Ứng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàng (LV thạc sĩ)

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Ứng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàngỨng dụng phân tích dữ liệu lớn trong thương mại điện tử góc nhìn từ phía doanh nghiệp và khách hàng 國立屏東科技大學熱帶農業暨國際合作系 Department of Tropical Agriculture and International Cooperation National Pingtung University of Science and Technology 博士學位論文 Ph.D Dissertation 以企業跟顧客的觀點來探討大數據分析對電子商務的衝擊 Applying Big Data Analytics in E-commerce: Aspects of Business and Customer 指導教授 Advisor: 廖世義博士(Shu-Yi Liaw, Ph.D.) 研究生 Student: 黎氏梅 (Le Thi Mai) 中華民國 107 年 06 月 01 日 June 1, 2018 摘要 學號:P10322019 論文名稱:以企業跟顧客的觀點來探討大數據分析對電子商務的 衝擊 總頁數:151 頁 學校名稱:國立屏東科技大學 系(所)別:熱帶農業暨國際合作系 畢業時間及摘要別:106 學年度第 學期博士學位論文摘要 研究生:黎氏梅 指導教授:廖世義 博士 論文摘要內容: 大數據分析應用已經在許多已開發國家中被各產業領域應用著。這 種新的分析工具提高了專家和研究人員對商業價值和企業挑戰的使用動 機。然而,目前研究在這部分較為缺乏以商業視角下評估大數據分析應 用的研究。本研究主旨在(1)應用大數據分析時,對公司的意義、企 業特色、企業價值和企業挑戰進行文獻回顧;(2)探索並確定應用大 數據分析在電子商務上對消費者反應的利弊影響;(3)評估知覺價值 維度和知覺風險的中介效應;(4)確定信任傾向的調節效果。通過使 用社會科學統計軟體和線性結構分析軟體進行數據分析,樣本回收越南 349 名受訪者之有效樣本。本研究從企業和客戶兩個角度進行分析。本 研究結果如下: (1)該研究綜合了多種大數據分析概念,為大數據分析在電子商務公 司的應用提供更深入的見解。值得強調的是近年來與電子商務相關的大 數據分析興趣增加。 大數據分析在電子商務中的應用可以分為創建透明 度、發現需求和提高績效、細分市場、更好的決策、新產品或商業模式 創新等五個方面。這些應用程序帶來了許多商業價值,但也會對其他想 要應用大數據分析的電子商務業者帶來一些挑戰。 I (2)研究結果發現訊息搜索、推薦系統、動態定價和客戶服務對消費 者反應有不同顯著的影響,整體而言,訊息搜索對消費者意向及改變消 費者行為的影響最大,而動態定價、推薦系統和客戶服務也對消費者意 向有顯著的影響,但消費者行為卻會降低。而另一方便,隱私、安全、 購物成癮和群眾效應對消費者反應有不同顯著的負面影響。具體而言, 購物成癮與群眾效應、隱私及安全相比,購物成癮對消費者意向及行為 都有具大的影響。因此不可否認的是,消費者正同時接收正面及負面的 影響。 (3)研究結果證實,功能和情感價值是大數據分析的積極性與消費者 反應之間關係的重要中介變數。但功能價值的中介效果與情感價值並無 顯著差異。這是一個重大的發現,現在的消費者不僅可以找到自己喜歡 的產品或服務,還可以享受在網上購物的趣味性。因此,如何有效地運 用大數據分析來促發消費者的功能價值和情感價值,這是給電子商務業 者的一個方向。 (4)研究發現,知覺風險不會調節大數據分析的負面因素與消費者反 應之間的關係。此外,客戶的信任傾向可以緩解大數據分析的負面因素 與客戶反應之間的關係及消費者感知到的風險。高信任傾向的消費者比 低信任傾向的反應更強烈。由於消費者對大數據分析應用的信任,因此, 當負面因素和知覺風險上升時,很容易對消費者行為有負面影響。 本研究有助於在以企業角度和消費者角度下增進對大數據分析應用 的理解,這提供給電商業者發展永續的消費者市場之重要作用。電子商 務可以依靠大數據分析來提升消費者行為,但過度使用可能會有一些負 面的影響。除此之外,本研究對未來的後續研究建議,理論和實踐方面 的挑戰進行了更廣泛的討論。 關鍵字:電子商務、大數據分析、消費者行為、知覺價值、知覺風險、 信任傾向 II ABSTRACT Student ID: P10322019 Title of Dissertation: Applying Big Data Analytics in E-commerce: Aspects of Business and Customer Total Page: 151 pages Name of Institute: Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology Graduate Date: June 1, 2018 Degree Conferred: Doctoral Degree Name of Student: Le Thi Mai Advisor: Liaw, Shu-Yi, Ph.D The Contents of Abstract in This Dissertation: The era of Big Data analytics (BDA) has begun in most industries within developing and developed countries This new analytics tool has raised motivation for experts and researchers to study its impacts to business values and challenges However, there is shortage of studies which evaluate the applications of BDA under business view and help to understand customers’ views towards the applications of Big Data analytic This research aims to (1) draw on a systematic review of the literature about definition, distinctive characteristics, business values and challenges of a company when applying Big Data analytics, (2) explore and determine the pros and cons of applying Big Data analytics that affects customers’ responses in an e-commerce environment, (3) evaluate the mediation effect of perceived value’s dimensions and perceived risk, (4) determine the moderation effect of trust propensity Data analyses were conducted by using the statistical package for social sciences and analysis of moment structures software in useful sample of 349 respondents in Vietnam Two aspects as business and customer views are reviewed, explored, discussed in this study III The major findings include: (1) The study synthesized diverse BDA concepts that provide deeper insight about application of BDA for e-commerce firms It is highlight that the increase in interest related to BDA in e-commerce in recent years BDA applications in e-commerce can be divided into five aspects like as creating transparency, discovering needs and improving performance, segmenting market, better decision making, new product or business model innovation These applications bring many business values but also raise some challenges when e-firms want to apply BDA (2) The findings found that information search, recommendation system, dynamic pricing, and customer services had different significant positive effects on customers’ responses Specifically, information search had a highest significant influence on customers’ intention and improved customers’ behavior Following by dynamic pricing, recommendation system and customers’ service also had significant impact on customers’ intention but decreased customers’ behavior On another hand, privacy and security, shopping addiction, and group influences were found to have different significant negative effects on customers’ responses Specifically, shopping addiction had a drastic change from intention to behavior compared to group influences and privacy and security It cannot be denied that customers receive positive and negative factors at the same time (3) The results confirmed that functional and emotional values play mediating roles between positive of applying BDA and consumers’ responses However, there weren’t significant different between mediator effect of functional value and emotional value This finding highlights the notification that customers nowadays not only find their products or services but also seek enjoyment when online shopping under Big Data era Therefore, e-firms should increase perceived value based on creasing equally functional and emotional values IV (4) The study found out that perceived risk don’t act mediate the relationship between negative of applying BDA and consumers’ responses Besides, customers’ trust propensity was found to moderate the relation of negative factor of applying BDA to customers’ responses and perceived risk to customers’ responses High trust propensity participants reported stronger responses than those with low trust propensity It due to customers’ trust on new applications of BDA, hence, it is easy to influence on customers as their negative response when negative factor and perceived risk are rising This study contributes to improve understanding of applications of Big Data Analytics under business view and customer view This could play an important role to develop sustainable consumers market E-vendors can rely on Big Data analytics but over usage may have some negative applications Besides that, the research also broader discussion regarding future research opportunities, challenges in theory and practice Keywords: E-commerce, Big Data Analytics, Customers’ Responses, Perceived Value, Perceived Risk, Trust Propensity V ACKNOWLEDGEMENTS This study has been carried out at the Department of Tropical Agriculture and International Cooperation (DTAIC), National Pingtung University of Science and Technology (NPUST), Taiwan This is the outcome of knowledge that I received from this university, my continuous efforts to learning, and consistent guidance of my advisor Firstly, I would like to express my sincere gratitude to my advisor, Professor Shu-Yi Liaw for continuous support of my Ph.D study and related research He has given me valuable guideline, patience, assistance, motivation and inspiration during Ph.D time His intellectual direction and critical reviews of research works helps me all the time and find a right tract towards the successfully competition of this dissertation He is the best teacher I have met Besides my advisor, I would like to thank the rest of my advisory committee: Dr Shi-Jer Lou, Dr Rong-Fang Chen, Dr Shih-Wei Chou, and Dr Pei-Chen Sun, for their insightful comments and encouragement My sincere thanks also goes to Dr Nguyen Tuan Anh who encourage me to join Ph.D program Many thanks to Dr Joey Lee, Dr Henry Chen and other faculties who provided for their encouragement and supports during my study I would like to thank Barbara, Sylvia (OIA), Sophia, Joanna and all DTAIC staff, Yang Ya-Chu, Lin Yi-Ru and other staff of computer center for their assistants I thank my fellow classmates for the discussions and fun time we had Also thank my international friends Mediana Purnamasari (Indonesia), Mr Chuang-Yeh Huang (Johnson), Mr Edgardo, Caleb Milk Breria (P&G), Miguel, Michael Qwanafia Bilau (Solomon Islands), Rudra (Nepal), Stanley, Jimmy, Adam, Guo Wei-Peng and other my friends for their support during VI the entire study Thanks to Vietnamese student association members and the time we have fun activities together I would like to thank NPUST and Chung Hwa Rotary Education Foundation for providing me the scholarship to pursue my doctoral degree Last but not the least, I extremely grateful to my family, my boyfriend and my relatives who have always given me encouragement and support to finalize my study in abroad VII TABLE OF CONTENTS 摘要 I ABSTRACT III ACKNOWLEDGEMENTS VI TABLE OF CONTENTS VIII LIST OF TABLES XII LIST OF FIGURES XIV CHAPTER I INTRODUCTION 1.1 Background of the Study 1.2 Statement of the Problem 1.3 Objectives of the Study 1.4 Contribution of the Study 1.5 Definition of the Operation Terms 1.6 Research Flowchart 1.7 Research Systematic Discussion CHAPTER II LITERATURE REVIEW 11 2.1 Concept of Big Data in E-commerce Environment 11 2.1.1 Big Data Analytics in the E-Commerce Environment 11 2.1.2 Big Data’s Distinctive Characteristics 13 2.1.3 Types of Big Data Used in E-commerce 18 2.2 Big data analytics in E-commerce: Aspect of business 22 2.2.1 Literature Review Research Approach 23 VIII 2.2.2 Business Values of Applying Big Data Analytics for E-commerce Firms 27 2.2.3 Challenges of Applying Big Data Analytics in E-commerce 30 2.3 Big data analytics in E-commerce: Aspect of Customer 34 2.3.1 Positive Factor of Applying BDA on Customers’ Responses 35 2.3.2 Negative effects of applying Big Data analytics on customers’ responses 40 2.3.3 The Mediating Role of Perceived Value and Perceived Risk 42 2.3.4 The Moderating Effect of Individual Trust Propensity 46 2.3.5 Behavior Consumer Responses Hierarchy Models 47 CHAPTER III RESEARCH METHODOLOGY 49 3.1 Research Model and Research Hypotheses 49 3.1.1 Mechanism of Applying Big data Analysis and Customers’ Responses 49 3.1.2 Perceived Value as the mediator for Positive Factor of Applying BDA and Customers’ Responses 50 3.1.3 The Mediating Role of Perceived Risk and Moderating of Trust Propensity 52 3.2 The Operational Definition and Measurement Design 55 3.3 Research Type 60 3.4 Pilot Test 61 3.5 Sample Size 62 3.6 Data Type and Data Collection Method 63 3.6.1 Data Type 63 3.6.2 Data Collection Method 63 3.6.3 Data Collection Procedure 64 3.7 Data Analysis Techniques 65 IX APPENDICES Appendix A Big Data analytics (BDA) applications in e - commerce BDA application in Ecommerce References Number Percent age (%) Creating transparency (Beath et al., 2012), (Tankard, 2012), (Waller and Fawcett, 2013), (Kiron et al., 2014), (Mithas et al., 2013), (Ashraf et al., 2015), (Balaraman and Chandrasekar, 2016), (Lokhande and Khare, 2015), (Ngai et al., 2017), (Akter and Wamba, 2016), (Akter and Wamba, 2016), (Song et al., 2017), (Arleo et al., 2017), (Gunasekaran et al., 2017), (Taniguchi et al., 2016), (Usama et al., 2017), (Bergamaschi et al., 2017), (Chan et al., 2014), (Liu, 2015), (Pan, 2015), (Huang and Rust, 2013), (Sahu et al., 2016) 22 18% 25 20% 13 11% Discovering needs and improving performance Segmenting market (Beath et al., 2012), (Chen et al., 2012), (Davenport et al., 2012), (Demirkan and Delen, 2013), (Fisher et al., 2012), (Tankard, 2012), , (Prasanna et al., 2015), (Livinus et al., 2016), (Al-Sakran, 2014), (Akter and Wamba, 2016), (Vossen, 2014), (Ma et al., 2017), (Buettner, 2017), (Song et al., 2017), (Akhbar et al., 2016), (He et al., 2016), (Gunasekaran et al., 2017), (Wamba et al., 2017), (Qi et al., 2016), (Singh et al., 2017), (Katarya and Verma, 2017), (Ragunathan et al., 2015), (Almohsen and Al-Jobori, 2015),(Chen et al., 2016), (Prando and de Souza, 2016) (Davenport et al., 2012), , (Wixom et al., 2013), (Beath et al., 2012), (Griffin, 2012), , (Tankard, 2012), (Wixom et al., 2013), (Constantiou and Kallinikos, 2015), (Akter and Wamba, 2016), (Ma et al., 2017), (Akhbar et al., 2016), (Wamba et al., 2017), (Yang and Shang, 2015), (Pabedinskaite et al., 2014) 137 BDA application in Ecommerce Better decision making New product or business model innovation References Number Percent age (%) (Beath et al., 2012), (Fisher et al., 2012), (Boyd and Crawford, 2012), (Demirkan and Delen, 2013), , (Griffin, 2012), (Sharma et al., 2014), (Kung et al., 2013), (Mithas et al., 2013) (Waller and Fawcett, 2013), (George et al., 2014), (Ashraf et al., 2015), (Addepalli et al., 2016), (Al-Sakran, 2014), (Sharma and Toshniwal, 2017), (Sun et al., 2017), (Tian and Liu, 2017), (Segarra et al., 2016), (Agrawal, 2014), (Ngai et al., 2017), (Akter and Wamba, 2016), (Ma et al., 2017), (Song et al., 39 31% 25 20% 124 100% 2017), (Akhbar et al., 2016), (Gunasekaran et al., 2017), (Wamba et al., 2017), (Qi et al., 2016), (Watkins, 2016), (Singh et al., 2017), (Ragunathan et al., 2015), (Kaur, 2015), (Gupta and Pathak, 2014), (Tao et al., 2017), (Shaozeng, 2015), (Kundu and Garg, 2015), (Zhang and Zhu, 2014), (Zhao et al., 2015), Pabedinskaite, Davidaviciene, & Milisauskas, 2014), (Sahu et al., 2016), (Kannan and Raja, 2016) (Ohata and Kumar, 2012), (Tankard, 2012), (Ohata and Kumar, 2012), , (Constantiou and Kallinikos, 2015), (Kiron et al., 2014), (Lim et al., 2013), (Fisher et al., 2012), (Wixom et al., 2013), (Kung et al., 2013), (Leavitt, 2013), (Livinus et al., 2016), (Balaraman and Chandrasekar, 2016), (Sharma and Toshniwal, 2017), (Lokhande and Khare, 2015), (Tian and Liu, 2017), (Akter and Wamba, 2016), (Akhbar et al., 2016), (Wamba et al., 2017), (Qi et al., 2016), (Gupta and Pathak, 2014), (Fan et al., 2017), (Anjali and Binu, 2014), (Sun et al., 2015), (Bühler et al., 2015), (Montgomery, 2015) Total 138 Appendix B QUESTIONNAIRE (English Version) Number: DISSERTATION RESEARCH Pros and Cons of Applying Big Data Analytics for Business and Customer in E-commerce context Dear Respondent, I am undertaking a research project to determine the effects on e-commerce users’ perception when e-vendors use Big Data analytics application I kindly request you can complete the following questionnaire regarding your feeling, thinking, preferences and your attitudes towards e-commerce service The following part is instruction to complete this survey Step Looking through the survey to ensure that you know what will you have to complete for survey This survey will ask you some factors: Recommendation system, Information Search, Dynamic Price, Customer Service, Perceived value, Privacy and Security, Shopping Addiction, Group influences, Perceived Risk, and Customers’ responses Each factor we will explain what meaning, example is and how to measure your feeling Please pay attention Step Navigate to the Amazon website (www.amazon.com) which is one of famous websites using big data analytics application This action is required to at least times on computer and goes through the procedure of buying any product on the website, but not actually purchase Step Complete the survey Your participation is vital to scientific research and is greatly appreciated Thank you very much in advance! Yours sincerely, Le Thi Mai Ph.D Student Supervisor: Dr Shu-Yi Liaw Associate Professor of College Management National Pingtung University of Science and Technology 139 Fill the blank with (X) sign on the answer you think most appropriate Demographic/ Personal Data Gender: Male Female   How many times last month you access e-commerce website?  Not at all  1-2 times  3-4 times  more than times  Kind of product was chosen for interaction with Amazon website  (1) Lightweight on-Ear Headphones – White  (2) Halston Heritage Women's Strapless Dress with Flared Skirt Please circle your response for each of the following items based on the following scale: Strongly Disagree Disagree disagree somewhat Neutral Agree Agree somewhat Strongly Agree Part A Based on your perception or ideas after interaction with e-vendor website, you feel the positive applications of Big Data analytics, using following statements as your consideration or your thinking So please give your agreement level for the following statements No IS1 IS2 IS3 IS4 RS1 RS2 I am able to search the useful information in the e-shopping website The information I search in the e-shopping site are detailed and completed Search result is provided quickly and fit to my need Search result is provided by shopping website is very realistic Shopping website can recommend substitute goods for the product I want to buy Shopping website can recommend 140 7 7 7 RS3 RS4 PD1 PD2 PD3 PD4 CS1 CS2 CS3 CS4 complementary goods for the product I want to buy Shopping website can recommend for you some products may be you like or best sellers of website I believe that the recommendation information is an act of kindness Providing different prices for individual customers at the same time Offer different prices at different times Providing different prices with different substitute products Providing different prices with different conditions on the same product The website can provide channels to support customers I expect that I am able to track my order 7 7 7 7 The shopping website which provides virtual experience can let me choose more suitable goods I can refer to the reviews of customers who bought the products before 141 Part B Based on your perception or ideas after interaction with e-vendor website, you feel the negative applications of Big Data analytics, using following statements as your consideration or your thinking So please give your agreement level for the following statements No PS1 PS2 PS3 PS4 SA1 SA2 SA3 SA4 SA5 Statement Online retailers may disclose my personal information (e.g email address, mailing address) to other companies Attracting a great deal of attention from cyber criminals Customer’s personal information will be stolen My information about payment method will be stolen Spending a lot of time to review products 7 7 I have often bought a product that it is not my intention As soon as I enter a shopping website, I have an irresistible urge to go into a shop and buy something I have felt somewhat guilty after buying a product, because it seems unreasonable There were some products that I bought, but I not show them to anyone for fear of being GI1 GI2 GI3 GI4 perceived as irrational in my buying behavior When I buy a product online, the reviews presented on the website are helpful for my decision making Reviews posted on the website affect my purchase decision Rating about usefulness of reviewers affects my purchase decision Presentation of the reviews affects my purchase decision 142 7 7 Part C Based on your perception after interaction with e-vendor website, using following statements as your consideration or your thinking So please give your agreement level for the following statements No Statement PV1 Information obtained from e-vendor website are easy to understand and useful PV2 I can buy product which I want from shopping website PV3 When using the shopping website, I feel relaxed and enjoy my time PV4 I feel I can save time to buy product on this evendor website 7 7 Part D Based on your perception after interaction with e-vendor website, using following statements as your consideration or your thinking So please give your agreement level for the following statements No Statement PR1 I am afraid that online purchase is risky because the product/service may fail to meet my expectation PR2 I believe that online purchases are risky because I will spend more money to buy other products PR3 I believe that online purchases are risky because I have to spend more time to view substitute and complementary products PR4 I believe that online purchases are risky because my personal information and credit information will be stolen 143 7 7 Please choose level of your trust propensity on something/someone Low High Part E Please indicate your agreement level with each of the following statements CA1 The applications on website catches my attention CA2 I had trying to read that information CI1 Continuously pay attention CI2 I want to get more information CD1 I want to buy the product CD2 I will continue to use this webpage for shopping CA3 I will have action to buy CA4 I will introduce this webpage to my friends and family 144 Appendix C QUESTIONNAIRE (Vietnamese Version) Số phiếu: Xin chào anh chị, Nhằm đánh giá tác động tích cực tiêu cực ứng dụng phân tích liệu lớn vào dịch vụ thương mại điện tử Anh/Chị vui lòng dành chút thời gian quý báu trả lời câu hỏi Xin chân thành cảm ơn! Hướng dẫn thực hiện: Bước Trước trả lời câu hỏi điều tra anh chị ý hỏi câu hỏi liên quan đến yếu tố tích cực như: Information Search(thơng tin tìm kiếm), Recommendation System (hệ thống giới thiệu), Price Dynamics (giá động), Customers Services (dịch vụ khách hàng), giá trị nhận (Perceived Value), xu hướng tin tưởng (Trust propensity), lòng tin (Customer Trust) ; yếu tố tiêu cực như: Privacy and Data Security (Tính riêng bảo mật liệu), Shopping Addiction (Gây nghiện mua sắm), Group influences (Ảnh hưởng nhóm), rủi ro nhận (Perceived Risk), Sự nghi ngờ (Customer distrust) từ đưa phản ứng khách hàng (Customers’ Responses) Bước Thực tương tác với trang web Amazon (www.amazon.com) sau: Lựa chọn sản phẩm (1) Lightweight on-Ear Headphones – White (2) Halston Heritage Women's Strapless Dress with Flared Skirt Thực tương tác việc tìm kiếm sản phẩm trang web Amazon đến tận bước cuối tốn tiền hàng khơng thực tốn ý đến ứng dụng có liên quan đến thơng tin tìm kiếm, giới thiệu, giá cả, dịch vụ khách hàng từ suy nghĩ liệu có tác động tiêu cực khơng Giả định rằng: bạn có ý định tìm kiếm mua sản phẩm có riêng khoản tài để mua Bước Hồn thành câu hỏi Hãy khoanh tròn câu trả lời bạn cho mục sau dựa thang điểm sau: 145 Rất không đồng ý Không đồng ý Không đồng ý phần Trung lập Đồng ý phần Đồng ý Rất đồng ý Phần Yếu tố tích cực việc ứng dụng phân tích liệu lớn Thơng tin tìm kiếm IS1 Tơi tìm kiếm thơng tin hữu ích trang web IS2 Các thông tin tìm kiếm cụ thể đầy đủ IS3 Kết cung cấp nhanh phù hợp với nhu cầu IS4 Kết cung cấp thực tế 7 Hệ thống giới thiệu RS1 Trang web giới thiệu sản phẩm thay cho sản phẩm tơi muốn mua RS2 Trang web giới thiệu sản phẩm bổ sung cho sản phẩm muốn mua RS3 Trang web giới thiệu số sản phẩm tơi thích bán tốt trang web RS4 Tôi tin tưởng thơng tin giới thiệu hành động lòng tốt 7 7 Giá động PD1 Cung cấp giá khác cho đối tượng khách hàng cá nhân thời điểm PD2 Cung cấp giá khác thời điểm khác PD3 Cung cấp giá khác sản phẩm thay khác PD4 Cung cấp giá khác cho sản phẩm với điều kiện khác 146 7 7 Dịch vụ khách hàng Trang web cung cấp kênh để hỗ trợ khách CS1 hàng CS2 Tôi mong muốn theo dõi đặt hàng tơi CS3 Trang web mua sắm cung cấp trải nghiệm ảo giúp tơi chọn hàng hóa phù hợp CS4 Tơi tham khảo ý kiến khách hàng mua sản phẩm trước 7 7 Giá trị nhận PV1 Thơng tin có từ trang web nhà cung cấp dễ hiểu hữu ích PV2 Tơi mua sản phẩm mà muốn từ trang web mua sắm PV3 Khi sử dụng trang web mua sắm cảm thấy thư giãn tận hưởng sống PV4 Tơi cảm thấy tiết kiệm thời gian mua sắm 7 7 Xu hướng tin tưởng Vui lòng lựa cho mực độ xu hướng tin tưởng bạn vào người điều TP Thấp Cao 147 Phần Yếu tố tiêu cực việc ứng dụng phân tích liệu lớn Tính riêng bảo mật liệu PS1 Các nhà bán lẻ trực tuyến tiết lộ thơng tin cá nhân tơi (ví dụ địa email, địa gửi thư) cho công ty khác PS2 Thu hút nhiều ý từ giới tội phạm mạng PS3 Thơng tin cá nhân khách hàng bị đánh cắp PS4 Thông tin việc tốn bị đánh cắp 7 Gây nghiện mua sắm SA1 Dành nhiều thời gian để xem xét sản phẩm SA2 Tôi thường mua sản phẩm mà thực không cần cần thiết 7 SA3 Ngay sau nhập vào trang web mua sắm, tơi có thơi thúc khơng thể cưỡng lại để vào cửa hàng mua SA4 Tơi cảm thấy chưa thực thích đáng sau mua sản phẩm, khơng hợp lý Ảnh hưởng nhóm GI1 Khi tơi mua sản phẩm trực tuyến, ý kiến sản phẩm trình bày trang web hữu ích cho việc định GI2 Nhận xét đăng trang web ảnh hưởng đến định mua hàng GI3 Xếp hạng khách hàng hữu dụng ảnh hưởng đến định mua hàng GI4 Sự có mặt đánh giá ảnh hưởng đến định mua hàng 148 7 Rủi ro nhận PR1 Tơi e mua hàng trực tuyến mạo hiểm sản phẩm /dịch vụ khơng đáp ứng mong đợi PR2 Tôi tin mua hàng trực tuyến mạo hiểm tơi dành nhiều tiền để mua thêm sản phẩm khác PR3 Tôi tin mua hàng trực tuyến mạo hiểm tơi phải dành nhiều thời gian để xem thay bổ sung sản phẩm PR4 Tôi cho mua hàng trực tuyến mạo hiểm thơng tin cá nhân tơi thơng tin tốn bị đánh cắp Phần Phản hồi khách hàng CA1 Những ứng dụng trang web nhận ý CA2 Tôi cố đọc thông tin cung cấp CI1 Tiếp tục ý tới thông tin ứng dụng CI2 Tôi mong muốn nhận thêm thông tin CD1 Tơi có mong muốn mua sản phẩm CD2 Tôi tiếp tục sử dụng trang web để mua sắm CA3 Tôi có hành động mua sản phẩm CA4 Tôi giới thiệu trang web cho bạn bè gia đình Điền vào chỗ trống với (X) dấu hiệu câu trả lời bạn nghĩ thích hợp Giới tính: Nam Nữ Khác Có lần tháng trước bạn truy cập trang web thương mại điện tử?  Không  1-2 lần  3-4 lần  nhiều lần Sản phẩm bạn lựa chọn để tương tác với trang web Amazon  (1) Lightweight on-Ear Headphones – White  (2) Halston Heritage Women's Strapless Dress with Flared Skirt 149 Biographical Sketch I Personal Information Name in Full: LE THI MAI Gender: Female Date of Birth: September 14, 1989 Place of Birth: Ha Tinh, Vietnam Areas of Studying and Researching: Accounting, Marketing, Big Data Applications Department: Department of Tropical Agriculture and International Cooperation Email: lemai.istnu@gmail.com II Education Ph.D Degree in Business and Management National Pingtung University of Science and Technology, Taiwan (20152018) Master of Business Administration Southern Luzon University, Philippines and Thai Nguyen University, Vietnam (2012-2014) Bachelor of Accounting & Auditing Banking Academy, Vietnam (2007-2011) III Professional Experience and Position Held 2011-2012: Collaborator at auditing of ACA group 2011-2012: Accountant at Maritime Bank (MSB) 2012-2015: Managing Staff at Academic Affairs and International Cooperation- International School-Thai Nguyen University- Vietnam 2012-2015: Teaching Assistant and Lecturer of Economic and Management Department - International School - Thai Nguyen University,Vietnam IV Professional Training Programs Banking (2011) - Topica Founder Institution, Vietnam Epage Website Construction (2014) – Computer Centre, NPUST, Taiwan 150 V Publications Le, T.M.; Liaw, S.-Y., (2017) Effects of Pros and Cons of Applying Big Data Analytics to Consumers’ Responses in an E-Commerce Context Sustainability, 9, 798 Le, T.M.; Liaw, S.-Y., (2016) How Certification Label and Website Language Affect on Purchase Intention? A Cross-culture Comparison The International Journal of Business & Management, (6) Liaw, S.-Y & Le, T M., (2017) Comparing Mediation Effect of Functional and Emotional Value in the Relationship between Pros of Applying Big Data Analytics and Consumers’ Responses International Journal of Marketing Studies, 9(4) Liaw, S.-Y & Le, T M., (2017) Under Interruptive Effects of Rarity and Mental Accounting, Whether the Online Purchase Intention Can Still Be Enhanced Even with Higher Search Costs and Perceived Risk International Journal of Business and Management; 12(8) Le, T M; Hediasri,Nur A D & Liaw, S.-Y (2018) A Cross-cultural Comparison in E bank based on Multiple Mediation of Trust Contemporary Management Research – ISSN: 1813-5498 Accepted VI International Conference Effects of Certification Label and Language Choice of Webpage on Purchase Intention International Conference on “Business, Economics, Social Science & Humanities-BESSH-2016 Seoul, Korea (02-03/07/2016) A Study of the Multiple Mediation of Trust in E-bank From a Crosscultural Comparison between Indonesia and Taiwan NETs 2017 International Conference on Internet Studies Bali Indonesia (14-16/07/2017) 151 ... responses and perceived risk to customers’ responses High trust propensity participants reported stronger responses than those with low trust propensity It due to customers’ trust on new applications
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