臺灣博碩士論文加值系統

旅遊對人類生活具有重要意義，不僅有助於促進身心健康、增進文化交流，還能帶動全球經濟發展，而旅遊活動通常涉及大量消費，信用卡因此成為旅遊支付的主要工具之一，因此旅遊刷卡的消費金額及其相關資訊，是了解旅遊消費行為和模式的重要資訊，尤其對於銀行和金融機構的行銷更是至關重要。然而，旅遊業極易受到各種異常事件的影響，如自然災害、地緣政治風險和全球疫情等，都極易對旅遊消費產生嚴重的負面衝擊。
通過分析COVID-19疫情對銀行旅遊類交易的影響，運用中斷時間序列分析（Interrupted Time Series Analysis, ITSA），分析疫情對信用卡旅遊交易的影響。而透過ITSA能夠在發生重大事件(如疫情)前後，分析時間序列數據的變化趨勢和影響程度，識別異常事件對信用卡交易的影響，從而提供更及時的分析結果。
結果指出，當發生大規模的傳染病恐慌情況時，會使人民的工作型態改變，甚至產生失業的情形，此類的不確定性，會進一步的衝擊影響個人或者整個家庭的旅遊消費模式，研究指出高資產族群以及已婚族群的旅遊刷卡總金額會出現較大的滑落。而軍警類、青年(20-29歲)與壯年(30-39歲)這兩個年齡區間的族群，則較不受到Covid-19疫情所影響。透過本研究，金融機構未來可於大規模負面衝擊後，快速找到合適的族群進行行銷，也可快速了解那些消費族群對負面衝擊具有一定的抵抗力。

Travel holds significant importance for human life, as it not only helps promote physical and mental well-being and enhance cultural exchange but also drives global economic development. Travel activities typically involve substantial spending, making credit cards a primary tool for travel payments. Therefore, the spending amounts and related information from travel credit card transactions are crucial for understanding travel consumption behaviors and patterns, especially for banks and financial institutions' marketing strategies. However, the travel industry is highly susceptible to various abnormal events, such as natural disasters, geopolitical risks, and global pandemics, which can severely negatively impact travel spending.
By analyzing the impact of the COVID-19 pandemic on bank travel-related transactions and using Interrupted Time Series Analysis (ITSA), this study examines the effects of the pandemic on credit card travel transactions. ITSA allows for the analysis of changes in time series data trends and impact levels before and after major events (such as pandemics), enabling the identification of abnormal events' effects on credit card transactions and providing more timely analytical results.
The results indicate that large-scale infectious disease outbreaks can change people's work patterns and even lead to unemployment. Such uncertainties further impact individual or entire family travel consumption patterns. The study found that high-asset groups and married individuals experienced a more significant decline in total travel credit card spending. In contrast, groups such as military and police personnel and individuals in the 20-29 and 30-39 age ranges were less affected by the COVID-19 pandemic. Through this study, financial institutions can quickly identify suitable target groups for marketing after large-scale negative impacts and quickly understand which consumer groups have a certain level of resilience to negative shocks.

目 錄
中文摘要 i
ABSTRACT ii
目 錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 背景動機 1
1.2 研究目的 3
1.3 研究流程 4
第二章 文獻回顧 5
2.1顧客分群 5
2.2家庭生命週期 6
2.2.1 家庭生命週之定義 6
2.2.2 家庭生命週期階段之劃分 7
2.3中斷時間序列分析之相關文獻 7
第三章 研究方法 9
3.1 研究資料來源 9
3.1.1 資料蒐集區間 9
3.1.2 其他公開統計資料 9
3.2 旅遊相關交易界定與顧客分群 10
3.2.1 商家類別代碼 (Merchant Category Codes) 10
3.2.2 A銀行之旅遊相關交易MCC認定 11
3.2.3 顧客分群方式 11
3.3 新冠疫情異常事件 12
3.3.1 事件起訖日 12
3.3.2 疫情期間發生之異常事件 13
3.4 中斷時間序列分析 (Interrupted Time Series Analysis) 21
第四章 研究結果 23
4.1 回撤 (Drawdown) 23
4.2 中斷時間序列分析 (Interrupted Time Series Analysisn) 30
4.2.1實證迴歸之敘述性統計結果 30
4.2.2 實證迴歸圖形結果解釋 35
4.2.3 各旅遊消費族群(group)的ITSA 模型實證迴歸分析 40
第五章 結論與建議 45
5.1 結論 45
5.2 研究建議 46
參考文獻 48

表目錄
表2.1 顧客分群變數類別 5
表3.1 其他公開統計資料 9
表3.2 A銀行旅遊相關回饋之MCC 11
表3.3 人口統計變數基本分群 11
表3.4 生命週期與家庭生命週期分群 12
表3.5 COVID-19疫情爆發初期(2020/1-2020/6) 13
表3.6 國內首次社區傳染期(2021/4-2021/9) 16
表3.7 國內Omicron疫情爆發時期 (2022/3-2022/8) 19
表4.1 各類型的 Drawdown數值 28
表4.2 Total Amount的ITSA 模型實證迴歸 30
表4.3 Total Transactions的ITSA 模型實證迴歸 31
表4.4 Married Amount的ITSA 模型實證迴歸 32
表4.5 Unmarried Amount的ITSA 模型實證迴歸 32
表4.6 Married Female Amount-年齡分類的ITSA 模型實證迴歸 33
表4.7 各旅遊消費族群(group)的總旅遊刷卡金額ITSA模型實證迴歸 41
表4.8 各旅遊消費族群(group)的總旅遊刷卡筆數 ITSA模型實證迴歸 43

圖目錄
圖1.2 每月新增本土病例數 2
圖1.3 本研究之流程 4
圖3.1 COVID-19疫情爆發初期(2020/1-2020/6) 13
圖3.2 國內首次社區傳染期(2021/4-2021/9) 16
圖3.3 國內Omicron疫情爆發時期 (2022/3-2022/8) 19
圖3.4 ITSA分析圖例 21
圖4.1 Total Amount與Total Transactions的Drawdown 23
圖4.2 各年齡層分布旅遊刷卡交易金額(Age Amount)的Drawdown 24
圖4.3 年薪分布旅遊刷卡交易金額(Income Amount)的Drawdown 25
圖4.4 已婚/未婚旅遊刷卡交易金額(Married/Unmarried Amount) 的Drawdown 26
圖4.5 已婚女性旅遊刷卡交易金額(Married Female Age Amount)-年齡分類的Drawdown 27
圖4.6 Total Amount (總旅遊刷卡金額)的ITSA 模型實證迴歸圖 35
圖4.7 Total Transactions (總旅遊刷卡筆數)的ITSA 模型實證迴歸圖 36
圖4.8 Married Amount的ITSA 模型實證迴歸圖 37
圖4.9 Unmarried Amount的ITSA 模型實證迴歸圖 37
圖4.10 Married Female Amount-年齡分類的ITSA 模型實證迴歸圖 39

參考文獻
中文文獻
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張芳華，2008，已婚婦女就業影響因素之探討：不同家庭生命週期的檢視，臺北大學社會學系碩士論文。
游慧仙，2007，家族治療之家庭生命週期(family life cycle)概念，網路社會通訊期刊，第64期。
江秀瓊，2009，台灣家計單位非酒精飲料消費之實證研究，國立中正大學國際經濟系碩士論文。

英文文獻
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其他資料來源
Merchant Category Codes (MCC): Definition, Purposes, and Examples(2023年8月17日)，Julia Kagan。2023年11月5日檢索：
https://www.investopedia.com/terms/m/merchant-category-codes-mcc.asp

台灣疫情期間每月新增病例數統計(2023年1月18日)報導者The Report。2023年11月5日檢索：
https://www.twreporter.org/a/covid-19-third-anniversary-coverage-data