本研究旨在探討台灣地區零售業目前行銷創新的現況。以往之文獻顯少涉及廠商特性與行銷創新間的關係，本研究則以細緻化後的行銷創新分類，實證探討台灣區零售業廠商特性與行銷創新間的關係。 首先，本研究探討了台灣地區零售業相較於非零售業之服務業而言，特別會進行哪些類型的行銷創新，以瞭解零售業的行銷創新特性；接著，本研究探討了會進行各種不同類型的廠商有何廠商特性，以瞭解零售業廠商特性與行銷創新間的相關性；最後，本研究進一步鎖定零售業廠商的市場範圍廣度及主要顧客類型此兩個構面，而去研究廠商的市場範圍廣度及主要顧客類型對零售業廠商進行行銷創新是否有顯著的影響。 本研究的分析方法採用probit迴歸模型，實證結果發現： 1.台灣地區零售業廠商行銷創新之比例高於非零售業之服務業的行銷創新之比例，且零售業廠商進行任何一種行銷創新之比例也都高於非零售業之服務業。零售業廠商所進行的各類行銷創新當中，有進行該創新的廠商比例為由高到低為：銷售通路、產品/外觀/形象設計、廣告促銷推廣、產品展示方式/管道、包裝、定價付款。 2.除了定價付款的創新之外，零售業廠商市場範圍包含海外市場數目越多，對於廠商進行其他五種行銷創新皆有正向影響；亦即，廠商的市場範圍越廣，則越容易進行此五類的行銷創新。 3.當零售業廠商的主要顧客類型包含消費者時，除了產品展示方式/管道的創新之外，此項廠商特性對於廠商進行其他五種類型的行銷創新皆有正向影響。當零售業廠商主要顧客類型包含非母企業集團之其他公司時，本研究結論無足夠證據支持其對進行此六種行銷創新是否有正向或負向之影響。當零售業廠商的主要顧客類型包含政府單位時，對於進行產品展示方式/管道的創新有正向影響。 / This research is to study the market innovation of the retail industry in Taiwan. Previous researches had rarely studied on the relationship between firm characteristics and market innovation. This research uses an empirical method to investigate the relationship between characteristics of retail firms in Taiwan and market innovation. This research first finds out what sorts of market innovation that the firms in the Taiwan retail industry tend to do, then identifies the relationship between firm characteristics and marketing innovation, and finally investigate the impacts of customer types and market area upon market innovations of these retail firms by probit regression model. The main findings of this research are in the following. I. Firms in the Taiwan retail industries have higher level of market innovation in every aspect as compared to firms in other service industries. The ranking of the percentage of each sort of market innovation, from high to low, is as follows: channel, design, promotion, display, package, and pricing. II. The increase of the number of overseas markets will contribute to higher level of five sorts of market innovation, except for the pricing innovation. In other words, the higher the retail firms’ market area, the more likely the firms will have marketing innovation. III. If the retail firms’ major customers include final consumers, firms are more likely to have channel, design, promotion, package, and pricing innovation, except for the display innovation. If the major customers include the government, retail firms are more likely to have display innovation, but less likely to have channel, design, promotion, package, and pricing innovation.

資料品質的好壞會影響決策品質以及各種行動的執行成果，所以資料品質在近年來越來越受到重視。本研究包含了兩個資料庫，一個是產業創新調查資料庫，一個是95年工商及服務業普查資料庫，資料品質的好壞對一個資料庫來說也是一個相當重要的議題，資料庫中往往都含有錯誤的資料，錯誤的資料會導致分析結果出現偏差的狀況，所以在進行資料分析之前，資料清理與整理是必要的事前處理工作。 我們從母體資料分佈與樣本資料分佈得知，在清理與整理資料之前，平均創新員工人數為92.08，平均工商員工人數為135.54；在清理與整理資料之後，我們比較兩個資料庫員工人數的相關性、相似性、距離等性質，結果顯示兩個資料庫的資料一致性極高，平均創新員工人數與平均工商員工人數分別為39.01與42.12，跟母體平均員工人數7.05較為接近，也顯示出資料清理的重要性。 本研究使用的方法為事後分層抽樣，主要研究目的是要利用產業創新調查樣本來推估95年工商及服務業普查母體資料的準確性。產業創新調查樣本在推估母體從業員工人數與母體營業收入方面皆出現高估的狀況，推測出現高估的原因是產業創新調查母體為前中華徵信所出版的五千大企業名冊為母體底冊，而工商及服務業普查企業資料為一般企業母體底冊。因此，我們利用和產業創新調查樣本所相對應的工商普查樣本做驗證，發現95年工商及服務業普查樣本與產業創新調查樣本的資料一致性極高。 / Data quality is good or bad will affect the decision quality and achievements in the implementation of various actions, so the data quality more and more attention in recent years. This study consists of two databases, one is the industrial innovation survey database, another is the industry and commerce census database in ninety five years. Data quality is good or bad of a database is also a very important issue, the database often contain erroneous information, incorrect information will result in bias of the analysis results. So before carrying out data analysis, data cleaning and consolidation is necessary. We can know from the parent and the sample data distribution. Before data cleaning and consolidation, the average number of innovation employees is 92.08, and the average number of industrial-commerce employees is 135.54. After data cleaning and consolidation, we compare the correlation, similarity, and distance of the number of employees in two databases. The results show the data consistency of the two databases is very high, the average number of innovation employees is 39.01, and the average number of industrial-commerce employees is 42.12, it is closer to the average number of parent employees 7.05. This also shows the importance of data cleaning. Method used in the study is post-stratified sampling, the main research objective is to use industrial innovation survey sample to estimate the data accuracy of the industry and commerce census in ninety five years. Use industrial innovation survey sample to estimate the number of employees and operating revenue in the industry and commerce census in ninety five years are both overestimated, we guess the reason is that the parent of the industrial innovation survey is five thousand large enterprises published by China Credit Information, and the parent of the industry and commerce census is general enterprises. Therefore, we use the corresponding industry and commerce census sample for validation. The results show that the data consistency of the industrial innovation survey sample and the industry and commerce census sample in ninety five years is very high.