Customer Relationship Research Articles

Supply chain risks and corporate innovation strategy: from the perspective of breakthrough innovation and incremental innovation

ABSTRACT This paper investigates the impact of supply chain risks on corporate innovation in China. Our findings indicate that firms with high supply chain risks are more inclined towards incremental innovation rather than breakthrough innovation. This relationship is further strengthened by high customer concentration and strong financial constraints, and it is more pronounced in industries characterized by higher levels of competitiveness and within the high-tech sector. Furthermore, the influence of supply chain risks on corporate innovation is jointly driven by supply and demand risks. Crucially, strategic adjustments in response to supply chain risks not only improve operational performance but also enhance the stability of customer relationships.

How Do Supply Chain Management Practices Influence Organizational Performance? A Case Study of Bangladesh's Agro-Food Industry

Introduction: Supply chain management practices are essential for enhancing organizational performance, particularly in the Agro-food industry. This study examines the impact of specific SCM factors—Strategic Supplier Partnership, Customer Relationship, Level of Information Sharing, and Quality of Information Sharing—on organizational performance in Bangladesh. Methodology: Data were collected via a structured questionnaire targeting key stakeholders in Bangladesh's Agro-food industry, using a 5-point Likert scale to measure responses. Structural equation modeling (SEM) was utilized to analyze the relationships between SCM practice factors and organizational performance factors such as marketing and financial performance. Findings: The analysis revealed that customer relationships, quality of information sharing, and strategic supplier partnerships have significant positive relationships with organizational performance, particularly in terms of financial and marketing outcomes. However, the level of information sharing did not show a significant impact on both financial and marketing performance. Managerial Implications: The study emphasizes the significance of prioritizing strategic supplier partnerships, customer relationships, and high-quality information sharing for improving organizational performance in Bangladesh's Agro-food industry. Strengthening these SCM practices can lead to better financial and marketing performance, providing valuable insights for managers to develop targeted strategies.

CUSTOMER SEGMENTATION USING RFM ANALYSIS

This research presents a comprehensive approach to customer segmentation using Recency, Frequency, and Monetary (RFM) analysis, combining statistical insights, data visualization, and machine learning techniques. The study utilizes a real-world dataset obtained from a retail environment, aiming to categorize customers based on their recent purchasing behavior, visit frequency, and monetary contributions to the store. The code begins with data preparation and exploration, ensuring data integrity by addressing issues such as negative quantities and missing customer identifiers. Following this, the Recency, Frequency, and Monetary metrics are computed, providing a holistic view of customer engagement and spending patterns. Visualizations, including violin plots, histograms, and box plots, are employed to intuitively convey the distribution of these metrics. The research then delves into the quantile-based segmentation of customers, allowing for a more granular classification. Quantiles are calculated to divide customers into four segments for each RFM metric. The resulting quantile labels are applied to the dataset, enabling the creation of a compound RFM quantile that combines recency, frequency, and monetary information. This combined quantile facilitates the definition of distinct customer segments. To further enhance the interpretability of customer segments, the study introduces a set of rules for labeling customers based on their RFM quantiles. These rules yield segments such as "Best Customer," "Loyal Customer," "Big Spender," "Dead Beats," and "Lost Customer." The resulting customer segmentation is presented visually through histograms and a pie chart, providing a clear and concise representation of the distribution of customers across different segments. Moreover, the research integrates machine learning models, including XGBClassifier, and CatBoostClassifier, to explore the potential of automating the segmentation process and predicting customer segments based on historical data. However, the machine learning aspect is introduced with commented-out sections, leaving room for further exploration and experimentation. In conclusion, this research contributes a comprehensive and detailed code implementation for RFM-based customer segmentation. The integration of visualization techniques aids in the interpretation of customer behavior, while the inclusion of machine learning models opens avenues for predictive analytics in customer segmentation. The presented approach provides valuable insights for businesses seeking to tailor marketing and customer relationship strategies based on individualized customer segments. KEYWORDS— Customer Segmentation, Frequency, Monetary Value, Recency, RFM Analysis.

ANALISIS MODEL BUSINESS CANVAS JASA CEK PLAGIASI THE LUCKY OF TURNITIN

Business Model Canvas (BMC) is a visual chart with nine elements that explain the value proposition of a company or product, infrastructure, customers and finances. The aim of implementing these nine BMC elements is to become a guide in running The Lucky of Turnitin services. BMC determines the best starting from planning The Lucky of Turnitin to realization, as a part of MSMEs managed by students who are still beginners in entrepreneurship. The research method uses descriptive methods, while data analysis uses qualitative analysis. Analysis has been carried out by compiling and verifying BMC. The results of the analysis of The Lucky of Turnitin using the BMC model show that; 1) The Lucky of Turnitin Customer Segments are lecturers and students. 2) Value Propositions using a fast application. 3) The Lucky of Turnitin channels will utilize social media to promote The Lucky of Turnitin through FB posts, official IG, WA stories, and word of mouth promotions. 4) Customer Relationship The Lucky of Turnitin will build relationships with customers by providing testimonials which can be done through assessment testimonials on official IG accounts, WA stories, posts on FB. 5) Revenue Streams for this business come from sales of The Lucky of Turnitin services. 6) Key Resources The Lucky of Turnitin, the main source used by Smartphones, the Internet and Vehicles. 7) Key Activities The Lucky of Turnitin will provide good and quality service quickly. 8) Key Partnership Tawanyesss works with several suppliers that The Lucky of Turnitin needs. 9) Cost Structure (Cost Structure). The costs incurred in this business are divided into several parts, namely Equipment Costs, Raw Material Costs, Packaging Costs and Operational Costs.

AI-Driven Innovations in Food and Beverage Service: A Roadmap to Future Hospitality

Artificial Intelligence (AI) is increasingly revolutionizing the food and beverage service industry by offering innovative solutions to enhance operational efficiency and personalize customer experiences. This abstract explores the transformative impact of AI technologies such as machine learning, predictive analytics, and robotics on various aspects of hospitality operations. AI-driven automation streamlines kitchen processes, optimizes inventory management, and reduces operational costs, thereby improving service consistency and profitability (Alsmadi et al., 2020; Li et al., 2021).AI facilitates personalized client interactions by analyzing vast datasets to understand individual preferences, dietary restrictions, and purchasing behaviors. This capability empowers establishments to offer tailored menu recommendations, personalized promotions, and interactive dining experiences that cater to diverse consumer needs (Chen et al., 2019; Huang et al., 2020). AI's integration into the food and beverage service industry significantly enhances customer satisfaction and loyalty while concurrently boosting revenue through targeted marketing and operational efficiencies. By leveraging AI technologies such as machine learning and predictive analytics, establishments can tailor offerings to meet individual preferences and anticipate consumer behavior, thereby fostering stronger customer relationships. Moreover, the future trajectory of AI in this sector foresees advancements in IoT integration, AR applications, and blockchain technology. These innovations are set to further transform operations by optimizing efficiency, enriching customer interactions, and ensuring robust supply chain transparency. IoT devices, for instance, will facilitate real-time data collection and analysis, thereby enhancing inventory management precision and optimizing resource allocation to meet demand fluctuations effectively. AR applications will offer immersive dining experiences, allowing customers to interact with digital menus or view culinary presentations. Blockchain technology will enhance transparency and traceability, ensuring food safety and compliance with regulatory standards. Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management (Jiang et al., 2023; Wang et al., 2023).

Innovation activity of the largest companies in the high-tech sector

Aim. To analyze the specific features of innovation activity of the largest transnational companies of the high-tech sector in order to identify general trends and assess their efficiency.Objectives. To study the expenditures on innovation in Alphabet, Apple, Microsoft and Amazon; to determine the ratio of expenditures on research and development (R&D) to total revenue and the share of innovation in total expenditures; to examine the market capitalization and the share of private investors in the ownership of shares of these companies; to analyze the share of intangible assets in total assets; to assess the intensity of innovation activity by the number of issued patents; to identify the general characteristics of innovative companies-leaders of the high-tech sectorMethods. This study utilizes financial indicators, statistical data and data on patent activity of Alphabet, Apple, Microsoft Corporation and Amazon.Results. The largest high-tech companies achieve improved innovation performance through a combination of understanding customer needs, active participation of management in the research and development program, and the creation of unique products and services based on new technologies. Financial success depends on successfully utilizing existing intellectual resources, creating long-term customer relationships, and fostering the development of human capital within companies. Companies that focus on customer satisfaction and continuously stimulate employee development demonstrate higher innovation performance and ensure commercial success in the market.Conclusions. The comparative analysis of the indicators of the largest transnational companies in the high-tech sector allows us to assess the efficiency of innovation activity, as well as to identify the key factors contributing to their sustainable development in a highly competitive market.

Knowledge retrieval and diagnostics in cloud services with large language models

Efficient customer support is the foundation for any service provider trying to improve customer relationships. An important measure of successful support is the mean time to resolve issues. The complexity and large scale of modern cloud environments make it unrealistic to reduce the resolution time without deploying intelligent solutions. The latest also provides an exceptional opportunity to leverage cross-customer product usage data for proactive solutions when the troubles of some users can be analyzed in advance to prevent similar issues of other users. We build a recommender system that matches customer support requests to other resolved support requests or knowledge base articles that contain valuable information for problem remediation. This system can be used by customers or support teams to quickly find problem-resolution tips or detect trending issues to warn vulnerable users. We utilize large language models, fine-tune for better performance, and discuss capabilities and possible improvements. During our research, we highlighted several evaluation metrics such as mean time to resolve issues and the accuracy of recommendations. However, estimating accuracy is challenging due to insufficient datasets with precise and comprehensive recommendations. Despite this, our support managers provided some estimates regarding the remediation durations. Typically, identifying and resolving an issue takes several days or weeks. With appropriate recommendations, this time can be significantly reduced to several hours and, in some simple cases, even lead to self-service capabilities.

The Effect of Perceived Risk and Customer Experience on Loyalty Intention for Mobile Banking: The Moderating Role of Customer Satisfaction

In the banking industry, providing a range of conventional and high-tech services is crucial to ensure customer satisfaction. One area that requires attention is mobile banking services, which need to offer an engaging experience to keep customers satisfied and loyal. However, empirically, the evidence regarding the impact of the role of mobile banking customer satisfaction on loyalty intention seems to leave room for improvement. Research indicates that customer experience and the risks involved can have a significant impact on consumer satisfaction and loyalty intention. The study is conducted using explanatory research methodology, employing a quantitative approach and adhering to the positivist paradigm. This methodology aims to elucidate the causal relationship between variables, both through direct and indirect means. The research data was analyzed using PLS-SEM, which stands for Partial Least Square Structural Equation Method. The findings indicate that perceived risk has a detrimental influence on customer satisfaction, whereas customer experience has a beneficial effect. Research has demonstrated that customer satisfaction has a clear and substantial impact on the intention of customers to remain loyal. While all indirect links are supported, it is not possible to establish a direct relationship between perceived risk and customer experience on loyalty intention. The significance of customer satisfaction lies in its ability to connect perceived risk and customer experience results with loyalty intention. This study explores the implications for literature and management about mobile banking applications and the integration of perceived risk and customer experience in shaping customer satisfaction and loyalty in the banking sector. These advancements emphasize the significance of excellent customer experiences in enhancing customer happiness and loyalty. Companies must enhance client experience and strive to surpass expectations. Gaining a comprehensive understanding of the concept of "customer satisfaction" and its role as a mediator can help marketing and customer relationship management strategies effectively retain and attract customers.

The Influence of customer relationship management (CRM) Systems on the efficiency and effectiveness of Small and Medium Enterprises in Saudi Arabia

Este estudio tiene como objetivo explorar los sistemas de gestión de relaciones con los clientes (CRM) en pequeñas y medianas empresas (PYME) en Arabia Saudita. Más específicamente, este estudio investiga los beneficios de CRM, los factores críticos de éxito (CSF) y el impacto de CRM en el desempeño de las PYMES. Este estudio se basa en un diseño cualitativo; Se emplearon entrevistas semiestructuradas para adquirir los datos necesarios. Los resultados reflejan que todos los negocios investigados están implementando CRM. Además, se ha determinado que ciertos factores de éxito, como la capacitación de los empleados, el apoyo de la alta dirección y la gestión del cambio, tienen un impacto significativo en el sistema CRM y aumentan el logro de los resultados deseados. El estudio crea conciencia sobre CRM y proporciona a los ejecutivos información sobre las prácticas de CRM, con el objetivo de mejorar la eficacia de CRM y confirmar sus beneficios y ventajas para las partes interesadas.

IMPACT OF ONLINE PAYMENT SYSTEMS ON CUSTOMER TRUST AND LOYALTY IN E-COMMERCE ANALYZING SECURITY AND CONVENIENCE

This study systematically reviews the impact of online payment systems on customer trust and loyalty in e-commerce, adhering to PRISMA guidelines. The findings highlight the critical importance of security, convenience, and transparency in shaping customer perceptions and behaviors. Secure payment systems, featuring robust encryption and multi-factor authentication, are essential in protecting sensitive information and mitigating fraud risks, thereby enhancing customer confidence. Convenience, characterized by user-friendly interfaces, multiple payment options, and quick transaction processes, significantly improves the overall shopping experience and fosters customer satisfaction. Transaction transparency, through clear communication about charges, detailed receipts, and real-time tracking, is pivotal in building and maintaining trust. The review underscores the strong relationship between trust and loyalty, revealing that secure, convenient, and transparent payment systems are vital for achieving long-term customer loyalty. E-commerce platforms that prioritize these aspects can significantly enhance customer trust and loyalty, ensuring sustained success in the competitive digital marketplace. The study provides valuable insights and practical recommendations for e-commerce businesses to optimize their online payment systems and build lasting customer relationships.

The Impact of Customer Relationship Management on Sustainable Competitive Advantage Mediator Brand Citizenship Behavior: An Analytical Study in a Sample of Iraqi Banks

The research aims to study the impact of customer relationship management on sustainable competitive advantage through brand Citizenship Behavior in a sample of Iraqi banks. The main problem of the research is summarized in the following questions: "have bank leaderships been able to employ customer relationship management in achieving sustainable competitive advantage? And have they been able to maximize this impact through the mediating role of brand Citizenship Behavior?" The comprehensive survey method was used to collect data, given the importance of the banking sector, both government and private banks in Baghdad were chosen as the focus of this study. Commercial and Islamic banks were considered for examination, and the researcher conducted field visits to six banks, including Rafidain Bank, Trade Bank of Iraq, Alnahrain Islamic Bank, Al Taif Islamic Bank, Iraqi Middle East Investment Bank, National Bank of Iraq. The research sample comprised 212 employees from these banks. Data analysis and hypothesis testing were conducted using appropriate statistical tools with the help of (SPSS and AMOS) software. The results revealed a significant impact of customer relationship management on sustainable competitive advantage through the mediating role of brand Citizenship Behavior.

Digital marketing as a determinant variable for improving the business performance

This study analyzes the complex relationships between digital marketing, Customer Relationship Management (CRM), сompetitiveness, and business performance in the creative industry Small and Medium Enterprises (SMEs), focusing on craft-leading products in urban areas of East Java province. According to the sample size, the number of survey respondents was 172. There were 86 male entrepreneurs (50%) and 86 female entrepreneurs (50%). Primary data were collected by distributing the questionnaires according to the number of respondents to SME entrepreneurs who are managers or company owners with competence in the business field and more than five years of business experience. The analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM). The results emphasize that digital marketing affects business performance through CRM by 0.126 with p-value of 0.006, and competitiveness by 0.260 with p-value of 0.000. It can be explained that digital media can identify, recruit, and turn potential customers into loyal customers. Higher demands from customers for online services are becoming increasingly crucial for digital marketing and business activities. The essence of the results of this study is that the more effective digital marketing is, the more influential the company’s CRM is to improve the company’s business performance, including profit growth, sales growth, and employment growth. The critical role of digital strategy, CRM, and competitiveness on business performance can overcome the challenges posed by the pandemic and drive success in creative industry SMEs of superior products in East Java, highlighted in this study. AcknowledgmentThe author would like to thank the Ministry of Education, Culture, Research and Technology. Directorate General of Higher Education, Research and Technology of The Republic of Indonesia, which has supported the research implementation and permitted publication of this article.

Impact of Employee Environmental Concerns on Sustainable Practices: Investigating Organizational Commitment and Job Satisfaction

Environmental issues are gradually increasing due to increases in carbon emissions, overproduction, plastic use, the burning of fossil fuels, etc. Therefore, ecological sustainability is a primary concern for society and the upcoming generation. Considering this, the present research objective is to examine the factors influencing employee eco-friendly behavior (EEBH) and firm environmental performance (FEP). Cross-sectional data were accumulated from 403 employees working in casual dining restaurants in China and analyzed through partial least squares structural equation modeling (PLS–SEM) approaches (i.e., PLS algorithm, bootstrapping, blindfolding, and PLS prediction). The findings support the significant effect of environmental concern (ENVC) on employee organizational commitment (EOC), employee job satisfaction (EJoS), and EEBH. In addition, EOC and EJoS directly and indirectly influence EEBH. Furthermore, EOC also mediates the link between ENVC and EJoS. Finally, the findings also show that EEBH stimulates environmental performance. By showing the links between these elements, it has been emphasized that EEBH is a precondition of good organizational productivity and how it contributes to enhancing the EJoS and overall firm’s health. It challenges managers to create a work environment that is conducive to sustainability for improved customer relations and a strong reputation for the organization in society.

Big Data Value Proposition in UK Facilities Management: A Structural Equation Modelling Approach

Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, founded on data delivering continuous value and achieving optimal performance, whilst also upgrading their own decision-making and reporting processes. This article aims to explore how UK FM organizations are currently capitalizing on BDA to drive innovation and ‘added value’ in their operations. The objective is to shed light on the initial BDA adoption efforts within the UK’s FM sector, particularly capturing the benefits experienced by FM organizations in relation to customer value and improved decision-making processes. Drawing upon exploratory sequential research including a qualitative stage with 12 semi-structured interviews and an industry-wide questionnaire survey with 52 responses, a novel fifteen-variable model for BDA outcomes was developed. Exploratory Factor Analysis (EFA) and a Higher-Order model using Partial Least Square Structural modelling (PLS-SEM) were used to validate the scale. The EFA output generated three dimensions with 14 items. The dimensions included Improved client value, FM business operations added value, and Improved efficiency added value. Furthermore, the results of PLS-SEM confirmed the validity of the scale items and the reflective–formative measurement model. The findings suggest that the contemporary digitization trend offers the FM service the unique opportunity to develop a smarter, client-centric strategy resulting in more personalized services and stronger customer relationships. Furthermore, efficient resource management and planning powered by analytics and data-driven insights emerge as a key driver for competitive differentiation in the field. As one of the first studies to develop and validate scale items measuring specific dimensions of BDA adoption outcomes, the study makes significant contributions to the literature.

Innovative digital marketing strategies for SMEs: Driving competitive advantage and sustainable growth

In the rapidly evolving digital landscape, small and medium-sized enterprises (SMEs) must adopt innovative digital marketing strategies to drive competitive advantage and sustainable growth. This review explores the critical components and benefits of leveraging digital marketing for SMEs, emphasizing how tailored strategies can enhance market presence, customer engagement, and business performance. SMEs face unique challenges in resource allocation and market competition, necessitating cost-effective and impactful marketing approaches. Innovative digital marketing strategies, including search engine optimization (SEO), social media marketing, content marketing, email campaigns, and data-driven analytics, provide SMEs with the tools to compete effectively against larger enterprises. By utilizing these strategies, SMEs can achieve greater visibility, attract a broader audience, and convert prospects into loyal customers. SEO and content marketing play a pivotal role in enhancing online visibility and organic traffic. By producing high-quality, relevant content and optimizing it for search engines, SMEs can improve their search rankings and attract targeted audiences. Social media platforms offer powerful channels for engaging with customers, building brand awareness, and fostering community. Through targeted advertisements and influencer partnerships, SMEs can reach specific demographics and drive engagement. Email marketing remains a cost-efficient strategy for maintaining customer relationships and promoting products or services. Personalized email campaigns can nurture leads, encourage repeat business, and build customer loyalty. Additionally, data-driven analytics enable SMEs to measure campaign performance, understand customer behavior, and make informed decisions to refine marketing efforts. The integration of innovative technologies such as artificial intelligence (AI) and machine learning further enhances digital marketing capabilities. AI-driven tools can automate tasks, provide insights into customer preferences, and optimize marketing strategies in real-time. For instance, chatbots can improve customer service, while predictive analytics can forecast trends and personalize marketing messages. In conclusion, innovative digital marketing strategies are essential for SMEs seeking to drive competitive advantage and sustainable growth. By embracing SEO, social media marketing, content marketing, email campaigns, and data-driven analytics, SMEs can maximize their marketing impact, enhance customer engagement, and achieve long-term business success. Leveraging these strategies allows SMEs to remain agile, responsive, and competitive in the dynamic digital marketplace. Keywords: Innovative, Digital Marketing, SMEs, Sustainable Growth, Competitive Advantage.

Enhancing efficiency and Personalization in Food and Beverage Service through AI: Future Trends and Challenges

Artificial Intelligence (AI) is increasingly revolutionizing the food and beverage service industry by offering innovative solutions to enhance operational efficiency and personalize customer experiences. This abstract explores the transformative impact of AI technologies such as machine learning, predictive analytics, and robotics on various aspects of hospitality operations. AI-driven automation streamlines kitchen processes, optimizes inventory management, and reduces operational costs, thereby improving service consistency and profitability (Alsmadi et al., 2020; Li et al., 2021).AI facilitates personalized client interactions by analyzing vast datasets to understand individual preferences, dietary restrictions, and purchasing behaviors. This capability empowers establishments to offer tailored menu recommendations, personalized promotions, and interactive dining experiences that cater to diverse consumer needs (Chen et al., 2019; Huang et al., 2020). AI's integration into the food and beverage service industry significantly enhances customer satisfaction and loyalty while concurrently boosting revenue through targeted marketing and operational efficiencies. By leveraging AI technologies such as machine learning and predictive analytics, establishments can tailor offerings to meet individual preferences and anticipate consumer behavior, thereby fostering stronger customer relationships. Moreover, the future trajectory of AI in this sector foresees advancements in IoT integration, AR applications, and blockchain technology. These innovations are set to further transform operations by optimizing efficiency, enriching customer interactions, and ensuring robust supply chain transparency. IoT devices, for instance, will facilitate real-time data collection and analysis, thereby enhancing inventory management precision and optimizing resource allocation to meet demand fluctuations effectively. AR applications will offer immersive dining experiences, allowing customers to interact with digital menus or view culinary presentations. Blockchain technology will enhance transparency and traceability, ensuring food safety and compliance with regulatory standards. Together, these innovations will enable food and beverage establishments to innovate and differentiate themselves in a competitive market landscape, ultimately shaping the future of dining experiences worldwide to further optimize operations and enhance transparency in supply chain management (Jiang et al., 2023; Wang et al., 2023).

Enhancing business performance: The role of data-driven analytics in strategic decision-making

In today’s highly competitive business landscape, organizations are increasingly turning to data-driven analytics to enhance performance and inform strategic decision-making. This approach leverages vast amounts of data, transforming it into actionable insights that drive efficiency, innovation, and growth. The role of data-driven analytics is multifaceted, encompassing predictive, prescriptive, and descriptive analytics, each contributing uniquely to the decision-making process. Predictive analytics forecasts future trends and behaviors, enabling proactive strategies. Prescriptive analytics provides recommendations for optimal actions based on various scenarios, while descriptive analytics offers a clear view of past performance, helping to identify patterns and opportunities for improvement. The integration of advanced data analytics tools and techniques has revolutionized how businesses operate, particularly in areas such as marketing, finance, supply chain management, and customer relationship management. By utilizing data analytics, companies can identify market trends, understand customer preferences, optimize operations, and reduce costs. For example, in marketing, data-driven insights allow for targeted campaigns and personalized customer experiences, leading to higher conversion rates and customer loyalty. In supply chain management, analytics improve demand forecasting and inventory management, enhancing overall efficiency. Furthermore, the adoption of data-driven analytics fosters a culture of evidence-based decision-making within organizations. Leaders can make informed decisions quickly, reducing the risks associated with gut-feel approaches and ensuring that strategies are aligned with market realities and organizational goals. This shift towards analytics-based decision-making is supported by advancements in artificial intelligence and machine learning, which enhance the accuracy and depth of insights generated from data. However, the journey to becoming a data-driven organization is not without challenges. It requires significant investments in technology, talent, and change management. Organizations must also address data privacy and security concerns to maintain stakeholder trust. Despite these challenges, the benefits of data-driven analytics in strategic decision-making are undeniable. It empowers businesses to navigate complexities, seize opportunities, and achieve sustainable competitive advantages. In conclusion, data-driven analytics is a critical enabler of enhanced business performance. Its strategic application in decision-making processes not only drives operational excellence but also positions organizations for long-term success in a dynamic market environment. Keywords: Decision-Making, Data-Driven, Analytics, Business Performance, Strategic.

Prioritizing Intellectual Capital Assets for Strategic Investment Using an Analytic Network Process Approach

Intellectual Capital Assets (ICAs) refer to the intangible resources that are critical to the creation of organizational value out of human, customer, innovation and process capital. High impact ICAs should be identified and prioritized for investment in by organizations as they strive to increase their value. This research aims at assessing and ranking ICAs by using Analytic Network Process (ANP) model for capturing the interactions between the assets. The method used to gather the pairwise data aimed at supervising CEOs, scientific supervisors, shareholders, employees and customers in order to determine the relative value each ICA contributes to value creation. A mathematical model is employed to translate qualitative assessments into quantitative results with regard to Consistency Index (CI) and Consistency Ratio (CR) to check the reliability of the judgments. Cost benefit analysis shows the ICAs which yield the greatest return on investment. Our findings suggest that knowledge sharing and innovation are the most dominant ICA that positively and significantly affects value creation. These assets involve moderate investment but have huge returns, while other assets such as employee training and customer relations also have greater potential but involve high investment. The results offer practical guidance to organizations in terms of how to effectively prioritize ICAs to support their achievement of strategic objectives in creating innovative value.

Factors influencing tourists' technology adoption in Saudi Arabia: examining determinants for effective use of mobile apps in tourism

Purpose The use of smartphone applications is the primary means by which modern technology improves the experiences of tourists in the tourism industry by bringing novel and superior features. The purpose of this study is to look into and examine the factors that affect tourists’ intention to use technology when looking for information in areas of tourism. Design/methodology/approach This study quantitatively uses online surveys. A total number of 315 questionnaires were collected from the tourism industry in Saudi Arabia. Partial least squares-structural equations model is used to analyze the data to evaluate the measurement and structural models. Findings Results of this study reveal that attitude, perceived usefulness, perceived ease of use and perceived enjoyment significantly and positively influence the intention of tourists, while customer relationship management and personal innovativeness in technology are insignificant. Furthermore, customer relationship management and personal innovativeness in technology indirectly impact tourists’ intentions. Originality/value This study offers novel insights by comprehensively analyzing the factors that influence tourists' intention to adopt technology in the context of destination information searches. Finding new avenues, the research emphasizes the distinct functions of customer relationship management, personal innovativeness in technology and enjoyment, offering insightful viewpoints for improving technology integration in the tourism industry.

Authenticity as a Strategic Weapon: Navigating the Social Media Battlefield to Enhance Brand Loyalty

Abstract In the dynamic landscape of the fashion industry, cultivating brand loyalty has become a challenging endeavour, intensified by the competitive realm of social media. This study addresses the crucial problem of fostering brand loyalty in the digital era by employing authenticity as a strategic weapon. Using an integrated Stimulus-Organism-Response (SOR) framework and social presence theory, we explore the diverse impact of social media marketing activity (SMMa) on brand authenticity and brand loyalty. Through empirical analysis of data from 365 consumers engaging with global fashion brands on social media, we find a direct and significant impact of SMMa on brand authenticity and brand loyalty. Brand authenticity partially mediates the relationship between SMMa and brand loyalty. While self-congruence does not moderate the effects, the research highlights the importance of authenticity in shaping effective brand strategies on social platforms. The implications of these findings are valuable for fashion marketers, guiding them in creating authentic brand images and fostering enduring customer relationships in the competitive world of digital marketing.