Customer Relationship Research Articles

“It’s more delicious because I like you”: commercial food influencers’ follower satisfaction, retention and repurchase intention

PurposeThis paper aims to investigate how commercial influencers retain their followers and successfully persuade them to consider purchasing newly recommended products and services within the food industry. We explored the impact of followers’ purchase satisfaction upon their repurchase intention for newly promoted food products and services, directly and by the mediating roles of followers’ affective commitment and loyalty toward commercial food influencers.Design/methodology/approachOur conceptual model design was supported by the tricomponent attitude model, which helps explain followers’ emotional attachment to the influencers. We validated the proposed model using a sample of 200 followers of renowned commercial food influencers in Iran. We used partial least squares structural equation modeling for data analysis, with the assistance of Warp PLS (version 8.0) software.FindingsWe found that followers’ purchase satisfaction exerts a positive influence upon their repurchase intention, both directly and through the mediating roles of affective commitment and loyalty toward commercial food influencers.Practical implicationsThis study elucidates the role of followers’ satisfaction with their previous purchases in influencing their intention to buy newly recommended products. There is a multiplicity of important implications for restauranteur’s business models, as this marketing approach rewards a digital equivalent of a strong customer relationship and an honest, high-quality product. Our results also suggest that food influencers can operate effectively in the affiliate marketing sphere by operating and sustaining enduring relationships.Originality/valueThis work addresses how the influencer–follower relationship, followers’ purchase satisfaction and emotional attachment toward influencers, shape both follower retention and future repurchase intentions. This is from the perspective of the tricomponent attitude model within the food industry.

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.

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

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.

GO-TO-MARKET STRATEGIES FOR CONSUMER TECHNOLOGICAL PRODUCTS WITH SUBSCRIPTION MONETIZATION MODEL

This study examines go-to-market (GTM) strategies for consumer technological products utilizing subscription monetization models. As the digital landscape evolves, businesses are increasingly adopting subscription-based approaches to generate recurring revenue and establish long-term customer relationships. Our research investigates the key components of successful GTM strategies in this context, focusing on market segmentation, value proposition development, pricing strategies, distribution channel selection, and customer acquisition and retention tactics. Through a comprehensive analysis of scientific papers, industry reports, and case studies, we identify best practices and trends in subscription-based GTM strategies for consumer tech products. Our findings highlight the importance of market segmentation, value proposition development, pricing strategies, marketing and communication, customer acquisition and retention practices in optimizing customer lifetime value. This study contributes to the growing body of literature on subscription business models and provides practical insights for managers and entrepreneurs in the consumer tech industry.

O IMPACTO DA IMPLEMENTAÇÃO DO MARKETING DIGITAL NO DESEMPENHO DE PEQUENAS EMPRESAS

This paper explores the strategic impact of digital marketing on small businesses, emphasizing the relevance of tools such as SEO (Search Engine Optimization), content marketing, and social media for strengthening online presence and competitiveness in the market. With the advancement of digitalization, these businesses have found digital marketing to be an accessible way to enhance their visibility, reach new audiences, and increase engagement with consumers, often more effectively and economically than traditional methods. The study highlights the importance of SEO for improving positioning in search engines, allowing small businesses to become more visible to their target audience. Additionally, content marketing emerges as a crucial tool for attracting and retaining customers, fostering trust and credibility through relevant and informative materials. The strategic use of social media is another central point, enabling direct and personalized communication with consumers, as well as offering opportunities for partnerships with influencers. These platforms allow companies to interact more authentically and engage their audiences in real-time, strengthening customer relationships and promoting loyalty. Thus, digital marketing, with its various facets, presents itself as a fundamental element for the growth and success of small businesses in today’s digital economy.

E-Business Online Experience, Satisfaction and Trust: Basis for Customer Loyalty among Selected SMEs in Bulacan

This research paper investigates the relationship between e-business online experience, satisfaction, and trust as predictors of customer loyalty among small and medium enterprises (SMEs) in a selected area of Bulacan. Considering the increasing reliance on digital platforms for e-commerce, issues surrounding online security and user privacy have arisen, raising concerns about consumer trust in e-business environments. Employing a quantitative research methodology, the study utilizes a quantitative descriptive and correlational design to measure and identify the mean and standard deviation of online experience, satisfaction, and trust as independent variables, with customer loyalty as the dependent variable. The findings reveal that SMEs in Bulacan have a strongly positive perception of their e-business online experience, particularly highlighting the simplicity, visual appeal, and effectiveness of the websites they use. They appreciate the quality of information provided by these e-business platforms, which facilitates e-purchases through up-to-date content and straightforward transactions. The study also indicates a strong agreement among SMEs regarding the effectiveness of e-business websites in meeting consumer expectations for repeat purchases, while also identifying areas for improvement in online performance features. Moreover, SMEs generally perceive a high level of security regarding e-business platforms, particularly regarding information transmission and electronic payment safety. However, there was a slightly lower perception of user privacy concerns. The regression analysis indicates that both e-business online experience and customer satisfaction are significant predictors of customer loyalty, underscoring the importance of enhancing these aspects to cultivate lasting customer relationships.

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.

Capabilities toward adoption of outcome-based contracts

PurposeManufacturing enterprises have started to offer the “outcome” derived from machines with the help of outcome-based contracts (OBCs). Offering OBCs leads to benefits such as increased revenues, stronger customer relationships and sustainability. However, implementing OBCs requires critical capabilities. Existing literature has focused on identifying these necessary capabilities, but the prioritization and interrelationships among them remain unexplored. This study aims to address this gap.Design/methodology/approachOur study employs a hybrid analytical hierarchy process and interpretative structural modeling approach to prioritize and map interrelationships among OBC-related capabilities within small and medium-sized enterprises (SMEs).FindingsThe findings highlight the importance of digitalization capabilities such as data privacy and security, remote monitoring, and data analytics; and organizational and governance capabilities, including quantifying, controlling, and monitoring risks, teamwork, and leadership, are highlighted.Research limitations/implicationsWe quantitatively prioritize OBC capabilities and establish their level-wise structural interrelationships, which will facilitate a more effective and efficient implementation of OBCs. Due to the emergent nature of OBCs, our study could identify just one SME case company meeting our selection criteria.Originality/valueExisting OBC literature focusses on the design of OBCs in large companies. Similarly, earlier capability-related OBC literature is oriented toward identifying the OBC capabilities to perform specific functions. However, in the current study, we propose a systematic decision-making approach that comprehensively prioritizes and identifies the interrelationships among the capabilities necessary to provide OBCs, thus complementing the existing scientific literature on OBCs. In addition, we focus on SMEs, that have specific limitations and characteristics.

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 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.

Machine learning, banking, industry, digitalization, innovation

Aim: The application of artificial intelligence (AI) offers an encouraging avenue for developments in the banking industry. Methods: The study adopted a scoping review of methodology to provide a comprehensive overview of relevant research regarding the potential uses and challenges of AI in banking. Consumer expectations have evolved, emphasizing features such as payment gateways, swift transactions, and resilient banking services. Results: Major utilization of the AI in banking industry includes enhanced customer engagement, satisfaction, and relationships, personalized financial guidance, smart digital wallets, regulation compliance and collaborative efforts, forecasting, tracking market trends, decision-making and problem-solving, anti-money laundering and fraud detection, automation of repetitive, large and time-intensive tasks, enhanced cybersecurity, and opportunity for digital financial inclusion. Conclusion: Users encounter challenges involving security issues, privacy concerns, and the complexity of technology in AI-driven banking services. Recommendation: While AI holds a crucial role in the banking industry, successful implementation demands ample allocation and consistent, predictable utilization of AI practices.

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.

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.

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.

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.