在藥品擴散方面之研究,但僅適用於藥品擴散預測,較難應用在其他產品的擴散情形。Bass (2001)首次發展針對科技產品耐久財之重覆購買擴散模型,並以PC、DRAM…等科技類產品為實證進行重覆購買擴散模型驗證。Bass認為在管理高科技產品方面,須特別著重在首次購買和重覆購買之間不同的產品、廣告宣傳和銷售策略等等所造成的影響程度。各世代產品每期銷售總量中的重覆購買量可反應顧客忠誠度,亦需探討重覆購買對於銷售量的影響。管理者冀望以準確、有效預測模型,建立產品銷售預測規劃,再依預測結果佐以不同行銷策略刺激消費者購買,藉此搶得市場先機,提高獲利。
本研究以Bass(2001)多世代產品重覆購買擴散模型為基礎架構,推導各世代產品內部影響參數與外部影響參數,解釋不同參數影響下,高科技產品世代交替的影響過程,並運用統計參數敏感度實驗探討經模式化後的擴散係數在不同變動下,對於多世代產品總收益、最佳演化代數與產品上市時間的影響情形。藉由產品上市時間點的掌握,與上市後獲利性的考量,求取整體產品線最大收益,希望可以適切表達各代產品競爭策略與產品生命週期對於多世代產品擴散過程的影響。
根據CPU歷史資料參數估計結果,在線性或指數模式下,所預測模型之配適性與預測能力都有不錯的效果,但以指數模式配適性較佳。在線性模式下,現有採用者所散佈的口碑宣傳影響效果較強烈;而指數模式下,則顯示大眾傳媒的效果較強烈。在線性模式下,現有採用者在產品通路或促銷等價格行銷的效果較強烈;指數模式下,價格的口耳相傳影響效果較強烈。而不論在線性或指數模式之下,現有採用者對產品創新比例變化程度上,口耳相傳的影響較大眾傳媒效果強烈,顯示大眾在採用CPU產品上較受到口耳相傳的影響。本研究經由重覆購買擴散模式與基因演算法求解產品上市時間與最大演化代數,並結合不同行銷策略最佳化過程,提供了更切合高科技產業在制定多世代產品行銷策略與決策規劃上之決策資訊。
The diffusion model that the Bass(1969) puts forward only considers the part of first adoption, and suppose to purchase one unit only. However along with product science and technology more and more advanced, repeated purchase phenomenon would also along with mass media or word-of-mouth and gradual creation. Since 1981, the scholar has repeated purchase and reset purchase to join diffusion model to carry on a study, apply repeated purchase and reset purchase in diffusion research of the aspect in the drugs, but being only applicable to drugs diffusion estimate, more difficult apply in the diffusion situation of other products. Bass(2001) is the original one develops a durable technological product repeated purchase to diffusion model with durable technological product like PC, DRAM …etc. for the evidence for repeated purchase diffusion model to identify again. In management of fast-tech products, special attention must be paid to the different needs of adopters and repeaters, which may require different product, advertising, and distribution-channel strategies. Each generation product can respond a customer loyalty on each period of repeated purchase quantity for a total amount of sell, it also need to inquired into repeated purchase influence of the sales volume. The manager looks forward to with accurate, valid estimate model to establish product sales forecasting programming, then depend on estimate result with different marketing policy stimulate the consumer purchase, robbing by this market first timing, raise profit
This research for the foundation configure with Bass(2001) have multi-generation product repeated purchase’s diffusion model, deducing each generation product internal influence parameter and exterior influence parameter, explaining different parameter’s influence, high-tech product generation on the influence process of the alternation, and use of an experiment on the parameter sensitive degree after model turning of diffusion under coefficient at dissimilarity change, for the influence situation of total income of multi-generation product, the best evolve generation and the timing that product to enter the market.
目錄
摘要 I
ABSTRACT II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與假設 3
1.4 研究方法與步驟 3
1.5 論文架構 4
第二章 文獻探討 7
2.1 產品定價相關研究 7
2.1.1 定價目標 7
2.1.2 定價策略 8
2.1.3 產品生命週期行銷策略 11
2.2 擴散模型 (DIFFUSION MODEL) 13
2.2.1 貝氏擴散模型 (Bass Diffusion Model) 13
2.2.2 採用者分類 15
2.2.3 參數估計 17
2.3多世代擴散模型 22
2.4 多世代重覆購買擴散模型 27
2.4.1 首次購買函數 28
2.4.2 重覆購買函數 29
2.4.3 重覆購買擴散模型 29
2.5 參數模式化 29
2.5.1 市場混合變數 30
2.5.2 其他變數 31
2.6 基因演算法 32
第三章 研究方法 38
3.1 研究模型建立 40
3.2 參數實驗設計 47
3.2.1 歷史資料參數估計 49
3.3 歷史銷售量隨機干擾 50
3.4 受價格、廣告支出、創新影響參數敏感度分析 50
3.5 預測未來產品世代價格與廣告支出 51
第四章 實證結果與分析 53
4.1 個案情境設定與實證產品CPU介紹 53
4.2 歷史資料參數估計結果 55
4.3 歷史資料干擾預測 61
4.4 參數敏感度分析 63
4.5 未來銷售預測分析 72
第五章 結論與未來研究方向 77
5.1 結論 77
5.2 未來研究方向 78
參考文獻 80
附錄A 83
附錄B 84


表目錄
表2.1 產品定價策略 9
表2.2 產品生命週期—行銷策略 12
表2.3 擴散模型相關研究 18
表2.4 運用META-ANALYSIS於擴散模型之參數估計 19
表2.5 BASS MODEL參數估計方法 32
表3.1 CPU市場線性及指數模式受價格、廣告支出影響參數水準設定 51
表4.1 CPU主要製造商市場佔有率 53
表4.2 CPU歷史資料線性模式下參數估計數值 56
表4.3 CPU產品原始價格與廣告支出 57
表4.4 CPU歷史資料指數模式下參數估計數值 59
表4.5 CPU線性模式外部影響參數敏感度分析 65
表4.6 CPU線性模式內部影響參數敏感度分析 67
表4.7 CPU指數模式外部影響參數敏感度分析 70
表4.8 CPU指數模式內部影響參數敏感度分析 72
表4.9 CPU歷史資料參數線性模式下價格與廣告支出結果 73
表4.10 CPU歷史資料參數指數模式下價格與廣告支出結果 75


圖目錄
圖1.1 論文架構圖 5
圖2.1 產品生命週期四階段 12
圖2.2 ROGER’S 採用者分類 16
圖2.3 BASS採用者分類 16
圖2.4 以功能別分類的多世代產品 26
圖2.5 NLS VS. GA 34
圖2.6 基因演算法之運算流程圖 36
圖3.1 本研究架構 39
圖3.2 參數設定與擴散過程( Q>P) 42
圖3.3 參數設定與擴散過程(Q≦P) 42
圖3.4 CPU產品1993-2004年市場銷售量統計圖 44
圖3.5 CPU歷史輸入資料與多代重覆購買擴散模型關係圖 48
圖4.1 CPU歷史資料線性模式下實際銷售量與預測銷售量關係圖 56
圖4.2 CPU歷史資料線性模式下預測銷售量與重覆購買量關係圖 57
圖4.3 CPU歷史資料線性模式下預測每代重覆購買量關係圖 57
圖4.4 CPU歷史資料指數模式下實際銷售量與預測銷售量關係圖 59
圖4.5 CPU歷史資料指數模式下預測銷售量與重覆購買量關係圖 60
圖4.6 CPU歷史資料指數模式下預測每代重覆購買量關係圖 60
圖4.7 CPU線性模式下歷史資料銷售量干擾與預測關係圖 62
圖4.8 CPU指數模式下歷史資料銷售量干擾與預測關係圖 62
圖4.9 CPU線性模式下外部影響參數K1、K2、K3變動關係圖 64
圖4.10 CPU線性模式下內部影響參數K4、K5、K6變動關係圖 66
圖4.11 CPU指數模式下外部影響參數X1、X2、X3變動關係圖 69
圖4.12 CPU指數模式下內部影響參數X4、X5、X6變動關係圖 71
圖4.13 CPU產品線性模式下最佳演化代數與利潤關係圖 73
圖4.14 CPU產品線性模式下各世代定價預測結果 74
圖4.15 CPU產品線性模式下各世代廣告支出預測結果 74
圖4.16 CPU產品指數模式下最佳演化代數與利潤關係圖 75
圖4.17 CPU歷史資料指數模式下各世代價格預測結果 76
圖4.18 CPU歷史資料指數模式下各世代廣告支出預測結果 76
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