Search Results (84)

Artificial Intelligence (AI) is increasingly reshaping customer relationship management (CRM) practices in service industries, yet its perceived effectiveness within emerging regional tourism economies remains underexplored. This study examined respondents’ perceptions of how AI-enabled capabilities influence CRM effectiveness within the tourism sector in Mthatha, in the Eastern Cape, South Africa. Existing AI–CRM research is largely concentrated in developed economies, limiting contextual understanding of its strategic value in resource-constrained and relational tourism environments. A positivist, quantitative explanatory design was adopted, and data were collected through a structured survey administered to managers and staff of tourism enterprises across the Eastern Cape (n = 121). Partial Least Squares Structural Equation Modelling was employed to assess the measurement model and test the hypothesized relationships. The model explained 63.2% of the variance in perceived CRM effectiveness. Sales forecasting and lead scoring exerted the strongest positive influence, followed by sentiment and feedback analysis, while personalization and automation showed positive but statistically insignificant effects. The findings suggest that tourism enterprises may achieve stronger relationship outcomes by prioritizing predictive and analytical AI tools while integrating automation within human-centered service strategies. The study extends AI–CRM theory to an emerging African tourism context and demonstrates that AI effectiveness is context dependent rather than universally transferable. Full article

Artificial intelligence (AI) increasingly permeates organizational work, yet research on AI–human collaboration remains fragmented and lacks a unified structure. This study provides a comprehensive bibliometric mapping of AI–human collaboration by examining its intellectual foundations and emerging research fronts across multiple disciplines. Using document co-citation and bibliographic coupling analysis, the study examines how research on AI–human collaboration has evolved and where it is heading. Data were collected from the Scopus database. A total of 2178 primary documents and 15,078 secondary documents were retrieved and analyzed using VOSviewer (1.6.20) software to visualize the thematic interconnectedness. Results from document co-citation revealed five significant research clusters underlying AI–human collaboration research, including psychological and social foundations of AI; organizational applications of AI in higher education; ethical–cognitive foundations of generative AI; AI literacy and educational transformation; and behavioral foundations of AI adoption. The bibliometric coupling results identified four active research fronts: AI governance, ethics, and humanization; AI–customer relationship management (CRM) adoption, capabilities, and organizational performance; anthropomorphic AI and consumer emotional response; and AI conversational agents and consumer experience dynamics. These findings suggest a thematic shift from technology-centered automation toward collaborative and human-centered integration. The study contributes theoretically by synthesizing insights across organizational behavior, psychology, and information systems to clarify the intellectual structure of this emerging domain. It also outlines implications for leaders designing AI-enabled workplaces that prioritize collaboration, ethical alignment, and adaptive capacity. Full article

Social media platforms have become critical infrastructures for customer relationship management (CRM), requiring scalable and intelligent solutions to handle high-volume interactions. In the luxury fragrance sector, digital promotion poses a unique challenge because olfactory attributes cannot be experienced online. As a result, physical fragrance samples remain essential, generating large volumes of sample requests or inquiries across social media. However, many requests remain unmanaged due to limitations in manual CRM (i.e., human-driven processes), revealing a design gap that may negatively affect perceived responsiveness and service quality. This study uses qualitative content analysis with NVivo 12 to examine large-scale sample request interactions on the Facebook pages of four luxury fragrance brands. Data was collected via NCapture and analyzed to identify recurring patterns, linguistic structures, and customer expressions related to sample requests. Findings confirm frequent repetitive requests, highlighting inefficiencies in traditional CRM systems under high demand. This research proposes an AI-driven CRM Sample Request Management Architecture (CRM–SRMA) that systematically captures and processes customer sample requests, collects the necessary mailing information, and seamlessly transfers validated data to the final dispatching stage. The proposed system also models individual fragrance preferences by analyzing customers’ interactions with samples, particularly in terms of top, middle, and base notes. By leveraging this information, the architecture enables the targeted promotion of new fragrance releases that closely align with customers’ demonstrated olfactory preferences. The insights of this research provide a scalable, intelligent mechanism that enables luxury social media managers and CRM systems to manage high-volume interactions while maintaining service quality. By automating sample request processing, the mechanism improves responsiveness and reduces operational burden. It also supports long-term relationship building through preference tracking and updating customers with any new relevant-fragrance releases. Although focused on fragrances, the mechanism is adaptable to other luxury cosmetic categories, thereby ideally enhancing overall social media-based customer service. Full article

Digital transformation in public institutions is increasingly understood as a socio-technical and organizational process rather than a purely technological upgrade. This study presents the design of an ICT-based digital transformation roadmap aimed at improving administrative efficiency and citizen service delivery in a municipal public utility in Ecuador. A mixed-methods diagnostic approach was adopted, combining qualitative evidence from direct observation and a semi-structured interview with the head of the IT department, and quantitative data from a structured online survey administered to citizens. Baseline Key Performance Indicators (KPIs) were established using institutional records, service logs, and workflow analysis conducted over a three-month diagnostic window. Post-implementation KPI values are explicitly treated as ex ante projections, derived from process redesign analysis, benchmarking with comparable public utilities, and scenario-based assumptions, rather than empirically observed outcomes. The empirical results demonstrate high citizen readiness and acceptance of proposed digital services, including remote service portals, electronic invoicing, and automated support channels. The projected operational improvements—such as reductions in response and administrative processing times and increased digital transaction rates—are therefore presented as expected performance scenarios. A risk and alternative scenario analysis further examines how organizational constraints, resource availability, governance capacity, and change-management factors may moderate these outcomes. The study contributes a transparent and replicable framework for diagnosing digital readiness and planning ICT-driven transformation initiatives in resource-constrained public utilities, while emphasizing the need for future longitudinal validation using post-implementation data. Full article

This study addresses member churn in a Brazilian agro-industrial cooperative by operationalizing a leakage-aware, governance-aligned machine-learning protocol within the organization’s Customer Relationship Management (CRM) system. Using real-world CRM data under confidentiality constraints, we followed a KDD-based workflow. This workflow includes: (i) multi-source integration; (ii) targeted preprocessing with explicit handling of severe class imbalance via undersampling; (iii) a unified validation scheme with stratified cross-validation, hyperparameter search, and controlled AutoML benchmarking; (iv) comparison of tabular learners (Random Forest, XGBoost, and Support Vector Classifier) and a voting ensemble; and (v) SHAP-based explainability to support transparent decision-making. Class rebalancing substantially improved minority-class performance; for instance, the “Inactive” recall increased from 0.27 to 0.74 with SVC. Across ten folds, AutoML achieved competitive mean ROC-AUC (0.8844), followed by XGBoost (0.8690) and Random Forest (0.8660); global metrics supported operational feasibility (accuracy 0.79–0.80; ROC-AUC up to 0.8876), while the ensemble delivered comparable discrimination (ROC-AUC 0.8845) with a modest precision gain. SHAP analyses yielded business-coherent drivers and enabled actionable, instance-level communication in the CRM. The resulting microservices-based module exposes ranked churn propensities and explanations in dashboards for risk stratification and prioritization of retention actions. Overall, the work provides an interpretable, reproducible, and production-ready methodological blueprint for predictive CRM in seasonal cooperative environments under governance and confidentiality constraints. Full article

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods. Full article

This study evaluated the relationship between information technology (IT) and competitiveness (CP), emphasizing the different dimensions of IT capabilities, including customer relationship management (CRM) and human resource management (HRM). Also, the mediating role of innovative performance (IP) was examined in the link between IT use and CP. Data were collected in 2023 through a standard questionnaire, whose validity and reliability were confirmed by experts and statistical tests. Then, 172 valid responses were analyzed after determining the minimum sample size using Cochran’s formula. SPSS version 25 was used for descriptive analyses and preliminary tests, while SmartPLS 3.3.3 was utilized for structural equation modeling and hypothesis testing. The findings indicated that the use of IT components enhances CP, and IP mediates this relationship. This research contributes to the theoretical development of innovation management and IT by highlighting the transmission mechanism of IP rather than focusing solely on the direct relationship. This study, conducted among Iranian small and medium-sized enterprises (SMEs), also fills a gap in global literature, especially in developing countries, and offers practical insights. Full article

Market volatility and resource constraints pose significant resilience challenges to small and medium-sized enterprises (SMEs). Although AI-agent automation, agile marketing, and customer relationship management (CRM) offer powerful individual solutions, their synergistic impact on SME resilience remains critically underexplored. This review bridges this gap by proposing an integrated, AI-driven resilience framework designed to enhance the adaptive capacity of smaller firms. Through a systematic analysis of 35 peer-reviewed articles, our study explicitly maps AI-agent automation, agile marketing, and CRM to the dynamic capabilities of sensing, seizing, and reconfiguring, clarifying the causal pathways to SME resilience. The framework defines key inputs (e.g., multi-channel customer data), processes (e.g., iterative sprints), and outputs (e.g., enhanced market responsiveness). We identify APIs and SaaS platforms as the critical technological backbone for implementation. The central finding is that this integrated model empowers SMEs to build dynamic resilience and achieve competitive parity through data-driven, automated workflows. Actionable recommendations include adopting API-first strategies, investing in workforce training, and prioritizing data security. Full article

Distributed operational data rarely translates directly into business decisions. Meanwhile, in almost all industries, including the automotive industry, especially in the premium segment, it is crucial to identify the factors conducive to closing the transaction at an early stage. The aim of this study is to develop classification models that make it possible to predict the probability of success of a particular Mercedes-Benz offer with regard to vehicle configuration. Such a tool enables optimal allocation of resources (salespeople’s time, media budgets, production capacity), which is confirmed by the literature on customer relationship management. This study evaluates the usefulness of four machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine with an RBF kernel (SVM-RBF)—in forecasting sales, which was encoded as the binary variable Success. Among the tested models, Random Forest achieved the best results with an accuracy of 84.3%, F1-score of 0.73, and AUC of 0.90, indicating a very good ability to distinguish between successful and unsuccessful transactions. The results can be used for lead prioritization, dynamic discounting, optimization of marketing campaigns, and distribution/production planning. Full article

Tourism and digitalization have become increasingly interconnected, yet the complex, nonlinear relationships between technological adoption and tourism performance remain underexplored. This study aims to examine how enterprise software solutions influence tourism indicators across European countries. Using a triangulated methodological approach, we employed factor analysis to identify underlying dimensions, neural network modeling to detect nonlinear relationships, and hierarchical clustering to group countries based on digital and tourism profiles. The results consistently highlight CRM (Customer Relationship Management) as the most influential technological factor linked to both the net occupancy rate of beds and the number of nights spent at tourist accommodations. While AI (artificial intelligence) technologies currently have less impact, their importance is growing, as seen in emerging patterns. Cluster analysis further confirms that countries with higher CRM adoption tend to cluster together and show better tourism performance, indicating a clear connection between digital maturity and sector competitiveness. These findings emphasize the strategic importance of CRM as a transformative tool in hospitality and tourism management, while also recognizing the potential of AI to shape future trends. The study offers empirical support for tailored digital policies across European regions to promote inclusive and sustainable tourism growth. Full article

This paper examines a number of features of online retailing and e-commerce, with a special focus on important topics including consumer behavior, multichannel marketing, and customer relationship management (CRM). According to existing research, online sales have several advantages for businesses, especially those with physical locations, such as better inventory control and increased profitability. The difficulties of integrating offline and online channels, maintaining consumer loyalty, and competing globally are all deeply analyzed. Small-business-specific CRM methods and innovative algorithms show improvements in client happiness and targeting. The study shows how e-commerce adoption and client loyalty are shaped by cultural variables, trust, and personalization. By covering the gaps in research on growing and regional markets, this review offers thorough insights into how online shopping is changing and how these changes affect retail tactics. Full article

This study examines Chinese hotel employees’ understanding of customer relationship management (CRM) practices, with a focus on loyalty programme behaviour (LPB). Specifically, it investigates how three factors—loyalty programme (LP) awareness, LP knowledge, and LP concerns—shape employees’ intentions to implement loyalty programme practices (LPP) in the hospitality industry. A quantitative research design was adopted to test the proposed hypotheses. Data was collected through a self-administered survey of 893 Chinese hotel employees. To evaluate the proposed hypotheses, path analysis was performed using SPSS Statistics 24 and Mplus 7.4. The findings reveal that LP awareness and LP knowledge are both positively associated with employees’ intentions to implement loyalty programme practices, whereas LP concerns showed no significant effect. Moreover, employees’ intentions were positively linked to loyalty programme behaviour and served as a mediator between awareness, knowledge, and behavioural outcomes. Theoretical insights and practical applications are also addressed. Full article

In the context of modern healthcare, the integration of sensor networks into electronic health record (EHR) systems introduces new opportunities and challenges related to data privacy, security, and interoperability. This paper proposes a secure distributed web system architecture that integrates real-time sensor data with a custom customer relationship management (CRM) module to optimize patient monitoring and clinical decision-making. The architecture leverages IoT-enabled medical sensors to capture physiological signals, which are transmitted through secure communication channels and stored in a modular EHR system. Security mechanisms such as data encryption, role-based access control, and distributed authentication are embedded to address threats related to unauthorized access and data breaches. The CRM system enables personalized healthcare management while respecting strict privacy constraints defined by current healthcare standards. Experimental simulations validate the scalability, latency, and data protection performance of the proposed system. The results confirm the potential of combining CRM, sensor data, and distributed technologies to enhance healthcare delivery while ensuring privacy and security compliance. Full article

Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be incorporated into customer segmentation—even in regulated markets such as the postal market. The article develops and applies a three-dimensional (3D) segmentation model of business customers in the Slovak postal market, utilizing cluster analysis within STATISTICA analytical software for operationalization of the segmentation criteria. The 3D model reacts to the three pillars of sustainable development and is verified under real conditions at Slovak Post, plc. By adopting a systems perspective, the research places customer segmentation as an integral component of the entire socio-technical system, emphasizing the interrelatedness of organizational, social, and environmental considerations. The study illustrates how a systems-based approach to segmentation enables postal operators to uncover key customer segments, optimize resource allocation, and support competitiveness and sustainability goals. The practical applicability of the model is illustrated by its potential for application in other regulated service industries, providing a solid framework for sustainable customer management and strategic decision-making in complex environments. The research underscores the critical role of systems thinking in addressing the complex challenges of sustainable development in regulated industries. Full article

A company’s competitive advantage largely depends on the longevity and quality of its customer relationships, making it essential to understand which tools best support these interactions. In particular, identifying the factors that shape the impact of Customer Relationship Management (CRM) systems on business performance is crucial. This study examines the influence of CRM on the business performance of Portuguese companies by employing a conceptual model structured around five dimensions: customer-centric management (CCM), CRM organization (CRMO), operational CRM (OCRM), customer service quality (CSQ), and technological turbulence (TT). Data were gathered via a questionnaire completed by employees of Portuguese firms using CRM systems, yielding a total of 228 valid responses. Of the nine hypotheses tested, eight were confirmed. The results indicate that CRM organization (CRMO) exerts the strongest positive influence on business performance (0.457), followed by customer service quality (CSQ), operational CRM (OCRM), and customer-centric management (CCM). The study also confirms that technological turbulence (TT) moderates the relationship between the CRM dimensions and business performance. These findings suggest that the proposed model is well-suited to the context of Portuguese SMEs and provide valuable insights for managers aiming to enhance competitiveness through the strategic use of CRM systems. Additionally, the results offer a relevant contribution to the academic literature on CRM and business performance. Full article