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

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 an

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

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。

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

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

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

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

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

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

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

1.1 Background of the Study 1

1.2 Statement of the Problem 2

1.3 Objectives of the Study 3

1.4 Contribution of the Study 4

1.5 Definition of the Operation Terms 5

1.6 Research Flowchart 6

1.7 Research Systematic Discussion 8

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

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

3.7.1 Descriptive Statistics Analysis 65

3.7.2 Reliability and Content Validity Analysis 65

3.7.3 Comparing Mean Test 66

3.7.4 Exploratory Factor Analysis 67

3.7.5 Structural Equation Model 68

3.7.6 Mediation Test 71

3.7.7 Moderation Test 72

CHAPTER IV RESULTS AND DISCUSSION 74

4.1 Descriptive Analysis and Mean Comparison 74

4.1.1 Descriptive Analysis 74

4.1.2 Mean Comparison 75

4.2 Reliability Analysis 76

4.3 Study I-Explore Positive and Negative Effects on Customers’ Responses 78

4.3.1 Exploratory Factor Analysis 78

4.3.2 Measurement model 80

4.3.3 Structural equation model 82

4.3.4 Discussion and Sub-conclusion 84

4.4 Study II-Evaluating the mediation effects of perceived value’s dimensions on relationship between PF and CR 88

4.4.1 Measurement Model 88

4.4.2 Structural Equation Model 90

4.4.3.Discussion and Sub-conclusion 93

4.5 Study III-Evaluating the Mediation Effects of PV and the Moderating of TP 95 4.5.1 Measurement Model 95

4.5.2 Structural Equation Model 97

4.5.3 Examining Moderating Effects 100

4.5.4 Discussion and Sub-conclusions 104

CHAPTER V CONCLUSIONS AND RECOMMENDATIONS 107

5.1 Conclusions of Research 107

5.2 Recommendations 110

5.3 Limitations and Future Studies Recommendation 113

REFERENCES 115

APPENDICES 137

Appendix A Big Data analytics (BDA) applications in e - commerce 137

Appendix B QUESTIONNAIRE (English Version) 139

Appendix C QUESTIONNAIRE (Vietnamese Version) 145

Biographical Sketch 150

LIST OF TABLES

Table 1 5Vs of Big Data Characteristics in business analytics 16

Table 2 Types of big data using in E-commerce 20

Table 3 Big Data analytics (BDA) applications in e - commerce 26

Table 4 Perceived value’ dimensions 44

Table 5 Dimensions and indicators of customers’ responses 56

Table 6 Dimensions and indicators of positive factor of applying BDA 57

Table 7 Dimensions and indicators of positive factor of applying BDA 58

Table 8 Dimensions and indicators of perceived value 59

Table 9 Dimensions and indicators of perceived risk 60

Table 10 Assessing Reflective Measurement Models 70

Table 11 Demographic descriptive (n = 349) 75

Table 12 T-test results by gender and survey items 76

Table 13 Anova results by experiences 76

Table 14 Reliabilities among the variables 77

Table 15 Correlation among variables 79

Table 16 Varimax-rotated component analysis factor matrix 79

Table 17 Standardized factor loadings, CR and AVE of the model 80

Table 18 The latent variable correlation matrix: discriminant validity 81

Table 19 Measurement model fit indicates 81

Table 20 Results of regression 83

Table 21 Reliability and validity of the constructs 89

Table 22 The latent variable correlation matrix: Discriminant validity 89

Table 23 Measurement model fit indicates 90

Table 24 Path comparison of indirect effects 92

Table 25 Reliability and validity of the constructs 96

Table 26 The latent variable correlation matrix: Discriminant validity 96

Table 27 Measurement model fit indicates 97

Table 28 Mediation effect of perceived risk 99

Table 29 T-Test between trust propensity groups 100

Table 30 Relationship between NF and CR, moderator effect by TP 102 Table 31 The finding of hypothesis analysis 108 Table 32 Future research questions for BDA in e-commerce 114

LIST OF FIGURES

Figure 1 Conceptual Framework of the research 8

Figure 2 The ease of capturing big data’s value, and the magnitude of its potential, vary across sectors 12

Figure 3 Characteristics and processing of Big Data 14

Figure 4 Selection criteria and evaluation framework 22

Figure 5 Distribution of articles by year 25

Figure 6 The evolution of consumer behavior 35

Figure 7 Model 1-Exploring and determining the mechanism of applying BDA 50

Figure 8 Model 2-The mediating role of perceived value 52

Figure 9 Model 3-The mediating role of perceived risk 54

Figure 10 A conceptual diagram of a simple mediation 71

Figure 11 A conceptual diagram of a simple mediator 72

Figure 12 A simple moderation model depicted as a statistical diagram 72

Figure 13 A combing mediation and moderation model depicted as a statistical diagram 73

Figure 14 A simple combing mediation and moderation conceptual model 73 Figure 15 The results of the research model 82

Figure 16 Results of regression 84

Figure 17 The results of direct effect 90

Figure 18 The results of mediation model 91

Figure 19 The results of direct effect 97

Figure 20 The results of mediation model 99

Figure 21 The results of moderating model 101

Figure 22 Moderator effect of TP in relation between NF and CR 103

Figure 23 Moderator effect of TP in relation between PR and CR 104

CHAPTER I INTRODUCTION 1.1 Background of the Study

With increasing advancement of internet technology, increasing amounts of data are streaming into contemporary organizations Data is getting bigger and more complicated because it is continuing to be generated from many devices and more sources as mobile phones, personal computers, government records, healthcare records, social media, etc An International Data Cooperation report estimated that the world would generate 1.8 zettabytes of data (1.8 × 1021 bytes) by 2011 (Gantz and Reinsel, 2011) By

2020, this figure will grow up to 35 zettabytes or more The Big Data era has arrived Why do researchers and practitioners be interested in understanding about the impacts of Big Data analytics? The simple reply

to this critical question is because Big Data enables to bring potential applications Big Data applications can help organizations; the government predicted the unemployment rate, the future trend for professional investors,

or cut spending, stimulates economic growth, etc For health care, Big Data can help to predict impact trend of a certain disease One of the most conspicuous examples of Big Data for health care is Google Flu Trend (GFT)

In 2009, Google has used Big Data to analyze and predict trends influence, spread of H1N1 flu Trend which Google drawn from the search keywords related to the H1N1 has been proven to be very close to the results from flu independent warning system Sentinel GP and Health Statistics launched The GFT program was designed to provide real-time monitoring of flu cases around the world based on Google searches that match terms for flu related activity For e-commerce firms, the inject Big Data analytics into their value

chain value 5-6% higher productivity than their competitors (McAfee et al.,

2012)

Big Data is generating remarkable attention worldwide There are some

definitions of Big Data term Manyika et al (2011) defined that Big Data as

a dataset with a size that can be captured, communicated, aggregated, stored, and analyzed Another definition is that Big Data are generated from

an increasing plurality of sources including internet clicks, mobile transactions, user generated content and social media as well as purposefully generated content through sensor networks or business transactions such

as customer information and purchase transactions (George et al., 2014)

Big Data has various forms are divided into two main forms as structured data and unstructured data Big Data owns distinctive characteristics (volume, variety, velocity, veracity and value) so it can easily distinguished from the traditional form of data used in analytics

Each industry moves a step closer into understanding the world of Big Data from how it is being applied to solve a lot of our problems Most industries are still estimating whether there is value in implementation of Big Data, while some industries have applied Big Data analytics already There are applications of Big Data in top ten industry verticals as banking & securities, communications, media& entertainment, healthcare providers, healthcare providers, education, manufacturing & natural resources, government, insurance, retail & wholesale trade, transportation, energy and utilities Even though, Big Data specific challenges will have to face but it is

to be noted that Big Data implementation has been encountered by the industries in these sectors

1.2 Statement of the Problem

The activity of retailing and wholesaling shapes both our economy as well as our daily life Consumers and businesses buy products and services every according to their needs and preference The retail and wholesale sectors contribute significantly in national economy In today competitive and complex business world, the company needs to rely on the data-structured and new type of data-unstructured or semi-structured to back up their decisions

BDA can bring benefits for e-vendors by improving market transaction cost efficiency (e.g buyer-seller transaction online), managerial transaction cost efficiency (e.g process efficiency) and time cost efficiency DBA also enables e-commerce firms to use data more efficiently, driver a higher conversion rate, improve decision making and empower customers However, DBA is new method for e-commerce vendors Therefore, they have to face some challenges when applying BDA

In addition, an increasing amount of published articles or books has focuses on DBA applications in e-commerce in recent years However, the literature still remains largely anecdotal and fragmented The research which provides dimensions and applications of Big Data analytics in e-commerce are limited Therefore, this research is to identify different conceptual dimensions of Big Data analytics in e-commerce and their relevance to its characteristics, different type of data and state business value and challenges From there, it can help the e-commerce enterprises enhanced business value and better respond to challenges of BDA applications

Furthermore, Big Data analytics applications were used in many industries Specifically, Big Data enables merchants to track each user’s behavior and connect the dots to determine the most effective ways to convert one-time customers into repeat customers in the e-commerce context Hence, the studies which determine the positive and negative of applying BDA and evaluate the customers’ responses under Big Data era are needed

1.3 Objectives of the Study

Although an increasing amount of published materials has focused on practitioners in this domain But it remains largely fragmented There is a paucity of research that provides a general taxonomy from which to explore the dimensions and applications of big data analytics in e-commerce This research intends to provide a thorough presentation of the conceptual dimensions of big data in e-commerce and their relevance to business values

and challenges The extant literature review shows that Big Data analytics could allow an e-commerce firm to achieve a range of benefits and face some difficulties Besides, this research focuses on the impact of positive and negative mechanism of Big Data analytics to customers’ responses in B2C e-commerce environments using application of Big Data analytics The specific objectives are:

i To draw on a systematic review of the literature about business values and challenges of a company when it applies Big Data analytics

ii To explore variables this has positive and negative effects on customers’ responses

iii To evaluate the mediation effects of perceived value’s dimensions on relationship between positive factor of applying BDA and customers’ responses

iv To evaluate the mediation effect of perceived risk on relationship between negative factor of applying BDA and customers’ responses and moderation effect of trust propensity

1.4 Contribution of the Study

This research intends to provide a thorough representation of the meaning of Big Data in the e-commerce context by drawing on a systematic review of the literature about business value and challenges of a company when it applies Big Data analytics and finding the mechanisms of applying Big Data analytics to customers’ responses

The study would suggest important implications in enhancing business values and minimum the challenges of applying Big Data analytics E-vendors could know what they need to prepare if they want to apply BDA in their system Identifying the factors of mechanisms of applying BDA affecting on customers’ responses, it would help for companies to understand

their customers’ behavior when they apply BDA and customers also can understand themselves under Big Data era

1.5 Definition of the Operation Terms

There are some keywords or operational terms used in this study which are needed to define as following:

1 E-commerce: E-commerce is the activity of buying or selling of products

and services online or over the internet Electronic commerce draws on technologies such as mobile commerce, electronic funds transfer, supply chain management; Internet marketing, online transaction processing, electronic data interchange (EDI), inventory management systems, and

automated data collection systems (Wikipedia)

2 Big Data: Big data is a data set that is so voluminous and complex that

traditional data processing, applications software is inadequate to deal with

them Big Data is similar to ‘small data ‘, but bigger in Size (Wikipedia)

3 Big Data Analytics (DBA): Big data analytics is where advanced analytic

techniques operate on big data sets Hence, big data analytics is really about two things - big data and analytics - plus how the two have teamed up to create one of the most profound trends in business intelligence (BI) today (Russom, 2011)

4 Structural Equation Modeling (SEM): SEM is multivariate technique

combining aspects of factor analysis and multiple regression that enables the researcher to simultaneously examine a series of interrelated dependence relationships among the measured variables and latent constructs as well as

between several latent constructs (Hair, 2010)

5 Reflective Measurement Model: a measurement model specification in

which it is assumed that the indicators are causes by the underlying construct

6 Positive factor: Variables related to positive factor of positive impact of

BDA application such as information search, recommendation system, dynamic pricing and customer’ services were considered in the analysis

7 Negative factor: Variables related to negative factor of negative impact of

BDA application such as privacy and security, shopping addiction and group influences were considered in the analysis

8 AIDA: AIDA model is a basic movement of marketing and advertisement

resulted from perception of customers

1.6 Research Flowchart

The objective of this research is that illustrate pros and cons of using application of Big Data Analytics: aspects of business and customers It includes two parts One is systematic review to see how company gets the business values and challenges of applying Big Data analytics Besides that, this research focuses on the impact of positive and negative mechanism of Big Data analytics to customers’ responses in B2C e-commerce environments The following Figure 1 presented overall implementation process of this research step by step

Firstly, this research has identified the research problem at the beginning process Next step, research topic has been considered pros and cons effects to company and customers After the research topic identification, the researcher tries to determine the research objective in this research under limitation of research content

Secondly, the literature reviews in this research are considered the earlier research These literature reviews consist of two parts One is systematic review by searching and reviewing published article from 2012 to

2017 for business view by showing Big Data definition, characteristics, earlier research to find out business values and facing challenges Second part takes related research about positive and negative factor of applying Big Data analytics to customers’ responses with mediator of perceived value and perceives risk, and moderating effect of trust propensity The customers’ view part is studied as study I, study II and study III which are mentioned in Chapter III of this research Data collection has been designed to collect to primary data from students in Vietnam The data collection was performed from December, 2016 to January, 2017 in Vietnam

Thirdly, collecting, clarifying, grouping techniques were used to systematic review for business aspect Regarding aspect of customers’ view, analyzing was done by using the Statistic Package for Social Science (SPSS) software version 22 and analysis of moment structures (AMOS 22.0) Description analysis, Anova test, Exploratory Factor Analysis, Confirmatory Factor Analysis, SEM, Mediation test and Moderation test were used in this research

Fourthly, the empirical results are analyzed and discussion, it uses the empirical results that obtains from analyzing part for explanation

Fifthly, the conclusion part has been set up to summarized the main descriptive results that obtain from analyzing process Moreover, this part also concludes some necessary contexts which relate the research objective and research hypotheses

Finally, there are some suggestions regarding company, implication, future and limitation research are talking in this area

Figure 1 Conceptual Framework of the research 1.7 Research Systematic Discussion

This research includes five chapters as introduction, literature review, methodology, results and discussion, conclusions and recommendations

Positive Factor Negative Factor

Other Related factors

to customers’

Responses

Customer View of Applying BDA

Questionnaire Design &

Conclusions and Recommendation

Define Problems Determine Objectives Literature Review

Introduction chapter contains about the background of the writing namely about effects of BDA to company and customer Based on this background seemed a problem that will be examined and look for the solution which will be discussed in more detail in Chapter IV The introduction includes background of study, statement of problem, research purposes, research contribution, definition of operation terms, and systematical discussion

Literature review chapter includes two main parts First part is

overview of previous studies about Big Data and Big Data Application for company From there, research draws on a systematic review of the literature about business values and challenges of a company when it applies Big Data analytics Second part is review previous research related to customers’ responses, pros and cons of applying BDA to customers, perceived value,

perceived risk and trust propensity

Research methodology chapter discussed about the research method that are used in this research, includes: Research model and research hypotheses, operational definition research variables and measure design, research pilot test, sample size, data type, data collection techniques and data analysis techniques The results of analysis will be discussed further in chapter IV

Results and discussion chapter present about results related to customer view The chapter is divided into five sections Each study includes the results

of research, analysis, interpretation of data, discussion and sub-conclusion Study I is to explore positive and negative effects on customers’ responses Study II is evaluating the mediation effects of perceived value’s dimensions

on relationship between positive factor of applying BDA and customers’ responses Study III is evaluating the mediation effect of perceived risk and moderation effect of trust propensity on the relationship negative factor of applying BDA and customers’ responses

Conclusions and recommendations chapter will close with an explanation of the conclusion of research discussion about results after analysis and provided constructive recommendation for business in accordance with the conclusions obtained Besides that, research also mentions limitation of research and future studies

CHAPTER II LITERATURE REVIEW

The literature review section includes the following: (Section 2.1) Concept of Big Data in E-commerce Environment, (Section 2.2) Big data analytics in E-commerce: Aspect of business, (Section 2.3) Big data analytics

in E-commerce: Aspect of customer

2.1 Concept of Big Data in E-commerce Environment

2.1.1 Big Data Analytics in the E-Commerce Environment

2.1.1.1 Introduction of Big Data

Big data, terabytes of data, mountains of data, no matter how to describe it, there is a going data explosion all around us that makes collections and storage of data merely trivial Generally the concept of big data refers to datasets who size is beyond the ability of typical database

software tool to capture, store, manage, and analyze (Manyika et al., 2011)

This definition is incorporation a moving definition of how big a dataset needs be considered as big data With another definition, Big Data is a collection of massive and complex data sets and data volume that include the huge quantities of data, data management capabilities, social media analytics and real-time data (Anuradha, 2015) Data is something so huge and complex that it is impossible to use traditional analytics tools to process and work on them The big data term has been used to refer to increasing data volumes in the mid-1990s And now, big data is very where The Big Data phenomenon has rapidly become pervasive across the spectrum of different industries and

sectors (McAfee et al., 2012; Davenport, 2013)

Figure 2 presents the ease of capturing big data’s value, and the magnitude of its potential, vary across sectors The big data value potential into account a sector’s competitive conditions, such as market turbulence, performance variability; structural factors, such as transaction intensity and the number of potential customers and business partners; and the quantity of data available The ease of-capture index takes stock of the number of

usefulness aspect of analytics has been the focus in other studies such as those

by Davenport and Harris (2007) and Bose (2009) BDA is explained by Davenport and Harris (2007) that BDA is processing with helps of mechanisms such as statistical analysis and the use of an explanatory and predicting model Bose (2009) described BDA as using different tools used to extract, interpret information as well as predict the outcomes of decisions

2.1.2 Big Data’s Distinctive Characteristics

Big data encompasses unstructured and structured data that correspond

to various activities Structured data entails data that is categorized and stored

in a file according to a particular format description, where unstructured data

is free-form that takes on a number of types (Kudyba, 2014) Big data is often

characterized by volume, velocity, and variety (the three Vs) (McAfee et al., 2012; Lycett, 2013; Goes, 2014; Wixom et al., 2014; Hashem et al., 2015)

Researcher have also extended big data characteristics include veracity

(Gillon et al., 2012; Goes, 2014) and value (Hashem et al., 2015) to make five

Vs (Patnaik et al., 2015)

We can classify the five Vs of big data into two subgroups base on the hierarchy as Data  Information  Knowledge  Intelligence (Goes, 2014) It means data has been processed to extract knowledge and intelligence from data information The Figure 3 shows five distinctive characteristics of big data and its processing Big data can be easily distinguished from traditional form of data used in analytics (Akter and Wamba, 2016) The following is detail about five Vs as describing with example in Table 1

Figure 3 Characteristics and processing of Big Data

1) Volume

With emergence of web technologies, there is an ever-increasing growth

in the amount of Big Data This aspect often comes to most people’s minds when they think of Big Data Data is collected in Big Data environment are often unstructured and can incorporated video, image or data generated from different technology For example, Walmart is one of the largest retailers in the world with over two million employees and 20,000 stores in 28 countries Walmart’s real-time transactional database consists of 40 petabytes of data Huge though this volume of transactional data, it includes most recent week’s data Large Hadron Collider (LHC) which is humanity’s biggest and most advanced physics experiment The LHC alone generates around 30 petabytes

of information per year – 15 trillion pages of printed text, enough to fill 600 million filling cabinets In addition to opportunities, the volume of big data brings challenges, especially integration of big data from different sources and formats Therefore, company has to have ability to deal with this data to understand and get right information

2) Variety

Variety refers to the different types of data we can now use Big Data can be collected from various sources which can be structured or unstructured Variety is another characteristic of Big Data as they are generated in different forms and formats including image, text, audio, video, web, log files, click-stream, etc (Russom, 2011) The variety of data requires the use of different analytical and predictive models which can enable information to be catch General, the variety of Big Data has a potential to add value to business

3) Velocity

Velocity refers to the speed at which new data is generated and the speed at which data moves around The term velocity of Big Data stated that how quickly big data should be used to add business value For example At Twitter, 140 characters per tweet, the data volume are estimated at 8 terabytes per day due to the high velocity of data (Dijcks, 2012) Retailer now can track their customers in the real time to know their changes in customer behavior

5) Value

Value of information is the most important attribution of Big Data technology trends Value is considering as the most significant to add business value that big data brings to enhance decision making process The owners have to be planned to use values of Big Data information for issues, problems or model your business

Table 1 5Vs of Big Data Characteristics in business analytics

records

(Davenport et al., 2012), huge

amount of storage (Russom, 2011)

 Walmart includes a 40-petabyte database of all the sales transactions in the previous weeks (Marr, 2016)

 Amazon introduced books for searching option consisting of 120.000 books (Davenport, 2006)

 The Large Hadron Collider alone generates around 30 petabytes of information per year – 15 trillion pages of printed text (Marr, 2016)

 On Facebook, 30 billion pieces of content are shared

every month (Manyika et al., 2011)

 Airbnb, launched in 2008, have collected a huge amount of data around 1.5 petabytes on people’s holiday habits and accommodation preferences (Marr, 2016)

Variety

Data comes from a greater variety of sources and formats (Russom, 2011;

Davenport et al.,

2012)

 Fitbit devices gather a range of structured data from users, including steps taken, floors climbed, distance walked/run, calorie intake, calories burned, active minutes

a day, sleep patterns, weight and BMI (Marr, 2016)

 Credit card company used website click-stream data and other data formats from call center to customize offers (Davenport and Patil, 2012)

 Apple’s focus is on internal data, generated by users of their products and services (Marr, 2016)

Table 1 5Vs of Big Data Characteristics in business analytics (Cont.)

processed, stored and analyzed (Anuradha, 2015)

 At Twitter, 140 characters per tweet, the data volume are estimated at 8 terabytes per day due to the high velocity of data (Dijcks, 2012)

 The 300 gigabytes per second of data provided by the

on the veracity

of the source data (Anuradha,

2015)

 Using data fusion, an organization can combine multiple less reliable sources to create a more accurate and

useful data point (Davenport et al., 2012)

 Montage Analytics has developed a tool that can predict extreme outlier data and other types risk (Ferguson, 2012)

Value

The extent to which big data

generates economically worthy insights

and or benefits

through extraction and

transformation

 GE, a computer hardware, by seeing the value of adopting advanced analytics technology, they have continued to act in the pioneering manner (Marr, 2016)

 IBM create value from the data that Twitter collect as their users tweet (Marr, 2016)

 Match.com reported more than 50% increase in revenue in the last two years׳ time, with more than 1.8 million paid subscribers in its core business, most of which driven through data analytics (Kiron and Shockley, 2011)

2.1.3 Types of Big Data Used in E-commerce

E-commerce refers to buying and selling products or services through internet E-commerce firm collects various forms of data, but they can be categorized into 4 types: (1) transaction or business activity data (2) Click-stream data (3) image and video data and (4) voice data (Table 2) This section is discussing different types of big data with their implication for e-commerce

1) Transaction or business activity data

The selling and buying activity between customer and firm over time will have transaction record or business activity data These structured data originate from retail transactions, customer profiles, distribution frequency, product consumption and service usage, nature and the occurrence of customer complaints It is evidence that e-vendor can get benefits across the using transaction data Amazon revealed at one point that 30% of sales were generated through its recommendation system by using customer data

(Manyika et al., 2011) Amazon collects data from users as they browse the

site – monitoring everything from the time they spend browsing each page, demographic details, and location to meet customer needs and desires

2) Click-stream data

Clickstream is the name given to the record of a customer’s actions on the internet Clickstream data, therefore, can show in detail exactly where a user goes and what they do, from search engine searches to websites visited, what they browse Previous studies found that many e-commerce firms like as Amazon, eBay, Alibaba analyze cookies and clickstream on customer browsers Clickstream data can be applied to identify patterns in consumers’ shopping habits and offer customized offers, advertisements and promotions (Mosavi and Vaezipour, 2013)

3) Image, video data

E-commerce firms tend to take image and video data besides business activity data or click-stream data These data have potential values to add into business value for e-commerce firms Some retailers used image analysis software liked to their video to track in-store traffic patterns and consumer

behavior (Manyika et al., 2011) Marr (2016) reported that Netflix have begun

automating this process, by creating routines that can take a snapshot of the content in Jpeg format and analyze what is happening on screen using sophisticated technologies such as facial recognition and color analysis The using of image or video data to analyze is essential for firms to make better business decision

4) Voice data

Voice data is another type of data which now is using to analyze in big data analytics Voice data typically originate from phone calls, call centers or customer services E-commerce firm was found to use advance method to

analyze text and transcript converted from voice call center (Schroeck et al.,

2012) Airbnb, the place that connects travellers with available accommodation around the world through website, launched in 2008 This website could find useful features for decisions about community growth, product development by using customer feedbacks as customer’s ‘voice’ into

a language more suitable for decision-making

Table 2 Types of big data using in E-commerce

information, service usage, distribution, rating

of customer, etc

 Amazon have used Big Data gathered from customers while they browse the site to build and fine-tune their recommendation engine (Marr, 2016)

 Walmart persuades its suppliers to monitor product movement by store to help plant promotions, reduce stock outs (Davenport, 2006)

 Harrah’s, US hotels and casino group, complies detailed customer profiles and used them to customize marketing to increase customer loyalty

(Manyika et al., 2011)

Click-stream

data

Click-stream data from the website, social media, online

advertisement, like Facebook, blog, tweets…

 Walmart, the analytics team can monitor 200 streams of internal and external data in real time (Marr, 2016)

 Netflix Inc analyzes web data of over one billion reviews of movies that were liked, loved, hated, etc to recommend movies that optimize customers’ tastes and inventory conditions (Davenport and Harris, 2007)

 LinkedIn track every move users make on the site: every click, every page view, every interaction

in order to aid decision making, and design powered products and features (Marr, 2016)

data- Esty, by monitoring and analyzing every click made by visitors and customers to their site, their data engineers are able to analyze what behavior leads to a sale, and what leads to customers leaving the site, unable to find what they are looking for (Marr, 2016)

Table 2 Types of big data using in E-commerce (Cont.)

Image or video

data

Image or video data from retail and other settings

 Airbnb used primarily internal data across a mixture of structured and unstructured formats as image data from host photos, location data, accommodation features (number of rooms/beds, Wi-Fi, etc), customer feedback and ratings, transaction data (Marr, 2016)

 Some retailers used image analysis software liked to their video to track in-store traffic

patterns and consumer behavior (Manyika et al.,

2011)

Voice data

Voice data from phone call, customer service, call centers

 Walmart translates customer’s ‘voice’ into a language more suitable for decision-making (Marr, 2016)

 E-business is using advance capabilities to analyze text in its natural state, such as transcripts of call center conversations

(Schroeck et al., 2012)

2.2 Big data analytics in E-commerce: Aspect of business

This part present a comprehensive review of literature related to applying Big Data Analytics about business values and challenges of a company in published journal in five recent years from 2012 and 2017 The review process is shown in Figure 4 The process is done by identifying the subject area correctly, relevant studies, materials, and inclusion and exclusion criteria From online database, searching criteria are set to filter articles related to BDA and E-commerce Each article was reviewed according business values or business challenges when applying Big Data analytics Generally, the criteria used to select and review paper contained an explicit or implicit indication of BDA in e-commerce regarding business values and business challenges

1 Database: 5 popular online databases

2 Description: “Big Data Analytics” and commerce or electronic commerce”

“e-3 Year: Published between 2012-2017

2.2.1 Literature Review Research Approach

This research reviewed literature to identify and appraise the current knowledge on business aspect about applying BDA in e-commerce related to business values and business challenges This research approach bases on a similar approach used by Akter and Wamba (2016), Vaithianathan (2010) and

Ngai et al (2009) in e-commerce and Lim et al (2013) in product research

The system approach was adopted a protocol that described the criteria, methodology for each step to deal with specific objective of this study

The review process focuses on the applying Big Data Analytics in commerce firm, how it could bring business values and raise challenges for company The process is done by identifying the subject area correctly, relevant studies, materials, and inclusion and exclusion criteria A search within the time from 2012 to 2017 was considered to be representative

e-The following online databases were searched in order to review the field as comprehensively as possible The scholar databases used in this study are:

 Science Direct (Elsevier)

 Web of knowledge (Thomson ISI)

 Business Source Complete (EBSCO host)

 Springer Link

 ABI/ Inform Complete (ProQuest)

The research focuses on e-commerce study as the source of material most relevant to Big Data and BDA analytics Addition, the search identified relevant publications by searching keywords that combined the key words

‘big data analytics’ with a different range of terms and phrases The combination keywords ‘big data analytics’ with the terms ‘electronic commerce’, ‘e-commerce’, ‘big data analytics and e-commerce’, and ‘big data analytics and electronic commerce’ are used for searching The searches were limited to the abstract, title and key words

The full text of each article was fast reviewed to eliminate those that were not actually related to aspect of business about BDA application in e-commerce The selection criteria were as follows:

 Only those articles had been published about BDA in e-commerce within 2012 -2017

 Only those articles which described how the mentioned BDA could be advantages or disadvantages for e-commerce firm were selected

 Conference papers, master thesis and doctoral dissertations, books and unpublished articles were excluded

The search was limited in abstract field A total article 33 articles matched with above criteria were downloaded and reviewed After that, cross-referencing is used in order to add more papers related to this topic The final list 81 papers were deemed relevant for our research objectives, so they were selected for classifications

2.2.1.1 Distribution of Articles by Year

Figure 5 presents the distribution of articles by year from 2012 to 2017

We can clearly see that research on BDA application grew exponentially during this time Publications related to big data analytics in e-commerce are

8 articles in 2012, 9 articles in 2013 This number keeps increasing of publication, ranging from 11 articles in 2014 to 16 articles in 2015, followed

by 17 articles in 2016 and by the end of 2017 is 20 articles Therefore, it is highlight that the increase in interest related to BDA in e-commerce

2.2.1.2 Distribution of Articles by Categories

We adopted a thematic analysis of literature review which provide by Braun and Clarke (2006) The literature is divided into five categories of BDA applications (Tankard, 2012) Five identified categories of BDA application are:

 Creating transparency by making relevant data more accessible, such

as by integrating data from R&D, engineering and manufacturing departments

to enable concurrent engineering to cut time to market and improve quality

 Discovering needs and improving performance by collecting more accurate and detailed performance data For example, e-commerce firms are using BDA to discover customers’ need and introduce suitable products or services to them

 Segmenting market to customize actions so that products and services

can meet actual needs For example, consumer goods and services companies can use big data analytics techniques to better target promotions and advertising

Figure 5 Distribution of articles by year