User Behaviour Research Articles

The Behaviour of Shopeefood Application Users Based on Unified Theory of Acceptance and Use of Technology (UTAUT) Modified Model in Indonesia

The use of the internet has made it easier for people to fulfill their needs and desires optimally. In order to take advantage of the convenience offered by the internet, many people carry out digital transactions using smartphones. This provides convenience for users in accessing the internet anytime and anywhere. The rapid development of technology and the availability of the internet have changed people's life patterns to become more practical and efficient. Everyone can take advantage of the convenience offered by the internet to fulfill their various daily needs. However, despite its great benefits, the use of applications and technology also creates certain challenges and problems when ordering food online using the Shopeefood application. This research aims to analyze the behavior of using the ShopeFood application based on the Modified Unified Theory of Acceptance and Use of Technology (UTAUT) Model in Indonesia. In this research, the UTAUT Model is used as a theoretical framework to understand the factors that influence ShopeFood application usage behavior. Data collection was carried out using an online survey method with purposive sampling technique. The research sample consisted of 340 respondents with the characteristics of Generation Y age range 25-40, Generation Z age range 15-25, installed the Shopee application, had used the Shopee food application, and had ordered food via the Shopeefood feature. The analysis model in data processing uses LISREL 8.8. The research results show that the variables performance expectancy, Effort Expectancy, sociol Influences, Facilitating Conditions, hedonic motivation, price saving orientation, habit, and Trust have a positive and significant effect on behavior intention. Then the Facilitating Conditions, habit and behavior Intention variables have a positive and significant influence on Shopeefood usage behavior (Use Of Behavior). Meanwhile, the Perceived of Risk variable has no significant effect on Behavioral Intention in using the ShopeeFood application.

Dynamic Bi-layer Graph Learning for Context-aware Sequential Recommendation

Sequential recommendations have received great attention in recent years due to their wide application in e-commerce, trip planning, and online education. Contexts reveal the intention of a user in a transaction such as consuming or purchasing an item, and highly affect her behaviour. However, existing techniques cannot fully capture the context-sensitive and dynamic sequential user behaviours for effective and efficient sequential recommendation. To address this limitation, we propose a novel Dynamic Bi-layer Graph learning (DBIG)-based framework for sequential recommendation, which learns the sequence dynamics of user behaviours and contexts. Specifically, we first propose a context graph to identify the relevant contexts to a target user. Then, we propose a transaction context graph that predicts the probability of a transaction happening under certain contexts. In addition, we apply BERT (Bidirectional Encoder Representations from Transformers) to our graph embedding for understanding the user sequential behaviours in bidirectional ways and generating recommendations. Finally, we propose a cluster-structure-based algorithm, which incrementally maintains the DBIG model and BERT over dynamic transaction updates. We evaluate our DBIG-based framework by conducting extensive experiments over five public datasets. The quantitative results demonstrate the superiority of our approach in terms of effectiveness and efficiency.

Provably secure data selective sharing scheme with cloud-based decentralized trust management systems

AbstractThe smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged. One potential issue that emerges is the risk that data may be transmitted by cloud servers to unauthorized users or individuals who have no interest in the particular data or user interests. The prior research lacks comprehensive solutions for balancing security, privacy, and usability in secure data selective sharing schemes inside Cloud-Based decentralized trust management systems. Motivating factors for settling this gap contain growing concerns concerning data privacy, the necessity for scalable and interoperable frameworks, and the increasing dependency on cloud services for data storage and sharing, which necessitates robust and user-friendly mechanisms for secure data management. An effective and obviously secure data selective sharing and acquisition mechanism for cloud-based systems is proposed in this work. We specifically start by important a common problematic related to the selective collection and distribution of data in cloud-based systems. To address these issues, this study proposes a Cloud-based Decentralized Trust Management System (DTMS)-connected Efficient, Provably Secure Data Selection Sharing Scheme (EPSDSS). The EPSDSS approach employs attribute-based encryption (ABE) and proxy re-encryption (PRE) to provide fine-grained access control over shared data. A decentralized trust management system provides participant dependability and accountability while mitigating the dangers of centralized trust models. The EPSDSS-PRE paradigm would allow data owners to regulate granular access while allowing users to customize data collection without disclosing their preferences. In our strategy, the EPSDSS recognizes shared data and generates short fingerprints for information that can elude detection before cloud storage. DTMS also computes user trustworthiness and improves user behaviour administration. Our research demonstrates that it’s able to deliver trustworthy and safe data sharing features in cloud-based environments, making it a viable option for enterprises seeking to protect sensitive data while maximizing collaboration and utilization of resources.

Assessing the Role of Autonomous Vehicles in Urban Areas: A Systematic Review of Literature

Autonomous vehicles (AVs) aim to improve safety and comfort of road users while contributing to the reduction of traffic congestion, air pollution, fuel consumption, and enabling mobility and accessibility of disabled and older people. As AV technology is rapidly advancing, there is an urgent need to explore how those new mobility services will impact urban transport systems, including the users, the infrastructure, and the design of future urban areas. This paper applies a systematic review to assess the role of AVs in urban areas. It reviews 41 articles published between 2003 and 2023, and uses inductive and deductive coding approaches to identify seven themes and thirty sub-themes within the literature. The seven include: benefits, attitudes, and behaviours and user perception, climate adaptation, climate mitigation, legislation and regulations, sustainability, and infrastructure. Studies related to benefits accounted for 25% of the sample, followed by behaviours and user perception (24%) and sustainability (22%). The least amount of research has been undertaken on the role of AVs to support climate adaptation. Geographically, almost half (#22) of the papers originate within Europe, followed by America (#10) and Asia (#7). There is only limited research originating from the Global South. This systematic review sets the scene for considering how AVs in public transport can be implemented in urban areas by establishing the current state of knowledge on user attitudes, perceptions, and behaviour, the benefits of AVs, the infrastructure and legislation and regulations required for AVs, and the role AVs have in climate mitigation, adaptation, and sustainability.

Deep Dive into Insta: Navigating Influencer on Instaflow

Instagram is a powerful tool for social media marketers, especially for influencers who have a lot of control over audience behavior and brand interaction. Examining how accurately Instagram's algorithms identify and promote top influencers is the goal of this study. This study delves deeply into the complex influencer ecosystem on Instagram's InstaFlow platform. The research seeks to offer a thorough grasp of influencers' roles and effects by traversing the complexity of influencer relationships. By means of methodical examination, this investigation aims to reveal significant perspectives on the connections and exchanges among the entities that comprise the InstaFlow ecosystem, thereby advancing a more sophisticated understanding of influencer participation on the widely used social media network. The nature of the content shared, the time of day, and the use of hashtags and hashtags, which may influence Instagram reach, will be gathered. In this paper we are discussing various methods applied to reach & predict the Instagram posts. Keywords: Deep dive, Machine Learning, Visualization, Feature extraction, Support vector machine, Prediction algorithms, Social network, User behaviour, stable server, feature space, page rank, user recommendation.

“Thank god for tags”—fanfiction as a reading paradigm

ABSTRACT While starting as an analogue practice in fanzines, fanfiction in its current form, that is, almost exclusively online, is closely entangled with the technology it is embedded in, both Web 2.0 features and specific website infrastructure. This is nowhere better exemplified than on the platform Archive of Our Own (AO3), which hosts over 13 million stories in thousands of fandoms. AO3 is one of the most popular online repositories for fanfiction and growing exponentially, precisely because of how it deals with this slew of data: its system of extensive hyperlinked labelling. Based on empirical participant data from an online survey and semi-structured interviews, I argue that tags offer distinct affordances and affect user behaviour in such a way that new reading practices arise. Tags create a literacy paradigm interwoven with virtuality, relying on links and networks, and featuring a unique set of characteristics that differentiate reading on AO3 from other digital reading platforms, as well as from print fiction. These include “informed” reading, whereby site users know what they can expect from works, “rhizomatic” reading by navigating across the platform along the metadata, even to unfamiliar fandoms, and reading in multiple, which is facilitated by the site’s searchability.

A Sequential Recommendation Model for Balancing Long- and Short-Term Benefits

Typically, user behaviour occurs continuously, and considering this dynamic sequence correlation can lead to more accurate recommendations. Sequential recommendation systems have, therefore, become an important means of solving the problem of network information overload. However, existing attention mechanisms are still insufficient for modelling users’ dynamic and diverse preferences. This paper presents a recommendation model based on a multiheaded self-attention mechanism and multitemporal embeddings of long- and short-term interests (MSMT-LSI). MSMT-LSI balances users’ long- and short-term benefits through two multihead self-attention networks and finally forms a hybrid representation for recommendation. After finding the most suitable parameter combinations for the MSMT-LSI model through parameter sensitivity analysis and verifying the advantages of the long- and short-term fusion strategy, related experiments on five well-known datasets and their analysis shows that the performance of MSMT-LSI is better than that of the classical model on the same benchmark dataset.

Space classification for indoor pedestrian navigation with morphological and functional characteristics

AbstractIndoor navigation networks serve as the foundation for indoor pedestrian navigation services. However, current graph‐based topological network models cannot easily represent complex indoor structures in a manner consistent with user behaviour and cognitive patterns, and thus cannot support fine‐grained indoor navigation analysis services. To enhance the rationality of the model, we considered the influences of morphological and functional characteristics of indoor spaces on pedestrian movement and proposed a method for classifying navigation spaces. This method, based on convex space segmentation, defines morphological and functional characteristic functions, classifying indoor navigation spaces as either corridors or open spaces, which suitable for representing as median axis models or visual graphs, respectively. These categories supported the creation of topological network models for route planning analysis. Indoor map experiments confirmed the effectiveness of our method in identifying various types of corridors and open spaces, that addressing the limitations of width‐based parameters, even in complex indoor settings.

Measuring spatial inequality of urban park accessibility and utilisation: A case study of public housing developments in Auckland, New Zealand

Poor spatial accessibility of urban green spaces affects disadvantaged populations, who are at greater risk of socioeconomic related health inequalities. We analyse mobile phone locational data from the Auckland Region of New Zealand to connect a user's 'home' location to nearby urban parks, specifically focusing on public housing tenants - a highly vulnerable group at greater risk of health inequalities.We use a number of traditional accessibility measures and four new park utilisation analyses to explore the relationship between accessibility to urban parks, neighbourhood area deprivation, and user's patterns of actual park usage. We develop four new metrics to assist in understanding the actual utilisation of green spaces available to residents: distance between closest park available and visited (DAT), used park opportunities (UPO), locality of park visits (LPV), and nearest park visitation rate (NPVR).We found that neighbourhoods with the most public housing tend to have a lower number of parks accessible, and those that are accessible are smaller in size. Additionally, people living in these areas tend to travel farther to access urban parks compared to areas with no public housing. More deprived neighbourhoods were revealed to have lower accessibility to urban greens in Auckland, highlighting the urgency to address the emerging divisions in accessibility and opportunities for these neighbourhoods. Our findings have important implications for policy-makers and urban planners. Furthermore, the use of mobile phone locational data can reveal insights into the mobility behaviour of users and can be applied to other locations and population groups.

Demystifying metaverse data from user-technology interaction

Metaverse data is the collection of detailed information generated from the user's interactions that take place within virtual and augmented realities. Metaverse data includes capturing motion information, recognising gestures, interaction and speech, emotion state assessment and evidence of eye metrics to understand user behaviour in virtual worlds. This paper will explore the different types of metaverse data as well as their implications for marketing efforts. It will also discuss how these technologies can help businesses better understand their customers and cater to their needs in an ever-evolving digital landscape.

Characterising travel behaviour patterns of transport hub station area users using mobile phone data

Understanding the travel behaviour of transport hub users is vital for improving transport services. Although previous research has explored passenger behaviour in and around transport hubs, there is a lack of comprehensive studies on travel patterns within hub station areas. To this end, this study harnessed mobile phone data to analyse the travel behaviour within a hub station area, using Beijing South Railway Station as a representative case. A series of recognition criteria was set up based on spatio-temporal information to categorize users within the station area. We categorised station area users into transport hub users (THU) and non-transport-hub users (NTHU) and examined their distinct travel patterns. The results reveal distinct travel patterns for THU and NTHU. NTHU are predominantly concentrated in and around the station area. THU, however, display longer trip distances and more widely distributed endpoints. In terms of travel time distribution, NTHU show evident morning and evening peak phenomena, while the travel time distribution of THU fluctuates throughout the day. The study also employed association rule analysis to illustrate strong connections between the station area, Beijing's core, central areas, and even urban fringe areas. The results reveal that the station area is most closely connected to the core and central areas of Beijing, from where many users are attracted to the station area. In addition, some urban fringe areas also have strong connections with the station area. These findings inform urban transport planning and personalised travel services.

Modeling airport choice for a multi-airport area using a random parameter logit model

Recent years have seen a growing demand for air transport which has led to an increase in the number of airports providing a significant number of flights in Spain and throughout the rest of Europe. Choosing which airport to use depends on the competition between the airlines operating from them and how accessible they are to the residents in the surrounding areas. This research introduces an efficient design based on D-error using a stated preferences survey to study user behaviour when choosing between competing airports. The data is modelled using a Multinomial Logit Model and a Random Parameter Error Component Logit (RPECL), a discrete choice model which allows us to study user preferences taking into account their varying systemic and random tastes, as well as find specific correlations which may exist between similar alternatives. This meant the authors were also able to calculate the direct and cross elasticities to examine the weight of the different variables involved in making the choice of airport. The results showed, for a study case in Spain (Bilbao and Seve Ballesteros - Santander airports), that increased access time to the furthest airport caused an important fall in demand (choice) for that airport. It was also shown that access fees had a lower elasticity than the flight cost and that the fewer the transfers required to reach the final destination increased the probability of choosing an airport. Therefore, in order for smaller airports to compete with larger airports they need to invest more in reducing the cost of flights, attracting more low-cost companies and offering more direct complementary destinations than those provided by larger airports.

User behaviour in sustainable technology: an exploration of endorsement strategy

ABSTRACT Given the lethargic acceptance behaviour of sustainable technologies (STs) by the public, the most crucial factor to contemplate is the influential form of organization endorsement strategy that can effectively enhance user intentions and behaviour. Using the social identity theory (SIT) and source credibility theory (SCT), this research examines the moderating potential of an attractive celebrity (CET) and social media influencer (IET) on user intention and behaviour in low- and high-involvement sustainable technologies (LISPs and HISPs). Based on an SEM-ANN analysis of data from 605 Chinese respondents, our empirical findings show that in the LISPs context, IETs moderate the intention-behaviour relationship positively whereas CETs have the opposite impact. In contrast, the moderating effect of CETs on HISPs is positive, whereas the effect of IETs is negative. Furthermore, it was shown that HISP users’ income level significantly influenced their behaviour, while education level had no significant impact on either category. These outcomes have both theoretical and practical implications in developing resilient strategies to retain users and provide guidance on how to efficiently optimise, integrate, and evaluate the STs.

Optimal control of a heat pump-based energy system for space heating and hot water provision in buildings: Results from a field test

This paper presents and discusses results from a field test that uses model predictive control (MPC) to optimise the operation of a multi-source heat pump-based energy system for space heating and hot water provision in a building. The objective of the optimisation is to minimise the electricity consumption of the heat pump using flexibility from the heat sources, the space heating and domestic hot water tanks while ensuring end-user comfort and system constraints. The field test was performed in the context of the RES4BUILD project. The integrated system consists of photovoltaic-thermal (PVT) collectors, an advanced vapour-compression multi-source heat pump and water buffer tanks for space heating and hot water needs. The heat pump utilises a solar buffer, a borehole thermal energy storage (BTES) and air as heat sources. The solar buffer is supplied with heat from the PVT collectors. The optimal operation of the system is obtained by monitoring and controlling the interactions between the different system components using MPC, taking into account the weather and heat load forecasts. Results have shown that MPC has a potential and added value for the optimal operation of multi-source heat pumps in real-life while considering system constraints and user behaviour to ensure thermal comfort. However, significant effort and expert knowledge are needed to develop the sufficiently accurate system models required by this control approach. The outcomes and conclusions of this work are therefore a basis for further development of such control approaches to improve their replicability and feasibility on a large scale, considering the diverse nature of energy system components in buildings.

The Impact of AI on Human Roles in the User Interface & User Experience Design Industry

The ever-evolving field of User Interface & User Experience UI/UX design prioritizes creating user-friendly interfaces. Traditionally, this has been a human-centred process. However, Artificial Intelligence AI offers new possibilities for automating design tasks and leveraging data for user insights. This research paper delves into the potential impact of AI on UI/UX design. The core question is whether AI tools can effectively replace human designers in crafting user experiences. Alternatively, can AI work alongside designers to enhance their capabilities? This paper explores these questions through a review of existing research and proposes a research methodology to further investigate the topic. The literature review analyses how AI can be used for tasks like user behaviour analysis, A/B testing, and prototype generation. However, it also acknowledges AI's limitations in understanding user emotions, implementing creative solutions, and adapting to unforeseen user needs. The suggested research methodology will give a combined approach, making a review of existing literature with interviews from UI/UX professionals and a case study analyzing a design project utilising AI tools. By investigating the strengths and limitations of AI in design tasks, how AI can be integrated into the workflow, and the ethical considerations surrounding AI-driven design decisions, this research helps to provide a better understanding of the relationship between AI and human designers in the UI/UX field. The findings can inform design education, industry practices, and the development of future AI tools specifically tailored for UI/UX design applications. Keywords: UI/UX Design, Artificial Intelligence, Human-Computer Interaction, User Experience, User Interface, Design Automation

Dimension of user behaviour

In this paper, I propose a dimension of user behaviour. This dimension could be seen from user behaviour itself. This dimension is like. This dimension of user behaviour could be seen from behaviour of people in everyday life. From smallest of the activities such as eating food to biggest of the activities such as pursuing professions like doctor, engineer and business, everyone’s behaviour is according to their likes.

Data mining

Data Mining Data mining is about finding patterns in the data [1]. In this paper, I put forward an important insight about similarity in branches of computer science and data mining. All branches of computer science could be termed as a procedure to carry out data mining. In this paper, I detail that. The computer works by finding patterns in the input and output [2]. Artificial Intelligence works by finding the patterns of functions of the related variables [3]. Machine learning works by mathematical justification of machine learning methods and results [4]. That is the pattern followed in machine learning. Social networking is about finding patterns in user behaviour and user engagement [5][6].

An exploratory study of information re-finding behaviour modes of Chinese college students on social media: video diary analysis from Chinese platforms

ABSTRACT This study aims to explore college students’ information-re-finding behaviour in the context of social media by identifying different behaviour modes to enrich the research on the information behaviour of social media users. Thirty-two college students volunteered to record their re-finding process once it occurred within two weeks by the video diary method. One hundred and forty-six valid events were collected and analysed qualitatively and quantitatively. It was shown that the behaviour process of information re-finding on social media experienced six stages: scene triggering, information demand generating, social media selecting, re-finding, information confirmation and information processing. Forty-eight kinds of information re-finding modes were illustrated and summarised into seven categories according to where and how the information was finally found: following, chat record, keyword, favourites, history, personal dynamics and the like. From further analysis, it was proved that significant differences did exist between varied re-finding behaviour modes in information needs and re-finding time rather than information processing behaviour. The study could expand the traditional information search model into mixed timing by considering recalling and re-finding. Practically, it generates useful practical implications for the design of re-finding functions, information recommendation and memory services of social media.

Human behaviour simulation for promoting usefulness and user-centric values in parametric design

Although contemporary architectural design education adopts the parametric design approach, which involves complex iterations between the arrangement of form components and building performance, this method limits students' exploration of the optimal matches between generated form and the behaviours of prospective users. Therefore, as a solution, this study hypothesises the effects of human behaviour simulation on students' exploration of user-centric values in the parametric design process. This paper compares the self-evaluation scores of 90 undergraduate students before and after simulation. Data were collected during an authentic digital design course that examined shopfront façade prototypes. Statistical analysis and interpretation indicate that simulation better facilitates students' search for matches between initially value-isolated forms, created using a parametric design tool, and useful user-centric values. However, simulation is not relevant for discovering unexpected forms. This study addresses the unknown affordances in simulation when assigning user-related value to form generation that lacks value.

Persistent interaction patterns across social media platforms and over time

Growing concern surrounds the impact of social media platforms on public discourse1–4 and their influence on social dynamics5–9, especially in the context of toxicity10–12. Here, to better understand these phenomena, we use a comparative approach to isolate human behavioural patterns across multiple social media platforms. In particular, we analyse conversations in different online communities, focusing on identifying consistent patterns of toxic content. Drawing from an extensive dataset that spans eight platforms over 34 years—from Usenet to contemporary social media—our findings show consistent conversation patterns and user behaviour, irrespective of the platform, topic or time. Notably, although long conversations consistently exhibit higher toxicity, toxic language does not invariably discourage people from participating in a conversation, and toxicity does not necessarily escalate as discussions evolve. Our analysis suggests that debates and contrasting sentiments among users significantly contribute to more intense and hostile discussions. Moreover, the persistence of these patterns across three decades, despite changes in platforms and societal norms, underscores the pivotal role of human behaviour in shaping online discourse.