User Expectations Research Articles

Paths to digital mobile payment platforms acceptance and usage: A topology for digital enthusiast consumers

The rapid development of digital mobile payment platforms (DMPPs) has significantlytransformed the financial services landscape, offering consumers unparalleled convenience and efficiency in payment options. This study delves into the acceptance and usability factors among digital enthusiast consumers, employing fuzzy set Qualitative Comparative Analysis (fsQCA) to uncover key determinants. Analyzing data from 325 digital enthusiasts, the research identified four distinct configural paths that significantly influence DMPP usage, revealing two primary user typologies: “systems readiness and ease of use” and “affection and secured platforms.” Performance expectancy emerged as a core condition, highlighting its critical role in driving DMPP adoption among digital enthusiasts. The study also underscored the importance of grievance redress and perceived trust as essential conditions influencing user adoption and continuous usage, emphasizing their pivotal role in enhancing user experience and fostering trust. These insights suggest that addressing users' concerns and building trust are fundamental for the sustained success of DMPPs. By tailoring DMPP features to align with the distinct preferences of the identified user typologies, service providers can significantly boost adoption rates. The fsQCA approach offers novel insights into the nuances of emerging technology acceptance and usage, underscoring the necessity of categorizing users to better understand DMPP adoption dynamics. This research provides valuable contributions to both businesses and policymakers,offering strategies to optimize DMPP design, ensure superior user experiences, and promote broader adoption. The findings highlight the dynamic interplay of user expectations and technological capabilities, essential for navigating the evolving digital financial landscape.

Global Quality of Service (QoX) Management for Wireless Networks

In the fast-changing technological landscape, novel applications are emerging with the potential to reshape the world. These applications, while promising, impose stringent requirements in terms of quality of service (QoS). The advent of wireless networks like 5G, 6G and Wi-Fi 6 brings about resource management solutions to ensure these requirements while meeting the user expectations within the interconnected environment. Nevertheless, user behaviors are also evolving, highlighting the importance of satisfaction and quality of experience (QoE). Furthermore, changes in user behavior trigger shifts in business models, where the quality of business (QoBiz) takes on a pivotal role. This evolving ecosystem, encompassing QoS, QoE, and QoBiz, demands a comprehensive and adaptable approach that conventional QoS management frameworks fail to perform. This paper introduces an implementation methodology for a global QoS management model named QoXphere. The implementation methodology is grounded in machine learning techniques and addresses the multifaceted aspects of quality of service (QoX) and their interconnections within wireless networks. The objective is to facilitate dynamic resource management that not only elevates user satisfaction but also optimizes provider benefits. Real-world examples illustrate the methodology’s applicability in widely deployed networks, complemented by simulated scenarios of modern network environments that further validate the approach.

Experiences when using different EHMR models: Implications for different designs and meeting user expectations

New elastomeric half-mask respirator (EHMR) models without exhalation valves (EVs) or with exhalation valve filters (EVFs) are facilitating expanded use in health settings to reduce workers' exposure to airborne hazards while acting as source control to prevent pathogen spread. The physical comfort of new models has not been assessed in comparison to previously used EHMRs with EVs. Researchers assessed 1,962 health care and emergency medical service personnels' self-reported adverse experiences from 2 cohorts while wearing EHMR models with EVs (cohort 1, n=1,080) and without EVs or with EVFs (cohort 2, n=882). Fisher exact test identified differences between the cohorts accounting for organizational factors when possible. Cohort 1 respondents experienced communication challenges and discomfort when wearing the EHMR >1hour statistically significantly more often than cohort 2. Cohort 2 respondents reported statistically significantly more instances of difficulty breathing, moisture buildup, being uncomfortable to wear <1hour, and being uncomfortably warm. Discomfort is prevalent among end users and more often among those wearing EHMRs without an EV/with an EVF. As EHMR research and development advances, prevalence in use may increase for emergency and routine situations. Organizations may not only need guidance about respirator selection but also model-specific selection.

What is helpful and unhelpful about psychological therapy from a Community Mental Health Team: Service user views

Routine quantitative outcome measures are commonly used within mental health services to provide an overview of change made by service users from the beginning to end of therapy. Whilst this is useful, these measures do not provide insight into why any changes may, or may not, have occurred. This article explores the results of a qualitative service evaluation conducted in an NHS community mental health team (CMHT). The questionnaire asked service users about aspects of therapy that they found helpful or unhelpful. Crucially, the questionnaire gave voice to service users who did not complete therapy and thus did not complete the final quantitative outcome measure. Analysis of data using a thematic analysis approach identified several themes, 1) whether it was the right time to access therapy, 2) challenges in maintaining progress outside of therapy, 3) meeting service user expectations of therapy, 4) whether service users can be vulnerable, 5) impact of therapy on the service users understanding of themselves and their emotions, and 6) flexibility within the service structure. Overall, service users found therapy to be challenging, yet the support and flexibility offered by their therapist was valued. The team used the results to reflect on their practice and to implement changes moving forward.

Capturing Usability Problems for People Living With Dementia by Applying the DEMIGNED Principles in Usability Evaluation Methods: Mixed Methods Study.

Dementia-related impairments can cause complex barriers to access, use, and adopt digital health technologies (DHTs). These barriers can contribute to digital health inequities. Therefore, literature-based design principles called DEMIGNED have been developed to support the design and evaluation of DHTs for this rapidly increasing population. This study aims to apply the DEMIGNED principles in usability evaluation methods to (1) capture usability problems on a mobile website providing information resources for people visiting a memory clinic, including those living with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia, and (2) investigate the realness of usability problems captured by the DEMIGNED principles in expert testing, specifically for mobile websites that act as a means of providing DHTs. First, a heuristic evaluation was conducted, with the DEMIGNED principles serving as domain-specific guidelines, with 3 double experts (experienced in both usability and dementia) and 2 usability engineering experts. Second, think-aloud sessions were conducted with patients visiting a memory clinic who were living with SCD, MCI, or dementia. The heuristic evaluation resulted in 36 unique usability problems. A representative sample of 7 people visiting a memory clinic participated in a think-aloud session, including 4 (57%) with SCD, 1 (14%) with MCI, and 2 (29%) with dementia. The analysis of the think-aloud sessions revealed 181 encounters with usability problems. Of these encounters, 144 (79.6%) could be mapped to 18 usability problems identified in the heuristic evaluation. The remaining 37 (20.4%) encounters from the user testing revealed another 10 unique usability problems. Usability problems frequently described in the think-aloud sessions encompassed difficulties with using the search function, discrepancies between the user's expectations and the content organization, the need for scrolling, information overload, and unclear system feedback. By applying the DEMIGNED principles in expert testing, evaluators were able to capture 79.6% (144/181) of all usability problem encounters in the user testing of a mobile website for people visiting a memory clinic, including people living with dementia. Regarding unique usability problems, 50% (18/36) of the unique usability problems identified during the heuristic evaluation were captured by the user-testing sessions. Future research should look into the applicability of the DEMIGNED principles to other digital health functionalities to increase the accessibility of digital health and decrease digital health inequity for this complex and rapidly increasing population.

Can GPT Help Improve Robo-advisory? The Construction of Robo-advisor for Users with Low Investment Experience Based on LLM

Robo-advisors have emerged as a significant innovation in investment management, offering automated financial advice to investors. However, user acceptance remains a challenge, particularly among those with limited investment experience. This paper explores the potential of large language models (LLMs) to enhance the interaction attributes of robo-advisor products and increase acceptance among novice investors. The study contributes to the existing literature by exploring the application of LLMs in robo-advisors, supplementing the exploration of interaction design, and systematically reviewing the service processes of current robo-advisor products. Findings suggest that existing robo-advisor products have room for improvement in interaction attributes and algorithmic mechanisms. Through theoretical exploration, this paper proposes methods for optimizing robo-advisor products by integrating LLMs. In conclusion, this research lays the groundwork for designing robo-advisor products with integrated LLM functionality, offering theoretical references for practitioners and researchers in financial technology. Future research directions include exploring user expectations and conducting controlled experiments to analyze the impact of LLM integration on user decisions.

Transforming Academic Libraries: Navigating Change and Embracing innovation in the Digital Era

Academic libraries are undergoing a profound transformation in response to technological advancements, changing user expectations, and shifts in scholarly communication paradigms, prompting a reevaluation of roles, services, and structures This study sought to explore the transformative processes within academic libraries prompted by the digital era, addressing the levels of technological integration, user experience, services, data management, open access, and resource allocation, investigating the influencing factors on the incorporation of change and innovation, and examining the lived experiences of librarians navigating change and embracing innovation. This mixed-method design study explored the challenges faced by academic libraries in the face of technological advancements. It revealed that while a moderate level of technological integration indicated a willingness to adopt technology, a strong commitment to data management and open access underscored modern library practices. However, areas for improvement emerged in User Experience and Services and Resource Allocation, prompting recommendations for a holistic strategy that integrated technology, prioritized user experience, and optimized resource allocation. Staff resistance is identified as the primary factor influencing change and innovation incorporation. The study also highlighted key challenges faced by librarians, such as budget allocation, staff adaptation, resistance to change, technological infrastructure issues, and user-centric concerns.

Online Medical Consultation Service-Oriented Recommendations: Systematic Review.

Online health communities have given rise to a new e-service known as online medical consultations (OMCs), enabling remote interactions between physicians and patients. To address challenges, such as patient information overload and uneven distribution of physician visits, online health communities should develop OMC-oriented recommenders. We aimed to comprehensively investigate what paradigms lead to the success of OMC-oriented recommendations. A literature search was conducted through e-databases, including PubMed, ACM Digital Library, Springer, and ScienceDirect, from January 2011 to December 2023. This review included all papers directly and indirectly related to the topic of health care-related recommendations for online services. The search identified 611 articles, of which 26 (4.3%) met the inclusion criteria. Despite the growing academic interest in OMC recommendations, there remains a lack of consensus among researchers on the definition of e-service-oriented recommenders. The discussion highlighted 3 key factors influencing recommender success: features, algorithms, and metrics. It advocated for moving beyond traditional e-commerce-oriented recommenders to establish an innovative theoretical framework for e-service-oriented recommenders and addresses critical technical issues regarding 2-sided personalized recommendations. This review underscores the essence of e-services, particularly in knowledge- and labor-intensive domains such as OMCs, where patients seek interpretable recommendations due to their lack of domain knowledge and physicians must balance their energy levels to avoid overworking. Our study's findings shed light on the importance of customizing e-service-oriented personalized recommendations to meet the distinct expectations of 2-sided users considering their cognitive abilities, decision-making perspectives, and preferences. To achieve this, a paradigm shift is essential to develop unique attributes and explore distinct content tailored for both parties involved.

Analyzing the Satisfaction of Coworking Space Users in Terms of Control

Due to technological developments that have changed the way people work, office interiors are also evolving and there has been an accelerating transition from conventional offices to shared workplaces since the rise of flexible working. These coworking spaces are intended to offset the negative effects of home-office and remote working, such as isolation and lack of collaboration. However, coworking places are more likely to improve performance and well-being when their users are given control. Accordingly, drawing on the concept of control developed by Evans and McCoy (1998), this study analyzed the satisfaction of coworking space users in terms of key control-related design factors, namely flexibility, indoor environmental control including thermal quality, air quality, visual quality, acoustic quality and furniture and layout, privacy, and territoriality. Data were collected via a survey conducted of the users of Originn Coworking Offices, İzmir, and via interviews with its founders. The findings indicate that users feel more satisfied resulted with an increased job satisfaction and productivity if they have control over their space. These findings can guide professionals in designing and constructing shared offices to meet the expectations of users, and in designing and developing existing coworking spaces.

Conversational AI and Chatbots: Enhancing User Experience on Websites

This research paper explores the transformative potential of conversational AI and chatbots in enhancing website user experience (UX). It addresses two key research questions: How do these technologies improve user engagement and satisfaction on websites, and what are the primary challenges in implementing them, along with effective solutions. The study examines case studies across diverse industries, including e-commerce, travel, healthcare, and finance, to gain insights into the underlying technologies powering conversational AI and chatbots, such as natural language processing (NLP), natural language understanding (NLU), and machine learning techniques. The paper highlights the significant benefits of integrating conversational AI and chatbots into websites, including providing personalized assistance, streamlining complex processes, ensuring 24/7 availability, and enhancing accessibility for users. However, the study also addresses the key challenges faced in implementation, ranging from handling ambiguity and context in natural language processing to ensuring data privacy and security, managing user expectations, and the need for continuous improvement and training. The research proposes solutions to these challenges, such as employing advanced NLP algorithms, robust API management tools, and establishing user feedback loops. Ethical considerations, including data privacy and addressing biases in AI responses, are also explored, emphasizing the importance of robust encryption, adherence to data privacy regulations, and advanced access control mechanisms. The paper concludes by providing a comprehensive overview of the current state and future directions of conversational AI and chatbots in enhancing website user experience, exploring emerging trends such as multimodal interactions, contextual awareness and personalization, integration with IoT devices, and the development of emotional intelligence and empathy in chatbots.

Improving Miscarriage Prevention Research: a survey exploring the expectations of service users and stakeholders (IMPRESS) – a study protocol for a UK-based survey

IntroductionInterventional clinical trials in recurrent miscarriage use varying expected effect sizes to inform their sample size calculations. Often these are not informed by what stakeholders consider a meaningful treatment effect....

An Analysis of Wearable Device Design for Pet Cats Based on Emotional Design Theory

With the booming development of the pet market in China, smart devices for pets are becoming increasingly diverse. While wearable smart products for pets, particularly pet cats, have begun to take shape, existing products often overlook the emotional needs between owners and pets while meeting basic pet needs. This paper proposes a design methodology for wearable devices for pet cats based on emotional design theory. By constructing interview outlines and questionnaires, this study delves into pet owners' emotional needs. Analyzing the survey results reveals user expectations for wearable devices for pet cats, providing a basis for subsequent designs. Finally, based on design scheme guidelines, this paper proposes the functional points of wearable devices for pet cats based on emotional design theory.

An Analysis of Wearable Device Design for Pet Cats Based on Emotional Design Theory

With the booming development of the pet market in China, smart devices for pets are becoming increasingly diverse. While wearable smart products for pets, particularly pet cats, have begun to take shape, existing products often overlook the emotional needs between owners and pets while meeting basic pet needs. This paper proposes a design methodology for wearable devices for pet cats based on emotional design theory. By constructing interview outlines and questionnaires, this study delves into pet owners' emotional needs. Analyzing the survey results reveals user expectations for wearable devices for pet cats, providing a basis for subsequent designs. Finally, based on design scheme guidelines, this paper proposes the functional points of wearable devices for pet cats based on emotional design theory.

Enhancing the Multi-User Experience in Fully Autonomous Vehicles Through Explainable AI Voice Agents

Fully autonomous vehicles (FAVs) operated by AI voice agents create a unique multi-user environment without a traditional driver, allowing users to engage in diverse activities such as entertainment and rest. In addition to the convenience of vehicle control, these agents face the challenge of resolving conflicts arising from multiple simultaneous user commands. This study investigates whether AI voice agents can effectively manage these command conflicts and enhance the multi-user experience (MUX). The research was conducted in two phases: In the preliminary study, online focus group interviews (FGIs) were conducted with 10 participants to explore their perceptions of using AI voice agents in FAVs. Participants shared their experiences and watched a concept video to discuss their expectations of FAV agents. Based on the FGI results, explainable AI voice agents were designed to prevent user conflicts between user commands, focusing on user tendencies and conflict contexts. In the main study, multi-user interactions with these agents were evaluated through online experiments with 89 participants. Two specific experiments were conducted based on the sources of conflict identified in the preliminary study: the first focused on control authority and the second on speech overlap. Participants watched scenario videos and assessed five variables, including sense of agency, trust, problem-solving ability, disappointment, and safety. The findings suggest that explanations provided by AI voice agents can effectively mitigate conflicts and improve MUX in FAVs. However, the effectiveness of these explanations varied across different contexts, indicating the need for alternative approaches. This research provides valuable insights for designing MUXs that prevent multi-user conflicts and meet user expectations in FAV.

A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations.

Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.

Security and Privacy Challenges in Software as a Service (SaaS)

This document In the rapidly evolving landscape of cloud computing, Software as a Service (SaaS) has emerged as a dominant model for delivering software applications over the internet. However, this model introduces significant security and privacy challenges. This research paper delves into these issues, highlighting the unique risks associated with SaaS platforms. The study explores common vulnerabilities such as data breaches, unauthorized access, data loss, and identity theft, which are exacerbated by the centralized nature of SaaS solutions where data is stored on remote servers managed by service providers. To address these challenges, the paper underscores the importance of robust security measures, including encryption, access controls, and secure software development practices. Additionally, it emphasizes the necessity for SaaS providers to adopt advanced privacy protection techniques like data anonymization and differential privacy to meet user expectations and comply with stringent legal regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Through a comprehensive review of literature and analysis of current SaaS security practices, the research identifies critical risks and provides strategic recommendations for enhancing security and privacy in SaaS environments. By integrating multiple layers of security throughout the software development lifecycle, enforcing strict access management protocols, and ensuring compliance with regulatory standards, SaaS providers can significantly mitigate risks and safeguard sensitive user data. The findings of this study highlight the imperative for continuous improvement in SaaS security strategies to keep pace with emerging threats and technological advancements. Ultimately, by prioritizing security and privacy, SaaS providers can not only protect their users but also gain a competitive edge in the increasingly crowded market of cloud services.

Air computing: A survey on a new generation computation paradigm

There is an ever-growing race between what novel applications demand from the infrastructure and what the continuous technological breakthroughs bring in. Especially after the proliferation of smart devices and diverse IoT requirements, we observe the dominance of cutting-edge applications with ever-increased user expectations in terms of mobility, pervasiveness, and real-time response. Over the years, to meet the requirements of those applications, cloud computing provides the necessary capacity for computation, while edge computing ensures low latency. However, these two essential solutions would be insufficient for next-generation applications since computational and communicational bottlenecks are inevitable due to the highly dynamic load. On the other hand, inadequate infrastructure considering rural areas and disaster sites makes the utilization of those solutions difficult. Therefore, a 3D networking structure using different air layers including Low Altitude Platforms, High Altitude Platforms, and Low Earth Orbits in a harmonized manner for both urban and rural areas should be applied to satisfy the requirements of the dynamic environment. In this perspective, we put forward a novel, next-generation paradigm called Air Computing that presents a dynamic, responsive, and high-resolution computation environment for all spectrum of applications. In this survey, we define the components of air computing, investigate its architecture in detail, and discuss its essential use cases and the advantages it brings for next-generation application scenarios. We provide a detailed and technical overview of the benefits and challenges of air computing as a novel paradigm and spot the important future research directions.

Applying the blended KTA-Sprint framework to a usability assessment of a digital health module for informal caregivers co-managing heart failure

AimsThe main purpose of this study was to design and evaluate core features of a patient-informal caregiver shared digital health module, Caretown, to support chronic disease management. The secondary purpose was to explore the efficacy of ongoing implementation of an innovative KTA-Sprint framework through usability testing of the Caretown module. MethodsGuided by the KTA-Sprint framework, seven core features of the Caretown module were designed through a multi-method approach: symptom live reports, care cards, trends, medication wallet, medical events timeline, messenger, and caregiver resources. Results and discussionWe tested the prototype of the module with seven informal caregivers. Findings from quantitative usability metrics and qualitative interviews were translated into required refinements (e.g., how to improve intuitiveness of icons, interpretation of presented data, and system navigation) and mapped onto a subset of Ravden checklist domains. Qualitative usability feedback highlighted opportunities to further improve: visual clarity, consistency, compatibility with user expectations, how to provide informative feedback with clear structure, functionality, flexibility and control. Throughout the design process, we identified considerations for supporting future implementation outcomes including equity, acceptability, adoption, appropriateness, and fidelity. Moreover, the KTA-Sprint framework effectively guided the design and evaluation process for the Caretown module. ConclusionsThis development and usability testing of the Caretown module illustrates the implementation of the rapid and iterative KTA-Sprint framework. Scenario-based testing and multi-method analyses of user feedback allowed for triangulation of feedback. Results informed key considerations for product deployment and future implementation.

A survey of ontology-enabled processes for dependable robot autonomy.

Autonomous robots are already present in a variety of domains performing complex tasks. Their deployment in open-ended environments offers endless possibilities. However, there are still risks due to unresolved issues in dependability and trust. Knowledge representation and reasoning provide tools for handling explicit information, endowing systems with a deeper understanding of the situations they face. This article explores the use of declarative knowledge for autonomous robots to represent and reason about their environment, their designs, and the complex missions they accomplish. This information can be exploited at runtime by the robots themselves to adapt their structure or re-plan their actions to finish their mission goals, even in the presence of unexpected events. The primary focus of this article is to provide an overview of popular and recent research that uses knowledge-based approaches to increase robot autonomy. Specifically, the ontologies surveyed are related to the selection and arrangement of actions, representing concepts such as autonomy, planning, or behavior. Additionally, they may be related to overcoming contingencies with concepts such as fault or adapt. A systematic exploration is carried out to analyze the use of ontologies in autonomous robots, with the objective of facilitating the development of complex missions. Special attention is dedicated to examining how ontologies are leveraged in real time to ensure the successful completion of missions while aligning with user and owner expectations. The motivation of this analysis is to examine the potential of knowledge-driven approaches as a means to improve flexibility, explainability, and efficacy in autonomous robotic systems.

When chatbots fail: exploring user coping following a chatbots-induced service failure

PurposeWhile the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.Design/methodology/approachThis study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.FindingsIt identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.Originality/valueOur study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.