User Inclination Research Articles

Understanding users’ news-sharing behaviors: roles of risk perception, believability, fake news awareness and social tie variety

PurposeThis study aims to investigate the impact of news quality on users’ risk perceptions toward online news and its subsequent influence on perceived believability and user engagement in sharing news. Additionally, we explore the moderating effects of fake news awareness and social tie variety.Design/methodology/approachDrawing upon the social amplification of risk framework, this study investigates the relationship between news quality and users’ news-sharing behaviors, along with its underlying mechanism. An online questionnaire involving 399 eligible participants was employed for hypotheses testing, and the structural equation model served as the main analytical method.FindingsThe influence of news quality on users’ news-sharing behavior is sequentially mediated by risk perception and perceived believability. Individuals with a heightened awareness of fake news or a diverse social tie are more inclined to perceive greater risks associated with news-sharing behavior and question news authenticity.Originality/valueThis study contributes to the existing literature on users’ news-sharing behaviors by examining the influence of risk perception on the relationship between news quality, perceived believability and users’ news-sharing behavior. Additionally, it explores the moderating effects of fake news awareness and social tie variety. Our findings offer valuable insights into comprehending user inclinations towards news sharing and mitigating the dissemination of fake news.

Literature Review: Why do we need innovative design methods for future Mobility-as-a-Service (MaaS)?

Since its inception in 2014, Mobility-as-a-Service (MaaS) has been the subject of numerous trials and pilot studies globally, predominantly yielding favourable outcomes. However, specific issues that need to be clarified regarding its long-term development have been observed. This paper outlines the development stages of MaaS and the trials conducted across different nations. It highlights an ambiguity within user-centred design in the extant literature, specifically the inadequate exploration of user requirements. The discourse propounds the necessity for a paradigm shift in the research focus—advocating for a more profound inquiry into user needs instead of the prevailing emphasis on the advancement of enabling technologies and the operations of digital platforms. Given the increasing demand for personalisation within intricate transportation systems, comprehending users’ cognitive and psychological preferences emerges as paramount. Such an understanding is imperative to allow the service framework to adapt and identify principal offerings that resonate with user inclinations. Moreover, establishing an enduring dialogue between service providers and users is essential. The paper argues for design researchers to formulate and implement an innovative methodology that fosters the alignment of supply with demand, thereby enhancing the efficacy of Mobility as a Service.

Understanding Behavioral Intention to Use of Air Quality Monitoring Solutions with Emphasis on Technology Readiness

This study aims to investigate the determinants of Behavioral Intention to Use (BIU) within the scope of Air Quality Monitoring Solution (AQMS), with a focus on Technology Readiness (TR). It explores nine crucial variables: Effort Expectancy (EE), Performance Expectancy (PE), Social Influence (SI), Facilitating Condition (FC), Hedonic Motivation (HM), Price Value (PV), Habit (HB), and TR using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. Data collection involved 371 participants via surveys and questionnaires, with demographic variables such as Gender (G), Age (A), and Location (L) serving as moderating factors. Analysis conducted with smart-pls 4.0 software revealed a notable correlation between TR and BIU, identifying HM as the most pivotal factor. The study’s theoretical and practical contributions offer a nuanced understanding of the integration between TR features and the UTAUT2 model, highlighting HM’s critical role in influencing AQMS user behaviors. Furthermore, it delivers strategic insights for developers and policymakers aimed at improving air quality monitoring systems. The research enhances the comprehension of technology adoption dynamics in environmental surveillance, setting the groundwork for refining AQMS’s deployment and efficacy. It aligns technological innovations with user inclinations, underscoring the combined effects of TR and HM on AQMS-related user actions. Ultimately, the findings illuminate TR’s significance and interplay within the UTAUT2 framework, providing actionable recommendations for crafting more effective air quality monitoring solutions.

Determinants Factor of Passengers’ Propensity to Utilise E-Hailing Services in Kota Kinabalu, Sabah

In the realm of public transportation, e-hailing services have emerged as a prominent mode of transportation in recent years. This study aims to elucidate the complex relationships between four key constructs, namely safety features, fare, ease of usage, and accessibility, in shaping user inclination towards the adoption of e-hailing services. Using the Stated Preference Survey (SPS) method, a meticulously designed online questionnaire was administered to three hundred respondents in Kota Kinabalu. Employing partial least squares-structural equation modelling for analysis, the results highlight the significance of ease of usage and accessibility as robust predictors influencing Kota Kinabalu residents' preference for using e-hailing services. The safety aspect displays notable disparities based on gender, as male and female consumers possess contrasting perspectives regarding the degree of protection provided by e-hailing services. In contrast, the concept of price value has a restricted ability to forecast user propensity, as surge pricing was universally enforced during peak hours in all e-hailing services. The study's findings provide useful insights for e-hailing businesses, indicating strategic enhancements in service delivery to guarantee customer comfort and safety, thereby increasing their service coverage, and attracting a wider user demographic.

Music Recommendation Using Facial Emotion Recognition

It can be very befuddling for people to choose which music to tune in to from a wide run of alternatives accessible. Different proposal frameworks have been made for particular spaces like music, feasting, and shopping, catering to the user's inclinations. Our essential objective is to supply music recommendations that adjust with the user's taste. By analyzing facial expressions and client feelings, ready to pick up experiences into their current mental or enthusiastic state. Music and recordings offer a extraordinary opportunity to show clients with a huge number of choices based on their slants and past data. It is well known that humans make use of facial expressions to express more clearly what they want to say and the context in which they meant their words. More than 60 percent of the users believe that at a certain point of time the number of songs present in their songs library is so large that they are unable to figure out the song which they have to play. By developing a recommendation system, it could assist a user to make a decision regarding which music one should listen to helping the user to reduce his/her stress levels. The user would not have to waste any time in searching or to look up for songs and the best track matching the user’s mood is detected, and songs would be shown to the user according to his/her mood. The image of the user is captured with the help of a webcam. The user’s picture is taken and thenas per the mood/emotion of the user an appropriate song from the playlist of the user is shown matching the user’s requirement.

Exploring the Convergence of Design, Security, and Human Dynamics in Social Networks in India

This Exploration dives into the complex exchange between social networks, zeroing in on their design, security measures, and the impact of human factors. As social networks keep on molding present day correspondence and connection, understanding their elements turns out to be progressively pivotal. The examination investigates the architecture and user-focused design rules that improve user experiences, encourage community associations, and empower self-expression. All the while, the review tends to the squeezing security challenges presented by these platforms, dissecting risks like identity theft and misinformation dispersal, and proposing techniques for powerful encryption and data protection. Moreover, the review digs into the domain of human behavior and brain research, clarifying how individual inspirations, cognitive biases, and social standards add to content creation, sharing, and commitment inside social networks. The bits of knowledge acquired give a complete perspective on what user activities mean for network elements and the development of online networks. By exploring these aspects, the review expects to illuminate platform developers and policymakers, offering important direction on upgrading design, carrying out powerful security measures, and figuring out user behavior. Eventually, this examination adds to an all-encompassing comprehension of social networks, introducing a diverse investigation of design, security, and human factors. The discoveries guide the advancement of more user-centric, secure, and socially significant social organization platforms that line up with user inclinations and contribute decidedly to the digital landscape.

Analysis of Factors Affecting User Inclination to use Virtual Education Exhibitions in the Post Pandemic Covid-19 Era: Case Study in Indonesia

Social distancing policies during the COVID-19 period have opened a space for interventions in the use of digital technology that was previously rejected by the public. Currently, most of the community activities have been carried out online, including those related to education, such as hosting virtual exhibitions with an educational purpose. Virtual exhibitions (VE) are online exhibitions that display information on specific topics. However, to the best of our knowledge, research on VE in the educational context is still lacking. This study aims to identify the variables that affect users' intentions to attend the virtual exhibition frequently. The channel expansion theory, flow theory, technology acceptance model, and expectation confirmation theory serve as the foundation for the research model that is constructed. The model was tested using data from 321 respondents who had visited VE, then analyzed using Structural Equation Modeling (SEM) with the aid of the SmartPLS application version 3.3.3. The findings show that 11 factors significantly influence the intention to continue using VE, while the other 2 factors have no significant influence. Thus, perceived usefulness has a significant effect on satisfaction and continued use intention to use VE, but not on perceived enjoyment. Furthermore, the perceived ease of use of VE has a significant impact on perceived usefulness and perceived enjoyment. However, media richness does not significantly affect perceived enjoyment. It's just that media richness influences perceived ease of use significantly. Confirmation also has a significant impact on perceived enjoyment, perceived usefulness, and satisfaction. Perceived enjoyment has a significant effect on satisfaction and continued use intention, and satisfaction has a significant effect on continued use intention to use VE. The findings are useful for VE providers and developers in developing a transformation strategy to increase the intention to use VE in every educational exhibition.

Why Were Innovators Motivated to Be Entrepreneurs? An Empirical Study of Taiwanese Start-Ups

Background: According to the lead user theory, lead users at the front of a market, benefiting significantly from new products, usually have more innovation intension than general users. However, little research depicts the entrepreneurship motivations that drive innovators to become businesspeople. Hence, this study investigates Taiwanese entrepreneurs to fill this gap in the research. Method: This study examines motivations for becoming an entrepreneur from small- and medium-sized enterprises in Taiwan. A multiple regression analysis is used to examine the relationship between lead user inclination and entrepreneurial motivations as well as to test the moderating effect of community involvement. Results: The research results summarise entrepreneurial motivations into five categories: product knowledge advantage, industry expertise, inducements to innovation, career expectations, and benchmarking and indicate the positive moderating effect of community involvement on the association between innovators and entrepreneurs. Conclusions: Additional research is suggested to catalyse motivations to aspire lead users to pursue business success as well as to enhance entrepreneurship education policy. This study contributes to understanding the inclination of lead users towards becoming entrepreneurs and, especially, to emphasise the role of community involvement, which increases the likelihood of innovators to be entrepreneurs.

Improvising personalized travel recommendation system with recency effects

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user's inclination towards travel destinations is subject to change over time. In this project, we have analyzed users' twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user's most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.

Visual Dialog Agent Based On Deep Q Learning and Memory Module Networks

In the past many years, it has been observed that there has been an increase in methods to solve problems and the solution involves a combination of Computer Vision and Natural Language Processing. New algorithms and systems are emerging and are being developed every day to solve the above-mentioned kind of problems. Visual Dialog Agent is one of them. This kind of system utilizes both Computer Vision and Natural Language Processing algorithms. With this technology many variants of Visual Dialog Agents have been designed till date and many exclusive algorithms are created for Visual Dialog Agent. In this paper we propose an idea to create a Visual Dialog Agent which utilizes the present state of art End to End Memory Module Networks along with Reinforcement Learning Policies to answer the questions prompted by the user and as well understand the inclination of the user in the conversation which it holds. The goal of the proposed Visual Dialog Agent is to have a more engaging conversation with the highest user inclination.

Do lead users cooperate with manufacturers in innovation? Investigating the missing link between lead userness and cooperation initiation with manufacturers

Past lead user research has provided strong empirical support for the claim that lead users tend to innovate independently. While the innovation activity itself is well documented, little is known about whether users' ideas and solutions also reach manufacturers' organizations. Recent research indicates that the majority of user discoveries remain limited only to local use and are rarely presented to a wider circle of other users, business angels, or manufacturers. This limitation presents a problem because if manufacturers do not have access to users’ needs, ideas, and solutions, they might fail to recognize essential opportunities for innovation.Therefore, this research studies the question of whether lead userness and expected recognition from manufacturers and peers explain the user's inclination to cooperate with manufacturers. We study cooperation initiated from two different ends: users pro-actively approaching manufacturers with problems and ideas and users participating in manufacturer-hosted innovation workshops. Drawing on lead user theory, we develop hypotheses and test them with two empirical studies with nurses and pharmaceutical technical assistants. The results provide support for the argument that a superior trend position and high expected benefits are strong and reliable predictors for both investigated forms of cooperation. The results on the effect of recognition from the manufacturer and peers are mixed, indicating that expected process benefits are less important.

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

Recommendation System (RS) has been broadly utilized in various areas and discovers product recommendations during an active user interaction in E-Commerce sites. Tremendous growth of users and products in recent years has faced some key challenges. There are numerous online sites that present many decisions to the user at once, which is strenuous. Moreover, finding active user or right product is an important task in RS. Existing works have been proposed to recommend a product by considering user inclination and socio-demographic behaviour. In this paper, we propose a Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features. It will classify the features using race classifiers from both quality and quantity aspects. The proposed approach also addresses the problems of the previous methods to efficiently evaluate user preference on products and balance feature analysis. The K-NN classification method has been qualified online and real-time to find user behaviour data coordinating to a specific user group containing the relationship between the similarity of many users and target users from a huge amount of data. The proposed experimental result is evaluated based on measures such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Squared Error (RMSE) with the lowest error of 0.7165, 0.7201 and 0.7322 separately. High predictive measures like Precision (P), Recall (R) and F1 are found to have values 0.8501, 0.2201 and 0.3507 respectively.

Modeling real-time data and contextual information from workouts in eCoaching platforms to predict users’ sharing behavior on Facebook

eCoaching platforms have become powerful tools to support users in their day-to-day physical routines. More and more research works show that motivational factors are strictly linked with the user inclination to share her fitness achievements on social media platforms. In this paper, we tackle the problem of analyzing and modeling users’ contextual information and real-time training data by exploiting state-of-the-art classification algorithms, to predict if a user will share her current running workout on Facebook. By analyzing user’s performance, collected by means of an eCoaching platform for runners, and crossing them with contextual information such as the weather, we are able to predict with a high accuracy if the user will post or not on Facebook. Given the positive impact that social media posts have in these scenarios, understanding what are the conditions that lead a user to post or not, can turn the output of the classification process into actionable knowledge. This knowledge can be exploited inside eCoaching platforms to model user behavior in broader and deeper ways, to develop novel forms of intervention and favor users’ motivation on the long term.

Social media addiction: What is the role of content in YouTube?

BackgroundYouTube, the online video creation and sharing site, supports both video content viewing and content creation activities. For a minority of people, the time spent engaging with YouTube can be excessive and potentially problematic.MethodThis study analyzed the relationship between content viewing, content creation, and YouTube addiction in a survey of 410 Indian-student YouTube users. It also examined the influence of content, social, technology, and process gratifications on user inclination toward YouTube content viewing and content creation.ResultsThe results demonstrated that content creation in YouTube had a closer relationship with YouTube addiction than content viewing. Furthermore, social gratification was found to have a significant influence on both types of YouTube activities, whereas technology gratification did not significantly influence them. Among all perceived gratifications, content gratification had the highest relationship coefficient value with YouTube content creation inclination. The model fit and variance extracted by the endogenous constructs were good, which further validated the results of the analysis.ConclusionThe study facilitates new ways to explore user gratification in using YouTube and how the channel responds to it.

The participact mobile crowd sensing living lab: The testbed for smart cities

In recent years, the widespread availability of smartphones provided with sensors has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling so-called mobile crowd sensing (MCS). While many efforts have been made to improve specific techniques for MCS - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing spectrum - to the best of our knowledge, thus far there have been no active experiments of MCS that involve all these techniques in a large-scale real-world experiment. Based on these considerations, we started the ParticipAct Living Lab testbed, an ongoing experiment at the University of Bologna involving 300 students for one year in crowd sensing campaigns that can passively access smartphone sensors and also require active user collaboration. In this article we describe the guidelines behind the design of ParticipAct, as well as its features and architecture. Moreover, we report some of the seminal results gathered during the first three months of its deployment, including accuracy of the classifier provided by ParticipAct client and user inclination to successfully complete tasks depending on the level of active collaboration required to executing them.

번들소프트웨어 디자인을 위한 다이어리 스터디 적용 연구

최근 사용자 경험의 중요성이 강조되면서 사용자를 이해하기 위한 다양한 리서치 방법론들이 시도 되고 있다. 특히 그간의 통계적인 분석을 중심으로 하는 정량적 조사 방식의 사용자 이해 방법이 한계에 부딪히게 되면서 인문학적 또는 문화 인류학적인 방법론의 도입 시도들이 지속적으로 일어나고 있는 추세이다. 이런 노력의 일환으로 문화 인류학의 대표적인 리서치 방법론인 에스노그래피 형식의 다양한 접근 방법이 시도되고 있는데 그중 다이어리 스터디(Diary Study method)도 효과적인 방법론의 하나로 거론 되고 있다. 이에 본 논문은 다이어리 스터디 방법론을 노트북 PC의 번들 SW의 사용자 태도 조사에 응용하여 적용함으로서 사용자 태도에 대한 심층적 연구를 시도 하였다. 또한 노트북 PC의 번들 SW의 사용자 수용도를 파악하는 프로젝트를 중심으로 다이어리 스터디가 사용자 이해에 적용된 구체적인 예를 보여준다. 이 과정을 통해 에스노그래피 리서치 방법에 대한 개괄을 알아보고, 프로젝트에 다이어리 스터디를 적용 설계하여 사용자 성향을 분석 도출 하는 과정을 통한 효과적인 리서치 방법론을 논의 하고자 하였다. 이는 구체적인 사례를 통해 연구 방법론을 실제 적용함으로서 교과서적인 논의에서 벗어나 실제 적용측면에서의 논의를 진행 하고자 하는 목적으로 진행 되었다. 본 연구를 통해 사용자의 노트북 PC SW에 대한 적극적인 사용자화 과정을 확인 할 수 있었으며, 앞으로 번들 소프트웨어에 대한 배포 및 SW UX전략을 수립하는데 있어 활용 될 수 있을 것으로 기대 된다. Recently as the user experience's importance has been emphasized, there has been many tries of various research methodology in order to understand the user. Specially, as the political investigation method's user understanding method based on the statistical analysis so far has encountered a limit, there has been a continuous tendency to try to introduce a humanitarian or cultural anthropological methodology. As part of this effort, there has been many tries of various approach methods of the Ethnography which is the cultural anthropological typical research methodology, but among those the Diary Study Method is mentioned as one of the effective methodologies. Therefore this thesis applies the Diary Study Method to the notebook PC bundle SW's attitude research and tries a deep rooted research about the user attitude. Also centered to the project of understanding the notebook PC bundle SW's user receptivity. it shows a specific example of the Diary Study applied to the user understanding. Through this process, we will examine the summary about the Ethnography research method, and will discuss an effective research methodology by through the process of analyzing and deducting the user inclination by applying and designing the Diary Study to the project. That is to say, it was progressed by actually applying the research methodology through a specific case, with the purpose of breaking away from the textbook discussion and progress with an application aspect discussion. Through the current research, we will verify an active user-inclined process about the user's notebook PC SW, and it is expected to be useful in establishing a SW UX strategy and distribution related to the bundle software.

Improvement of CSCW software implementation in NPD: the CAM mechanism for a better adoption by users

Cooperation between disseminated actors is a key factor in improving new product development (NPD) performance. In the last years, numerous CSCW software applications have been introduced in the industry to support NPD with a low success rate. This is partly due to the limited insight of the organisational and human factors influencing user motivation during the first stages of CSCW software implementation. This research suggests that users adopt more rapidly those features of CSCW software with whom they are more familiar and that the adoption of other features of the application is reinforced by the various links connecting them with the preferred feature. We propose a CSCW integration protocol called the 'catching module mechanism' (CAMM) which exploits user's inclination towards a particular feature of a CSCW application by training them on that same feature. The CAMM was tested in the NPD context of Saint-Gobain Glass.

The Critical Dictionary and the Wiki World

ABSTRACTA new utopian lexicography – online? Today, dictionary users are Web users: they imagine that a static paper text is necessarily less richly informative than a hyperlinked megatext. They expect to move among texts as interest and curiosity direct, not to be constrained by linear, editorially directed matter of the old-fashioned kind. The nodes of an infinite hyperlinked search may map a universe of small, classically organized texts, but those who travel the Web for information and ideas expect to click around on impulse. Further, they often expect to participate in the public construction of know-ledge, contributing and editing Web content whenever they feel competent to do so. They are hyper-Romantics who have conflated their imaginations and the scapes of human know-ledge into a hypertextual hyperuniverse. Future dictionaries of English should acknowledge these new user inclinations to the extent compatible with the highest lexicographical standards. These ideas are discussed further.