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

This researchaims to 1 draw on a systematic review of the literature about definiton,distnctve characteristics, business values and challenges of a company whenapplying Big Data analytic

國國國國國國國國國國國國國國國國國國 Department of Tropical Agriculture and Internatonal Cooperaton Natonal Pingtung University of Science and Technology

國國國國國國 Ph.D Dissertaton

國國國國國國國國國國國國國國國國國國國國國國國國國

Applying Big Data Analytcs in E-commerce: Aspects of

Business and Customer

國國國國 Advisor: 國國國國國(Shu-Yi Liaw, Ph.D.)

國國國 Student: 國國國 (Le Thi Mai)

國國國國 107 國 06 國 01 國

June 1, 2018

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國國國國國國國國國國國國國國國國國國國國國國國國國國國國國國國 國國國國

III III

Student ID: P10322019

ABSTRACT

Title of Dissertation: Applying Big Data Analytics in E-commerce: Aspects of

Business and CustomerTotal Page: 151 pages

Name of Insttute: Department of Tropical Agriculture and International

Cooperation, National Pingtung University of Scienceand Technology

Graduate Date: June 1, 2018 Degree Conferred: Doctoral DegreeName of Student: Le Thi Mai Advisor: Liaw, Shu-Yi, Ph.D TheContents of Abstract in This Dissertation:

The era of Big Data analytcs (BDA) has begun in most industries withindeveloping and developed countries This new analytics tool has raisedmotivaton for experts and researchers to study its impacts to businessvalues and challenges However, there is shortage of studies which evaluatethe applicatons of BDA under business view and help to understandcustomers’ views towards the applicatons of Big Data analytic This researchaims to (1) draw on a systematic review of the literature about definiton,distnctve characteristics, business values and challenges of a company whenapplying Big Data analytics, (2) explore and determine the pros and cons ofapplying Big Data analytics that affects customers’ responses in an e-commerce environment, (3) evaluate the mediaton effect ofperceived value’s dimensions and perceived risk, (4) determine themoderation effect of trust propensity Data analyses were conducted by usingthe statistcal package for social sciences and analysis of moment structuressoftware in useful sample of 349 respondents in Vietnam Two aspects asbusiness and customer views are reviewed, explored, discussed in this study

IV IV

The major findings include:

(1) The study synthesized diverse BDA concepts that provide deeperinsight about applicaton of BDA for e-commerce firms It is highlight thatthe increase in interest related to BDA in e-commerce in recent years BDAapplicatons in e-commerce can be divided into five aspects like as creatngtransparency, discovering needs and improving performance, segmentngmarket, better decision making, new product or business model innovation.These applicatons bring many business values but also raise some challengeswhen e-firms want to apply BDA

(2) The findings found that information search, recommendationsystem, dynamic pricing, and customer services had different significantpositive effects on customers’ responses Specifically, information searchhad a highest significant influence on customers’ intention andimproved customers’ behavior Following by dynamic pricing,recommendation system and customers’ service also had significant impact

on customers’ intention but decreased customers’ behavior On anotherhand, privacy and security, shopping addiction, and group influences werefound to have different significant negative effects on customers’ responses.Specifically, shopping addiction had a drastic change from intenton tobehavior compared to group influences and privacy and security Itcannot be denied that customers receive positve and negatve factors atthe same time

(3) The results confirmed that functonal and emotional values playmediating roles between positve of applying BDA and consumers’ responses.However, there weren’t significant different between mediator effect offunctional value and emotonal value This finding highlights the notificatonthat customers nowadays not only find their products or services but alsoseek enjoyment when online shopping under Big Data era Therefore, e-firms should increase perceived value based on creasing equally functonaland emotional values

V V

(4) The study found out that perceived risk don’t act mediate therelatonship between negative of applying BDA and consumers’ responses.Besides, customers’ trust propensity was found to moderate the relation ofnegative factor of applying BDA to customers’ responses and perceived risk

to customers’ responses High trust propensity partcipants reported strongerresponses than those with low trust propensity It due to customers’ trust onnew applications of BDA, hence, it is easy to influence on customers as theirnegative response when negative factor and perceived risk are rising

This study contributes to improve understanding of applicatons of BigData Analytics under business view and customer view This could play animportant role to develop sustainable consumers market E-vendors canrely on Big Data analytics but over usage may have some negatveapplicatons Besides that, the research also broader discussion regardingfuture research opportunites, challenges in theory and practice

Keywords: E-commerce, Big Data Analytics, Customers’ Responses,

Perceived Value, Perceived Risk, Trust Propensity

VI VI

ACKNOWLEDGEMENTS

This study has been carried out at the Department of TropicalAgriculture and Internatonal Cooperation (DTAIC), National PingtungUniversity of Science and Technology (NPUST), Taiwan This is the outcome ofknowledge that I received from this university, my contnuous efforts tolearning, and consistent guidance of my advisor

Firstly, I would like to express my sincere grattude to my advisor,Professor Shu-Yi Liaw for contnuous support of my Ph.D study and relatedresearch He has given me valuable guideline, patence, assistance,motvaton and inspiration during Ph.D time His intellectual directonand critical reviews of research works helps me all the time and find a righttract towards the successfully competton of this dissertaton He is the bestteacher I have met

Besides my advisor, I would like to thank the rest of my advisorycommittee: Dr Shi-Jer Lou, Dr Rong-Fang Chen, Dr Shih-Wei Chou, and Dr.Pei-Chen Sun, for their insightul 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 andother faculties who provided for their encouragement and supports during

my study I would like to thank Barbara, Sylvia (OIA), Sophia, Joanna and allDTAIC staff, Yang Ya-Chu, Lin Yi-Ru and other staff of computer center fortheir assistants

I thank my fellow classmates for the discussions and fun time we had.Also thank my internatonal 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

VIII VIIIV

TABLE OF CONTENTS

國國 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 Contributon of the Study

4 1.5 Definiton 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 Distinctve Characteristics 13

IXI XIX

2.1.3 Types of Big Data Used in E-commerce 182.2 Big data analytics in E-commerce: Aspect of business 222.2.1 Literature Review Research Approach

23

IX IX

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 Positve 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 Moderatng 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 Positve Factor of Applying BDA and Customers’ Responses

50 3.1.3 The Mediatng Role of Perceived Risk and Moderatng 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

X X

623.6 Data Type and Data Collection Method

633.6.1 Data Type

633.6.2 Data Collecton Method

633.6.3 Data Collecton Procedure

643.7 Data Analysis Techniques

65

X X

3.7.1 Descriptve Statistcs 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 Mediaton Test

71 3.7.7 Moderaton Test

72 CHAPTER IV RESULTS AND DISCUSSION 74

4.1 Descriptve Analysis and Mean Comparison

74 4.1.1 Descriptve 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 mediaton effects of perceived value’s dimensions on relatonship between PF and CR

88 4.4.1 Measurement Model

XI XI

884.4.2 Structural Equation Model

904.4.3.Discussion and Sub-conclusion 934.5 Study III-Evaluatng the Mediaton Effects of PV and the Moderating of TP 954.5.1 Measurement Model

954.5.2 Structural Equation Model

97

XI IX

4.5.3 Examining Moderatng 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) applicatons 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 Characteristcs 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 positve factor of applying BDA 57

Table 7 Dimensions and indicators of positve 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 Reflectve Measurement Models 70

Table 11 Demographic descriptve (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 96Table 26 The latent variable correlation matrix: Discriminant validity 96Table 27 Measurement model fit indicates

97Table 28 Mediaton effect of perceived risk 99Table 29 T-Test between trust propensity groups

100

XIII XIIIX

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

XIV XIVX

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 evaluaton framework

22 Figure 5 Distributon of artcles 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 statistcal diagram

72 Figure 13 A combing mediation and moderaton model depicted as a statistcal diagram

73 Figure 14 A simple combing mediation and moderaton 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

XV XVX

Figure 18 The results of mediation model

91Figure 19 The results of direct effect

97Figure 20 The results of mediation model

99Figure 21 The results of moderatng model

101Figure 22 Moderator effect of TP in relaton between NF and CR 103Figure 23 Moderator effect of TP in relaton between PR and CR 104

CHAPTER I INTRODUCTION1.1 Background of the Study

With increasing advancement of internet technology, increasingamounts of data are streaming into contemporary organizatons Data isgetting bigger and more complicated because it is contnuing to be generatedfrom many devices and more sources as mobile phones, personal computers,government records, healthcare records, social media, etc An InternationalData Cooperation report estimated that the world would generate 1.8zettabytes 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 hasarrived Why do researchers and practitioners be interested inunderstanding about the impacts of Big Data analytics? The simple reply

to this critcal queston is because Big Data enables to bring potentialapplicatons Big Data applications can help organizations; the governmentpredicted the unemployment rate, the future trend for professionalinvestors, or cut spending, stmulates economic growth, etc For healthcare, Big Data can help to predict impact trend of a certain disease One ofthe most conspicuous examples of Big Data for health care is Google FluTrend (GFT) In 2009, Google has used Big Data to analyze and predict trendsinfluence, spread of H1N1 flu Trend which Google drawn from the searchkeywords related to the H1N1 has been proven to be very close to the resultsfrom flu independent warning system Sentinel GP and Health Statistcslaunched The GFT program was designed to provide real-timemonitoring of flu cases around the world based on Google searches thatmatch terms for flu related actvity For e-commerce firms, the inject Big Dataanalytics into their value chain value 5-6% higher productvity than theircompettors (McAfee et al.,

2012)

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

to be noted that Big Data implementaton has been encountered by theindustries in these sectors

1.2 Statement of the Problem

The activity of retailing and wholesaling shapes both our economy aswell as our daily life Consumers and businesses buy products and servicesevery according to their needs and preference The retail and wholesalesectors contribute significantly in natonal economy In today competitveand complex business world, the company needs to rely on the data-

3structured 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 costefficiency (e.g buyer-seller transaction online), managerial transaction costefficiency (e.g process efficiency) and time cost efficiency DBA alsoenables e-commerce firms to use data more efficiently, driver a higherconversion 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 additon, an increasing amount of published artcles or books hasfocuses on DBA applicatons in e-commerce in recent years However, theliterature stll remains largely anecdotal and fragmented The research whichprovides dimensions and applications of Big Data analytics in e-commerceare limited Therefore, this research is to identify different conceptualdimensions of Big Data analytics in e-commerce and their relevance to itscharacteristics, different type of data and state business value andchallenges From there, it can help the e-commerce enterprises enhancedbusiness value and better respond to challenges of BDA applicatons

Furthermore, Big Data analytcs applicatons were used in manyindustries Specifically, Big Data enables merchants to track each user’sbehavior and connect the dots to determine the most effective ways toconvert one-time customers into repeat customers in the e-commercecontext Hence, the studies which determine the positve and negative ofapplying BDA and evaluate the customers’ responses under Big Data era areneeded

1.3 Objectves of the Study

Although an increasing amount of published materials has focused onpracttioners in this domain But it remains largely fragmented There is apaucity of research that provides a general taxonomy from which toexplore the dimensions and applicatons of big data analytics in e-commerce.This research intends to provide a thorough presentaton of the conceptual

5dimensions of big data in e-commerce and their relevance to businessvalues

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

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

ii To explore variables this has positve and negatve effects on customers’responses

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

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

1.4 Contribution of the Study

This research intends to provide a thorough representation of themeaning of Big Data in the e-commerce context by drawing on a systematicreview of the literature about business value and challenges of a companywhen it applies Big Data analytics and finding the mechanisms of applyingBig Data analytics to customers’ responses

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

7understand

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

1.5 Definition of the Operation Terms

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

1 E-commerce: E-commerce is the actvity of buying or selling of productsand services online or over the internet Electronic commerce draws ontechnologies such as mobile commerce, electronic funds transfer, supplychain management; Internet marketing, online transacton processing,electronic data interchange (EDI), inventory management systems, andautomated data collecton systems (Wikipedia)

2 Big Data: Big data is a data set that is so voluminous and complex thattraditonal data processing, applicatons software is inadequate to dealwith them Big Data is similar to ‘small data ‘, but bigger in Size (Wikipedia)

3 Big Data Analytics (DBA): Big data analytics is where advanced analytictechniques operate on big data sets Hence, big data analytics is really abouttwo 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 Equaton Modeling (SEM): SEM is multivariate techniquecombining aspects of factor analysis and multiple regression that enables theresearcher to simultaneously examine a series of interrelated dependencerelatonships among the measured variables and latent constructs as well asbetween several latent constructs (Hair, 2010)

5 Reflective Measurement Model: a measurement model specification inwhich it is assumed that the indicators are causes by the underlyingconstruct

6 Positive factor: Variables related to positive factor of positve impact ofBDA application such as information search, recommendation system,

9dynamic pricing and customer’ services were considered in the analysis.

7 Negative factor: Variables related to negatve factor of negatve impact ofBDA application such as privacy and security, shopping addicton and groupinfluences were considered in the analysis

8 AIDA: AIDA model is a basic movement of marketng and advertsementresulted from perception of customers

1.6 Research Flowchart

The objectve of this research is that illustrate pros and cons of usingapplicaton of Big Data Analytics: aspects of business and customers Itincludes two parts One is systematic review to see how company gets thebusiness values and challenges of applying Big Data analytics Besides that,this research focuses on the impact of positve and negatve mechanism ofBig Data analytics to customers’ responses in B2C e-commerceenvironments The following Figure 1 presented overall implementatonprocess of this research step by step

Firstly, this research has identfied the research problem at thebeginning process Next step, research topic has been considered prosand cons effects to company and customers After the researchtopic identfication, the researcher tries to determine the research objective

in this research under limitaton of research content

Secondly, the literature reviews in this research are consideredthe earlier research These literature reviews consist of two parts One

is systematic review by searching and reviewing published artcle from 2012to

2017 for business view by showing Big Data definition, characteristics, earlierresearch to find out business values and facing challenges Second part takesrelated research about positve and negative factor of applying Big Dataanalytics to customers’ responses with mediator of perceived value andperceives risk, and moderating effect of trust propensity The customers’view part is studied as study I, study II and study III which arementoned in Chapter III of this research Data collection has been designed

to collect to primary data from students in Vietnam The data collection wasperformed from December, 2016 to January, 2017 in Vietnam

Thirdly, collecting, clarifying, grouping techniques were used tosystematic review for business aspect Regarding aspect of customers’ view,analyzing was done by using the Statistc Package for Social Science (SPSS)software version 22 and analysis of moment structures (AMOS 22.0).Descripton analysis, Anova test, Exploratory Factor Analysis, ConfirmatoryFactor Analysis, SEM, Mediaton test and Moderation test were used in thisresearch

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

Fifthly, the conclusion part has been set up to summarized the maindescriptve results that obtain from analyzing process Moreover, this partalso concludes some necessary contexts which relate the research objectiveand research hypotheses

Finally, there are some suggestions regarding company, implicaton,future and limitaton research are talking in this area

Define Problems

Determine Objectves Literature Review

to customers’ Responses

Data Collecton

Data Analysis

Results Explanaton Discussion

Conclusions and RecommendatonFigure 1 Conceptual Framework of the research1.7 Research Systematic Discussion

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

recommendations

Introduction chapter contains about the background of the writingnamely about effects of BDA to company and customer Based on thisbackground seemed a problem that will be examined and look for thesolution which will be discussed in more detail in Chapter IV Theintroducton includes background of study, statement of problem, researchpurposes, research contribution, definiton of operaton terms, andsystematical discussion.

Literature review chapter includes two main parts First part isoverview of previous studies about Big Data and Big Data Application forcompany From there, research draws on a systematic review of theliterature about business values and challenges of a company when it appliesBig Data analytics Second part is review previous research related tocustomers’ responses, pros and cons of applying BDA to customers,perceived value, perceived risk and trust propensity

Research methodology chapter discussed about the researchmethod that are used in this research, includes: Research model andresearch hypotheses, operatonal definiton research variables and measuredesign, research pilot test, sample size, data type, data collecton techniquesand data analysis techniques The results of analysis will be discussedfurther in chapter IV

Results and discussion chapter present about results related tocustomer view The chapter is divided into five sectons Each study includesthe results of research, analysis, interpretation of data, discussion and sub-conclusion Study I is to explore positve and negatve effects on customers’responses Study II is evaluating the mediation effects of perceived value’sdimensions on relationship between positive factor of applying BDA andcustomers’ responses Study III is evaluatng the mediation effect ofperceived risk and moderation effect of trust propensity on the relationshipnegatve factor of applying BDA and customers’ responses

Conclusions and recommendations chapter will close with anexplanation of the conclusion of research discussion about results afteranalysis and provided constructive recommendaton for business inaccordance with the conclusions obtained Besides that, research alsomentons limitation of research and future studies.

CHAPTER II LITERATURE REVIEWThe literature review section includes the following: (Secton 2.1)Concept of Big Data in E-commerce Environment, (Secton 2.2) Big dataanalytics 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 todescribe it, there is a going data explosion all around us that makescollections and storage of data merely trivial Generally the concept of bigdata refers to datasets who size is beyond the ability of typical databasesoftware tool to capture, store, manage, and analyze (Manyika et al., 2011).This definition is incorporation a moving definiton of how big a datasetneeds be considered as big data With another definiton, Big Data is acollection of massive and complex data sets and data volume that includethe huge quantites of data, data management capabilites, social mediaanalytics and real-time data (Anuradha, 2015) Data is something so hugeand complex that it is impossible to use traditonal analytics tools to processand work on them The big data term has been used to refer to increasingdata volumes in the mid-1990s And now, big data is very where The BigData phenomenon has rapidly become pervasive across the spectrum ofdifferent industries and sectors (McAfee et al., 2012; Davenport, 2013).Figure 2 presents the ease of capturing big data’s value, and themagnitude of its potential, vary across sectors The big data value potentialinto account a sector’s competitve conditons, such as market turbulence,performance variability; structural factors, such as transacton intensity andthe number of potential customers and business partners; and the quantity

of data available The ease of-capture index takes stock of the numberof

Big data encompasses unstructured and structured data thatcorrespond to various activites Structured data entails data that iscategorized 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, andvariety (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 thehierarchy as Data  Information  Knowledge  Intelligence (Goes,2014) It means data has been processed to extract knowledge andintelligence from data information The Figure 3 shows five distnctvecharacteristcs of big data and its processing Big data can be easilydistnguished from traditonal form of data used in analytcs (Akter andWamba, 2016) The following is detail about five Vs as describing withexample in Table 1

Source: Shim et al (2015)

Figure 3 Characteristics and processing of Big Data1) Volume

With emergence of web technologies, there is an ever-increasinggrowth in the amount of Big Data This aspect often comes to most people’sminds when they think of Big Data Data is collected in Big Data environmentare often unstructured and can incorporated video, image or data generatedfrom different technology For example, Walmart is one of the largestretailers in the world with over two million employees and 20,000 stores in

28 countries Walmart’s real-time transactional database consists of 40petabytes of data Huge though this volume of transactonal data, it includesmost recent week’s data Large Hadron Collider (LHC) which is humanity’sbiggest and most advanced physics experiment The LHC alone generatesaround 30 petabytes of information per year – 15 trillion pages of printedtext, enough to fill 600 million filling cabinets In additon to opportunites,the volume of big data brings challenges, especially integration of big datafrom different sources and formats Therefore, company has to have ability

to deal with this data to understand and get right information