INTRODUCTION
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 (Shu-Yi Liaw, Ph.D.)1.8 × 10 21 bytes) by 2011 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)GFT) In 2009,Google has used Big Data to analyze and predict trends influence, spread ofH1N1 flu Trend which Google drawn from the search keywords related to theH1N1 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 (Shu-Yi Liaw, Ph.D.)McAfee et al., 2012).
Big Data is generating remarkable attention worldwide There are some definitions of Big Data term Manyika et al (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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.
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 (Shu-Yi Liaw, Ph.D.)e.g buyer-seller transaction online), managerial transaction cost efficiency (Shu-Yi Liaw, Ph.D.)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.
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.
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.
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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)BI) today (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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.
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 inChapter 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 (Shu-Yi Liaw, Ph.D.)SPSS) software version 22 and analysis of moment structures (Shu-Yi Liaw, Ph.D.)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.
Business View of Customer View of
Other Related factors to customers’
Data Analysis Results Explanation Discussion
Research Systematic Discussion
This research includes five chapters as introduction, literature review, methodology, results and discussion, conclusions and recommendations.
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.
LITERATURE REVIEW
Concept of Big Data in E-commerce Environment
2.1.1 Big Data Analytics in the E-Commerce Environment
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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)2007) and Bose (Shu-Yi Liaw, Ph.D.)2009) BDA is explained by Davenport and Harris (Shu-Yi Liaw, Ph.D.)2007) that BDA is processing with helps of mechanisms such as statistical analysis and the use of an explanatory and predicting model Bose (Shu-Yi Liaw, Ph.D.)2009) described BDA as using different tools used to extract, interpret information as well as predict the outcomes of decisions.
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 (Shu-Yi Liaw, Ph.D.)Kudyba, 2014) Big data is often characterized by volume, velocity, and variety (Shu-Yi Liaw, Ph.D.)the three Vs) (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)Gillon et al., 2012; Goes, 2014) and value (Shu-Yi Liaw, Ph.D.)Hashem et al., 2015) to make five
Vs (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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 (Shu-Yi Liaw, Ph.D.)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
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 (Shu-Yi Liaw, Ph.D.)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.
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 (Shu-Yi Liaw, Ph.D.)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.
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 (Shu-Yi Liaw, Ph.D.)Dijcks, 2012) Retailer now can track their customers in the real time to know their changes in customer behavior (Shu-Yi Liaw, Ph.D.)Manyika et al., 2011).
Another characteristic of Big Data relates to the uncertainly associated with certain forms of data With high volume, velocity and variety of data, it is not possible that all the data is going to be totally correct there will be dirty data The data accuracy of analysis depends on the veracity of the source data(Shu-Yi Liaw, Ph.D.)Anuradha, 2015) Therefore, verification is necessary to generated authenticated and relevant data and to have ability to take out bad data(Shu-Yi Liaw, Ph.D.)Beulke, 2011) Because bad data would take resources of firm to analysis and would have little relevance in adding business value Montage Analytics has developed a tool for predict extreme outlier data in organization and other types of risk (Shu-Yi Liaw, Ph.D.)Ferguson, 2012).
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
Walmart includes a 40-petabyte database of all the sales Huge data transactions in the previous weeks (Shu-Yi Liaw, Ph.D.)Marr, 2016). consist of a Amazon introduced books for searching option large number of consisting of 120.000 books (Shu-Yi Liaw, Ph.D.)Davenport, 2006). records The Large Hadron Collider alone generates around 30 Volume (Shu-Yi Liaw, Ph.D.)Davenport et petabytes of information per year – 15 trillion pages of al., 2012), huge printed text (Shu-Yi Liaw, Ph.D.)Marr, 2016). amount of On Facebook, 30 billion pieces of content are shared storage every month (Shu-Yi Liaw, Ph.D.)Manyika et al., 2011)
(Shu-Yi Liaw, Ph.D.)Russom, 2011) Airbnb, launched in 2008, have collected a huge amount of data around 1.5 petabytes on people’s holiday habits and accommodation preferences (Shu-Yi Liaw, Ph.D.)Marr, 2016).
Fitbit devices gather a range of structured data from Data comes users, including steps taken, floors climbed, distance from a greater walked/run, calorie intake, calories burned, active minutes variety of a day, sleep patterns, weight and BMI (Shu-Yi Liaw, Ph.D.)Marr, 2016).
Variety sources and Credit card company used website click-stream data formats and other data formats from call center to customize offers (Shu-Yi Liaw, Ph.D.)Russom, 2011; (Shu-Yi Liaw, Ph.D.)Davenport and Patil, 2012).
Davenport et al., Apple’s focus is on internal data, generated by users of
2012) their products and services (Shu-Yi Liaw, Ph.D.)Marr, 2016).
Table 1 5Vs of Big Data Characteristics in business analytics (Shu-Yi Liaw, Ph.D.)Cont.)
Velocity refers At Twitter, 140 characters per tweet, the data volume to increasing are estimated at 8 terabytes per day due to the high speed which velocity of data (Shu-Yi Liaw, Ph.D.)Dijcks, 2012). data is created, The 300 gigabytes per second of data provided by the
Velocity processed, seven CERN stored and sensors (Shu-Yi Liaw, Ph.D.)Marr, 2016). analyzed All of the 6000 tweets that are posted every second are (Shu-Yi Liaw, Ph.D.)Anuradha, available for IBM to analyze in real time (Shu-Yi Liaw, Ph.D.)Marr, 2016). 2015)
The data Using data fusion, an organization can combine accuracy of multiple less reliable sources to create a more accurate and analysis depends useful data point (Shu-Yi Liaw, Ph.D.)Davenport et al., 2012).
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.
2 Description: “Big Data Analytics” and
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 (Shu-Yi Liaw, Ph.D.)2016), Vaithianathan (Shu-Yi Liaw, Ph.D.)2010) and Ngai et al (Shu-Yi Liaw, Ph.D.)2009) in e-commerce and Lim et al (Shu-Yi Liaw, Ph.D.)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 e- 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.
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 (Shu-Yi Liaw, Ph.D.)Elsevier)
Web of knowledge (Shu-Yi Liaw, Ph.D.)Thomson ISI)
Business Source Complete (Shu-Yi Liaw, Ph.D.)EBSCO host)
ABI/ Inform Complete (Shu-Yi Liaw, Ph.D.)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.
Figure 5 Distribution of articles by year.
2.2.1.2 Distribution of Articles by Categories
We adopted a thematic analysis of literature review which provide by Braun and Clarke (Shu-Yi Liaw, Ph.D.)2006) The literature is divided into five categories of BDA applications (Shu-Yi Liaw, Ph.D.)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.
Better decision making with automated algorithms to improve decision making and minimize risks by unearthing valuable insights in order to find out hidden patterns For example, McKinsey using big data analytics to automatically fine-tune inventories in response to real-time sales.
New product or business model innovation For example, a manufacturer can use data obtained from actual use of its products to improve the development of the next generation of products.
Table 3 presents the distribution of articles by categories of BDA applications in e-commerce First, we can notice that many of the publications covered more than one type of applications of BDA Clearly, the vast majority of the publications are in ‘Better decision making’ (Shu-Yi Liaw, Ph.D.)39 articles or 31% of all publications) One explanation of this high level of publication on ‘Better decision making” is e-commerce depends on real time business decision making Followed by ‘Discovering needs and improving performance’ and
‘New product or business model innovation’, with 25 articles for each category (Shu-Yi Liaw, Ph.D.)or 20% of all publication), followed by ‘Creating transparency’ with
22 articles or 18% of all publications Finally, we have ‘market segmentation’ with 13 articles (Shu-Yi Liaw, Ph.D.)or 11% of all publications) The research related to five identified categories of DBA application were shown clearly in Appendix A.
Table 3 Big Data analytics (Shu-Yi Liaw, Ph.D.)BDA) applications in e - commerce
No BDA application in E-commerce Number Percentage (%)
2 Discovering needs and improving performance 25 20%
5 New product or business model innovation 25 20%
2.2.2 Business Values of Applying Big Data Analytics for E-commerce Firms
Big data analytics in E-commerce: Aspect of Customer
The changes in consumer behavior had strong influences on all enterprises throughout time; a decision moment being in the 1970’s when a significant macroeconomic change on the law of supply and demand had happened Until 1960, the economic perspective of consumer behavior and the models that described it relied on the assumption that all consumers are always rational in their purchases, so they will always buy the product that will bring the higher satisfaction Before that, three types of models were developed There was Economic Model, Learning Model and Psychoanalytic
& Sociological Model (Shu-Yi Liaw, Ph.D.)Kahneman and Thaler, 2006) During this time, consumers had conservative behavior because they were buying the same products, consumer behavior being an emergent phenomenon that has evolved along with human development This diversification of needs is the main cause leads the researchers to study the consumer behavior Back to year 2008 because of the economic and financial crisis that spread all over the world led consumers think twice before buying a product Consumers were buying less products, their behavior began to be a defensive one The online marketing began to take a role in purchase People started use internet to order and compare their prices and characteristics of product Today, consumers face a problem is to information and diverse, the opportunity cost for a making the decision process more and more complicated, their behavior began to be unpredictable.
The purchase decision process began to be study follow the evolution of consumer behavior (Shu-Yi Liaw, Ph.D.)Figure 6) While customers can tell what they think,researchers can tell what the customers actually want, because data on actual consumer behavior and experiences is now available to be measured and analyzed Big Data was used and developed in order to understand more the customer’s behavior Like all technologies, using Big Data in doing business especially e-commerce has advantages and disadvantage at the same time.
Rational Perspective Behavior Perspective PSYCHOANAYTIC and SOCIOLOGICAL
ECONOMIC MODEL LEARNING MODEL MODEL
Daniel Bernoulli John Von Neumann and
Daniel Kahneman and Rick Smolan, Jennifer
Amos Tversky Erwitt “The Human marginal utility “Theory of Game and
“Prospect Theory: an Face of Big Data”
Economic Behavior”-risk and uncertainty Analysis of Decision under Risk”
Figure 6 The evolution of consumer behavior
Based on what Big Data analytics can bring to business values, the author select out some influencing factors: recommendation system, information search, dynamic pricing and improve customer interaction and proposes an integrated model to explore the effects of Big Data mechanism on consumer behavior on the model B2C e-commerce.
2.3.1 Positive Factor of Applying BDA on Customers’ Responses
Positive factor of applying Big Data analytics application includes offering information search, recommendation system, dynamic pricing and customer service to interact with the community member.
Emotionally driven consumers are easy to induce their purchase desire and demand by network information The quick and convenience of gathering online information is one of the perceiving values for customer when they shop online The website using Big Data analytics tool can filter and browse a large number of data to customer information Text miner technical is used to solve within the web and text search and note the relevance of history with libraries, catalogs, and coincidences Big Data is all about relevancy and offering the right products or services to the right person for the right price via the right channel at the right time For example, Google personalizes its search results based on users profile and Amazon offers different homepages with different products on offer to almost every visitor It comes back to completely knowing your customer by combining different data sources to really know what they are looking for.
Information search indicates that information quality and searching service quality Information quality is a measure of value perceived by output provided by a website Information characteristics, such as update, useful, detailed, accurate, and completed has been viewed as important components of information quality (Shu-Yi Liaw, Ph.D.)Bharati and Chaudhury, 2004) Searching service quality can be defined as overall customer evaluations regarding quality of searching service as quickly responsiveness (Shu-Yi Liaw, Ph.D.)Delone and McLean, 2003), suitable and realistic Based on customer’s choice and action, online retailer using Big Data analytics can provide real-time services to customers This action may become one of the sources of competitive advantages to gain customer’s satisfaction (Shu-Yi Liaw, Ph.D.)Luo and Seyedian, 2003).
Recommendation system operated under recognized famous sites likeAmazon, eBay, Netflix, Monster, and other Retail stores where everything is a recommended This involves a relationship between e-vendors and buyers whereby the buyers provide their information as hobbies, preferences, while the e-vendors offer a recommendation fitting their needs whilst benefiting both Details are given on basic principles behind recommendation systems:user-based collaborative filtering which used similarities in user rankings to predict their interests and item-based collaborative filtering as points in a space of items Collaborative filtering systems use customer interactions and product information with ignoring other factors to make suggestions (Shu-Yi Liaw, Ph.D.)Huang et al., 2007; Lee et al., 2012) Recommendation system has chosen some algorithms to use in recommendation model like K-nearest neighbor is a collaborative filter based on measures of association between items or users. Cold starting recommends typical products popular across your customer base to new website visitors Association rules automatically recommend related items as you browse or place an item in the cart Clustering is an algorithm to group similar users or items together to streamline analysis of massive data matrices Slope one estimates preferences for new items based on average difference in preference value (Shu-Yi Liaw, Ph.D.)ratings) between a new item and the other items a user prefers.
Recommendation system which is based on the customer’s purchase behavior can evaluate commodity information, study on interest of customer, product matching and recommend customers to substitute or complementary products Recommender systems help individuals to identify items that might be of interest to them from a large collection of items by aggregating inputs from all individuals (Shu-Yi Liaw, Ph.D.)Resnick and Varian, 1997) In these systems, recommendations are usually made based on a mixture of past purchasing or browsing behavior, characteristics of the items being considered, and demographic and personal preference information of shoppers (Shu-Yi Liaw, Ph.D.)Shardanand and Maes, 1995) Chevalier and Mayzlin (Shu-Yi Liaw, Ph.D.)2006) indicated that other internet consumers’ product recommendations had an impact on consumer purchasing behavior at online retailer sites.
E-commerce recommendation system can help consumers to choose favorite products that can be implemented in real networks, like Amazon,Taobao, Google and other websites to promote the sale (Shu-Yi Liaw, Ph.D.)Hongyan andZhenyu, 2016).
Dynamic pricing is an individual-level price discrimination strategy where prices are charged according to customer, location, product, or time (Shu-Yi Liaw, Ph.D.)Kotler and Armstrong, 2000) Dynamic pricing often referred to in economic term as individual-level price discrimination which has become much more common with increased prevalence of internet marketing Dynamic pricing is mostly defined as the buying and selling of products in markets where prices are free to adjust in responses to supply and demand conditions at the individual transaction level Thus, dynamic pricing can attract most retailers with the ability to use the newly available information to individually set prices based on a given customer’s willingness to pay (Shu-Yi Liaw, Ph.D.)Garbarino and Lee, 2003).
The purpose of dynamic pricing is to maximize the seller’s profit by charging consumers with the highest prices each consumer is willing to pay by manipulating the magnitude and the temporal proximity of price differences they will employ Consumers’ reactions to this pricing scheme strategy will have a significant impact on their satisfaction with purchases and their subsequent behavioral intentions Example, Amazon normally changes the price of items sold on its website on a daily, weekly, or monthly basis by 5%, 10%, or 15% Dynamic pricing practices by sellers in responses to segment and individual level differences have been made more feasible as online customers’ behavior increases (Shu-Yi Liaw, Ph.D.)Haws and Bearden, 2006; Erevelles et al., 2016).
Consistent with the recommendations of Bolton et al (Shu-Yi Liaw, Ph.D.)2003), the present research investigates the effects of various dynamic pricing contexts and can be consider as an additional transaction characteristics Economic theory argues that dynamic pricing (Shu-Yi Liaw, Ph.D.)i.e., individual-level price discrimination) is naturally good for the profitability of the firm because it allows the firm to capture a larger share of the consumer surplus However, evidence from recent retail experiments with internet based dynamic pricing suggests that consumers react strongly against this practice.
Providing the high-quality customer service is an important key to keep the customers happy Big Data enables you to drastically improve your services Using deep data analytics, you can optimize your customer service resulting in happier customers Some customers may not only complain of products or services through the official channels offered by website, but may also go social about their groups You need to have data of such customers and exercise extra caution so that complaints of such customers are addressed double-quick Big Data is used to enhance business processes Retailers can optimize their stock based on predictions from web search trends, customers’ responses and weather forecasts One special application for business process is the analytics in supply chain or delivery route Based on geographic position and radio frequency identification, sensors are used to track goods or delivery vehicles This process enables customers to track their orders From that, the customer services can be improved and increase customer satisfaction.
Amazon used Big Data analytics to save what customers have placed inside their virtual shopping cart These items have recently viewed or take a purchasing action in the past The technique used here is item to item collaborative filtering Another application is virtual presence which enables online shoppers to interact with shopping experience Unlike traditional online retailing, online shopping has shopping information being suggested to consumers through several channels including product trial, product offering or services Virtual reality or virtual product experience lets customers to interact with online products and experience a much wider range of those products’ characteristics (Shu-Yi Liaw, Ph.D.)Jiang and Benbasat, 2002) Earlier exploratory studies have recommended that virtual reality has the potential to enhance the consumers’ product knowledge and brand attitude including enhancement in their purchase intentions (Shu-Yi Liaw, Ph.D.)Jiang and Benbasat, 2002; Daugherty et al., 2005).
A review of customer is a feedback of a product or service made by consumer who has purchased It evaluates the product or service quality and give comments on the website instead of professional reviews Interested customers can see the feedback of previous customers who has interacted with the website before This service can guarantee shoppers of products trustworthiness They can also inspire other customers to share their thoughts about the products being sold.
2.3.2 Negative effects of applying Big Data analytics on customers’ responses
Besides the benefits of applying BDA bringing the customer values, applying BDA may give customers some negative effects The detailed description is as following subsections.
RESEARCH METHODOLOGY
Research Model and Research Hypotheses
According to the literature review and the results of the former research, it needs to make research model in order to match with study purposes This research shows three models to solve different research purposes.
Hypothesis is specific prediction, based on theory, about degree and direction between variables to be tested in the research study (Shu-Yi Liaw, Ph.D.)VanderStoep and Johnson, 2008) Based on the research purposes and theory concept, the research model and research purposes as following:
3.1.1 Mechanism of Applying Big data Analysis and Customers’ Responses
By collecting different data in Big Data era like geographic distribution,emotional tendencies, customers’ responses on shopping as well as social connection, hobbies; companies can achieve demand orientation preference orientation, relationship orientation, and other ways to satisfy customers E- commerce vendors used information and communication technologies through using different data mining techniques to provide personalized services to customers, redesign the website to provide better services(Shu-Yi Liaw, Ph.D.)Astudillo et al., 2014) Akter and Wamba (Shu-Yi Liaw, Ph.D.)2016) indicated that e-vendors apply Big Data analytics to create personalized offers, set dynamic price, and offer the right channel to provide consumer value Applying Big Data analytics by offering virtual shopping experience, a more direct experience of personalized products will stimulate consumers desire to buy products(Shu-Yi Liaw, Ph.D.)Guangting and Junxuan, 2014) All these four positive applications of positive factor above will help catch customers’ intention, bring good customers’ behavior and finally lead them take action to buy a product or service from e-vendors Accordingly, the following hypothesis is suggested:
Hypothesis (H 1 ): Positive factor of applying Big Data analytics is positively associated with customers’ responses.
Negative factor include privacy and security problem, shopping addiction and group influences Customers feel uncomfortable and embarrassed when they think that e-vendors know more about them (Shu-Yi Liaw, Ph.D.)Kshetri,
2014) Guangting and Junxuan (Shu-Yi Liaw, Ph.D.)2014) said that analyzing the Big Data has negative impact on the consumers’ willingness Negative factors will decrease customers’ intention and stimulate their negative behavior, finally drive them to refuse taking action to buy products or services As discussed above, we propose the following hypothesis:
Hypothesis (H 2 ): Negative factor of applying Big Data analytics is negatively associated with customers’ responses.
Figure 7 presents model 1 which is built to explore and determine mechanism of applying big data analysis and customers’ responses.
Evaluation of Intentional application of BDA Outcome
In orm t on I formation Search ation Search ion Search eation Search ch r
Figure 7 Model 1-Exploring and determining the mechanism of
3.1.2 Perceived Value as the mediator for Positive Factor of Applying BDA and Customers’ Responses
Since perceived value is more important nowadays, firms can enhance consumer purchase intentions through product value(Shu-Yi Liaw, Ph.D.)Steenkamp and Geyskens, 2006) It could not only be a important determinant in maintaining long-term customer relationships, but also play a key role in affecting purchase intentions (Shu-Yi Liaw, Ph.D.)Zhuang et al., 2010) Perceived value was found to be a powerful predictor of purchase intention (Shu-Yi Liaw, Ph.D.)Zeithaml, 1988) as well as mediator (Shu-Yi Liaw, Ph.D.)Chen, 2012) Therefore, perceived value’s dimensions play the mediating role in the positive factor of applying BDA–customers’ responses relation need to be examined.
With four applications of applying BDA as mention above, information search can offer information available with needed information, recommendation system offer different choices for customers to customize products, dynamic pricing enables customers to save cost and customers’ services are enhanced Therefore, positive factor of applying BDA will affect to functional value, and functional value can enhance positive customers’ responses Therefore, we propose the hypothesis as following:
Hypothesis (H 3 ): Functional value mediates the relationship between positive factor of applying BDA and customers’ responses.
Using BDA application in the website, the customers may receive different joy which is offered by recommendation system, customers’ services.
Hypothesis (H 4 ): Emotional value mediates the relationship between positive factor of applying BDA and customers’ responses.
Figure 8 shows the built model 2 to evaluate the multiple mediating effect of perceived value ‘dimensions in relation between positive factor of applying BDA and customers’ responses.
Evaluation of Multiple Intentional application of BDA Mediators Outcome
Figure 8 Model 2-The mediating role of perceived value
3.1.3 The Mediating Role of Perceived Risk and Moderating of Trust
3.1.3.1 Mediating Role of Perceived Risk
Park et al (Shu-Yi Liaw, Ph.D.)2005) referred that lower perceived risk may be related to higher purchase intention Compared to traditional shopping, online commercial establishments are less known to consumers (Shu-Yi Liaw, Ph.D.)Lee and Turban,
2001), and the absence of face-to-face interaction has introduced more uncertainty and risk (Shu-Yi Liaw, Ph.D.)Wu and Chen, 2005) In e-commerce, Vijayasarathy andJones (Shu-Yi Liaw, Ph.D.)2000) found that consumers’ perceived risk was an important factor that influenced intention to online shopping Shoppers’ confidence in judging quality of products or in making decisions to purchase products may reduce perceived risk, as consumers develop shopping experience from the Internet(Shu-Yi Liaw, Ph.D.)Yoh et al., 2003) The higher the perceived risk, the more uncertain consumers feel in purchasing decision (Shu-Yi Liaw, Ph.D.)Chen and Chang, 2013).
Market research has reported that the growing concerns about perceived risk associated with online shopping E-commerce is more applied technology so the concern about perceived risk also will increase Customers are often reluctant to buy a product or use a service because of perceived risk from transaction (Shu-Yi Liaw, Ph.D.)Gregg and Walczak, 2008) The concept of perceived risk have been identified as critical mediators that influence customers’ online purchase intention (Shu-Yi Liaw, Ph.D.)Lim, 2003; Chau et al., 2007; Chen and Barnes, 2007; Lin, 2008). Moreover, the outcome of this process depends on the behavior of the e- marketer and this is not within the consumer’s control (Shu-Yi Liaw, Ph.D.)Lee and Turban,
2001) Therefore, perceived risk is a relevant parameter, especially in the early stages of a process of customer adoption of online purchase of products and services (Shu-Yi Liaw, Ph.D.)De Ruyter et al., 2001; Hsu and Chiu, 2004) Therefore, we propose that perceived risk may be a mediator between negative factor and customer’s responses.
Hypothesis (H 5 ): Perceived risk is a mediator of the relationship between negative factors of applying BDA and customer’s responses.
3.1.3.2 Moderating Role of Trust Propensity
Trust propensity can be seen as one kind of personal trait, it affects to specific customers’ perception to e-vendor It is a vital factor of customers’ responses and other various perceptions about the web site and the company.
A strong trust propensity tends to be associated with increased honesty, raise positive feelings and accepting of things at the first sight (Shu-Yi Liaw, Ph.D.)Graziano andTobin, 2002) Customers with low trust propensity tend to have cautious or even negative views when faced with uncertain situations (Shu-Yi Liaw, Ph.D.)Falcone et al.,2001; Graziano and Tobin, 2002) Low trust propensity lead to break customers’ desire and reluctance to try new things Lee and Turban (Shu-Yi Liaw, Ph.D.)2001) revealed that trust propensity is positively moderator in the relationship between perception about internet vendors to customers’ trust in online shopping However, perceived risk is existence and is threaten that will guide lower consumers’ intention to continue to online purchase (Shu-Yi Liaw, Ph.D.)Chen et al., 2015).
Under BDA era brings some negative factors to customers, but good first good feeling from customer will fall quickly when risks are received. Especially, customers with high trust propensity will not think of bad results as the low trust propensity group did Therefore, we propose that trust propensity is a moderator effect the process from receiving negative factors to customers’ responses under mediating of perceived risk.
Hypothesis (H 6-1 ): Trust propensity is a moderator of the relationship between negative factors of applying BDA and customers’ responses.
Hypothesis (H 6-2 ): Trust propensity is a moderator of the relationship between negative factors of applying BDA and perceived risk.
Hypothesis (H 6-3 ): Trust propensity is a moderator of the relationship between perceived risk and customers’
Figure 9 shows the built model 3 to evaluate the mediating effect of perceived risk in relation between negative factor of applying BDA and customers’ responses and moderating effect of customers trust propensity.
Evaluation of Mediator and Intentional application of BDA Moderator Outcome
Figure 9 Model 3-The mediating role of perceived risk and moderating of trust propensity
The Operational Definition and Measurement Design
According to Blumberg et al (Shu-Yi Liaw, Ph.D.)2008), an operational definition is a definition stated in terms of specific testing or measurement criteria These terms must have empirical standards (Shu-Yi Liaw, Ph.D.)that is must be able to count, measure, or in some other way gather the information through our sense) The definition must specify characteristics and how they are to be observed, making it easier to show the data collection tool which is suitable for used in this research In Structural Equation Modeling (Shu-Yi Liaw, Ph.D.)SEM), there are exogenous variable and endogenous variable According to (Shu-Yi Liaw, Ph.D.)Bowen and Guo, 2011), exogenous variable is variable which didn’t explained or predicted by any other variables in the model, meanwhile endogenous variable is variable which explained or predicted by one or more other variables Operational definition variable of this research, as follows:
Endogenous variable using in this study is customers’ responses variables. Customers’ responses were measured base on AIDA model The AIDA model contains a four-step formula to get attention, attract interest, create desire, and then take action, which is making a purchase AIDA model is very useful in assessing the impact of advertising by controlling every step of the psychological transformation that starts from the individual level to see the effect to purchase made by the individuals involved (Shu-Yi Liaw, Ph.D.)Kojima et al., 2010) The measurement items are adapted from previous study (Shu-Yi Liaw, Ph.D.)Ehrenberg, 2000; Lee et al., 2013) and presented in Table 5.
Table 5 Dimensions and indicators of customers’ responses
No Dimension Items Indicators Sources
Attention CAI1 The applications on website catches my attention
(Shu-Yi Liaw, Ph.D.)CAI) CAI2 I had trying to read that information
Interest CI1 Continuously pay attention
2 CI2 I want to get more information
(Shu-Yi Liaw, Ph.D.)CI)
Desire CD1 I want to buy the product
I will continue to use this webpage for
(Shu-Yi Liaw, Ph.D.)CD) shopping.
Action CAC1 I will have action to buy
I will introduce this webpage to my friends
(Shu-Yi Liaw, Ph.D.)CAC) and family.
(Shu-Yi Liaw, Ph.D.)Ehrenberg, 2000; Lee et al., 2013)
Exogenous variable in this research includes positive factor and negative factor of applying DBA analytics
Positive Factor of Applying Big Data Analytics
Applying Big Data analytics could bring special benefit to customers It includes information search, recommendation system, dynamic pricing and customer services The measurements for each variable are shown in Table 6.
Table 6 Dimensions and indicators of positive factor of applying BDA
No Dimension Items Indicators Sources
IS1 I am able to search the useful information in the e-shopping website Information IS2 The information I search in the e-shopping (Shu-Yi Liaw, Ph.D.)Tang site are detailed and completed
IS3 The result is provided quickly and fit to my
(Shu-Yi Liaw, Ph.D.)IS) 2015) need IS4 Search result provided by shopping website is very realistic.
RS1 Shopping website can recommend substitute goods for the product I want to buy.
RS2 Shopping website can recommend complementary goods for the product I want (Shu-Yi Liaw, Ph.D.)Tang
System (Shu-Yi Liaw, Ph.D.)RS) RS3 Shopping website can recommend for you
2015) some product may be you like or best sellers of website RS4 I believe that the recommendation information is an act of kindness.
PD1 Providing different prices for individual customer at the same time
Price Dynamic PD2 Offer different prices at different time New
3 PD3 Providing different prices with different measure
(Shu-Yi Liaw, Ph.D.)PD) substitute products -ment
PD4 Providing different prices with different conditions on the same product
CS1 The website provides channel to support customers CS2 I expect that I am able to track my order (Shu-Yi Liaw, Ph.D.)Tang Customer CS3 The shopping website which provides
Services (Shu-Yi Liaw, Ph.D.)CS) virtual experience can let me choose more 2015) suitable goods.
CS4 I can refer to the reviews of customers who bought the products before
Negative Factor of Applying Big Data analytics
From literature review, applying BDA may bring some negative factor to customers It is privacy and security, shopping addiction and group influence Table 7 presents the measurements for each variable.
Table 7 Dimensions and indicators of positive factor of applying BDA
No Dimension Items Indicators Sources
PS1 Attracting a great deal of attention from
1 Security PS2 Customer’s personal information will be stolen New
(Shu-Yi Liaw, Ph.D.)PS) measurement
PS3 My information about payment method will be stolen SA1 Spending a lot of time to review products
I have often bought a product that I did not
SA2 need, while knowing that I had very little
Shopping money left (Shu-Yi Liaw, Ph.D.)Lejoyeux
2 Addiction As soon as I enter a shopping website, I have and
(Shu-Yi Liaw, Ph.D.)SA) SA3 an irresistible urge to go into a shop and buy Weinstein, something 2013)
SA4 I have felt somewhat guilty after buying a product, because it seemed unreasonable When I buy a product online, the reviews GI1 presented on the website are helpful for my decision making
Group GI2 Reviews posted on the website affect my
3 Influences purchase decision (Shu-Yi Liaw, Ph.D.)Mana and
Mirza, 2013) (Shu-Yi Liaw, Ph.D.)GI) GI3 Reviewers’ rating of usefulness of the review affects my purchase decisionPopularity of web site that present the reviewsGI4 affect my purchase decision
Mediation variable: Perceived value and perceived risk are evaluated in this research.
Perceived value is considered as multiple constructs with functional and emotional value The functional value is defined as a perceived utility of the attributes of the products and services Emotional value consists of the feelings or the affective states generated by the experience of consumption (Shu-Yi Liaw, Ph.D.)Carlos et al., 2006) Dimensions and indicators of perceived value that used in this research presented on Table 8.
Table 8 Dimensions and indicators of perceived value
1 Functional FV1 Information obtained from e-vendor website are
Value easy to understand and useful
(Shu-Yi Liaw, Ph.D.)FV) FV2 I can buy product with acceptable quality and price which I want from shopping website
2 Emotional EV1 When using the shopping website, I feel relaxed
Value and enjoy my time
(Shu-Yi Liaw, Ph.D.)EV) EV2 I feel I can save time for shopping
(Shu-Yi Liaw, Ph.D.)Carlos et al
(Shu-Yi Liaw, Ph.D.)2006); Sanchez et al.,
Perceived risk is defined as a consumer’s belief about the potential uncertain negative outcomes from the online transaction E-commerce industry under Big Data era, perceived risk is mentioned to four main risks:privacy, financial, product performance, psychological, and time risk These indicators of perceived risk that used in this study are presented in Table 9.
Table 9 Dimensions and indicators of perceived risk
1 PR1 I am afraid that online purchase is risky because the product/service may fail to meet my expectation Forsythe
2 PR2 I believe that online purchases are risky because I will spend and Shi more money to buy other products (Shu-Yi Liaw, Ph.D.)2003),
3 PR3 I believe that online purchases are risky because I have to spend (Shu-Yi Liaw, Ph.D.)Lim, more time to view substitute and complementary products 2003)
4 PR4 I believe that online purchases are risky because my personal information and credit information will be stolen.
Moderation variable: Customer trust propensity is considered as a moderator in this research Propensity to trust is a dispositional variable that concerns a person’s general willingness to trust others Trust propensity in this study is measured by using 7 points from low to high trust propensity. Low trust propensity customer means that customer is a difficult person to trust a new thing In contrast, high trust propensity customer means that customer is an easy person to trust a new thing.
We separated 349 respondents into two groups: Low and high trust propensity based on standardized value of trust propensity to define High andLow Risk The standardized value higher than 0, it belongs to high trust propensity group In contrast, the standardized value less than 0, it belongs to low trust propensity group Among all respondents, 144 respondents belong to low trust propensity and 205 respondents belong to high trust propensity.
Research Type
Doing research means a systematic investigation, including research development, testing and evaluation, designed to develop or contribute generalizable knowledge Activities which meet this definition constitute research for purpose of this policy, whether or not they have conducted or supported under a program which is considered research for other purpose (Shu-Yi Liaw, Ph.D.)Steneck, 2009).
A quantitative technique is used in this study According to Creswell and Creswell (Shu-Yi Liaw, Ph.D.)2017) quantitative research is a means of testing objectives theories by examining the relationship among variables These variables, in turn, can be measured, typically on instruments, so that numbered data can be analyzed using statistical procedures.
Accordance with the problem formulation and research objectives, this research can be categorized as a type of explanatory research Explanatory research goes beyond description and attempts to explain the reason for the phenomenon that the descriptive research only observed This research uses theories or at least hypotheses to account for the forces that caused a certain phenomenon to occur (Shu-Yi Liaw, Ph.D.)Cooper et al., 2006) According to Gravetter andForzano (Shu-Yi Liaw, Ph.D.)2012), explanatory research is involved in explaining how something happens, and assessing causal relationship between variables.Therefore this research objective is to get the description about the relationship and influence between several independent variables on the dependent variable That is the explanation of the reasons for choosing explanatory research as a type of research.
Pilot Test
A pre-test was conducted to ensure reliability and validity of the adapted scales Fifty respondents (Shu-Yi Liaw, Ph.D.)nP) were invited to participate in the pre-test study Their feedbacks were analyzed in order to adjust the questionnaire. Cronbach’s alpha will be used to examine the reliability analysis These coefficients will be exploited to test the internal consistence of the variables.
If Crobach’s alpha (Shu-Yi Liaw, Ph.D.)α) is greater than 0.7, it means high reliability If the) is greater than 0.7, it means high reliability If theCronbach’s α) is greater than 0.7, it means high reliability If the ranges between 0.5 and 0.7, it means the internal consistency of the variable should be accepted Otherwise, if the item has Cronbach’s α) is greater than 0.7, it means high reliability If the coefficient under 0.35 and Cronbach’s α) is greater than 0.7, it means high reliability If the if item deleted is higher than total
Cronbach’s α) is greater than 0.7, it means high reliability If the then it should be deleted (Shu-Yi Liaw, Ph.D.)Hair, 2010) On the other hand, these participants were pre-tested with the questionnaire according to its understanding and clarity Changes in wording were made in the questionnaire on the pre-test.
After doing pre-test, one item of privacy and data security was deleted because of the Crobach’ Alpha if item deleted was higher than totalCronbach’s α) is greater than 0.7, it means high reliability If the of variable.
Sample Size
According to Gravetter and Forzano (Shu-Yi Liaw, Ph.D.)2012), a sample is a set of individuals selected from a population and usually is intended to represent the population in research study For normally distributed data, Bentler and Chou (Shu-Yi Liaw, Ph.D.)1987) suggested sample size requirements in SEMs as a ratio as low as 5 cases per free parameters would be sufficient when latent variables have multiple indicators and that a ratio of at least 10 subjects per variable would be sufficient for other distributions In our examination of the published research, there are many articles used from 250 to 500 samples.
The minimum sample for three models bases on number of free parameters of model Model 1 as Figure 7 in chapter 3 includes 29 free parameters Therefore, the minimum requirement data is 145 samples The minimum requirement data for second model (Shu-Yi Liaw, Ph.D.)Figure 8) and third model (Shu-Yi Liaw, Ph.D.)Figure 9) are 205 and 290, respectively.
A total 430 questionnaires were returned First 350 samples were used for test the hypothesis in Study 1 (Shu-Yi Liaw, Ph.D.)Figure 7) After deleting 77 missing data and outlier data, thereby yielding 273 valid questionnaires (Shu-Yi Liaw, Ph.D.)78%) which was much higher than the recommended value of at least 145 samples for Model
1 Study 2 and study 3 (Shu-Yi Liaw, Ph.D.)Figure 8 & Figure 9) have the appearances of mediator and moderator Therefore, the sample size used to test in these models is 349 samples.
Along with the rapid development of the internet, Asia is becoming one of the e-commerce markets with the fastest growth in the world Besides China, Korea, Japan, China and the others, e-commerce in Vietnam has grown rapidly, resulting in Vietnamese consumers now gradually changing in their habit of online shopping According to the Internet World Stats 2011, Vietnam has more than 30 million internet users, representing more than 30% of the nationwide population Vietnam has been become a country with the fastest growing internet market in South East Asia It has become a more dynamic and favorable market for e-commerce For those reasons, Vietnam depicts a special case being chosen to in this study.
The survey data was collected from a sample of student, since college students have had experiences in using the internet The feasibility of using students as sample has been demonstrated in many previous studies (Shu-Yi Liaw, Ph.D.)Gefen,2002; Kuo et al., 2009; Zhang et al., 2011) Besides, according to a report(Shu-Yi Liaw, Ph.D.)Agency, 2016) stated that students are the key convenient shoppers and become potential customers of e-commerce market The data collection was performed from December, 2016 to January, 2017 in Vietnam.
Data Type and Data Collection Method
The data used in this research is the primary data and secondary data. Primary data is data which made by researcher in order to solving the research problems Data was collected from respondent’ answers through survey given to student in Vietnam Secondary data is data which collected by researcher, in order to solving the problem, to build the systematic review Source of secondary data is information, literature from previous studies, internet.
Data collection aims to obtain the necessary information in order to achieve the research objectives The research objectives expressed in the form of hypotheses Method of data collection can be through questionnaires and interviews Relating with this matter, the techniques used in the collecting of relevant with research problems, they are documentation and questionnaire. Documentation is done by collecting and studying the documents that can be a form of published articles, writing papers, magazines, monumental work, related to research objectives A questionnaire normally includes a list of questions or statements which require respondents to answer or indicate the extent to which they agree or disagree with the given statement In this study, a closed-ended multiple choice questionnaire will be used to collect the data. The survey consists of two sections: Section 1 - Main questions and section 2- Respondents’ personal information The questionnaires were provided in detail in Appendix B and C.
The study was conducted in the computer center of Thai NguyenUniversity, Vietnam for the purpose of understanding the research,mechanism of Big Data analytics and minimizing interference during the survey participation The respondents were asked to navigate to the Amazon website (Shu-Yi Liaw, Ph.D.)www.amazon.com) which is one of the famous websites using BigData analytics application This action is required to do at least two times on computer and goes through the procedure of buying one of two products on the website, but not actually purchase This ensured that the respondents have interaction experience with the website applying Big Data analytics in e- commerce Meanwhile, respondents also was explained the applications ofBig Data Analytics After that, the respondents were required to complete the survey Based on the report of Vietnam e-commerce (Shu-Yi Liaw, Ph.D.)Agency, 2014; Agency,2015; Agency, 2016) in 2013, 2014 and 2015, two products are fashion item and electronics item which best sellers of e-commerce market in Vietnam.
Data Analysis Techniques
The data obtained from the questionnaires will be coded, captured and edited The Statistic Package for Social Science (Shu-Yi Liaw, Ph.D.)SPSS) software version 22 and analysis of moment structures (Shu-Yi Liaw, Ph.D.)AMOS 22.0) were used for the data analysis The procedure of data analysis and the techniques used will be as follow:
Lind et al (Shu-Yi Liaw, Ph.D.)2005) defined that descriptive statistics is methods of organizing, summarizing, and presenting data in an informative way Masses of unorganized data, such as the census of population and the weekly earning of thousands of computer programmers, are of little value as is However, statistical techniques are available to organize this type of data in a meaningful form.
This analysis can be done by interpreting the data processing derived from answers to the question from the questionnaires that has been distributed The results of data processing can be presented through tables, graphs, pie charts, pictograms, calculation mode, mean median, calculating percentiles, calculations of data through the calculation of average and standard deviation, and percentage calculation This descriptive statistics analysis is used for all three research model of this research.
3.7.2 Reliability and Content Validity Analysis
Hair (Shu-Yi Liaw, Ph.D.)2010) said that the researcher’s goal of reducing measurement error can follow several paths In assessing the degree of measurement error present any measure, reliability and validity are important characteristics of measurement.
Reliability is an assessment of the degree of consistency between multiple measurements of variable One form of reliability is test-retest, by which consistency is measured between the responses for an individual at two points in time The objective is to ensure that responses are not too varied across time periods so that a measurement taken at any point in time is reliable A second and more commonly used measure of reliability is internal consistency, which applies to the consistency among the variables in a summated scale The rationale for internal consistency is that the individual item or indicators of the scale should all be measuring the same construct and thus be highly inter-correlated (Shu-Yi Liaw, Ph.D.)Hair, 2010).
In this test, Cronbach’s alpha will be used to assess the consistency of the entire scale It aims to examine the measurement scale of individual items in the questionnaire whether they are consistent or not If the Cronbach’s α) is greater than 0.7, it means high reliability If the is greater than 0.7, indicating that it has high reliability If the Cronbach’s α) is greater than 0.7, it means high reliability If the is between 0.5 and 0.7, it means that the internal consistency of variable should be accepted Otherwise, if the item has coefficient of Cronbach’s α) is greater than 0.7, it means high reliability If the is under 0.35, and then it should be deleted (Shu-Yi Liaw, Ph.D.)Hair, 2010).
Related to validity testing of the instrument according to Malhotra (Shu-Yi Liaw, Ph.D.)2002) validity is the extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured, rather than systematic random errors Validity test performed related to the accuracy of measuring instrument against the concept that measured so that really measuring what should be measured Content validity depends on the extent to which an empirical measurement reflects a specific domain of content (Shu-Yi Liaw, Ph.D.)such as concept and constructs) It means that it is necessary to construct items that reflect the meaning associated with each dimensions The reliability and validity analysis are used for all three research model of this research.
Comparing mean test is known as Student’s T-test and Anova test.Student’s T-Test compares two averages and shows there is different between them or not T-test was used to analyze and describe more detail about whether there are any significant differences between demographic characteristic (Shu-Yi Liaw, Ph.D.)age and survey products) and customers’ responses The Analysis of Variance (Shu-Yi Liaw, Ph.D.)ANOVA) is used to compare the mean between more than two groups mean, Anova is the suitable method instead of the t-test (Shu-Yi Liaw, Ph.D.)Field, 2013) It measure the relative size of variance amongst group means compared to the average variance within groups (Shu-Yi Liaw, Ph.D.)Kim, 2014) One-way Anova was used to analyze and describe more detail about whether there are any significant differences between demographic characteristic (Shu-Yi Liaw, Ph.D.)experience) and customers’ responses.
Exploratory Factor Analysis (Shu-Yi Liaw, Ph.D.)EFA) is used when a researcher wants to discover the number of factors influencing variables and to analyze which variables “go together” (Shu-Yi Liaw, Ph.D.)DeCoster, 1998) The purpose of factor analysis is to summarize data so that relationships and patterns can be easily interpreted and understood It helps to isolate constructs and concepts by regrouping variables into a limited set based on shared variance A factor loading for a variable is a measure of how much the variable contributes to the factor, so the high loading scores indicate that the dimensions of the factors are better accounted for by the variables The correlation r must be equal or greater than 0.30 since anything lower would suggest a weak relationship between variables (Shu-Yi Liaw, Ph.D.)Tabachnick and Fidell, 2007).
In this study from 16 items of positive factor and 11 items of negative factor of applying Big Data analytics that can be reduced to smaller set, to get at an underlying concept and to facilitate interpretations (Shu-Yi Liaw, Ph.D.)Rummel, 1988) It is easier to focus on some key factors rather than having to consider too many variables that may be trivial, and so EFA is useful to apply in research model
1 in Figure 7 in Chapter 3 for placing variables into meaningful categories.
The decision to use either formative or reflective indicators for a construct should be based on the nature of the causal relationship between the indicators and the latent variables in the measurement model According to (Shu-Yi Liaw, Ph.D.)Vinzi et al., 2010), the reflective mode has causal relationships from the latent variable to the manifest variables in its block Direction of causality is from latent variable to manifest variables (Shu-Yi Liaw, Ph.D.)indicators), thus constructs explaining variance measurement In this research, models using reflective indicator models Confirmatory factor analysis (Shu-Yi Liaw, Ph.D.)CFA) was used to measure for the internal consistency reliability, convergent validity and discriminant validity.
According to Henseler et al (Shu-Yi Liaw, Ph.D.)2009) reflective measurement models should be assessed with regard to their reliability and validity Based on Hair et al (Shu-Yi Liaw, Ph.D.)2016) usually, the first criterion which is checked is internal consistency reliability The traditional criterion for internal consistency is Cronbach’s alpha, which provides an estimate of the reliability based on the inter-corellations of the observed indicator variables Cronbach’s alpha assumes that all indicators are equally reliable Moreover, it is more appropriate to apply a different measure of internal consistency reliability, which is referred to as composite reliability (Shu-Yi Liaw, Ph.D.)CR- ρ c) The composite reliability varies between 0 and 1, with higher values indicating higher levels of reliability It is generally interpreted in the same way as Cronbach’s alpha.Specially, composite reliability values of 0.60 to 0.70 are acceptable in exploratory research, while in more advanced stages of research, values between 0.70 and 0.90 can be regarded as satisfactory Values above 0.95 are not desirable because they indicate that all indicator variables are measuring the same phenomenon and are therefore unlikely to be valid measure of the construct Finally, composite reliability values below 0.60 indicate a lack of internal consistency reliability.
For the assessment of validity, two validity subtypes are usually examined: the convergent validity and the discriminant validity Convergent validity signifies that a set of indicators represents one and the same underlying construct, which can be demonstrated through their unidimensionality Henseler et al (Shu-Yi Liaw, Ph.D.)2009) suggested using the average variance extracted (Shu-Yi Liaw, Ph.D.)AVE) as a criterion of convergent validity An AVE value of at least 0.5 indicates sufficient convergent validity, meaning that a latent variable is able to explain more than half of the variance of its indicators on average.
High outer loadings on a construct indicate that the associated indicators have much in common, which is captured by the construct This characteristic is also commonly called indicator reliability A common rule of thumb is that the (Shu-Yi Liaw, Ph.D.)standardized) outer loadings should be 0.70 or higher Generally, indicators with outer loadings between 0.40 and 0.70 should be considered for removal from the scale only when deleting the indicator is the extent to which its removal affects content validity Indicators with very low outer loadings are sometimes retained on the basis of their contribution to content validity. Indicators with very low outer loadings (Shu-Yi Liaw, Ph.D.)below 0.40) should, however, always be eliminated from the scale (Shu-Yi Liaw, Ph.D.)Hair, 2010).
Discriminant validity is a rather complementary concept: Two conceptually different concepts should exhibit sufficient difference (Shu-Yi Liaw, Ph.D.)i.e the joint set of indicators is expected not to be one-dimensional) The Fornell – Larcker criterion assesses discriminant validity on the construct level In statistical terms, the AVE of each latent variable should be greater than the latent variable’s highest squared correlation with any other latent variable.
RESULTS AND DISCUSSION
Descriptive Analysis and Mean Comparison
Total 430 respondents’ answers of students from Vietnam were collected during in the end of 2016 and early of 2017 for three models of this study Based on the descriptive analysis, regarding the outliers using SPSS, we need to delete some cases in order to get effective questionnaire So, the usable respondents in this study were 349 effective questionnaires.
Data used in this study were obtained from structured questionnaires designed to target those were student Therefore, characteristics as age, occupation, income and education will not consider to test The descriptive analysis which based on usable respondent consists of gender, experience of last month and kinds of product Table 11 presents the distribution frequency of characteristic of sample structure and general descriptions of respondents’ characteristics are as follows:
Table 11 Demographic descriptive (Shu-Yi Liaw, Ph.D.)n = 349)
How many times last 1-2 times 110 31.5 month 3-4 times 80 22.9 above 4 times 92 26.4
Kind of product was Fashion item 174 49.9 chosen for survey Electronics item 175 50.1
Source: Data processed by SPSS 22
Gender Based on the data from Table 11, it can be explained that from
349 respondents, there are 129 persons or 37% of male respondents and 220 persons or 63% of female respondents.
How many times last month to access e-commerce website It can be shown in Table 11 from 349 respondents The number of respondent who is
“not at all” is 67 persons or 19.2 % of respondents, 1-2 times is 110 persons or 31.5 % of respondents, 3-4 times is 80 persons or 22.9 % of respondents, and above 4 times is 92 persons or 26.4 % of respondents.
Kind of product was chosen for survey There are two popular kinds of products was used to do the survey Based on Table 11, 174 persons or 49.9 % of respondents chose fashion item for survey and 175 persons or 50.1
% of respondents chose electronics items for doing survey.
Independent sample T-test and one way Anova were used to compare mean between different demographic characteristics and customers’ responses Table 12 presents that gender and survey items are not significant different to responses of customers, with t-value 0.873 and 1.790 respectively (Shu-Yi Liaw, Ph.D.)p>0.05).
Table 12 T-test results by gender and survey items
Demographic Characteristics Mean Std t p -value
The result from Anova test is shown in Table 13 With F-value is 1.858 (Shu-Yi Liaw, Ph.D.)p>0.05) that the online shopping experience in last month didn’t affect to customers’ responses Because none of the demographic characteristics had a significant effect on the results, they were not investigated further.
Table 13 Anova results by experiences
Sum of Squares df Mean Square F p -value
Reliability Analysis
To test the internal consistency of the indicators of each factor, the most common method is calculating the Crobach’s α) is greater than 0.7, it means high reliability If the value (Shu-Yi Liaw, Ph.D.)Maichum, Parichatnon,
& Peng, 2017) and item-total correlation (Shu-Yi Liaw, Ph.D.)correlation between a factor and its variables) Hair (Shu-Yi Liaw, Ph.D.)2010) and Nunnally and Bernstein (Shu-Yi Liaw, Ph.D.)1978) suggested that Cronbach’s α) is greater than 0.7, it means high reliability If the value should be higher than 0.700 or at least 0.65 (Shu-Yi Liaw, Ph.D.)Lee and Kim,
1999) From the results one question was removed from the questionnaire because it wasn’t feed the criteria Cronbach’s α) is greater than 0.7, it means high reliability If the were calculated for internal validity, the Cronbach’s α) is greater than 0.7, it means high reliability If the value after removed one item, all variables ranged from 0.672 to 0.900 and the lowest corrected item- total correlation was higher than 0.3 (Shu-Yi Liaw, Ph.D.)presented in Table 14) Therefore, all variables were internally consistent and reliable to conduct in this study.
Table 14 Reliabilities among the variables
Variable Items total correlation Cronbach’s α α value if item deleted
Information Search (Shu-Yi Liaw, Ph.D.)IS) 4 0.880 0.660 0.875
System (Shu-Yi Liaw, Ph.D.)RS)
Price Dynamic (Shu-Yi Liaw, Ph.D.)PD) 4 0.873 0.691 0.852
(Shu-Yi Liaw, Ph.D.)CS)
Perceived Value (Shu-Yi Liaw, Ph.D.)PV) 4 0.672 0.404 0.636
(Shu-Yi Liaw, Ph.D.)PS)
(Shu-Yi Liaw, Ph.D.)SA)
Group Influence (Shu-Yi Liaw, Ph.D.)GI) 4 0.789 0.496 0.785
Perceived Risk (Shu-Yi Liaw, Ph.D.)PR) 4 0.856 0.663 0.831
(Shu-Yi Liaw, Ph.D.)Attention, Interest) 4 0.838 0.620 0.820
(Shu-Yi Liaw, Ph.D.)CI)
(Shu-Yi Liaw, Ph.D.)Desire, Action) 4 0.873 0.710 0.859
(Shu-Yi Liaw, Ph.D.)CB)
Study I-Explore Positive and Negative Effects on Customers’ Responses
This study explored variables which has positive and negative effects on customers’ responses The data was collected in the end of year 2016 and useful data is 273 samples The results are as followings:
From Table 15, it has been found that several variables are highly correlated The exploratory factor analysis (Shu-Yi Liaw, Ph.D.)EFA) was performed to solve this problem by determining the latent constructs The value of KMO (Shu-Yi Liaw, Ph.D.)Kaiser- Meyer-Olkin) value is found to be 0.825 and significant value p 0.900 0.979
Comparative Fit Index (Shu-Yi Liaw, Ph.D.)CFI) >0.950 0.992
Root Mean Square Error of 0.046
Approximation (Shu-Yi Liaw, Ph.D.)RMSEA)