Customer Big Data Research Articles

Big Data Technology Acceptance Model (TAM) in Indonesian state-owned financial services and banking

The aim of this study in general is to examine the influence of perceived ease of use, perceived usefulness, and perceived risk on intention to use. The subject of this study is the account officer of the big data user of a state-owned company in Malang, East Java, Indonesia. Specifically, this research has the purpose of finding out both the simultaneous and partial influence of perceived use, perceived usefulness, and perceived risk on the intention to use the big data of State-Owned Bank customers. This study is a survey employing both quantitative and qualitative data analysis. The samples of the subjects used were 95 account officers of a state-owned bank in East Java who used customer big data. The analysis conducted in this study included multiple linear regression testing and hypothesis testing for both simultaneous and partial The result of the F testing indicates that collectively, the perceived ease of use, perceived usefulness, and perceived risk variables have a simultaneous and positive influence on the intention to use the customer's big data. However, based on the T-test, it was observed that perceived usefulness is the only variable that has a partial influence on the intention to use the big data of the customers. While the other two variables, perceived ease of use and perceived risk, were found to have no significant influence on the intention to use the bank’s customers' big data.

The Effects of the Covid-19 Pandemic on Big Data Artificial Intelligence

The Covid-19 pandemic has changed people's behavior. Artificial Intelligence is made based on big data of people's behavior. Financial institutions use AI in making credit decisions. The AI basis of credit decisions is made based on existing customer Big Data. This customer's Big Data has changed as a result of the Covid-19 pandemic. Changes in Big Data mean that the AI of financial institutions in decision making must be adapted to the new behavior. This study aims to discuss the changing community behavior variables related to the granting of credit approvals. This study uses a qualitative method with a literature review. This study concludes that length of stay, home ownership, occupation, length of work, expense ratio, age, bank account, awareness of fintech, usage of fintech and demographic variables have changed. These variables need to be adjusted as a result of changes in the behavior of the Covid-19 pandemic.

O2O customer big data analysis system based on embedded technology

O2O customer big data analysis system based on embedded technology

O2O customer big data analysis system based on embedded technology

O2O customer big data analysis system based on embedded technology

INDUSTRIAL B2B BIG DATA

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software &for buying and selling of personal and consumer data. The use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from B2B firms. The study finds that the use of customer big data significantly fosters sales growth and enhances the customer relationship performance. However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.

ICT-enabled CRM system adoption: a dual Indian qualitative case study and conceptual framework development

PurposeThe paper aims to develop a comprehensive framework for adopting an information and communication technology (ICT)-enabled customer relationship management (CRM) system, toward strengthening and expanding the customer basis of the organization.Design/methodology/approachAdvanced ICT-enabled CRM includes mobile CRM, AI integrated CRM and social CRM and several business organizations have already started exploring their advanced utilization for direct financial gain and indirect “soft” benefits. Most of them, however, are challenged in the process, due to lower-than-demanded adoption of such CRM systems. To deal with the issue, the paper methodologically applies a dual qualitative case study approach that ultimately develops an advanced comprehensive CRM adoption framework.FindingsThe key findings pertaining to the evaluation of organizations’ readiness to adopt advanced ICT-enabled CRM systems in terms of infrastructure and resources, the identification of the critical functional areas of the organization’s focus and the differences in the requisite approaches across industries and organization types.Practical implicationsIn the context of the internet and social media, the analysis and management of customer big data and their transcription into useable information is crucial. Traditional and conventional CRM was deemed unable to address this need, thus necessitating the use of advanced ICT-enabled CRM. The proposed corresponding framework tangibly and practically, thus, directs businesses toward the successful deployment of ICT-based CRM systems.Originality/valueThis study constitutes a novel attempt to identify the issues of deployment of ICT-based CRM systems in an organization and proposes a comprehensive framework, which will enable organizations to overcome the barriers when adopting a new system.

Control, use and ownership of big data: A reciprocal view of customer big data value in the hospitality and tourism industry

Abstract While the large-scale harvesting of consumer data is a common practice in the hospitality and tourism industries, the seemingly unassailable right of companies to collect and share consumer data is not without critics. The purpose of this paper is to explore the nature of data-based value co-creation under varying conditions of consumer control and benefits. Academic and press articles were used to explore the nature of big data value co-creation in a wide range of hospitality, tourism, and other industries. A forward-looking approach was adopted by considering the implications of policy and technology as key mechanisms for sharing the power dynamic regarding the ownership, control and use of personal data. The results suggest that reciprocal big data value creation can be seen as a function of the level of benefit and control afforded to consumers regarding the use of their data. Four types of reciprocal big data value creation are proposed.

Fostering B2B sales with customer big data analytics

This study focuses on the use of big data analytics in managing B2B customer relationships and examines the effects of big data analytics on customer relationship performance and sales growth using a multi-industry dataset from 417 B2B firms. The study also examines whether analytics culture within a firm moderates these effects. The study finds that the use of customer big data significantly fosters sales growth (i.e. monetary performance outcomes) and enhances the customer relationship performance (non-monetary performance outcomes). However, the latter effect is stronger for firms which have an analytics culture which supports marketing analytics, whereas the former effect remains unchanged regardless of the analytics culture. The study empirically confirms that customer big data analytics improves customer relationship performance and sales growth in B2B firms.

Incentive mechanism on customer knowledge collaborative acquisition with relational contract under double-sided moral hazard in big data context

In the big data context, the customer data are distributed out of the enterprise boundary. It’s the key to maintain competitive advantage that applying the customer knowledge acquisition to the enterprise innovation. From the perspective of the supply chain, there is complementarity of customer knowledge for the manufacturer and the retailer. Sharing the collaborative acquisition of customer knowledge can improve the efficiency of both sides, and even of the whole system. However, since the imbalance of the information between both sides and the imbalance aggravated because of the pending verification of customer big data value, result in speculation behaviors that called bilateral moral hazard. Reasonable design of synergy incentive contract is an important way to solve bilateral moral hazard in customer knowledge collaborative acquisition. As the big data customer knowledge needs to be examined, the relational contract model of customer acquisition knowledge is established and contract analysis of the incentive effects between the formal contract and relational contract is made. Consequently, the incentive of formal contract will result in system profit loss, while relational contract incentive has positive inspiration for collaboration between the manufacturer and the retailer, which can improve the efficiency of the acquisition. In the process, when the discount rate is large enough to reach a certain threshold, the optimal system revenue can be achieved through the relational contract.

TELECOM BIG DATA FOR URBAN TRANSPORT ANALYSIS – A CASE STUDY OF SPLIT-DALMATIA COUNTY IN CROATIA

Abstract. Today, big data has become widely available and the new technologies are being developed for big data storage architecture and big data analytics. An ongoing challenge is how to incorporate big data into GIS applications supporting the various domains. International Transport Forum explains how the arrival of big data and real-time data, together with new data processing algorithms lead to new insights and operational improvements of transport. Based on the telecom customer data, the Study of Tourist Movement and Traffic in Split-Dalmatia County in Croatia is carried out as a part of the “IPA Adriatic CBC//N.0086/INTERMODAL” project. This paper briefly explains the big data used in the study and the results of the study. Furthermore, this paper investigates the main considerations when using telecom customer big data: data privacy and data quality. The paper concludes with GIS visualisation and proposes the further use of big data used in the study.

Big plateaus of big data on the big island

In ancient texts, 40 was a magic number. It meant a "lot" or "a long time." 40 years represented the time it took for a new generation to arise. A look back at 40 years of VLDB suggests that this applies to database researchers as well -- the young researchers of the early VLDBs are now the old folks of the database world, and a new generation is creating afresh. Over this period many plateaus of "Big Data" have challenged the database community and been conquered. But there is still no free lunch -- database research is really the science of trade-offs, many of which are no different today than 40 years ago. And of course the evolution of hardware technology continues to swing the trade-off pendulum while enabling new plateaus to be reached. Todd will take a look back at customer big data plateaus of the past. He will look at where we are today, then use his crystal ball and the lessons of the past to extrapolate the next several plateaus -- how they will be the same and how will they be different. Along the way we will have a little fun with some VLDB and Teradata history.

Combining How Net and Extension Strategy Generation Method to Improve Customer Values

Abstract This paper proposesto combine HowNet and extension strategy generation method to analyze customer data in order to improve customer values. In view of the problems that enterprises are difficult to analyze customer big data, the paper shows how to combine HowNet structure to enhance extension strategy generation methods. The extension strategy generation systemcan rebuild its knowledge base structure and eliminate semantic conflicts. This will more effectively treat customer big data and improve customer values. The paper presents the experiment results of the extension strategy generationsoftware which successfully analyze customer data and generate strategies to improve customer values.