Customer Relationship Management Research Articles

Use of Artificial Intelligence in the Banking Industry: A Case Study of Pakistan

This study investigates the adoption and impact of Artificial Intelligence (AI) in Pakistan’s banking sector, with a focus on Habib Bank Limited (HBL), Faysal Bank, and Bank Alfalah. The objective is to understand the extent to which AI technologies have transformed core activities within customer service, operations, fraud detection, and risk management in these banks. Data were collected from semi-structured interviews with ten respondents, selected based on their experience with AI implementation. This qualitative methodology allows for in-depth insights into the functionality and challenges associated with AI in banking. Results reveal that AI has notably improved operational efficiency by streamlining risk management and enabling real-time fraud detection. Additionally, AI-driven chatbots and virtual assistants have enhanced customer experiences by offering faster and more personalized services. Despite these benefits, the study identifies significant challenges, particularly regarding data privacy and regulatory compliance, as well as the need for human resources to adapt to AI-enabled tools. Key pressures in the industry include building capabilities to work alongside AI while addressing concerns over automation. To maximize the advantages of AI, the study emphasizes the need for comprehensive policies that balance technological adoption with ethical considerations, data security, and staff training. Strategic planning in Pakistan’s banking sector will be crucial to harnessing AI’s potential while managing its risks.

Evaluation of the fintech era on the performance of Moroccan banks: analysis through non-performing loans

Background This study aims to contribute to a better understanding of the impact of the financial technologies (fintech) era on the performance in the banking sector, measured through non-performing loans (NPL) and their coverage by provisions for NPL. It is a question of knowing whether banking investment in fintech makes it possible to better evaluate the granting of credits, and therefore makes it possible to reduce overdue credits. Methods To this end, the method used consists of using a regression analysis and a Pearson correlation applied to the financial data of Moroccan banks observed during two distinct periods, namely 2007-2014, considered pre-fintech, and the period 2015-2022, considered as the fintech period. Results With the emergence of the fintech era, the Moroccan banking situation improved slightly compared to the pre-fintech period: bad debts did not increase despite the significant increase in net banking income and the size of banking assets during the fintech era. Conclusions The implementation of fintech has improved customer relationship management, credit risk analysis and loan monitoring services, which ultimately reduces non-performing loans and improves the coverage of non-performing loans by provisions. The main implication of the results allows us to deduce that the implementation of fintech makes it possible to have a positive impact on overdue credits, and they are also likely to serve as a lever for the inclusion of those excluded from banking services.

Determinants of Financial Information Disclosure Online: Empirical Evidence in Portugal

The rapid advancement of information technology, particularly the Internet, has significantly transformed communication and corporate reporting practices. The evolution of the Internet and Information and Communication Technologies (ICT) has enabled companies to disseminate extensive financial and non-financial information quickly and cost-effectively. Typically published on company websites, this information is accessible to users worldwide. Digital financial reporting has evolved to meet the needs of both information users and companies. The Internet is increasingly the primary channel for companies to share financial information, driven by technological advancements and the growing demand for online company data. However, the rise of technology in financial reporting raises concerns about information overload, potentially diminishing the quality and relevance of economic data. Additionally, managing the costs associated with technology, such as website development, customer relationship management systems, hardware, software, and other digital platforms, is crucial. This study aims to determine the extent of online financial information disclosure among the 150 largest Portuguese companies and identify the factors influencing this disclosure. The research methodology involves content analysis of the websites of 52 of the largest and best-performing companies operating in Portugal, as ranked by Revista Exame, using a predefined disclosure index to evaluate the degree of financial information disclosure on the Internet. The objective is to enhance understanding of the financial reporting practices adopted by major Portuguese companies and identify factors determining the level of disclosure. The results confirm that the disclosure index has an average value of 0.52147 and that there is a relationship between company size and sector of activity and the level of internet financial reporting.

Big Data Analytics And Predictive Analysis In Enhancing Customer Relationship Management (CRM): A Systematic Review Of Techniques And Tools

This systematic review examines the transformative role of big data analytics and predictive techniques in Customer Relationship Management (CRM), focusing on how these advancements enhance customer engagement, satisfaction, and retention strategies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzed a final selection of 100 articles, exploring CRM applications such as predictive modeling, real-time data processing, data integration, and Natural Language Processing (NLP). Findings indicate that predictive analytics enables CRM systems to anticipate customer behaviors and personalize interactions, while real-time data processing supports immediate responses to customer needs, essential in high-demand sectors. Data integration helps overcome silos, creating a cohesive customer view that enhances personalized engagement across all touchpoints. NLP applications, including sentiment analysis and chatbots, further improve CRM by fostering empathy and responsiveness in customer interactions. Despite these advancements, challenges related to data privacy and regulatory compliance remain central, necessitating strict data protection and ethical handling practices. This review underscores that while CRM technology is evolving to meet modern demands, effective implementation depends on a balance between technical innovation and adherence to ethical standards in data management, ultimately fostering meaningful and sustainable customer relationships.

Enhancing Customer Satisfaction

Customer satisfaction is crucial for the sustained success of e-commerce platforms, necessitating effective Customer Relationship Management (CRM) strategies to bolster relationships and overall performance. While existing research provides insights into factors like payment methods, shipping times, and customer demographics that influence satisfaction, significant gaps remain in fully understanding these dynamics, particularly within specific contexts such as Olist, a prominent Brazilian online marketplace. This study aims to address these gaps by leveraging advanced machine learning techniques on a comprehensive dataset comprising 100,000 orders spanning 2016 to 2018. Through meticulous analysis, which includes rigorous feature selection and the application of predictive models like logistic regression, decision trees, and random forests, we pinpoint crucial determinants of customer satisfaction. Our findings underscore the pivotal role of operational efficiencies in shaping customer experiences and offer actionable recommendations for enhancing e-commerce performance. By bridging these research gaps, this study not only contributes to the existing body of knowledge but also informs practical strategies for optimizing customer satisfaction and guides future research endeavors in the realm of e-commerce and CRM.

Impact of customer relationship management, financial literacy and social influence on online banking adoption: the moderating role of personal innovativeness

Impact of customer relationship management, financial literacy and social influence on online banking adoption: the moderating role of personal innovativeness

Digital Transformation in Healthcare : Leveraging Technology to Enhance Patient Care

This article examines the successful implementation of Pega's Healthcare Customer Service and Care Management solutions at a major healthcare provider, demonstrating how digital transformation can revolutionize patient care delivery while optimizing operational efficiency. The implementation, which focused on integrated care coordination, patient case management, and compliance frameworks, resulted in significant improvements, including a 20% reduction in hospital readmissions, a 30% reduction in administrative costs, and a 25-35% reduction in operational costs. The organization achieved enhanced patient outcomes, streamlined operations, and improved regulatory compliance through comprehensive data integration, automated workflows, and user-centric design. The article highlights how strategic digital transformation initiatives, aligned with industry best practices and supported by robust change management, can successfully address healthcare's complex challenges while positioning organizations for future innovation in care delivery.

The Mediating Effect of Innovative Performance on the Relationship Between the Use of Information Technology and Organizational Agility in SMEs

The current study has four main objectives. First, it aims to investigate the effect of the relationship between information technology (IT) dimensions (customer relationship management, knowledge management, and human resource management) and innovative practices on organizational agility in small and medium-size companies (SMEs). Second, it seeks to measure the relationship between IT components and innovative performance. Third, it examines the impact of innovative performance on organizational agility. Fourth it explores the mediating role of innovative performance in the relationship between IT and organizational agility. These objectives provide a clear roadmap for the research and guide the analysis and interpretation of the findings. This paper’s statistical population was composed of senior managers in SMEs in Khorsaran Razavi, Iran. The data were collected using standard questionnaires, 172 which were received in 2023 and analyzed using SPSS version 25 and SmartPLS version 4 software. The results demonstrate that using customer relationships, human resources, and knowledge management as three dimensions of IT and innovative performance can enhance organizational agility. Moreover, innovative performance plays a crucial role as a mediator, strengthening the impact of information IT dimensions on organizational agility. These findings underscore the practical relevance for companies operating in a dynamic economic environment. Special attention to organizational agility and practical factors will increase flexibility, speed of response, etc., and, ultimately, companies’ success in this tense economic environment. The innovation of this research is that the three dimensions of IT, including evaluating customer relationship management, human resource management, and knowledge management, is a growing research field in organizational agility. Therefore, this research is vital in empowering SMEs to increase agility. By evaluating the effect of the four variables of knowledge management, customer relationship management, human resource management, and innovative performance on organizational agility in SMEs, on the one hand, this research expands the theoretical literature and, on the other hand, helps such companies.

Role of Technological Advancements on Customer Satisfaction in Japan’s Hospitality

Purpose: The aim of the study was to analyze the role of technological advancements on customer satisfaction in Japan’s hospitality. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Technological advancements have significantly enhanced customer satisfaction in Japan’s hospitality industry by improving service efficiency, personalization, and overall guest experience. Innovations such as AI-driven customer service robots, automated check-in systems, and smart hotel rooms equipped with IoT devices offer convenience and seamless interactions for guests. These technologies reduce waiting times, streamline operations, and allow for personalized services, such as customized room settings and tailored recommendations, increasing guest satisfaction. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM), service quality model (SERVQUAL) & expectancy-confirmation theory (ECT) may be used to anchor future studies on the role of technological advancements on customer satisfaction in Japan’s hospitality. Hospitality businesses should adopt integrated management systems that combine customer relationship management (CRM), property management systems (PMS), and online booking platforms. Industry regulators should promote standards for technology integration in the hospitality sector.

A Real-Time Associative Feature-Based Customer Relationship Management and Enterprise Resource Planning Integration Model for Small- and Medium-Sized Enterprises

Abstract Customer relationship management (CRM) and enterprise resource planning (ERP) have been extensively discussed in the research literature respectively. However, existing studies do not reach a complete agreement on the CRM/ERP integration method, especially for small- and medium-sized enterprises (SMEs). Generally, this work proposed a novel method for CRM/ERP integration approach via associative feature technology. A new feature type, Real-time Data Link Board (RDLB), is developed and detailed as a generic information carrier solution for multisystem integration between CRM and ERP packages. Valuable data elements are mapped into such data boards dynamically and synchronized across different data sources, structures, and databases. The contribution of this work is to present a well-defined and generically reusable data carrier definition, alongside related methods for ensuring consistent data modeling during the system integration phase across various digitalization engineering implementation projects. With this approach, the consistent management of data integration across intricate systems is achievable throughout application lifecycles, supported by the object-oriented software engineering foundation.

Re-examining the chatBot Usability Scale (BUS-11) to assess user experience with customer relationship management chatbots

AbstractIntelligent systems, such as chatbots, are likely to strike new qualities of UX that are not covered by instruments validated for legacy human–computer interaction systems. A new validated tool to evaluate the interaction quality of chatbots is the chatBot Usability Scale (BUS) composed of 11 items in five subscales. The BUS-11 was developed mainly from a psychometric perspective, focusing on ranking people by their responses and also by comparing designs’ properties (designometric). In this article, 3186 observations (BUS-11) on 44 chatbots are used to re-evaluate the inventory looking at its factorial structure, and reliability from the psychometric and designometric perspectives. We were able to identify a simpler factor structure of the scale, as previously thought. With the new structure, the psychometric and the designometric perspectives coincide, with good to excellent reliability. Moreover, we provided standardized scores to interpret the outcomes of the scale. We conclude that BUS-11 is a reliable and universal scale, meaning that it can be used to rank people and designs, whatever the purpose of the research.

Impact of Customer Service Management on the Survival of Micro, Small and Medium Scale Enterprise (MSMEs) in Asaba, Delta State, Nigeria

Impact of Customer Service Management on the Survival of Micro, Small and Medium Scale Enterprise (MSMEs) in Asaba, Delta State, Nigeria

Impact of AI-Driven CRM on Customer Relationship Management and Business Growth in the Manufacturing Sector

The impact of AI technologies has changed major aspects of human lifestyle along with the operations of the business world at contemporary world. The dynamic shift has developed new processes and techniques within the manufacturing sector that is closely connected with the business improvement and customer relationship management. Based on this change, the research adopted a secondary qualitative design along with a case study approach to analyse the impact of AI-drive CRM on business growth and customer relationship management within the manufacturing industry. The findings suggested overall improvement of the business and vital enhancement of customer relationships within the industry as AI-driven CRM systems have improved customer satisfaction through seamless demand fulfilment.

Developing a Customer Satisfaction Model for Non-Wage Recipients at BPJS Employment Office, West Kalimantan Province

This study aims to develop a customer satisfaction model for BPJS Employment participants who are non-wage recipients (BPU) in West Kalimantan Province. The research focuses on the impacts of Customer Relationship Management (CRM), service quality, government role, and customer trust on participant satisfaction. Using a quantitative methodology with Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 384 respondents was analyzed. Findings indicate that CRM and service quality are significant factors in customer satisfaction, with customer trust enhancing these effects. The government’s role further moderates satisfaction, suggesting policy and regulatory support are essential for public sector service providers.

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Consumer Satisfaction Benchmarking Analysis Using Group Decision Support System (GDSS) PROMETHEE Methodology in a GIS Environment

In today’s competitive environment, multi-branch companies allocate their stores with the aim of expanding their territorial coverage to attract new customers and increase their market share. Consumer satisfaction surveys either produce global performance results or they are not able to differentiate consumer perceptions using location analytics. This research develops a novel framework to assist multi-branch companies in mapping the consumer satisfaction performance of their stores, expanding conventional customer relationship management to the spatial context. The framework developed proposes a decision model that combines the Group Decision Support extension of the PROMETHEE and CRITIC methods in a GIS environment to generate satisfaction performance mappings. The developed decision-making framework converts consumer responses into satisfaction performance maps, allowing the company’s stores and their competitors to be evaluated. Moreover, it provides insight into the potential opportunities and threats for each store. The performance of the proposed framework is highlighted through a case study involving a multi-branch coffeehouse company in a Greek city. Finally, a tool developed to assist the computational part of the framework is presented.

Pengaruh Custemer Value, Custemer Relationship Management, serta Custemer Experience terhadap Custemer Loyalty Debitur Lending yang terdapat di Bank OCBC NISP Cabang Purwakarta

This research aims to analyze the influence of customer value on customer loyalty, determine and analyze the influence of customer relationship management on customer loyalty, and determine and analyze the influence of customer experience on customer loyalty for lending debtors. The method used in this research is descriptive verification. With this type of associative research. The population used in this research is Bank OCBC NISP Purwakarta branch customers. The results of this research show that customer value influences the loyalty of lending debtors. Customer relationships influence loyal customers of lending debtors, customer experience has an influence on customer loyalty of lending debtors at OCBC NISP bank.

An empirical evidence on the impact of social customer relationship management on the small and medium enterprises performance

Social media has swiftly established itself as a primary source of products’ information for customers. Nowadays, Small and Medium-size Enterprises (SMEs) can use social media to develop Customer Relationship Management platforms (social CRM). Firms, particularly SMEs in developing countries need to understand the factors affecting their performance by implementing social CRM. However, there is a dearth of awareness on the impact of social CRM on the performance of SMEs. This study proposes an integrated model that aims to investigate the effects of social CRM on SMEs’ performance. The model is constructed by incorporating three dominant theoretical frameworks: the Fit-Viability Model (FVM), Network Externalities, and the Resource-Based View (RBV). A cross-sectional survey was used to gather data from 149 SMEs managerial staff. Findings revealed that almost 50% of the variability in the performance of SMEs is explained by the fitness and viability of social CRM. In addition, network externalities of social media significantly impact the social CRM fitness in the context of SMEs with path coefficient 0.617. Furthermore, the internal financial resources factor makes sCRM viable for SMEs as the results show significant relationship between the internal financial resources and the sCRM viability with 0.536 path coefficient. Manager innovativeness, IT knowledge, top management support, and government assistance, on the other hand, do not contribute significantly to the viability of social CRM for SMEs. The model aids SMEs in making well-informed decisions regarding the adoption of social CRM by evaluating both the suitability of social media for CRM tasks and the enterprise preparedness to implement social CRM, leading to enhanced performance.

Antecedents and Outcomes of Big Data Adoption in Supply Chain: A Meta-Analytic Investigation

This paper aims to provide a comprehensive understanding of the big data–performance relationship based on the existing empirical evidence. Using a meta-analysis approach, big data adoption (BDA) related 446 effect sizes reported in 133 prior empirical studies were gathered from 118848 informants in more than 30 countries. Results confirm ten significant antecedents and eight outcomes of BDA based on identified literature. We further estimate the heterogeneity based on subgroup analysis by considering two types of moderators as (a) economic regions (developed vs developing), and (b) type of industry. We find that organizations in developed countries adopt big data largely due to environmental and organizational factors. Further, developed countries can harness the potential of big data for better performance (e.g., supply chain integration, collaboration, customer relationship management, and innovation). This study provides multifaceted insights for practitioners and academia alike regarding the use of big data.