1. Introduction
With increasing advancement of Internet technology, increasing amounts of data are streaming into contemporary organizations. Data are getting bigger and more complicated due to the continuous generation of data from many devices and sources such as mobile phones, personal computers, government records, healthcare records, and social media. An International Data Cooperation report estimated that the world would generate 1.8 zettabytes of data (1.8 × 10
21 bytes) by 2011 [
1]. By 2020, this figure will grow up to over 35 zettabytes. The Big Data era has arrived. Why are researchers and practitioners interested in understanding the impacts of Big Data analytics? The simple answer of this critical question is that Big Data enables to bring potential applications. Big Data analytics (BDA) applications can help organizations predict the unemployment rate, stimulate economic growth, and provide the future trend for professional investors and other sectors. In health care, big data could help to predict impact trends of certain diseases. One of the most conspicuous examples of Big Data for health care is Google Flu Trend (GFT). In 2009, Google used Big Data to analyze and predict trends influence, a spread of H1N1 flu virus. The trend which Google has drawn from the search keywords related to the H1N1 has been proven to be very close to the results from flu independent warning system Sentinel GP and Health Statistics launched. The GFT program was designed to provide real-time monitoring of flu cases around the world based on Google searches that match terms for flu related activity.
Big Data is generating remarkable attention worldwide with various definitions of Big Data. Big Data is a dataset with a size that can be captured, communicated, aggregated, stored, and analyzed [
2]. Another definition is that Big Data is 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 [
3]. Big Data owns distinctive characteristics (volume, variety, velocity, veracity, and value) that can be easily distinguished from the traditional form of data used in analytics.
Each industry moves a step closer to understanding the world of Big Data from how it is being applied in solving problems. Most industries are still estimating whether there is value in implementing big data, while some other industries have already applied Big Data analytics.
Applications of Big Data were shown in top ten industries such as banking and securities, communications, media and entertainment, healthcare providers, healthcare providers, education, manufacturing and natural resources, government, insurance, retail and wholesale trade, transportation, energy and utilities. Even though Big Data faces specific challenges, its implementation has been practiced by industries in these sectors.
The activity of retailing and wholesaling is part of our economy as well as daily life. Consumer and business markets buy products and services everyday according to their needs and preference. The retail and wholesale sectors contribute significantly to the countries national economy. In today’s competitive and complex business world, the company needs to rely on the data-structured and new type of data-unstructured or semi-structured to back up their decisions. BDA can bring benefits for e-vendors by improving market transaction cost efficiency (e.g., buyer–seller transaction online), managerial transaction cost efficiency (e.g., process efficiency) and time cost efficiency. Specifically within the e-commerce context, Big Data enables merchants to track individual user’s behavior and determine the most effective ways to convert one-time customers into regular customers. The injection of big data analytics into a company’s value chain equates to 5–6% higher productivity compared to their competitors [
4]. Recent studies are focusing on positive mechanisms of applying Big Data analytics with little attention to the negative effects of applying Big Data analytics such as privacy and security [
5], shopping addiction [
6] and group influences [
7]. However, the positives and negatives of applying big data analytics on customers’ responses have not been reported.
Before 2008, three models of consumers’ behavior were discovered; the customers intended to consume more products. In 2008, the global economic and financial crisis that occurred all over the world has led customers to think twice before buying. Consequently, customers were purchasing less and their behavior became defensive. Today, customers face massive and diverse information. Therefore, the opportunity cost for decision process is more complex and their behavior became unpredictable. It requires a new method to understand customers’ behavior and Big Data analytics can be a potential method. Many previous studies reported that the impact of Big Data analytics to business values and business challenges [
8,
9]. However, it is lacking research on customers’ views, to see how customers think about the application of Big Data analytics for online shopping. Thus, the research on customers’ responses towards the influence of pros and cons in applying Big Data analytics is becoming an advanced trend in marketing strategy.
From the marketing perspective, the AIDA model is explored and used to measure the responses of customers by four stages: attention, interest, desire and action [
10,
11]. The AIDA model was developed to represent the four stages that an e-vendor takes their customers in the selling process. This model illustrates that the buyers as passing through attention, interest, desire and action. E-vendors have to firstly get the customer’s attention and then push their interest in the product or service. Strong interest should create desire to have a product or service usage. The action in the AIDA model depicts customer getting to make a purchase and closing the sale. Based on the AIDA model, this study explores consumer responses by two stages: Intention and Behavior.
This research focuses on exploring and determining the positive and negative factors of applying Big Data analytics influence on customers’ responses in B2C e-commerce environments using application of Big Data analytics. Through analysis, the influencing factors of applying Big Data analytics can help enterprises to adjust strategy and meet consumer demand when they apply BDA. Customers also can understand themselves under Big Data era.
4. Results
We have used a two-stage analytical procedure to present results. First, a confirmatory factor analysis was done to assess the measurement model. Second, the structural model and regression analysis were examined.
4.1. Measurement Model
The factor loading should be greater than 0.70 [
39,
49]. For this study, all standardized factor loadings were significant, ranging from 0.759 to 0.993. The construct reliability was tested using composite reliability measures that assess the extent to which factors in the construct measure the latent concept. Convergent validity of the CFA results should be supported by composite reliability (CR) and average variance extracted (AVE). Hair [
39] and Maichum, et al. [
49] stated that the estimates of CR and AVE should be higher than 0.700 and 0.500, respectively. As presented result in
Table 5, the CR and AVE value ranged from 0.851 to 0.927 and 0.657 to 0.762, respectively, passing their recommended levels. Discriminant validity is established using the latent variable correlation matrix, which has the square root of AVE for the measures on the diagonal, and correlations among the measures as the off-diagonal elements (
Table 6). Discriminant validity is determined by looking down the columns and across the rows and is deemed satisfactory if the diagonal elements are larger than off-diagonal elements [
51].
Table 7 shows the CFA results for measurement model fit indicators. The recommended acceptance of a model fit requires that the obtained goodness of fit index (GFI), the adjusted goodness of fit index (AGFI), and the normed fit index (NFI) should be greater than 0.900; the comparative fit index (CFI) should be greater than 0.950; and the root mean square error of approximation (RMSEA) should be less than 0.080 [
49,
52]. The ratio of the chi-square value to degree of freedom is 1.571, which is below the recommended value of 5.000. Furthermore, the other fit index values for GFI, AGFI, NFI, CFI and RMSEA were 0.970, 0.947, 0.979, 0.992 and 0.046, respectively, which are suitable considering the recommended values. Thus, the measurement model had a good fit.
4.2. Structural Equation Model
The results of the structural model and the standardized path coefficient indicated effect among the constructs of the model was shown in
Figure 2. The positive relationship between positive factors and customer responses (H
1: β1 = 0.789,
t = 14.852,
p 0.001) pointed out that H
1 was supported. Regarding H
2, the negative estimate of coefficients between negative factor and customer responses has significant negative effects (H
2: β2= − 0.124,
t = 2.542,
p 0.05). However, the comparison between the path coefficient of positive factors and negative factors (β
PF = 0.789, β
NF = 0.124), respectively) clarifies the different roles that positive factor and negative factor in customer responses. The comparison clearly demonstrates the critical role of positive factor of applying Big Data analytics in customer responses.
Table 8 and
Figure 3 present the results of regression analysis. Regression analysis indicates that all positive and negative factors can explain 49.3% variance of Intention. Customer intention is positively significantly influenced by information search, recommendation system, dynamic pricing and customer services. Conversely, customer intention is negatively significantly influenced by privacy and security, shopping addiction and group influences. All factors of applying Big Data analytics would influence customer’s intention.
The results also indicated that all the positive and negative factors can explain 47.4% variance of customer behavior. Behavior is positively significantly influenced by information search, recommendation system and dynamic pricing. Moreover, customer behavior is negative significant influenced by privacy and security, shopping addiction and group influences. Compared to all factors of applying Big Data analytics, information search is the most significant influential factor that would improve customer behavior on buying products of e-vendor. However, customer service factor is no more an important effect to customer behavior.
In different stages of the consumer responses hierarchical model, the effects of Big Data application on consumers are different. In the stage of customer intention to its applications, customers pay more attention and more interest of positive applications than negative applications. In the stage of customer behavior, most positive factors and negative factors of application had significant impact on customer responses, except customer services. In this case, customers care more about other application factors than customer service.
Positive factors of applying Big Data analytics have positive influence on customer responses. Among four positive factors, we can see that the information search is the most important factor. All negative factors of applying Big Data analytics have negative influence on customer responses.
5. Discussion and Conclusions
The aim of this study is to explore the factors of applying Big Data analytics and how it effects to customers’ responses in the B2C e-commerce environments using application of Big Data analytics. The first result showed that information search, recommendation system, dynamic pricing and customer services were found to have significant positive effects on customers’ responses. Privacy and security, shopping addiction and group influences were found to have significant negative effects on customers’ responses. Tang and Wu [
48] stated that decision making influences the recommendation system, information search, reputation system, and virtual experience on online consumers in the Big Data environment. Data privacy and data security is an unavoidable problem in Big Data era [
55]. The paper attempts to address several implications that may help in developing marketing strategies for e-commerce under Big Data era. Recommendation system allows consumers to locate and match their preferences and interest easily, thereby increasing the customers’ intention and behavior. Moreover, from customer’s perspective, customers want to receive from recommendation system to improve their decision making in buying, so the products meet their own preferences with more satisfaction. Furthermore, products are recommended by a system are more reliable, which leads to gaining more customers’ confidence, and feeling pleasure and motivation. With the development of information technology and e-commerce, the problem of information overloading and complexity of choice shall be more and more regular. Therefore, e-vendors should improve the matching result information search for reducing their cognitive costs. Dynamic pricing and improved customer service are applications that should be noticed to make customers more satisfied.
Positive and negative effects simultaneously coexist, thus affecting customer responses. This is consistent with two distinct constructs being able to coexist at the same time [
56,
57,
58]. The results showed that positive and negative factors activate different effects to customers’ responses, implying that they are distinct constructs which are associated with different neurological process. Especially, the positive factor is connected with brain liked to anticipating rewards, positive emotion. Negative factor is related with brain liked to intense negative emotions, fear, and loss. The result also illustrated that the role of positive factor was clearly influenced on customers’ responses than negative factor. This finding highlights notification that consumers using website applied Big Data analytics paying more attention with positive factor. Therefore, e-vendors should try to apply Big Data analytics to attract customers’ intention toward their applications. This could help in influencing the customers to do online purchase.
Positive and negative factors had different impacts on customer’ intention and customer’s behavior. In positive part, information search, recommendation system, dynamic pricing and customer services has high significant effect on the intention with recommendation system having a strongest influence followed by information search, dynamic pricing and lastly customer service. With recommendation system exerting a strong impact, e-vendors should apply this Big Data analytics application to catch customers’ intention. Information search had highest influence to customer behavior, followed by recommendation system and dynamic pricing. Customer service had no significant effect to behavior. As demonstrated in
Figure 3, these findings indicated that recommendation system firstly catches highest customers’ intention but it has reduced customers’ behavior. This can be explained that recommendation system is an application which gives to customers with passive condition. Information search had significant influence customers’ intention and after that improved customer behavior. A customer in active condition might want to search information. The information provided by Big Data analytics application satisfied customers. In negative part, privacy and security, shopping addiction and group influence had significant negative effect on customers’ intention and behavior. Privacy and security problem has the strongest influence to customers’ intention but the lowest impact to customers’ behavior. It can be explained because e-commerce firms are able to identify security and fraud detection under Big Data analytics [
8]. Therefore, privacy and security is not the biggest customer concern about negative of applying Big Data analytics. The result implied as the change of slope of effect value, the shopping addiction had big slope change than group influences, privacy and security. It raises a question for e-vendors that customers had to realize biggest issue about shopping addiction because of the benefit of applying Big Data analytics. Therefore, customers should understand and control themselves away shopping addiction.
Applying Big Data analytics has emerged as the new innovation and new method of the e-commerce landscape. Applying Big Data analytics increasingly provide positive value to customers by using dynamic, processes, and technologies to analyze data to customize consumers’ need. Leading e-commerce applying Big Data analytics such as Google, eBay, Amazon, Taobao, and others have already applied and gained much business value. However, applying Big Data analytics also brings some negative issues to customers’ responses. This study presents several positive factors and negative factors and their effects to customer responses for application of Big Data analytics. Regarding Big Data era, studies reflect that, after 2017, data analysis technique will be a competitive necessity. Therefore, companies need to start to adapt to the trend using Big Data analytics in order to survive in the dynamic and digitalized markets. This is a process that deals with data, sources, skills, and systems to create competitive advantages. The concept of big data has been developed, and should be applied now to improve strategies, prediction and decision making for better customer relations. However, applying Big Data analytics can also have pros and cons. E-vendors can optimize the advantages of applying BDA but do not inclined to over reliance on BDA in order to avoid negative aspects. Validating checks with their real case would make suitable and effective marketing strategies.
In addition, e-vendors that would like to work with Big Data analytics would require having enough data. This procreates, as a form of rule with the online citizen behavior. Not only customers contribute their information but also e-vendors also add data to build big data.