Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 395-434
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce

2021 ◽  
pp. 885-924
Author(s):  
Steven Chan Siang Hui ◽  
Omkar Dastane ◽  
Zainudin Johari ◽  
Mardeni Roslee
Keyword(s):  
Big Data ◽  
Customer Satisfaction ◽  
Information Search ◽  
Dynamic Pricing ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics ◽  
System Dynamic ◽  
Mediation Effect ◽  
Repurchase Intention

Based on the empirical research, this chapter investigated the impact of big data-based techniques typically used in big-data driven E-commerce such as information search, recommendation system, dynamic pricing, and personalisation on the online repurchase intention in Malaysia. This study also investigated the mediating effect on customer satisfaction. Therefore this study utilised the quantitative research method with an explanatory study to predict the link between dependent and independent variables. Additionally, the snowball sample method was used to select a sample size of 318 working adults in Klang Valley. Next, a self-administered online questionnaire was used to collect the necessary data. The IB, SPSS 22 software was then used to assess the reliability and normality of the variables at the first stage. Next, the Confirmatory Factor Analysis and Structural Equation Modelling were examined via IBM SSS AMOS 22. The findings showed that the big data analytic factors like information search, recommendation system, dynamic pricing, and personalisation had a positive significant impact on customers' repurchase intention. Nonetheless, the mediation effect of customer satisfaction on information search, recommendation system, and dynamic pricing did not encourage the repurchase intention. Then, this chapter discussed the managerial implication, limitations, and future research scope. Finally, this study suggested strategies to enhance online repurchase intention via application of big-data analytics in E-commerce.

scholarly journals Exploring the Relationship between Perceived Big Data Advantages and Online Consumers’ Behavior: An Extended Hierarchy of Effects Model

2020 ◽  
Vol 13 (6) ◽  
pp. 73
Author(s):  
Jean-Luc Pradel Mathurin Augustin ◽  
Shu-Yi Liaw
Keyword(s):  
Big Data ◽  
Information And Communication Technologies ◽  
Customer Service ◽  
Information Search ◽  
Dynamic Pricing ◽  
Recommendation System ◽  
Tourism Industry ◽  
Social Awareness ◽  
Significance Level ◽  
Hierarchy Of Effects

This study intends to extend the hierarchy of effects model into the reality of the tourism industry after incorporation of information and communication technologies. Data analyses were conducted on 260 online questionnaires. The findings indicated consumer behavior follows a three-layer model: Attention-Intention/Desire-Action/Sharing-Social Awareness. Among big data advantages, recommendation system, information search and improved customer service are important to Attention-Intention; information search, dynamic pricing are important to Desire-Action with customer service (lower significance level); only customer service is important to Sharing-Social awareness. This model allows understanding of consumers’ behavior in online tourism as tourists are often sharing their experiences and raise awareness on service quality from e-vendors. Organizations might use big data to guarantee customers’ satisfaction and attract positive feedback particularly from the third layer of behavior.

Big data analytics adoption model for small and medium enterprises

2020 ◽  
Vol 11 (4) ◽  
pp. 483-513 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Wan Khairuzzaman Wan Ismail ◽  
Morteza Ghobakhloo
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Small And Medium Enterprises ◽  
Big Data Analytics ◽  
Mediation Effect ◽  
Unified Framework ◽  
Content Type ◽  
Medium Enterprises ◽  
The Relationship

Purpose Big data analytics (BDA) is recognized as a turning point for firms to improve their performance. Although small- and medium-sized enterprises (SMEs) are crucial for every economy, they are lagging far behind in the usage of BDA. This study aims to provide a single and unified model for the adoption of BDA among SMEs with the integration of the technology–organization–environment (TOE) model and resource-based view. Design/methodology/approach A survey of 112 manufacturing SMEs in Iran was conducted, and the data were analysed using structural equation modelling to test the model of this study. Findings The results offer evidence of a BDA mediation effect in the relationship between technological, organizational and environmental contexts, and SMEs performance. The findings also demonstrated that technological and organizational elements are the more significant determinants of BDA adoption in the context of SMEs. In addition, the result of this study confirmed that BDA adoption could enhance the financial and market performance of SMEs. Practical implications Providing a single unified framework of BDA adoption for SMEs enables them to appreciate the importance of most influential elements (technology, organization and environment) in the adoption of BDA. Also, this study may encourage SMEs to be more willing to use BDA in their businesses. Originality/value Although there are studies on BDA adoption and firm performance among large companies, there is a lack of empirical research on SMEs, in particular, based on the TOE model. SMEs differ from large companies in terms of the availability of resources and size. Therefore, this study aimed to initiate a conceptual framework of BDA adoption for SMEs to assist them to be able to take advantage of the adoption of such technology.

scholarly journals Big Data Analytics: A Spotify Case Study

2021 ◽  
Vol 9 (10) ◽  
pp. 1823-1829
Author(s):  
Suraj Ingle
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Customer Loyalty ◽  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Exploratory Data ◽  
Insight Into

Abstract: By developing products that are in line with consumer needs, anticipating their profitability and manufacturing them, Big Data has opened up a lot of possibilities for building customer loyalty and commercial business by proactively engaging and comprehensively streamlining offers across all customer touch points. The use of big data to determine the best, most efficient ways to engage and interact with their customers will be discussed in this paper. An insight into how Spotify intends to provide music lovers additional ways to find their favourite songs, interact with artists, and improve Spotify recommendations has been provided. Keywords: Big Data, Data Analytics, Customer Satisfaction, Exploratory Data Analysis

Intelligence-Based Health Recommendation System Using Big Data Analytics

2019 ◽  
pp. 227-246 ◽  
Author(s):  
Abhaya Kumar Sahoo ◽  
Sitikantha Mallik ◽  
Chittaranjan Pradhan ◽  
Bhabani Shankar Prasad Mishra ◽  
Rabindra Kumar Barik ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Recommendation System ◽  
Big Data Analytics

Movie Analytics for Effective Recommendation System using Pig with Hadoop

2016 ◽  
Vol 3 (2) ◽  
pp. 82-100 ◽  
Author(s):  
Arushi Jain ◽  
Vishal Bhatnagar
Keyword(s):  
Big Data ◽  
Science Fiction ◽  
Data Analytics ◽  
Recommendation System ◽  
18Th Century ◽  
Big Data Analytics ◽  
Data Set ◽  
The People ◽  
Late 18Th Century

Movies have been a great source of entertainment for the people ever since their inception in the late 18th century. The term movie is very broad and its definition contains language and genres such as drama, comedy, science fiction and action. The data about movies over the years is very vast and to analyze it, there is a need to break away from the traditional analytics techniques and adopt big data analytics. In this paper the authors have taken the data set on movies and analyzed it against various queries to uncover real nuggets from the dataset for effective recommendation system and ratings for the upcoming movies.

scholarly journals Comparing Mediation Effect of Functional and Emotional Value in the Relationship between Pros of Applying Big Data Analytics and Consumers’ Responses

2017 ◽  
Vol 9 (4) ◽  
pp. 66
Author(s):  
Shu-Yi Liaw ◽  
Thi Mai Le
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Perceived Value ◽  
Mediation Effect ◽  
Functional Value ◽  
Perceived Values ◽  
Emotional Value ◽  
The Relationship ◽  
Positive Effect

Applying Big Data analytics application brings many benefits for e-vendors and customers. Exploring the effect of consumer perceived value to consumers’ responses under applying Big Data analytics is lacking. And, what kind of perceived values do customers have more concerns under Big Data era. Therefore, the aims of this study are to analyze relationship between pros of applying Big Data analytics and Consumers’ responses under multiple mediators of perceived values as functional value and emotional value. Data analysis was done in a sample of 349 respondents. The results show that applying Big Data analytics have significant positive effect on customers’ responses. Functional and emotional values act as important mediators on the relationship between applying Big Data analytics and consumers’ responses. There are no significant different between mediator effect of functional value and emotional value. The findings of this study will have implications for e-vendors to understand the important mediator of perceived value on customers’ responses under Big Data analytics era.

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