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標題: | 電子書銷售樣態歸納分析與平台銷售策略發展 eBook Sales Pattern Analysis and the Development of Platform Selling Strategies |
作者: | 顏利芳 Li-Fang Yen |
指導教授: | 藍俊宏 Jakey Blue |
關鍵字: | 數位出版,電子書銷售記錄,k-means集群分析,銷售樣態解析, Digital Publishing,eBook Sales,k-means Clustering,Sales Pattern Analytics, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | 數位出版產業發展數十年間,已近成熟。在臺灣的發展,隨著2017年國內最大網路書店博客來開始提供電子書服務。隨後在2019年,國際領先的電子書平台Amazon Kindle也進入繁體中文書店市場,進一步擴大了繁體中文數位出版市場。在過去三年間,尤其受惠於新冠肺炎疫情,產值更是年年攀升,成長到占整體出版產業產值5%到10%,已為出版方不可忽視之營收來源,國內電子書平台也呈現數強鼎立的狀態,可說十分熱鬧。
過去的數位出版相關研究,多數著重在介面設計、產業轉型、使用者體驗、公共圖書館借閱等議題,較少對於繁體中文電子書銷售數據的直接研究。本研究詳細回顧數位出版產業發展之歷程,再以某出版社之2020年到2022年的所有新舊出版電子書銷售資料為數據基礎,運用k-means群集分析法對電子書的銷售樣態進行分群。研究步驟包括確認資料範圍、收集數據、處理數據、採用k-means分群法和判斷分群效果。對於電子書銷售數據的分析,並使用了Dynamic Time Wrapping(DTW)方法來衡量時間序列的相似性。 研究還探討了電子書銷售的週期表現是否存在同質性或規律性。根據分群結果,對各群進行分析,解釋了每個群集的銷售週期表現差異和背後的意義。並根據不同群集的書籍組成,解釋了各群的特徵。 研究結果可用於推論電子書的銷售狀態,預測未來的銷售週期表現。該研究的意義在於提供了對電子書銷售週期性的理解,以及基於分群結果的銷售預測。這些結果對於制定電子書營運策略具有參考價值。 The digital publishing industry has matured over the past few decades. In Taiwan, this development was marked by the launch of e-book services by the country's largest online bookstore, Books.com.tw, in 2017. Subsequently, in 2019, the leading international e-book platform, Amazon Kindle, entered the market for traditional Chinese language books, further expanding the digital publishing market in traditional Chinese. Over the past three years, particularly due to the COVID-19 pandemic, the industry's value has consistently increased, growing to account for 5% to 10% of the overall publishing industry's revenue. Digital publishing has become an indispensable source of income for publishers, and the domestic e-book platform landscape has become highly competitive and vibrant. Previous research in the field of digital publishing has mainly focused on topics such as interface design, industry transformation, user experience, and public library borrowing, with relatively little direct research on sales data for traditional Chinese e-books. In this study, we provide a detailed review of the development of the digital publishing industry and utilize sales data from a specific publisher's e-books from 2020 to 2022 as the basis for analysis. We apply the k-means clustering analysis method to identify patterns in e-book sales. The research process includes defining the data scope, collecting and processing the data, applying the k-means clustering method, and evaluating the clustering results. To analyze e-book sales data, we also employ the Dynamic Time Wrapping (DTW) method to measure the similarity of time series. The study also explores whether there is homogeneity or regularity in the performance of e-book sales cycles. Based on the clustering results, we analyze and explain the differences in sales cycle performance among each cluster and the underlying implications. Furthermore, we examine the characteristics of each cluster based on the composition of books within them. The findings of this study can be used to infer the sales status of e-books and predict future sales cycle performance. The significance of this research lies in providing an understanding of the periodicity of e-book sales and sales forecasting based on clustering results. These findings serve as valuable references for developing e-book business strategies. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88574 |
DOI: | 10.6342/NTU202302506 |
全文授權: | 未授權 |
顯示於系所單位: | 工業工程學研究所 |
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