Identifying Customer Perceived Value (CPV) Components and Their Impact on Customer Satisfaction. Using Big Data Analytics

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

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

Research Anthology on E-Commerce Adoption, Models, and Applications for Modern Business ◽

10.4018/978-1-7998-8957-1.ch046 ◽

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.

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

Handbook of Research on Innovation and Development of E-Commerce and E-Business in ASEAN - Advances in Electronic Commerce ◽

10.4018/978-1-7998-4984-1.ch021 ◽

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.

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

International Journal of Marketing Studies ◽

10.5539/ijms.v9n4p66 ◽

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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Big Data and Analytics

Advances in Systems Analysis, Software Engineering, and High Performance Computing - Handbook of Research on Pattern Engineering System Development for Big Data Analytics ◽

10.4018/978-1-5225-3870-7.ch004 ◽

2018 ◽

pp. 55-66 ◽

Cited By ~ 1

Author(s):

Misbahul Haque ◽

Mohd Imran ◽

Mohd Vasim Ahamad ◽

Mohd Shoaib

Keyword(s):

Big Data ◽

Customer Satisfaction ◽

Data Analytics ◽

Care Delivery ◽

Big Data Analytics ◽

Cost Savings ◽

Heterogeneous Data ◽

Future Perspective ◽

Clear Understanding ◽

Health Organizations

In today's world, humungous and heterogeneous data are being generated from every action of researchers, health organizations, etc. This fast, voluminous, and heterogeneous generation leads to the evolution of the term big data. Big data can be computationally analyzed to uncover hidden trends and patterns that help in finding solutions to the problems arising in various fields. Analysis of big data for manufacturing operational acquaintance at an unparalleled specificity and scale is called big data analytics. Proper utilization of analytics can assist in making effective decisions, improved care delivery, and achieving cost savings. Recognizing hidden trends and useful patterns can lead us to have a clear understanding of the valuable information that these data holds. This chapter presents a quality overview of big data and analytics with its application in the field of healthcare industries as these industries requires their stream of data to be stored and analyzed efficiently in order to improve their future perspective and customer satisfaction.

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Big Data Analytics: The Next Big Thing

The Management Accountant Journal ◽

10.33516/maj.v54i5.20-24p ◽

2019 ◽

Vol 54 (5) ◽

pp. 20

Author(s):

Dheeraj Kumar Pradhan

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

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Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

WiSt - Wirtschaftswissenschaftliches Studium ◽

10.15358/0340-1650-2020-5-11 ◽

2020 ◽

Vol 49 (5) ◽

pp. 11-17

Author(s):

Thomas Wrona ◽

Pauline Reinecke

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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