Search Results (78)

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

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

Business problems today are complicated and involve considering numerous dimensions to be weighed against each other, leading to opposing goals that must be compromised on to discover the best solution. Multi-Criteria Decision Making or MCDM plays an essential role in this situation here. MCDM techniques and procedures analyze, score, and select between options that have various conflicting criteria. This systematic review investigates applications of MCDM methods within Management Information Systems (MIS) based on evidence from 40 peer-reviewed articles selected from the Scopus database. Key methods discussed are Analytic Hierarchy Process (AHP), TOPSIS, fuzzy logic-based methods, and Analytic Network Process (ANP). These methods were applied across MIS strategic planning, re-source assignment, risk assessment, and technology selection. The review contributes further by categorizing MCDM application into thematic decision domains, evaluating methodological directions, and mapping the strengths of each method against specific MIS problems. Theoretical guidelines are suggested to align the type of decision with an appropriate MCDM strategy. The study demonstrates how the addition of MCDM enhances MIS capability with data-driven, transparent decision-making power. Implications and directions for future research are presented to guide scholars and practitioners. Full article

Integrating smart devices from the Internet of Things (IoT) with Customer Relationship Management (CRM) systems presents significant opportunities for enhancing customer experience through real-time data utilization. This article explores the technological frameworks and practical solutions for achieving seamless integration of IoT data within CRM platforms. By leveraging distributed Web systems, this study demonstrates how companies can improve scalability, responsiveness, and personalization in managing customer relationships. This paper outlines key architectural designs for distributed Web systems that ensure efficient real-time data processing while addressing challenges such as security, system integration, and the demands of analytics. This research provides insights into overcoming these challenges with strategies like load balancing, edge processing, and advanced encryption protocols. Results from simulations and practical implementations underscore the effectiveness of these approaches in optimizing operational efficiency and delivering hyper-personalized customer experiences. This study aims to bridge the gap between theoretical possibilities and real-world applications, offering actionable guidelines for organizations to fully leverage IoT-driven CRM systems. Full article

Customer relationship management (CRM) is a key factor driving innovation and organizational growth. The present study investigated the relationship between data-driven CRM (DDCRM) and innovation in Taiwan. We developed a research model involving CRM theory, innovation theory, and the technology adoption model (TAM) theory to account for the cultural and organizational contexts of Taiwan and investigate this relationship. The study distributed questionnaires to employees and stakeholders within Taiwanese firms to understand their firms’ innovation and CRM practices. The results indicate that technology adoption and organizational culture have mediating effects and industry dynamics and organizational size have moderating effects on the relationship between DDCRM and innovation. That is, adopting new technology and having an organizational culture that supports innovation and company-wide collaboration can enhance the effects of implementing DDCRM practices. In addition, certain industries (e.g., the technology industry) are more likely to effectively leverage DDCRM practices to drive innovation, and although large organizations have more resources and can therefore more easily implement CRM systems, small and medium-sized enterprises (SMEs) can more quickly adapt and innovate on the basis of CRM insights. These findings highlight the importance of DDCRM in driving innovation and reveal key factors influencing the effectiveness of CRM in doing so. The study features comprehensive suggestions of operable strategies and measures for Taiwanese SMEs, hopefully assisting them in gaining a market advantage and elevating their innovation capabilities by leveraging DDCRM practices. Full article

This research explores opportunities for generative artificial intelligence (GenAI) in higher education constituent (customer) relationship management (CRM) to address the industry’s need for digital transformation driven by demographic shifts, economic challenges, and technological advancements. Using a qualitative research approach grounded in the principles of grounded theory, we conducted semi-structured interviews and an open-ended qualitative data collection instrument with technology vendors, implementation consultants, and HEI professionals that are actively exploring GenAI applications. Our findings highlight six primary types of GenAI—textual analysis and synthesis, data summarization, next-best action recommendations, speech synthesis and translation, code development, and image and video creation—each with applications across student recruitment, advising, alumni engagement, and administrative processes. We propose an evaluative framework with eight readiness criteria to assess institutional preparedness for GenAI adoption. While GenAI offers potential benefits, such as increased efficiency, reduced costs, and improved student engagement, its success depends on data readiness, ethical safeguards, and institutional leadership. By integrating GenAI as a co-intelligence alongside human expertise, HEIs can enhance CRM ecosystems and better support their constituents. Full article

We developed a method for the evaluation and selection of customer business analysis in two stages. First, using the bank’s existing expert model, artificial rules of thumb were used to evaluate the value of each field of the data and establish screening rules. Secondly, the machine learning feature screening method was applied based on the customer’s transaction data to find out whether the customer’s contribution to the bank had a significant impact as a feature of the model. Based on the results, the best classification model was selected through data verification. The effectiveness of the proposed model was validated through actual case analysis, taking wealth management in banks as an example. The classification method, using support vector machines (SVMs), effectively assists banks in identifying potential customers efficiently and in planning to manage customers. This method helps to avoid the traditional blind spots, which emerge based on subjective judgment, and allows bank wealth managers to promote customer relationship management (CRM). Full article