隨著時尚產業面臨環境和社會影響，永續時尚的概念越來越被大眾所接受。為了在市場上獲得相對於快時尚業務的競爭優勢，永續時尚企業可以優化其價值鏈。在本研究中，我們將研究改善永續服裝產品的營運活動以及行銷和銷售活動。
價值鏈中的營運活動包括設計、裁剪和將布料組裝成服裝產品。永續時尚的未來可能會朝著永續紡織品邁出重大一步。然而，這方面的研究仍然有限。因此，本研究的第一階段使用多標準決策來評估購買新一代紡織產品的標準。首先，透過回顧以往研究的主要成果，建立了4個屬性和10個子屬性的層次結構和網絡結構。其次，利用層次分析法（AHP）和網絡分析法（ANP）來確定每個屬性和子屬性的重要性權重。 6位在永續時尚領域擁有7年以上經驗的專家參與了這一步驟。研究結果表明，專家將製造流程列為選擇新一代紡織品時最關鍵的因素。此外，耐用性和感官是第二和第三重要標準，而價格被認為是最不重要的。對於子屬性，道德勞動實踐、持久、水和能源消耗以及可生物降解被認為是重要的。這結果顯示新一代紡織品的研發應聚焦在這些面向。
另一方面，價值鏈中的行銷和銷售活動向潛在客戶通報產品並說服他們購買。社交網路服務（SNS）因其以視覺為中心的平台和廣泛的用戶群而成為行銷的強大工具。 Instagram 是最受歡迎的 SNS 平台之一，在促進商業行銷方面發揮著重要作用。 Instagram 影響者是指因其技能、魅力或創新而在 Instagram 上獲得大量粉絲的人。近年來，專門從事永續時尚的影響者數量不斷增加。他們透過永續的時尚內容影響大眾，但目前尚未有這方面的研究。因此，本研究的第二階段旨在透過研究永續時尚影響者 Instagram 貼文和追隨者評論中的基本主題來解決這一差距。本研究使用了自然語言處理技術。我們分析了從 10 位 Instagram 永續時尚影響者收集的 400則貼文內容的資料集。首先，我們使用稱為潛在狄利克雷分配（LDA）的主題建模技術來識別抽象主題。此外，我們根據比例並使用Rank-1方法對這些問題進行排名。其次，為了確定評論中使用者的情緒基調，我們使用 SentiStrength 軟體進行情緒分析。結果，找到了 9 個主題：服裝推薦、永續時尚實施、時裝週、黑色星期五活動、有害勞動行為、永續時尚業務、節儉時尚/升級改造服裝、氣候變遷和合作。總共有 34.7% 的正面評論、52.3% 的中立評論和 13.0% 的負面評論。永續時尚業務經理在未來與影響者合作時可以考慮這些結果

As the fashion industry faces with its environmental and social impacts, the concept of sustainable fashion is accepted by the public more and more widely. To gain competitive advantages over fast fashion business in the market, sustainable fashion businesses can optimize their Value Chain. In this research, we will study the aspects of improving Operations activities and Marketing and Sales activities for sustainable apparel products. 
The Operations activities in Value Chain includes designing, cutting and assembling the fabric into an apparel product. Future of sustainable fashion may see a significant move toward sustainable textiles. However, the research in this area is still limited. Therefore, the first phrase of this study evaluates criteria in purchasing new-generation textile products using Multi-criteria decision making. First, through reviewing key findings from previous researches, a hierarchy structure and network structure of 4 attributes and 10 sub-attributes was established. Second, using the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP), the importance weight of each attribute and sub-attributes was determined. 6 experts with over seven years in the field of sustainable fashion took part in this step. The findings demonstrated that experts rank Manufacturing process as the most crucial factor when choosing new-generation textiles. Furthermore, Durability and Sensory as the second and third important criteria, while Price is deemed to be of the least important. For sub-attributes, Ethical Labor Practices, Long-lasting, Water and Energy Consumption and Biodegradable are considered important. This result indicates that the new-generation textile research and development should focus in those aspects. 
On the other hands, Marketing and Sales activities in Value Chain inform potential customers about the product and persuade them to buy it. Social Network Service (SNS) has become a powerful tool in marketing due to its visual-centric platform and extensive user base. Instagram, one of the most popular SNS platform, plays a significant role in boosting business marketing. Instagram influencers are people who have gained a large following on Instagram because of their skill, charisma, or innovation. In recent year, the number of influencers specialized in sustainable fashion is increasing. Their influence the public with sustainable fashion content. However, there have not been research on this aspect. Therefore, the second phase of this study aims to address this gap by examining the underlying topics in sustainable fashion influencers Instagram posts and follower’s comment. Natural Language Processing techniques were used in this study. A dataset of 400 post contents gathered from 10 Instagram sustainable fashion influencers was analyzed. First, we identify the abstract topics using a topic modeling technique called Latent Dirichlet Allocation (LDA). Furthermore, we rank these issues based on proportion. Second, in order to determine the user's emotional tone in the comments, we employ sentiment analysis with SentiStrength software. As a result, 9 topics were found: Outfits recommendation, Sustainable fashion implementation, Fashion Week, Black Friday event, Harmful labor practices, Sustainable fashion business, Thrift fashion/ upcycling clothes, Climate change and Collaboration. In total, there are 34.7% positive comments, 52.3% neutral comments and 13.0% negative comments towards those topics. Sustainable fashion business managers can consider these results when collaborating with influencers in the future.

Table of Contents
Table of Contents	i
List of Tables	iii
List of Figures	iv
Chapter 1	Introduction	1
1.1	Research background and objectives	1
1.2	Research questions	5
1.3	Research process	6
Chapter 2	The first study	8
2.1	Literature review	8
2.1.1	New-generation textiles	8
2.1.2	Instrumental attributes	10
2.2	Methods	13
2.2.1	Research process	13
2.2.2	Selecting sub-attributes under Manufacturing process	13
2.2.3	Selecting sub-attributes under Durability	15
2.2.4	Selecting sub-attributes under Sensory: Touch and Sight	16
2.2.5	Selecting sub-attributes under Price	17
2.2.6	Analytic Hierarchy Process	21
2.2.7	Analytic Network Process	25
2.3	Results	27
2.3.1	Analytic Hierarchy Process	27
2.3.2	Analytic Network Process	37
Chapter 3	The second study	48
3.1	Literature review	48
3.1.1	Instagram influencers	48
3.1.2	Natural language processing	50
3.2	Methods	51
3.2.1	Collecting data	51
3.2.2	Text pre-processing	57
3.2.3	Topic modelling with LDA	59
3.2.4	Sentiment analysis	60
3.3	Results	61
3.3.1	Finding topics	61
3.3.2	Topic distribution	66
3.3.3	Topic ranking	67
3.3.4	Applying SentiStrength	69
Chapter 4	Discussion	72
4.1	The first study	72
4.2	The second study	75
Chapter 5	Managerial implications, limitations and future research	76
5.1	Managerial implications	76
5.2	Limitations and future research	77
References		79

 
List of Tables
Table 1 Attributes and sub-attributes with definitions	19
Table 2 Saaty’s fundamental scale. Source: Saaty (1987)	23
Table 3 Random Index (RI). Source Saaty (2008)	24
Table 4 Descriptive data of interview experts	28
Table 5 Pairwise comparison of four attributes after geometric	30
Table 6 Weights and ranking of each attribute	31
Table 7 Calculating the consistency ratio (CI)	32
Table 8 Relative weights of sub-attributes	35
Table 9 The pairwise comparisons of attributes with respect to “Manufacturing process”	39
Table 10 The pairwise comparisons of attributes with respect to “Durability”	39
Table 11 The pairwise comparisons of attributes with respect to “Sensory”	39
Table 12 The pairwise comparisons of attributes with respect to “Price”	39
Table 13 The super-matrix of attributes before convergence	40
Table 14 ANP ranking of attributes	41
Table 15 The super matrix of sub-attributes before convergence	44
Table 16 The super-matrix of sub-attributes after convergence	46
Table 17 Descriptive statistics value of Instagram influencers accounts and posts	55
Table 18 A comparison of a sentence’s before and after text pre-processing	59
Table 19 Top 30 words in each 9 topics	64
Table 20 Topic description	65
Table 21 Topic ranking by proportions	69
Table 22 Comparisons of the finding of AHP and ANP	73
 
List of Figures
Figure 1 Porter’s Value Chain Model (Source: Porter, 1985)	2
Figure 2 Research process	7
Figure 3 AHP model for ranking attributes and sub-attributes	27
Figure 4 AHP questionnaire for attributes	29
Figure 5 Experts’ opinions of 4 attributes	30
Figure 6 Normalized pairwise comparison matrix and achieve weights in MS Excel	31
Figure 7 AHP questionnaire for sub-attributes	33
Figure 8 Geometric values of sub-attributes pairwise comparison	34
Figure 9 The interdependence among attributes	37
Figure 10 ANP questionnaire for attribute	38
Figure 11 Input value for comparisons with respect to Manufacturing process	40
Figure 12 The super matrix of attributes after convergence	41
Figure 13 The interdependence among sub-attributes	42
Figure 14 Example of ANP questionnaire for sub-attributes	42
Figure 15 The pairwise comparisons of each sub-attribute	43
Figure 16 Inputting sub-attributes network in SuperDecisions software and ANP results	45
Figure 17 Searching for Instagram influencers	52
Figure 18 Example of an Instagram post scraping	54
Figure 19 Example of comments under influencer’s post	57
Figure 20 Latent Dirichlet Allocation (Source: Blei et al, 2003)	60
Figure 21 Coding for text pre-processing	61
Figure 22 Coding for finding topics number	62
Figure 23 LDA tuning	62
Figure 24 Coding for listing top 30 words in each topic	63
Figure 25 Coding for topic distribution	66
Figure 26 Frequency distributions of topics in each Instagram influencer	66
Figure 27 Coding for topic ranking	68
Figure 28 Sentiment classification distribution of users comment in each account post	71
Figure 29 Sentiment classification distribution of users comment in the whole corpus	71

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