Changes In User Behavior Research Articles

Adapting Gamification Strategies According to Targeted Health Behavior Change: Findings from a Focus Group.

In healthcare, improving lifestyle behaviors like medication adherence remains challenging, with an impact on treatment success and patient outcomes. Mobile health (mHealth) applications offer promising solutions by leveraging smartphone ubiquity and digital accessibility. However, sustaining user engagement and behavior change presents obstacles. This study explores the specificities of gamification-integrating game-like elements into non-game contexts-to improve mHealth applications for 3 health behaviors. During a focus group, 15 participants discussed the potential of 17 gamification strategies to improve medication adherence, physical activity, and diet within mHealth applications. Individual choices varied widely, with preferences influenced by targeted behaviors. Physical activity focused on self-monitoring and challenges, while dieting emphasized cooperation. Simulation models were particularly intriguing, projecting long-term benefits of behavior changes. Future research should explore personalized design options and consider individual characteristics. Iterative design cycles with end-users are crucial for refining concepts and sustaining user engagement. Overall, gamification presents an innovative avenue for empowering patients and transforming healthcare delivery.

System dynamics model for analysis of demand response on incorporating autoswitch in the Colombian electrical sector

This study investigates the potential outcomes of transforming the Colombian electricity market—70% of which is regulated—into a deregulated market. Given the impact of climate change and the need for greater control over the Colombian retail energy sector, it is crucial to examine future scenarios where two policies, successful in other countries, could improve market management. A simulation was conducted using a system dynamics model to analyze residential user behavior by manipulating demand response and implementing an autoswitch policy. The results validate the effectiveness of regulating electricity consumption through price management and the autoswitch policy, where customers can automatically switch suppliers. By 2033, these policies could reduce electricity prices by 11%–14%, depending on weather and user behavior changes, leading to significant customer savings, especially for higher-income groups, while ensuring suppliers' profitability. Although the conclusions suggest a positive outlook, the implementation of these policies requires regulatory changes and the adoption of smart energy meters. he proven policies could help Colombian government in managing consumption during energy crises.

Verify and trust: A multidimensional survey of zero-trust security in the age of IoT

The zero-trust (ZT) model assumes that all users, devices, and network traffic should not considered as trusted until proven. The Zero-trust model emphasizes the importance of verifying and authenticating every user and device, and limiting access to resources based on the principle of least privilege. Under the principle of the zero-trust model, devices are granted access after they have been successfully presented with their authentication credentials and access rights based on different factors, such as user identity, device health, location, and behaviour. Access controls are then continuously evaluated and updated as user properties, locations and behaviour change. The zero-trust model can be applied in various domains (healthcare, manufacturing, financial services, government etc.) to provide a comprehensive approach to cybersecurity that helps organizations to reduce risk and protect critical assets. This paper aims to provide a comprehensive and in-depth analysis of the zero-trust model, its principles, and its applications, as well as to propose recommendations for organizations looking to adopt this approach. We explore the major components of the zero-trust framework and their integration across different practical domains. Finally, we provide insightful discussions on open research issues within the zero-trust model in terms of the security and privacy of users and devices. This paper should help researchers and practitioners understand the importance of a zero-trust framework and adopt the zero-trust model for effective security, privacy, and resilience of their networks.

Exploring Antibiotic Resistance Through Artificial Intelligence: A Novel Perspective

Microbial resistance has long been linked to antibiotic resistance, a serious worldwide health issue. But as technology advances quickly in this day and age, a related phenomenon in the field of artificial intelligence [AI] is beginning to take shape. In order to better understand the idea of "antibiotic resistance" in the context of artificial intelligence [AI], this study will compare and contrast the evolution of bacterial resistance with potential obstacles in the design and implementation of intelligent systems. The increasing prevalence of AI systems across several industries highlights the striking similarities between their capacity to adapt and withstand hostile attacks and changing surroundings, and the biological resistance mechanisms seen in bacteria. This study explores the causes behind AI resistance, looking at how data drift, adversarial manipulations, and changing user behavior might cause machine learning systems to lose their effectiveness over time. The paper also examines the ethical ramifications of AI resistance, addressing issues with biases, unforeseen outcomes, and the influence of intelligent systems on society that are resistant to change or intervention. The area of antibiotic stewardship in medicine serves as an inspiration for the paper's discussion of potential mitigation techniques for AI resistance. Through the identification of parallels between AI resistance and antibiotic resistance in bacteria, this study adds to a better comprehension of the difficulties pertaining to the long-term viability and efficiency of intelligent systems. Since AI will continue to be a major influence on the future, it is critical to address the problem of "antibiotic resistance" in this context in order to ensure that AI is developed responsibly and ethically.

Peak shaving and self-consumption maximization in home energy management systems: A combined integer programming and reinforcement learning approach

This paper proposes a novel framework for Home Energy Management System based on the combination of integer programming and Reinforcement Learning (RL) for achieving efficient home-based Demand Response (DR). In particular, RL is exploited to manage the charge and discharge of Battery Energy Storage System (BESS), and Mixed Integer Linear Programming is exploited for load scheduling. The idea is to focus the RL specifically on BESS management, as its behavior is stochastic and is mainly affected by Photovoltaic (PV) production and user behavior changes. The scheduling decisions of household appliances, Electric Vehicles (EVs), and charging/discharging batteries can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR-induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV, PV panels, and electric storage. The test results demonstrate the effectiveness of the proposed home energy management framework under the application of different demand-side flexibility strategies.

Penerapan Financial Technology (Fintech) pada UMKM Binaan Bank NTT di Sabu Raijua

In today's modern era, humans have a life with all activities that can never be separated from technological developments. One of the technological developments that is currently popular in Indonesia is Financial Technology. Financial Technology (FINTECH) is the latest innovation in the financial service system which is touched by modern technology which can make it easier for Small and Medium Enterprises).to run their businesses with the help of Fintech. The aim of this research is to determine the application of Fintech to Small and Medium Enterprises assisted by Bank NTT in Sabu Raijua and to determine the advantages and disadvantages of implementing Fintech in Small and Medium Enterprises assisted by Bank NTT in Sabu Raijua. This research uses a qualitative research method, which is a research method carried out by means of in-depth interviews in the field, using a phenomenological approach. The data source in this research is primary data obtained directly from Small and Medium Enterprises actors in Sabu Raijua, while secondary data is in the form of interviews, observations and documentation. The research results show that the application of Financial Technology (Fintech) in Small and Medium Enterprises assisted by Bank NTT in Sabu Raijua is experiencing positive developments in line with technological advances and changes in user behavior, ease of access, and transaction security are getting better. However, in its implementation there are several obstacles, namely weak internet connections, elderly Small and Medium Enterprises players find it difficult to understand how to use Financial Technology.

A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

Functional Framework for Multivariant E-Commerce User Interfaces

Modern e-businesses heavily rely on advanced data analytics for product recommendations. However, there are still untapped opportunities to enhance user interfaces. Currently, online stores offer a single-page version to all customers, overlooking individual characteristics. This paper aims to identify the essential components and present a framework for enabling multiple e-commerce user interfaces. It also seeks to address challenges associated with personalized e-commerce user interfaces. The methodology includes detailing the framework for serving diverse e-commerce user interfaces and presenting pilot implementation results. Key components, particularly the role of algorithms in personalizing the user experience, are outlined. The results demonstrate promising outcomes for the implementation of the pilot solution, which caters to various e-commerce user interfaces. User characteristics support multivariant websites, with algorithms facilitating continuous learning. Newly proposed metrics effectively measure changes in user behavior resulting from different interface deployments. This paper underscores the central role of personalized e-commerce user interfaces in optimizing online store efficiency. The framework, supported by machine learning algorithms, showcases the feasibility and benefits of different page versions. The identified components, challenges, and proposed metrics contribute to a comprehensive solution and set the stage for further development of personalized e-commerce interfaces.

Changing User Behavior in Decisions to Share COVID-19 Misinformation: An Implicit Association Test Study

Making medical decisions while distracted when receiving COVID-19 misinformation can majorly impact a person's life and even lead to death. Blatantly sharing COVID-19 misinformation is a significant problem of human behavior that triggers a speed-up and acceleration in the propagation and diffusion of misinformation in social media. While the latest research has focused on understanding the psychological dimensions of this phenomenon, few studies have explored the role of selective exposure and technological prevention when a person considers sharing COVID-19 misinformation, primarily through an Implicit Association Test (IAT). Our study identified and intervened in the association of user exposure between misinformation and implicit truth evaluations by using the Implicit Association Test (IAT) with "Misinformation vs. Fact Information or Positive vs. Negative Words”, 38 from 150 participants were either exposed to misinformation headlines or actual new headline posts on stimulants, in the form of images. We then measured participants' implicit truth evaluations and self-reported perceived accuracies of actual and of misinformation headlines using the Visual Selective Attention System (VSAS). After intervening, participants exposed to fake news headlines had lower implicit truth evaluations and increased perceived accuracy. This implies that exposure to fake news headlines after the intervention with the VSAS system may have directly affected implicit evaluations and changed user behavior in sharing COVID-19 misinformation.

Measureing Carbon NetZero and the IRISCAST Project

Moving towards NetZero requires robust information to enable good decision making at all levels: covering hardware procurement, workload management and operations, as well as higher level aspects encompassing grant funding processes and policy framework development. The IRISCAST project is a proofof-concept study funded as part of the UKRI DRI Net-Zero Scoping Project. IRISCAST performed an audit of carbon costs across a multi-site heterogeneous infrastructure by collecting and analysing snapshots of actual usage across different facilities within the IRIS community (https://iris.ac.uk). Combining usage information with an analysis of the embodied carbon costs and careful mapping and consideration of the underlying assumptions resulted in an estimate of the overall carbon cost, an understanding of the key elements that contribute to the carbon cost, and the important metrics needed to measure it. IRISCAST makes reccomendations to allow high level feedback of carbon costs to funding bodies to inform strategic decisions as well as low level feedback of carbon costs to users and user communities to drive changes in user code bases and behaviors to be more carbon efficient. IRISCAST carbon modeling shows that estimates of carbon costs can vary by factors of 10, hence there is significant opportunity for the carbon footprint of Digital Research Infrastructures to be reduced.

Preferensi Konsumen Terhadap Layanan M-banking

Technological developments have had a huge impact on the banking system, one example of the impact of developments is increasingly modern payment systems such as mobile banking. Bank NTT is one of the banks that provides mobile banking services called B'pung mobile. This research aims to determine consumer preferences for mobile banking services at Bank NTT. This research uses a qualitative descriptive approach, by conducting structured interviews with informants and NTT bank customers. The research results show that consumer preferences for mobile banking services at NTT banks continue to experience positive developments in line with technological advances and changes in user behavior, ease of access, transaction security, feature and functional innovations are getting better. However, in its implementation there are several problems, namely a weak internet connection, no fingerprint feature and also a top up feature that is difficult to access.

Identifying Behavior Change Techniques in an Artificial Intelligence-Based Fitness App: A Content Analysis.

In the field of artificial intelligence-based fitness apps, the effective integration of behavior change techniques (BCTs) is critical for promoting physical activity and improving health outcomes. However, the specific BCTs employed by apps and their impact on user engagement and behavior change are not explored sufficiently. This study investigates the Freeletics fitness app through a mixed-methods approach to evaluate the use of BCTs. In the quantitative analysis, fifteen unique BCTs were identified based on the Behavior Change Technique Taxonomy (V1). In the qualitative analysis, user reviews (n=400) were examined to understand perspectives on the app's effectiveness in promoting behavior change. Goal setting, action planning, self-monitoring of behavior, and social support were among the most prevalent BCTs identified in the Freeletics app, and their effectiveness in enhancing user engagement and promoting behavior change was also highlighted by user reviews. Among the areas of improvement identified in the study were the need for simplifying personalization options and addressing user concerns regarding the specificity of feedback. The study underscores the importance of integrating BCTs effectively within AI-based fitness apps to drive user engagement and facilitate behavior change. It contributes valuable insights into the design and implementation of BCTs in fitness apps and offers recommendations for developers, emphasizing the significance of goal setting, feedback mechanisms, self-monitoring, and social support. By understanding the impact of specific BCTs on user behavior and addressing user concerns, developers can create more effective fitness apps, ultimately promoting healthier lifestyles and positive behavior change.

Adapting to Lockdown: Exploring Stress Coping Strategies on Short Video Social Media During the COVID-19 Pandemic.

The study addresses a gap in research on media use during the COVID-19 pandemic, focusing on the overlooked role of short video social media platforms in stress management. It builds on transactional stress model and coping flexibility to advance the understanding of the strategy-situation fit, particularly the alignment of coping strategies with platform affordances. Our study investigated the detailed use of short video platforms and coping strategies during the pandemic lockdown through an online survey of 1569 respondents conducted immediately after the lockdown. We characterized lockdown duration as a situational stressor attribute and explored variations in the coping process. Assessing changes in user behavior across the top four Chinese short video platforms, we examined anxiety induced by lockdown and the use of problem-focused and emotion-focused coping strategies. Significant changes in user behavior in response to lockdown-induced stress were observed. Structural model analyses reveal that people employed both coping strategies to manage heightened anxiety, primarily through increased viewing behavior over interaction behavior. The findings underscore the positive implications of short video platforms on individual well-being. This study argues for a deeper research focus on coping flexibility within the realm of social media, accompanied by a comprehensive assessment of platform user behavior. Future research should also consider the influence of algorithms on platform functionality.

Load Data Analysis Based on Timestamp-Based Self-Adaptive Evolutionary Clustering

Smart grid system can obtain users' daily load data, and by clustering, we can get users' load profiles to divide them into industrial, commercial and residential types. Load data has the characteristic of changing periodically. Within a period, the load profiles are relatively stable. However, load profiles often changes significantly according to reasons like holidays and season changing. When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results be very different even if user behaviors are almost the same. Evolutionary clustering (EC) can be taken into consideration. EC doesn't ignore historical clustering results and makes results more stable in a period. However, when dramatic changes happen in user behaviors, the quality of EC's clustering results will decrease significantly. This paper proposed an optimized evolutionary clustering algorithm: Timestamp-Based Self-Adaptive Evolutionary Clustering (TBSAEC). TBSAEC is based on evolutionary clustering, and takes a heuristic approach to pick the evolutionary parameter to maximize the total quality for one certain timestamp. TBSAEC maintains the stability of continuous-time clustering results while better adapting to changes in user behaviors. Besides, TBSAEC optimize the running efficiency of the algorithm by picking samples in equal portions from historical data instead of the whole data. We applied TBSAEC to the load data of a certain region in east China in 2015, and the results showed that TBSAEC is 3% to 9% higher than the ordinary evolutionary clustering algorithm in total quality, and 87% faster than the ordinary algorithm. Applying TBSAEC to power grid systems can provide more precise users' load profiles, which makes them create more accurate and profitable power plans.

A bibliometric analysis of the use of the Gamification Octalysis Framework in training: evidence from Web of Science

The Octalysis framework, which systematically organizes gamified elements in non-game settings, such as training, has become one of the most famous persuasive tools for changing user behaviour. The field of Gamification Octalysis Framework research is expanding rapidly, moving beyond elementary questions like “what” and “why” to more complex ones like “how” and “when”. However, there remain empirical and theoretical challenges such as demonstrating the effectiveness of the Gamification Octalysis Framework and codifying the principles underpinning effective gamification design. This paper uses bibliometric analysis and scientific mapping to characterize the structure and development of the academic field of the Gamification Octalysis Framework, with the help of intellectual, conceptual, and social network structures of the framework. Our findings provide insight into the field’s research frontiers and intellectual structures, the interconnections between articles, authors, and keywords, the current collaborative networks, the hottest themes, and the most cited authors, publications, and sources, for instance, that in recent years, the Gamification Octalysis Framework has attracted significant research attention in the training field. Interestingly, 66.6% of cutting-edge research is done in the social sciences, as compared to science and technology. Researchers, academicians and professionals should be made aware of Octalysis framework gamified method because it has been shown to increase user engagement and motivation outside of social sciences game setting. We recommend that countries outside Europe integrate the Gamification Octalysis Framework in training into their learning programs and launch corresponding research centres and journals.

How do management factors influence digital adoption in the case of a large-scale digital transformation project – A process perspective

Abstract Although practitioners and scientists agree that user adoption of new technologies is a key success factor in digital transformations, little is known about how specific management factors are related to user behavior. In particular, the temporal nature of digital transformation projects is largely neglected. Therefore, we propose a systematic, theory-based framework for the management of digital adoption (MDA) and derive specific process-oriented hypotheses for content-, process-, and context-related management factors, their relationships to user adoption, and underlying psychological processes (e.g., performance expectancy or social influence). We applied the MDA framework in the context of a large digital transformation project in a logistics company in a two-wave research design. We tested the process-oriented hypotheses based on latent change score analysis among 1,095 users. The results support the assumption that changes in management factors, largely mediated by changes in the psychological processes, lead to changes in user behavior.

A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure

As Electric vehicle (EV) adoption increases worldwide, public charging infrastructure must be expanded to meet the growing charging demand. Furthermore, insufficient and improperly deployed public charging infrastructure poses a real risk of slowing EV adoption. The infrastructure thus needs to be expanded intelligently and flexibly while accounting for uncertain dynamics in future charging demand. Nevertheless, current methods for demand-based expansion often rely on rigid and error-prone tools, such as travel surveys and simple rules of thumb. The former is more appropriate for long-term, equilibrium scenarios, where we consider the charging network as a whole rather than incrementally expanding it. At the same time, the latter relies on business experience in a rapidly changing field. We propose a predictive optimization approach for intelligent incremental expansion of charging infrastructure. At each time step, we estimate the future charging demand through a Gaussian Process, which is subsequently used in a linear chance-constrained optimization method to expand the charging infrastructure incrementally. To develop and validate this framework, we account for environmental feedback by simulating user behavior changes based on historical charging records and considering an optimized charging network at every iteration. We apply this approach to a case study of EV charging in Dundee, Scotland. We compare different strategies and reasons for their pros and cons for monthly incremental expansion of the charging network. In particular, combining machine learning and optimization results in the cheapest expansion and one that serves the most demand.

Shaping the Media Landscape: Exploring Media User Consumption in the Age of Social Media

The innovation in social media and digital technology has profoundly transformed media user consumption patterns and habits, significantly impacting the Media, Entertainment, and Information (MEI) industry. Among the various sectors affected, the news industry stands out as one of the most profoundly influenced. This article employs the news industry as a case study and delves into changes in audience behavior and media organizational relationships, alterations in news content, and dissemination methods to further dissect the shifts in the ways users access news. Consequently, it derives insights into how media user behavior changes impact the news industry.

Dynamic recommendation algorithms for a COVID-19 restrictions scenario in the restaurant industry

研究目的利用关于不同国家餐厅及其顾客反馈的数据, 我们探索了以下问题：(i) 在餐饮行业, 用户行为和偏好在COVID-19限制期间如何改变, (ii) 这些变化如何影响推荐算法的性能, 以及 (iii) 可以提出哪些方法来改进封锁情景下的餐厅推荐质量。研究方法为了评估用户行为和偏好的变化, 本研究进行了定量和定性数据分析, 对比了COVID-19限制期前后的情况。为了评估非稳态环境中餐厅推荐系统的性能, 我们测试了最先进的协同过滤算法。我们提出并研究了一种基于过滤的方法, 以提高封锁情景下推荐算法的质量。研究发现研究发现, 在COVID-19限制期间, 平均评分和评论数量发生了变化。实验研究证实：(i) 在COVID-19限制期间, 所有最先进的餐厅行业推荐系统的性能显著下降; (ii) 使用滑动窗口和后过滤方法可以改进非稳态环境下餐厅推荐的准确性和稳定性。实践意义我们提出了两种新方法：基于CatBoost分类模型的关闭餐厅后过滤和滑动窗口方法。这些方法可以应用于经典的协同过滤推荐算法, 并在非稳态条件下提高指标值。这些方法对于推荐系统的开发者和大规模餐厅和酒店聚合平台都有帮助。因此, 这对于应用的最终用户和企业主都有好处, 因为当用户得到良好的推荐时, 他们会诚实地对餐厅进行评价, 而不会因为外部因素降低评分。研究创新本文首次提供了COVID-19限制对不同国家餐厅推荐系统有效性影响的广泛多方面的实验研究, 并提出和验证了两种解决餐厅推荐性能下降问题的新方法。

TransJakarta Service Evaluation in Controlling COVID-19 Transmission Using Twitter Sentiment Analysis

This study attempted to understand passenger perception of using public transport by utilizing Twitter data about the services of the TransJakarta Busway. Tweets were the main data source to capture users’ responses toward these services. Users’ perceptions were analyzed by sentiment analysis using a naïve Bayes algorithm. Furthermore, content analysis was used to inform improvements in service maintenance. The findings showed that the pandemic had a major impact on TransJakarta services, from a decrease in users, route closures, and fleet reductions to changes in user behavior. Most Tweets were negative regarding (1) poor bus frequency, leading to long queues and passenger overcrowding at bus stops and inside buses; (2) failure to maintain social distancing measures; (3) frequent violations of the 50% bus capacity reduction during peak hours, and showing a lack of consideration in measuring demand size during peak hours; (4) staff’s weak control of implementing the health protocol exacerbated poor services. This study suggests service improvement based on peak hour demand analysis to offset the implications of a 50% capacity restriction by providing proper bus frequencies and headway arrangements considerable enough to avoid crowding, followed by optimal monitoring of health protocol by staff. Tweet data may inform poor management in controlling the transmission of COVID-19 on public transportation. Hence, using Twitter data could replace conventional data collection methods like user interviews. Beneficial information from Tweet data can be captured at relatively low costs. Therefore, it may aid the evaluation of PPKM policy implementation to create more resilient public transportation during pandemics.