Developing crossplatform software applications to enhance compatibility across devices and systems

The rapid advancement of artificial intelligence (AI) has fundamentally transformed the nature of customer–organization interactions in the digital era. With increasing customer expectations for instant, accurate, and personalized service, organizations are increasingly adopting AI-driven technologies to enhance service delivery and operational efficiency. Among these technologies, AI chatbots have emerged as a widely implemented solution in customer service functions due to their ability to provide real-time responses, automate routine queries, and deliver 24/7 assistance across multiple digital platforms. Industries such as e-commerce, banking, healthcare, telecommunications, and hospitality have witnessed extensive deployment of AI chatbots to improve customer engagement and service accessibility. Despite their growing adoption, the impact of AI chatbots on customer satisfaction remains a subject of ongoing academic and managerial debate. While chatbots offer advantages such as reduced response time, cost efficiency, and scalability, concerns persist regarding their limitations in handling complex queries, emotional intelligence, and contextual understanding. These contrasting perspectives highlight the need for a systematic examination of how AI chatbot characteristics influence customer satisfaction outcomes. This study adopts an analytical and descriptive research approach to examine the impact of AI chatbots on customer satisfaction. The research is based on a comprehensive review of existing literature and an analysis of user perceptions related to chatbot-based interactions. Key factors such as responsiveness, accuracy of information, personalization, ease of use, availability, and perceived reliability are identified as critical determinants influencing customer satisfaction. By synthesizing theoretical insights and user-oriented observations, the study develops a conceptual understanding of the relationship between AI chatbot functionalities and customer satisfaction. The primary contribution of this research lies in offering an integrated conceptual perspective that explains how functional and experiential aspects of AI chatbots collectively shape customer satisfaction. The findings of the study provide valuable insights for organizations seeking to design customer-centric chatbot systems and also serve as a foundation for future empirical research in the domain of AI-enabled customer service and digital user experience.

Implementation of a Chatbot System using AI and NLP

Computer-Aided Design (CAD) software has long served as a foundation for planning and modeling in Architecture, Engineering, and Construction (AEC). In recent years, the introduction of Augmented Reality (AR) and Virtual Reality (VR) has significantly reshaped the CAD landscape, offering novel interaction paradigms that bridge the gap between digital prototypes and real-world spatial understanding. These technologies have enabled users to engage with 3D architectural content in more immersive and intuitive ways, facilitating improved decision making and communication throughout design workflows. As digital design services grow more complex and span multiple media platforms—from desktop-based modeling to immersive AR/VR environments—evaluating usability and User Experience (UX) becomes increasingly challenging. This paper presents a longitudinal usability and UX study of a multiplatform house design pipeline (i.e., structured workflow for creating, adapting, and delivering house designs so they can be used seamlessly across multiple platforms) comprising a web-based application for initial house creation, a mobile AR tool for contextual exterior visualization, and VR applications that allow full-scale interior exploration and configuration. Together, these components form a unified yet heterogeneous service experience across different devices and modalities. We describe the iterative design and development of this system over three distinct phases (lasting two years), each followed by user studies which evaluated UX and usability and targeted different participant profiles and design maturity levels. The paper outlines our approach to cross-platform UX evaluation, including methods such as the Think-Aloud Protocol (TAP), standardized usability metrics, and structured interviews. The results from the studies provide insight into user preferences, interaction patterns, and system coherence across platforms. From both participant and evaluator perspectives, the iterative methodology contributed to improvements in system usability and a clearer mental model of the design process. The main research question we address is how iterative design and development affects the UX of the heterogeneous service. Our findings highlight important considerations for future research and practice in the design of integrated, multiplatform XR services for AEC, with potential relevance to other domains.

The evolution of Salesforce's user interface (UI) development has been marked by significant advancements, with Lightning Web Components (LWC) emerging as a pivotal technology. LWC represents a modern approach to building user interfaces on the Salesforce platform, enabling developers to create highly responsive, efficient, and scalable applications. This paper explores the significance of LWC in modern Salesforce UI development, highlighting its architectural advantages, performance improvements, and alignment with web standards. By leveraging LWC, developers can build applications that not only enhance the user experience but also streamline development processes, reduce technical debt, and ensure long-term maintainability. LWC is built on native web standards, such as Web Components, which allows developers to utilize reusable components across multiple applications. This modularity simplifies development, reduces redundancy, and promotes a cleaner, more maintainable codebase. Additionally, LWC's adherence to modern JavaScript frameworks and its compatibility with standard HTML and CSS make it an attractive choice for developers familiar with these technologies. This compatibility allows for seamless integration with other web development practices and tools, reducing the learning curve and accelerating the adoption of LWC in Salesforce projects. One of the critical advantages of LWC is its performance efficiency. Unlike its predecessor, Aura, LWC leverages native browser capabilities, resulting in faster load times and improved application responsiveness. The lightweight nature of LWC components ensures that applications can handle large volumes of data and complex user interactions without compromising performance. This efficiency is particularly crucial in enterprise environments where Salesforce applications must support thousands of users and handle significant data processing tasks. Moreover, LWC's design aligns with Salesforce's commitment to providing a robust, scalable, and secure platform. The framework includes built-in security features, such as Locker Service, which enforces security best practices by isolating components and preventing unauthorized access to sensitive data. This emphasis on security makes LWC an ideal choice for organizations that prioritize data protection and regulatory compliance. LWC also offers enhanced customization capabilities, enabling developers to tailor Salesforce applications to meet specific business requirements. Through LWC, developers can create custom user interfaces that integrate seamlessly with Salesforce's backend services and third-party APIs. This flexibility allows businesses to deliver unique, branded experiences to their users while maintaining the robustness and reliability of the Salesforce platform. The paper also delves into the ecosystem surrounding LWC, including the availability of pre-built components, tools, and resources that accelerate development. Salesforce provides an extensive library of base components that developers can leverage to quickly assemble complex UIs. Additionally, the Salesforce community and marketplace offer a wealth of third-party components and solutions, further expanding the possibilities for UI customization and enhancement. The adoption of LWC also fosters collaboration among development teams. Its modular architecture allows multiple developers to work on different components simultaneously, reducing bottlenecks and speeding up the development cycle. This collaborative approach is supported by Salesforce's developer tools, such as Salesforce CLI and Visual Studio Code extensions, which streamline the development, testing, and deployment of LWC-based applications. In conclusion, Lightning Web Components represent a significant leap forward in Salesforce UI development, offering a modern, efficient, and secure framework for building high-quality applications. By embracing LWC, organizations can enhance their Salesforce applications' user experience, improve performance, and future-proof their investments in the Salesforce platform. As businesses continue to demand more sophisticated and responsive applications, LWC will play a crucial role in meeting these expectations and driving innovation within the Salesforce ecosystem.

This study delves into the realm of customer satisfaction within the context of online shopping applications, which have seen a significant surge in usage in recent years. With the proliferation of e-commerce platforms, understanding the factors influencing customer satisfaction becomes paramount for businesses striving to maintain competitiveness in the digital marketplace. The objective of this research is to identify the key determinants that impact customer satisfaction towards online shopping apps, thereby providing insights for app developers and e-commerce companies to enhance user experience and foster customer loyalty. The quantitative aspect involves a large-scale survey distributed among users of various online shopping applications, assessing their satisfaction levels and preferences. Concurrently, qualitative interviews are conducted to delve deeper into the nuanced aspects of user experiences, elucidating underlying motivations and concerns. The research considers a range of factors potentially influencing customer satisfaction, including app design, user interface, product variety, pricing, security, delivery services, and customer support. By analyzing both quantitative and qualitative data, the study aims to discern patterns, correlations, and user sentiments regarding these factors. Furthermore, it explores the interplay between customer satisfaction and. By understanding the drivers of satisfaction and areas for enhancement, companies can tailor their offerings expectations online retail.

The vision of user experience is making life of users of products as convenient as possible, especially during the interaction with a productor a service. An important aspect of perceived convenience is the user experience of a product. The visual design and especially the interaction design has a major influence on this perception. In order to achieve the vision, user experience experts apply different types of tasks. One type of task is to analyze how users carry out tasks and what user's needs or problems are. Another type of task is to design user experience solutions and other typical task type deals with carrying out usability evaluations in order to find problems in using software application. In the course of user experience activities, many data are being collected. Many of the collected data relates to certain activities of users. For the user experience area there exist just a few tools, which support typical tasks in different ways. None of the tools supports linking results of user experience work to user experience meta-data. Why is it a problem? The current tools do not support an access to user experience project data with generic search and filter criteria like case etc. This makes the access to user experience research data difficult and the comparison of user experience project data between different projects inefficient. In general, results of different user experience projects are difficult to reuse. The core idea of data pattern for allocating user experience Meta-Data to User Experience research data is to associate user experience project data with user experience meta-data. The data pattern considers associating user experience project data with user experience meta-data partially automatically and partially manually by the user. The key idea is that we want to reuse project data like the project sponsor, the application area, the industry, use cases etc. as user experience meta-data and assign them to user experience research data. The benefits of the data pattern are: Reusing results of user experience research projects. Making access to available results more efficient. Direct comparison of available results is supported and more efficient. UX Office is the typical instance for application.

For global-market-oriented software applications, it is required that their user interface be translated to local languages so that users from different areas in the world can use the software. A long-term practice in software industry is to hire professional translators or translation companies to perform the translation. However, due to the large number of user-interface labels and target languages, this is often too expensive for software providers, especially cost-sensitive providers such as personal developers of mobile apps. As natural language processing and machine techniques advance, more mature machine translation techniques are providing a cheap though imperfect alternative, and the Google Translation service has already been widely used for translating websites and apps. However, the effect of lower translation quality on user experience has not been well studied yet. In this paper, we present a user study on 6 popular Android apps, which involves 24 participants performing tasks on app variants with 4 different translation quality levels and 2 target languages: Spanish and Chinese. From our study, we acquire the following 3 major findings, including (1) compared with original versions, machine translated versions of apps have similar task completion rate and efficiency on most studied apps; (2) machine translated versions have more tasks completed with flaws such unnecessary steps and missed optional steps, and (3) users are not satisfied with the GUI of machine translated versions and the two major complaints are misleading labels of input boxes, and unclear translation of items in option lists.

Artificial Intelligence (AI)-powered chatbots have emerged as a transformative technology, fundamentally changing how businesses and organizations engage with their customers by providing real-time, personalized communication. These chatbots, driven by sophisticated algorithms, utilize Natural Language Processing (NLP) and Machine Learning (ML) to understand, interpret, and respond to human language in a manner that is contextually appropriate and relevant. As a result, AI-powered chatbots enhance both user experience and operational efficiency by automating routine interactions, reducing response times, and providing consistent, high-quality service. The integration of AI chatbots spans multiple sectors, including customer service, healthcare, education, and e-commerce. In customer service, chatbots are deployed to manage high volumes of inquiries, troubleshoot issues, and offer personalized assistance around the clock, thereby freeing human agents to focus on more complex tasks. In healthcare, AI-powered chatbots are utilized to facilitate patient engagement by providing initial diagnoses, managing appointment schedules, offering medication reminders, and delivering health information. Educational institutions employ these chatbots to interact with students, answer frequently asked questions, facilitate administrative processes, and support learning through personalized tutoring. Meanwhile, in e-commerce, chatbots serve as virtual shopping assistants, offering product recommendations, guiding users through their purchasing journey, and resolving post-purchase concerns. This paper delves into the development and deployment methodologies of AI-powered chatbots, examining the various approaches and technologies used to build robust and efficient chatbot systems. The discussion highlights key components such as NLP, ML, reinforcement learning, and deep learning techniques that contribute to the chatbot’s ability to understand user intent, handle natural language conversations, and learn from past interactions to improve future responses. Additionally, the paper analyzes chatbot architecture, including front-end interfaces, dialogue management systems, and backend integration, to provide a comprehensive understanding of the chatbot ecosystem. The literature review presented in this paper synthesizes findings from recent studies and publications, identifying the current trends, advancements, and challenges in implementing AI chatbots across different domains. It evaluates the effectiveness of chatbots in achieving key performance indicators such as customer satisfaction, response accuracy, operational efficiency, and cost savings. The review also highlights areas where AI chatbots have proven to be most effective and identifies potential limitations, including data privacy concerns, integration challenges with existing legacy systems, and the limitations of current NLP models in understanding context, sarcasm, or nuanced language. This paper further discusses the benefits and challenges associated with deploying AI-powered chatbots. Benefits such as 24/7 availability, scalability, reduced operational costs, and enhanced customer engagement are explored in detail, demonstrating how chatbots can deliver substantial value to organizations. Conversely, the paper also addresses challenges such as ensuring data security and privacy, overcoming natural language understanding (NLU) limitations, mitigating biases in AI models, and managing customer expectations when interacting with non-human agents. In addition, this paper provides a forward-looking perspective on the potential future developments of AI chatbots. It explores emerging trends such as multimodal chatbots that integrate voice, text, and visual inputs; advancements in emotion recognition to enable more empathetic and human-like interactions; and the rise of explainable AI, where chatbots can provide transparency in their decision-making processes.To illustrate these concepts, the paper includes diagrams that depict the architecture of AI chatbot systems, the flow of natural language processing, and the integration of various components such as databases, machine learning models, and user interfaces. These visual aids provide a clearer understanding of the technical and functional aspects of chatbot development and deployment. Overall, this paper aims to provide a comprehensive analysis of AI-powered chatbots, detailing their applications, benefits, challenges, and future potential. It serves as a guide for businesses, researchers, and technology developers interested in leveraging AI chatbots to enhance communication, streamline operations, and create a more engaging user experience. By critically examining both the opportunities and the limitations, this research offers valuable insights into the strategic implementation of AI chatbots across diverse industries.

The rapid growth of internet use has transformed shopping behavior in Indonesia, particularly in the fashion e-commerce sector. Increasing competition requires online platforms to enhance user experience and electronic service quality to sustain customer satisfaction and loyalty. This study examines the effects of user experience and electronic service quality on customer satisfaction and loyalty in Indonesia’s online fashion industry. Data were collected from 285 respondents who had purchased fashion products through the official websites of five top brands: Adidas, Nike, Converse, H&M, and Uniqlo. Using the disjoint two-stage Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, the results reveal that user experience significantly affects electronic service quality and satisfaction, while electronic service quality positively influences satisfaction. Customer satisfaction strongly predicts loyalty. Furthermore, both electronic service quality and customer satisfaction play significant mediating roles. User experience indirectly enhances satisfaction through electronic service quality and indirectly promotes loyalty through satisfaction. This study validates an integrated model in Indonesia’s fashion e-commerce context using a two-stage analytical approach.Keywords: Customer loyalty; Customer satisfaction; E-service quality; E-commerce; User Experience

As an application, Halodoc provides a user interface (UI) and user experience (UX) for its users to access health services more easily. When compared to many competitors that are similar to the application, Halodoc has interesting advantages that deserve to be thoroughly investigated. The mixed method is used because the research aims to find out the relationship between UI & UX based on data sources and measured data sources. The quantitative data sources are taken from the user surveys, and then the qualitative method is conducted to describe studies related to UI & UX principles and visual elements in a design. Thus, the research shows that UI & UX have a positive relationship. The better the UI & UX of the Halodoc application, the more it affects the users’ motivation to use application-based health services. This study is useful for determining the relationship between UI and UX in meeting the needs of Halodoc application health service users, as well as how the level of UX is perceived by the users.
 Keywords: User Interface, User Experience, Halodoc, Application

The rapid expansion of internet access and the growth of Indonesia’s digital economy have accelerated the adoption of digital financial services, particularly mobile banking. In this increasingly competitive environment, Islamic digital banking providers face a dual challenge: ensuring Sharia compliance while delivering a seamless user experience (UX) that meets modern customer expectations. This study examines the effect of UX on Customer Satisfaction (CS) and Customer Loyalty (CL) among Generation Y and Z users of Jago Syariah mobile banking in Jabodetabek. A quantitative design was employed using an online survey (May–June 2025) with purposive sampling, yielding 203 valid responses from active users aged 17–44. UX was measured using the User Experience Questionnaire (UEQ) with six dimensions (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty), while CS and CL were assessed using established indicators. Data were analyzed through descriptive statistics and Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4. The measurement model met validity and reliability criteria (AVE > 0.50; HTMT < 0.90; CR > 0.70). Structural results showed that UX positively and significantly influenced CS (β = 0.817; t = 21.176) and CL (β = 0.498; t = 6.716), and CS also positively affected CL (β = 0.443; t = 5.529), confirming the mediating role of satisfaction. The model explained 66.5% of variance in CS and 80.3% in CL. These findings highlight UX as a critical driver of satisfaction and loyalty in Islamic mobile banking, emphasizing the need for continuous improvements in usability, reliability, and feature performance to sustain customer retention and advocacy among digitally literate generations.

This article provides an introduction to the usage of software engineering approaches and tools for the design of user experience (UE). This approach was developed for three main reasons: (a) to provide UE design teams a rigorous, methodical, encompassing method that can be applied throughout the entire product development cycle; (b) to provide designers a way to work with abstract concepts to establish the conceptual design of the system before progressing to the realization of the design to the appropriate platform; and (c) to communicate the intended design to the implementation team in a manner that is concise, complete, and familiar. The approach is the application of Unified Modeling Language concepts to describe users and the UE design rather than the technical architecture. It is believed that designing the UE design separately from the technical architecture will help ensure teams deliver a usable and delightful UE. The methods in this article have been applied to a handful of real-world projects in the last year and a half. Thus far, the application of software engineering approaches and tools have been found to be very powerful aids that have enabled the creation of highly useful Web experiences as well as software applications. Our intent is to address the full gamut of issues involved in creating a total user experience (UE)-from discovery of users needs through high-level and low-level design, ensuring fidelity during development, and deployment, with a full assessment of how well the design meets users' needs. We believe that through a concerted use of such a rigorous approach we can greatly improve not only the UE of products we create, but also the experience of designing and developing them. We are not the only team taking this direction in UE design. It has become clear to many in our field that a more rigorous, thorough, and accurate approach to UE design is not only possible-it is necessary, for many of the same reasons that software engineers long ago embraced such practices. As we are evolving our approaches in other phases, this article focuses on the discovery and design phase activities.

Understanding and improving user experience is key to strengthening uptake and realizing the potential of chatbots for customer service. In this paper, we investigate customer satisfaction surveys as a source of insight into such user experience. A total of 5,687 customer satisfaction reports on users’ interactions with a customer service chatbot, and the corresponding chatbot interactions, are analyzed. The findings demonstrate that customer satisfaction reports are closely associated with the degree to which the problems motivating users’ chatbot interactions are resolved. Furthermore, the findings show substantial variation in the performance of different chatbot intents in terms of customer satisfaction and problem resolution. This implies that user experience varies substantially depending on the problems motivating users to interact with the chatbot. Finally, we identify key characteristics of the intents associated with particularly high or low customer experience, suggesting paths towards efficient improvement of chatbot user experience. Based on the findings, we point to key implications for theory and practice and suggest directions for future research.

Identity and Access Management (IAM) has emerged as a cornerstone of information security in multi-cloud environments, where organizations leverage diverse services across multiple platforms. As enterprises increasingly adopt cloud-first strategies and distribute resources across an average of nearly five cloud providers, maintaining consistent identity controls presents significant challenges. The complex nature of these distributed architectures often creates fragmented security policies, inconsistent authentication mechanisms, and critical visibility gaps that adversaries actively exploit. This article presents a comprehensive framework for strengthening IAM in multi-cloud settings through five essential components: unified identity management, adaptive authentication, policy harmonization, federated access solutions, and advanced monitoring capabilities. Organizations can address the inherent security challenges of multi-cloud environments by centralizing identity repositories, implementing dynamic verification based on contextual factors, standardizing security policies, enabling seamless cross-platform authentication, and leveraging AI-enhanced monitoring. The framework addresses how these integrated components enable enterprises to maintain robust security controls despite the heterogeneous nature of diverse cloud ecosystems, providing practical guidance for implementing effective IAM strategies that balance security requirements with operational efficiency and user experience.

Aim/Purpose: This study examined how service fairness and SERVQUAL dimensions influenced customer satisfaction and loyalty in mobile financial applications (MFAs) in Phnom Penh, Cambodia. As digital transformation reshapes financial services, understanding these factors is crucial for improving user experiences and fostering long-term engagement. Introduction/Background: Mobile financial applications (MFAs) have become an essential part of Cambodia’s urban financial ecosystem by providing accessible and cost-effective financial solutions. Despite their increasing usage, limited research exists on the factors influencing customer satisfaction and loyalty in this sector. This study aimed to bridge this gap by examining how service fairness dimensions (distributive, procedural, interpersonal, and informational) and SERVQUAL dimensions (reliability, responsiveness, assurance, and empathy) impacted customer satisfaction and loyalty. Methodology: A quantitative research methodology was employed to explore the relationships among service fairness, SERVQUAL factors, customer satisfaction, and loyalty. The study targeted individuals aged 18 and above who actively use MFAs in Phnom Penh. Due to the absence of precise population data, stratified purposive sampling was used to ensure diverse representation across Phnom Penh’s districts. Using Cochran’s formula with a 95% confidence level and a 5% margin of error, the study aimed for a sample size of 400 respondents, but 560 surveys were distributed to enhance reliability, yielding 470 valid responses. Data collection took place between May and July 2024 through online surveys and self-administered questionnaires distributed across various digital platforms. The survey instrument was structured into four key sections: demographic information, MFA usage experience, behavioral factors, and service fairness/SERVQUAL dimensions, which were measured on a five-point Likert scale. Survey items were adapted from prior studies to ensure measurement reliability and validity. The questionnaire was pretested with 30 respondents, and refinements were made to improve clarity and relevance. Advanced statistical techniques were used to analyze the data. Descriptive statistics provided an overview of respondent demographics and usage behavior. Cronbach’s Alpha was employed to assess reliability, while Confirmatory Factor Analysis (CFA) validated the construct dimensions. Structural Equation Modeling (SEM) was utilized to test the relationships among service fairness, service quality, customer satisfaction, and loyalty, with results presented through detailed statistical models and tables. Findings: The study revealed that both service fairness and SERVQUAL dimensions significantly influenced customer satisfaction, which, in turn, drives customer loyalty. Among service fairness dimensions, distributive and procedural fairness had the strongest effects on satisfaction, highlighting the importance of transparent processes and fair service outcomes. Customers responded positively to equitable resource allocation and well-structured procedural guidelines. Regarding SERVQUAL dimensions, responsiveness and assurance emerged as the most influential factors shaping customer satisfaction. Customers highly valued prompt responses to inquiries and the expertise and reliability of service providers. While empathy contributed to satisfaction, its impact was comparatively less pronounced. Customer satisfaction was found to mediate the relationships between service fairness, service quality, and loyalty. This underscored the necessity of fostering positive user experiences to enhance long-term customer retention and loyalty. The findings suggest that MFA providers should prioritize procedural fairness and responsiveness to optimize customer satisfaction and long-term engagement. Contribution/Impact on Society: This study provides empirical evidence of the role of service fairness and SERVQUAL factors in shaping customer satisfaction and loyalty in the Cambodian MFA market. While previous research has examined these factors in other industries, this study uniquely focused on MFAs in a developing Southeast Asian context. The findings emphasize the importance of fair service design and high-quality service delivery in building customer trust and loyalty. By implementing transparent service processes and prioritizing customer support quality, MFA providers can foster financial inclusion and promote sustainable business growth in emerging markets. Recommendations: For MFA providers, the study recommends emphasizing transparency, equitable service outcomes, and efficient customer support to enhance satisfaction and trust. Specifically, providers should: Improve distributive and procedural fairness by implementing clear, transparent, and fair resource allocation policies. Enhance responsiveness by ensuring customer inquiries are addressed promptly and effectively. Strengthen assurance through professional training programs that enhance staff expertise and reliability. Invest in customer service training to improve staff empathy and engagement with users. For researchers, further exploration into additional variables such as trust, security, and digital literacy could offer a more comprehensive understanding of MFA user behavior. Research Limitation: This study had several limitations. Its cross-sectional research design captured data at a single point in time, limiting the ability to infer causal relationships. Additionally, the study focused exclusively on Phnom Penh, restricting the generalizability of findings to other regions or countries. The reliance on self-reported data may have introduced potential bias, including social desirability bias. Future studies should adopt longitudinal designs and include a broader geographic scope to enhance research validity Future Research: Future studies should expand on this research by incorporating additional factors such as perceived security, trust, and digital literacy to provide deeper insights into customer satisfaction and loyalty in MFAs. Comparative studies across different regions or countries could reveal cultural differences in how service fairness and quality affect customer perceptions. Additionally, longitudinal studies tracking changes in satisfaction and loyalty over time would provide a dynamic perspective on user behavior and evolving service expectations.