User Engagement Research Articles

Enhancing User Experience in Private Bank Mobile Banking: Insights from Management Information System Analysis

This research explores the impact of Management Information Systems (MIS) on user experience in private bank mobile banking, focusing specifically on the effectiveness of m-BCA. Utilizing a descriptive quantitative approach, the study aims to understand how mobile banking features, including accessibility, user interface, security, and functionality, influence user satisfaction. Data were collected through direct experimentation with the m-BCA application, complemented by analysis from secondary data sources and a survey of 30 users. Results indicate that 93.3% of users find m-BCA effective for routine banking needs, attributing high ratings to its convenience and functionality. However, while 80% of respondents appreciate the current security measures, 20% cite the absence of advanced security features such as fingerprint and facial recognition as a drawback. The study also reveals mixed feedback on the user interface; while the straightforward design aids usability, 10% of users express a desire for a more visually appealing interface. Despite these areas for improvement, m-BCA’s comprehensive set of features is generally seen as effective, supporting users in performing essential transactions with ease and reliability. The findings suggest that enhancing interface aesthetics and integrating advanced security options could further boost user engagement and trust in mobile banking applications, underscoring MIS’s role in meeting evolving customer expectations in digital banking.

User engagement in digital curation: A systematic review and synthesis

This review article synthesizes current research findings to propose effective strategies for measuring and enhancing user engagement in digital curation. This review article synthesizes current research findings to propose effective strategies for measuring and enhancing user engagement in digital curation. By establishing comprehensive frameworks and methodologies, researchers and practitioners can gain deeper insights into user behaviors, preferences, and interactions with curated content. This understanding will improve digital curation practices, ensuring curated resources meet user expectations and needs. The review identifies best practices, offers actionable recommendations, and highlights the critical need for robust user engagement strategies. Through systematic analysis of existing literature, it empowers the digital curation community to develop more effective strategies for managing and curating digital resources, ultimately enhancing user experience and engagement across diverse digital platforms.

AI Enabled E-commerce Platform

An overview of AI-based e-commerce plat forms and their effects on the online retail sector is given in this survey. Businesses are starting to use artificial intelligence (AI) to improve customer experience, streamline processes, and boost revenue development in the e-commerce industry as a result of the technology’s rapid advancement. This research offers a thorough examination of the state of AI-based e-commerce platforms today, stressing their salient characteristics, advantages, difficulties, and potential. The first part of the poll introduced artificial intelligence (AI) and how it relates to e-commerce. It looks at how artificial intelligence (AI) technologies like computer vision, machine learning, natural language processing, and recommendation systems are being incorporated into e-commerce platforms to provide intelligent customer support, enhance product search and discovery, and offer personalized shopping experiences. Additionally, it explores the role of AI in enhancing customer engagement through chatbots, virtual assistants, and personalized marketing campaigns

Uncovering Key Factors That Drive the Impressions of Online Emerging Technology Narratives

Social media platforms play a significant role in facilitating business decision making, especially in the context of emerging technologies. Such platforms offer a rich source of data from a global audience, which can provide organisations with insights into market trends, consumer behaviour, and attitudes towards specific technologies, as well as monitoring competitor activity. In the context of social media, such insights are conceptualised as immediate and real-time behavioural responses measured by likes, comments, and shares. To monitor such metrics, social media platforms have introduced tools that allow users to analyse and track the performance of their posts and understand their audience. However, the existing tools often overlook the impact of contextual features such as sentiment, URL inclusion, and specific word use. This paper presents a data-driven framework to identify and quantify the influence of such features on the visibility and impact of technology-related tweets. The quantitative analysis from statistical modelling reveals that certain content-based features, like the number of words and pronouns used, positively correlate with the impressions of tweets, with increases of up to 2.8%. Conversely, features such as the excessive use of hashtags, verbs, and complex sentences were found to decrease impressions significantly, with a notable reduction of 8.6% associated with tweets containing numerous trailing characters. Moreover, the study shows that tweets expressing negative sentiments tend to be more impressionable, likely due to a negativity bias that elicits stronger emotional responses and drives higher engagement and virality. Additionally, the sentiment associated with specific technologies also played a crucial role; positive sentiments linked to beneficial technologies like data science or machine learning significantly boosted impressions, while similar sentiments towards negatively viewed technologies like cyber threats reduced them. The inclusion of URLs in tweets also had a mixed impact on impressions—enhancing engagement for general technology topics, but reducing it for sensitive subjects due to potential concerns over link safety. These findings underscore the importance of a strategic approach to social media content creation, emphasising the need for businesses to align their communication strategies, such as responding to shifts in user behaviours, new demands, and emerging uncertainties, with dynamic user engagement patterns.

The influence of AR & VR-induced reality congruence on customer engagement in e-commerce websites: a mediation analysis

The influence of AR & VR-induced reality congruence on customer engagement in e-commerce websites: a mediation analysis

Online and Offline Evaluation in Search Clarification

The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the performance of these models, it is crucial to employ assessment approaches that encompass both real-time feedback from users (online evaluation) and the characteristics of clarification questions evaluated through human assessment (offline evaluation). However, the relationship between online and offline evaluations has been debated in information retrieval. This study aims to investigate how this discordance holds in search clarification. We use user engagement as ground truth and employ several offline labels to investigate to what extent the offline ranked lists of clarification resemble the ideal ranked lists based on online user engagement. Contrary to the current understanding that offline evaluations fall short of supporting online evaluations, we indicate that when identifying the most engaging clarification questions from the user’s perspective, online and offline evaluations correspond with each other. We show that the query length does not influence the relationship between online and offline evaluations, and reducing uncertainty in online evaluation strengthens this relationship. We illustrate that an engaging clarification needs to excel from multiple perspectives, and SERP quality and characteristics of the clarification are equally important. We also investigate if human labels can enhance the performance of Large Language Models (LLMs) and Learning-to-Rank (LTR) models in identifying the most engaging clarification questions from the user’s perspective by incorporating offline evaluations as input features. Our results indicate that LTR models do not perform better than individual offline labels. However, GPT, an LLM, emerges as the standout performer, surpassing all LTR models and offline labels.

Application and Empirical Analysis of Fuzzy Neural Networks in Mining Social Media Users’ Behavioral Characteristics and Formulating Accurate Online Marketing Strategies

In the current digital social environment, social media platforms have become an important position for user behavior insights and precision marketing. User behavioral data on social media contain rich information, but they are often fuzzy, uncertain and highly complex. Fuzzy neural network (FNN), as an advanced model combining fuzzy logic and neural network theory, provides a powerful tool for processing and analyzing social media user behavioral features. This study is dedicated to exploring the application of fuzzy neural networks in social media user behavior analysis and their key role in the design of accurate online marketing strategies. We construct and optimize a fuzzy neural network model by meticulously classifying and quantifying user behavioral features, including behavioral frequency features, content topic features, social interaction features, and time series features, as well as applying fuzzy set theory to deal with fuzzy features such as emotional states. Through empirical analysis, we will show how fuzzy neural networks can reveal the intrinsic laws behind user behaviors, and how these insights can be used to design and implement precise online marketing strategies to improve advertising effectiveness, user engagement, and brand loyalty.

Human-Machine Engagement (HME): Conceptualization, Typology of Forms, Antecedents, and Consequences

Artificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME.

Effectiveness of Live Selling Marketing Strategies as a Key Tool to Enhance Sales in the Fashion Business

This research examines how live selling marketing strategies can boost sales for a fashion brand. Data was gathered through a questionnaire on the brand’s Instagram, and Smart PLS was used for statistical analysis to understand the relationships between different factors. The results show that live selling significantly enhances customer engagement, sales conversion rates, and brand loyalty. The study confirms the reliability of the research model and highlights key factors that contribute to successful live selling, along with the challenges faced. Practical recommendations are provided to help optimize live selling strategies in the fashion industry. While the findings are specific to the selected brand, they offer valuable insights for other fashion brands looking to implement similar strategies.

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.

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.

DESIGN AND IMPLEMENTATION OF A SUPERMARKET MANAGEMENT SYSTEM USING UML FOR STREAMLINED INVENTORY, SALES, AND CUSTOMER MANAGEMENT

The growing demands on supermarket management have necessitated the adoption of robust systems to streamline operations such as inventory control, sales tracking, and customer management. This study introduces a UML-based design for a supermarket management system that addresses the core needs of store administration through structured modules and defined workflows. The system encompasses store registration, inventory management, sales tracking, and customer engagement, further facilitating seamless product browsing, checkout, and post-sale support. Through structured modeling, the proposed system simplifies process flows, thereby enhancing the operational efficiency and user experience in a supermarket environment. This paper details the UML diagrams used, outlines the functional processes, and discusses the potential benefits of the system for modern retail management.

Strategic Technological Innovations Influencing Competitive Advantage Among Micro and Small Enterprises in Nairobi County, Kenya

Purpose: The ability of businesses to integrate information technology in their operations has been found to be an integral driver to their success. Through information technology, firms can utilize technological innovations to strengthen the uniqueness of their products in the market thus enhancing their competitive advantage. However, with limited evidence in the local context especially among the micro and small enterprises which continue to face a continuous decline, it warrants the study to assess how strategic technological innovations have been embraced among the small and micro enterprises and the role the innovations have played in enhancing the competitive advantage of the enterprises. Specifically, the study will examine the influence of enterprise’s IT Capabilities on competitive advantage among Micro and Small Enterprises (MSEs); determine the influence of Technological resources on competitive advantage among Micro and Small Enterprises (MSEs); explore the influence of adopted new technologies on competitive advantage among Micro and Small Enterprises; and establish the influence of Technological Processes and Products (TPP) on competitive advantage among Micro and Small Enterprises (MSEs) in Nairobi County, Kenya. The objectives will be anchored on dynamic capabilities theory, Schumpeter theory of innovation, technology acceptance model and diffusion theory of innovation. Methodology: Using a descriptive research approach, the study will collect and analyse both qualitative and quantitative data. This data was obtained from 386 micro and medium enterprises in Nairobi Central Business District, drawn from a population of 11,245 MSEs registered by the Nairobi City County in the CBD. A questionnaire was the main instrument of data collection, which was pilot-tested for validity and reliability. The collected data was analyzed using descriptive and inferential statistics. Findings: The findings were presented using frequency tables and graphs. In conclusion, the findings emphasize the critical role of technology in driving market expansion, customer engagement, and operational efficiency. The study highlights the importance of strategic support and intervention to address technological adoption challenges. By encouraging technological development and integration, policymakers and other stakeholders can assist MSEs in improving their operations, boosting the economy of Nairobi County. Investing in technological education and infrastructure is essential for MSEs to optimally benefit from TPP in the contemporary digital economy. Unique Contribution to theory, policy and practice: Training and development on how MSEs can use the platforms effectively, online marketing and selling needs to be promoted to help MSEs build their capacities. Therefore, when equipped with the appropriate knowledge and tools, the policymakers can help the MSEs to adopt e-commerce and increase their market access and productivity.

Enhancing Visual Perception in Immersive VR and AR Environments: AI-Driven Color and Clarity Adjustments Under Dynamic Lighting Conditions

The visual fidelity of virtual reality (VR) and augmented reality (AR) environments is essential for user immersion and comfort. Dynamic lighting often leads to chromatic distortions and reduced clarity, causing discomfort and disrupting user experience. This paper introduces an AI-driven chromatic adjustment system based on a modified U-Net architecture, optimized for real-time applications in VR/AR. This system adapts to dynamic lighting conditions, addressing the shortcomings of traditional methods like histogram equalization and gamma correction, which struggle with rapid lighting changes and real-time user interactions. We compared our approach with state-of-the-art color constancy algorithms, including Barron’s Convolutional Color Constancy and STAR, demonstrating superior performance. Experimental results from 60 participants show significant improvements, with up to 41% better color accuracy and 39% enhanced clarity under dynamic lighting conditions. The study also included eye-tracking data, which confirmed increased user engagement with AI-enhanced images. Our system provides a practical solution for developers aiming to improve image quality, reduce visual discomfort, and enhance overall user satisfaction in immersive environments. Future work will focus on extending the model’s capability to handle more complex lighting scenarios.

Penerapan Kursus Berbasis Rapid Rural Appraisal Sebagai Upaya Internasionalisasi Konten Media Pembelajaran Augmented Reality

This research aims to develop and implement a Rapid Rural Appraisal (RRA) based course to internationalize augmented reality (AR) based learning media content. The RRA method is used to identify international needs and preferences for AR learning content. The results of RRA become the basis for developing strategies that include content development, platforms, marketing and promotions. Implementation involves developer training, international collaboration, and regular evaluation and monitoring to ensure successful internationalization. The findings suggest that the integration of local cultural and linguistic elements in AR content as well as the use of platforms that support multilingualism can increase user engagement in international markets. Evaluations also show increased understanding and appreciation of AR learning materials in various countries.

Factors Influence Online Customers’ Purchase Intention on Luxury Fashion through Customer Engagement

Factors Influence Online Customers’ Purchase Intention on Luxury Fashion through Customer Engagement

Empowering Consumer Decision-Making: Decoding Incentive vs. Organic Reviews for Smarter Choices Through Advanced Textual Analysis

Online reviews play a crucial role in influencing seller–customer dynamics. This research evaluates the credibility and consistency of reviews based on volume, length, and content to understand the impacts of incentives on customer review behaviors, how to improve review quality, and decision-making in purchases. The data analysis reveals major factors such as costs, support, usability, and product features that may influence the impact. The analysis also highlights the indirect impact of company size, the direct impact of user experience, and the varying impacts of changing conditions over the years on the volume of incentive reviews. This study uses methodologies such as Sentence-BERT (SBERT), TF-IDF, spectral clustering, t-SNE, A/B testing, hypothesis testing, and bootstrap distribution to investigate how semantic variances in reviews could be used for personalized shopping experiences. It reveals that incentive reviews have minimal to no impact on purchasing decisions, which is consistent with the credibility and consistency analysis in terms of volume, length, and content. The negligible impact of incentive reviews on purchase decisions underscores the importance of authentic online feedback. This research clarifies how review characteristics sway consumer choices and provides strategic insights for businesses to enhance their review mechanisms and customer engagement.

Enhancing Knowledge Sharing on Social Media: The Role of Functionalities in User Engagement and Information Quality

This study investigates functionalities on social media platforms that promote effective knowledge sharing, focusing on information quality and user engagement. A systematic literature review (SLR) was conducted to analyze relevant research. The findings reveal that functionalities like content creation and curation tools, user interaction and feedback mechanisms, community building and gamification elements, and knowledge filtering and recommendation systems all contribute to a positive knowledge-sharing environment. These functionalities empower users to create high-quality content, engage in discussions that refine information, and discover trustworthy sources. The study highlights the potential of social media platforms for knowledge sharing but acknowledges that the effectiveness of functionalities can vary depending on the platform and audience. Future research is recommended to explore the most impactful functionalities in specific contexts and the interplay between functionalities, user behavior, and information quality.

Evaluating omni channel retailing in the emergence of industry 5.0: A perspective of South Asian generation Z

This study investigates the influence of Omnichannel-Retailing (OCR) on customer-retention (CR) for Generation Z (Gen Z) in the nascent Industry 5.0 era. Employing a survey with South Asian respondents, the research explores their experiences with retailer-provided omnichannel services. A novel model examines the relationships between OCR, customer-satisfaction (CS), CR, and customer-engagement (CE). Additionally, the model investigates the moderating role of Man-Machine-Collaboration (MMC) on the OCR-CR association. Process macro analysis is employed to assess mediation and moderation effects. The findings reveal positive associations between OCR and both CR and CE. Interestingly, CS does not mediate the OCR-CR relationship, suggesting a more nuanced effect of OCR on retention. However, CE emerges as a significant mediator. Furthermore, the moderating role of MMC is confirmed. This research holds novelty by examining the early stages of Industry 5.0 and its potential disruption of marketing practices, particularly for CR strategies targeting Gen Z in South Asia.

Leveraging AI to Revolutionize Subscription Business Models

This article explores the transformative impact of Artificial Intelligence (AI) on subscription-based business models across various industries. It examines how AI is revolutionizing key aspects of subscription services, including personalization, customer retention, pricing strategies, customer support, operational efficiency, and fraud detection. The article highlights specific AI applications such as content recommendations, dynamic user interfaces, churn prediction, advanced customer segmentation, dynamic and usage-based pricing, AI-powered chatbots, and sentiment analysis. Additionally, it discusses how AI enhances operational efficiency through automated billing and inventory management, and improves security via anomaly detection and behavioral biometrics. Case studies of Adobe Creative Cloud and Amazon Web Services (AWS) are presented to illustrate real-world applications of AI in subscription services. The article concludes by emphasizing the paradigm shift AI represents in customer engagement, operational optimization, and revenue generation for subscription businesses, forecasting continued innovation and opportunities in this rapidly evolving sector.