我們對於類別的概念構築了生活的每一個部分，在普遍以類別來分類的市場 中，多數研究聚焦在產品層級的討論，對於類別層級的討論相對較少。然而，聚焦 在產品層級的討論，或者只從單一市場參與者和產品的討論，限制了我們綜觀整個類別演化的過程，不易理解類別在出現與消逝的過程中發生何種變化。
本文藉由美國 Goodreads 書籍評論網站的書籍與評論數據，結合三個具代表性 的知名圖書獎，並加入亞馬遜暢銷書排行榜數據，建構了一個 2007 年到 2021 年 的 panel data 數據集，採用固定效果卜瓦松迴歸模型測量獎項授予類別地位後是否 影響生產者與觀眾對於類別的選擇，以及對於類別內部階層的影響，進而找出類別 變化的過程。透過檢視生產者與觀眾對於類別地位認可的反應，期望能填補理論中 類別隨時間演變過程中所空缺的成長的概念。從數據結果中發現獎項確實影響生 產者和觀眾對於產品類別的選擇，而類別的成長則來自於生產者與觀眾對於類別 獲得地位後更廣泛的標籤使用。

Our conception of categories structures every part of our lives, and among markets that are generally categorized by categories, most studies focus on the product level, with less discussion of the category level. However, focusing on the product level, or on a single set of market participants and products, limits our understanding of the evolution of entire categories, and makes it difficult to understand how categories change between emergence and demise. This paper constructs a panel data set from 2007 to 2021 comprising Goodreads' book and review data, combining three representative and well-known book awards, and adding Amazon's bestseller list data to measure whether the awards' conferring of category status affects producers' and audiences' choices of categories by fixed-effects Poisson model, as well as the impact on the levels within categories, to find out the process of category evolution. By examining producers' and audiences' reactions to the conferring of category status, it is hoped to fill in the gaps in the concept of category growth. The data results show that awards do influence producers' and audiences' choices of product categories, and that category growth comes from the wider use of labels by producers and audiences as categories obtain conferred status.

Thesis Validation Letter i
誌謝 ii
摘要 iii
Abstract iv
1. Introduction 1
2. Literature Review 6
2.1 Market Intermediaries and Status 6
2.2 The Impact of Market Intermediaries on Producers and Categories 8
2.3 The Impact of Market Intermediaries on Audiences and Review labels of Categories 10
2.4 The Impact of Market Intermediaries on the Structural Composition within Categories 13
3. Data and Methods 17
3.1 Why Choose the Book Market? 17
3.2 Source of Data 18
3.2.1 Goodreads 18
3.2.2 Book Awards 19
3.2.3 Amazon's Bestseller 20
3.3 Data Overview 20
3.3.1 Data Scraping Method 20
3.3.2 The Time Period of Data Scraping 22
3.3.3 Data Screening and Cleaning 22
3.3.4 The Taxonomy of Goodreads's Genres 23
3.4 Variables 24
3.4.1 Dependent Variables 24
3.4.2 Independent Variables 25
3.4.3 Control Variables 26
3.5 Statistic Analysis 27
4. Results 30
4.1 Results for Hypothesis 1 31
4.2 Results for Hypothesis 2 33
4.3 Results for Hypothesis 3 36
5. Discussion and Conclusion 38
References 42
Appendix 46

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