Understanding User Behavior Research Articles

Special features of service design in microservices architecture in cloud computing with container virtualization

The article addresses the issue of service design in microservices architecture in cloud computing under conditions of container virtualization. Service design is the process of modeling a service to facilitate interaction between the provider and users. The foundation of service design lies in understanding user behavior, their needs, and motivations to create a service that is convenient and memorable, increasing the chances of reuse. Microservices architecture technology is an approach that involves breaking down applications into smaller and more manageable blocks (microservices), each performing a specific function. This approach differs from monolithic programs where all components are tightly interconnected and reside in a single codebase. The new approach opens up unique possibilities for utilizing various technologies, including containerization, orchestration, hybrid cloud, and more, offering excellent opportunities for optimizing applications through microservices. To address the identified challenges in the article: a) it examines how the new approach introduces complexities, such as the lack of proper monitoring and event logging; the possibility of incorrect service detailing; complexities in data management and synchronization; weaknesses in communication and automation processes; manifestations of bottlenecks in terms of productivity and scalability, among others; b) it is demonstrated that all these complexities can be resolved through effective service design in microservices architecture. This means that when designing microservices architecture in cloud computing with container virtualization, the challenge of effective service design is addressed by creating a balance between the detailing of services and their functionality to achieve maximum productivity and system functionality. Solving this task is possible primarily through an understanding of the principles of construction and application of microservices in cloud computing under conditions of container virtualization.

Immersive Study Analyzer: Collaborative Immersive Analysis of Recorded Social VR Studies.

Virtual Reality (VR) has become an important tool for conducting behavioral studies in realistic, reproducible environments. In this paper, we present ISA, an Immersive Study Analyzer system designed for the comprehensive analysis of social VR studies. For in-depth analysis of participant behavior, ISA records all user actions, speech, and the contextual environment of social VR studies. A key feature is the ability to review and analyze such immersive recordings collaboratively in VR, through support of behavioral coding and user-defined analysis queries for efficient identification of complex behavior. Respatialization of the recorded audio streams enables analysts to follow study participants' conversations in a natural and intuitive way. To support phases of close and loosely coupled collaboration, ISA allows joint and individual temporal navigation, and provides tools to facilitate collaboration among users at different temporal positions. An expert review confirms that ISA effectively supports collaborative immersive analysis, providing a novel and effective tool for nuanced understanding of user behavior in social VR studies.

Investigating Technology Acceptance Model (TAM) for E-Da’wah: A Systematic Literature Review

This literature review research aims to conduct a systematic review of the use of the Technology Acceptance Model (TAM) in the context of E-Da’wah. E-Da’wah is a new approach in da'wah that uses information and communication technology as a means to convey religious messages to the community. Information and communication technology has opened up new opportunities to spread da'wah more effectively and widely, but the acceptance and adoption of this technology by the public remains a relevant and interesting issue to be researched. This article will present a summary and analysis of previous studies that have applied the technology acceptance model, namely the Technology Acceptance Model (TAM), in the context of E-Da’wah. This literature review will cover some of the main variables present in the TAM model, such as perceived usefulness (perceived utility) and perceived ease of use (perceived ease of use), as well as other factors that influence the acceptance of technology in the context of E-Da’wah. In addition, this article will also discuss the results of re- cent research that has been conducted to understand user behavior in adopting E-Da’wah technology. The results of this article are expected to provide in-depth insights into the application of technology acceptance models in the E-Da’wah environment, provide a better understanding of the factors that influence the adoption of this technology, and provide direction for future research in optimizing the use of technology information and communication in da'wah efforts.

Understanding Digital Marketing Strategies and Their Implications: A Case Study of Huawei

In today’s fast-moving business environment, digital marketing has become a key tool for organizations to connect with their target audience and strengthen their brand identity. Taking global technology giant Huawei as a case study, this article provides an in-depth look at its multi-dimensional digital marketing strategy, covering search engine optimization, pay-per-click advertising, email marketing, content marketing, and social media marketing. Through a deep understanding of user behavior, market trends, and cultural differences, Huawei has crafted strategies that resonate with diverse audiences worldwide. These strategies have enabled Huawei to build outstanding online visibility and deep engagement with users globally, thereby realizing its business objectives. This paper summarizes the key takeaways from Huawei’s digital marketing, including precise targeting, personalized interactions, content diversity, and cultural sensitivity. By learning from Huawei’s digital marketing successes, companies can optimize their digital marketing strategies and increase their market presence. Huawei’s digital marketing strategy applies to the tech industry and inspires all industries to succeed in the digital era.

Dynamics of Social Media Engagement in Pakistan: A Comprehensive Analysis of User Trends Across Platforms

Abstract The purpose of this research paper is to conducts a comprehensive examination of social media engagement in Pakistan, focusing on user trends across diverse platforms. [1]A longitudinal observational research design was adopted to capture temporal variations in user interactions on major social media platforms. The data collection process involved sourcing monthly engagement metrics from publicly available platform analytics, ensuring accuracy and reliability. The analysis begins with Facebook, revealing distinctive fluctuations in engagement percentages over the observed months. Peaks in November 2022 and subsequent recoveries underscore the impact of seasonality, content dynamics, and platform updates on user behavior. Twitter, as a microblogging platform, maintains a consistent but comparatively lower level of engagement, resiliently serving audiences seeking real-time and concise content. Visual-centric platforms like YouTube and Instagram exhibit dynamic patterns, with YouTube's fluctuating percentages indicating a diverse content landscape and an uptick in Instagram engagement signaling evolving user preferences for visually-driven experiences. Beyond mainstream platforms, the study extends its focus to niche audiences on platforms such as Pinterest, LinkedIn, Reddit, and others, revealing the coexistence of platforms catering to specific interests. the findings contribute to a distinctive understanding of user behavior, uncovering the impact of various factors on engagement percentages across different social media platforms in Pakistan. Future research endeavors can build upon these insights, exploring additional dimensions of social media engagement and it's evolving trends within the digital landscape of Pakistan.

Living Lab-Based Service Interaction Design for a Companion Robot for Seniors in South Korea.

A living lab is a valuable method for designing tangible and intangible service elements, ensuring a comprehensive user experience. Developing a digital companion service, which users may be unfamiliar with, requires observing user behavior in real-world environments and analyzing living and behavioral patterns. A living lab starts with understanding user characteristics and behaviors. Living lab methods have an impact on the accuracy and precision of service design. The number of seniors in South Korea is rapidly increasing, leading to a rise in social issues like solitary deaths and suicide. Addressing these problems has led to a growing demand for companion robots. To design effective companion services, understanding seniors' living environments and their cognitive and behavioral traits is essential. This opinion piece, based on a national R&D project, presents the development of a digital companion for seniors. It offers insights, providing a comprehensive overview of living lab-based service interaction design and proposing methodologies about living lab environment construction and experimentation and considerations when designing robot interaction functions and appearance. The living lab environment includes real living spaces, laboratories, virtual reality settings, and senior welfare centers. Using the research findings, we created service scenarios, analyzed senior language characteristics, and developed the concept and facial expressions of the digital companion. To successfully introduce a novel service, it is crucial to analyze users' real-life behavior and adjust the service accordingly.

Research on Virtual Reality Experience and User Behavior from the Perspective of Psychology

Virtual reality (VR) technology has attracted a lot of attention in many fields, including entertainment, healthcare, education and industrial applications. As technology continues to advance, VR has become a powerful tool for psychological research to delve deeper into human cognition, emotion, and behavior. This thesis aims to study the relationship between virtual reality experience and user behavior from the perspective of psychology. By combining virtual reality technology and psychological theory, we explore how virtual reality affects users' perceptual, emotional, and cognitive processes, as well as users' behavior in virtual environments. We first review the development of virtual reality technology, including its definition, technical composition and application areas. We then propose a theoretical framework of psychology to explain the psychological mechanisms of virtual reality experience. This includes important concepts such as perception, attention, emotion, and cognitive load, which play a key role in understanding user behavior and experience in virtual reality. The experience of virtual reality includes not only visual and auditory perception, but also the emotional experience and physical perception of the user. We'll delve into how virtual reality creates immersion, evokes emotions, and changes the user's physical perception. At the same time, we will focus on the user's behavior in the virtual environment, including their actions, interactions and reactions. The research method is an important part of exploring virtual reality experience and user behavior. We will cover experimental design, data acquisition tools, and analytical methods to help researchers delve deeper into this area. In the empirical research section of this paper, we will present a series of specific studies that demonstrate how virtual reality affects user attention, emotion, cognition, and behavior. These studies will provide insights that will help us better understand the psychological effects of virtual reality. Finally, in the discussion and conclusion section, we will summarize the findings, discuss their connection to psychological theory, and make suggestions for future research. These studies are not only of great significance to the field of psychology, but also provide strong support for the application and improvement of virtual reality technology.

Provectories: Embedding-based Analysis of Interaction Provenance Data.

Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult to extract and describe analysis strategies based on interaction provenance. In this paper, we propose a novel visual approach to the meta-analysis of interaction provenance. We capture single and multiple user sessions as graphs of high-dimensional application states. Our meta-analysis is based on two different types of two-dimensional embeddings of these high-dimensional states: layouts based on (i) topology and (ii) attribute similarity. We applied these visualization approaches to synthetic and real user provenance data captured in two user studies. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.

Gender Classification of Twitter Users Using Convolutional Neural Network

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and

A study of user switching intention for ERP systems based on push-pull-mooring model: Focusing on the important role of information quality for users

Enterprise resource planning (ERP) systems have become indispensable within companies due to their substantial functions and benefits. With a plethora of ERP systems available in the market, management is offered a broad array of options. This paper endeavours to identify the determinants influencing the switching intention of ERP users. Utilizing a conceptual model that adapts the push-pull-mooring paradigm, we seek to construct a formation mechanism of switching intention. Through structural equation modeling conducted on data collected from 236 users, our study uncovers several key findings. The study indicates that system quality, information quality, and top management support exert considerable influence on switching intention via satisfaction. Notably, we found that user satisfaction has a negative impact on switching intention. Our analysis also reveals that alternative attractiveness significantly determines switching intention. These findings provide valuable insights for organizations and ERP vendors to better understand user behaviour and to strategically manage user retention and switching decisions.

TEMPAT SAMPAH PINTAR “MYSMARTRECYLEBIN” BERBASIS TEKNOLOGI INTERNET OF THINGS (IOT) DAN BIG DATA

This research aims to develop "MySmartRecyleBin," a smart trash bin that utilizes Internet of Things (IoT) technology and Big Data in many cities around the world that face serious challenges in waste management. This research was conducted to face the urgency of handling the increasingly pressing waste problem. The aim is to help governments and waste managers overcome waste management challenges in a more sustainable manner, reduce waste management costs, and strengthen waste management systems. This research involves researchers and developers of IoT and Big Data technology collaborating to create a smarter and more efficient waste management system. By utilizing IoT and Big Data technology, this research produces a system that allows real-time data collection from various devices connected to the internet, such as sensors on trash cans and waste transport fleets. This data is then analyzed to understand user behavior and waste collection patterns. Through this technology, the waste management system can become more effective and efficient. This technology uses a Microcontroller, Load Cell, RFID Tag, and RestFull API Technology as a communication medium to the Waste Management Information System (MySipah). The test results show the success of sending data over the last 1 year, namely January 2022 is 152 kg, February 2022 is 109 kg, March 2022 is 93 kg, April 2022 is 113 kg, May 2022 is 126 kg, June 2022 is 161 kg, July 2022 is 188 kg, August 2022 is 143 kg, September 2022 is 102 kg, October 2022 is 113 kg, November 2022 is 116 kg, December 2022 is 124 kg.

A Self-adaptive and Secure Approach to Share Network Trace Data

A large volume of network trace data are collected by the government and public and private organizations and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis, because they contain sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data need to be anonymized before being shared. Most of the existing anonymization tools have two major shortcomings: (1) they cannot provide provable protection, and (2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This article proposes a self-adaptive and secure approach to anonymize network trace data and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques.

Popularity Bias in Online Dating Platforms: Theory and Empirical Evidence

Problem definition: Generating recommendations of compatible dating partners is a challenging task for online dating platforms because uncovering users’ idiosyncratic preferences is difficult. Thus, platforms tend to recommend popular users to others more frequently than unpopular users. This paper studies such popularity bias in an online dating platform’s recommendations and its consequences for users’ likelihood of finding dating partners. Methodology/results: Motivated by the empirical evidence that a user’s chance of being recommended by the platform’s algorithm increases significantly with the user’s popularity, we study an online dating platform’s incentive that generates popularity bias by modeling the platform’s recommendations and users’ subsequent interactions in a three-stage matching game. Our analysis shows that the recommendations that maximize the platform’s revenue and those that maximize the number of successful matches between users are not necessarily at odds, even though the former leads to a higher bias against unpopular users. Unbiased recommendations result in significantly lower revenue for the platform and fewer matches when users’ implicit cost of evaluating incoming messages is low. Popular users help the platform generate more revenue and a higher number of successful matches as long as these popular users do not become “out of reach.” We validate our theoretical results by running simulations of the platform based on a machine learning–based predictive model that estimates users’ behavior. Managerial implications: Our result indicates that an online dating platform can increase revenue and users’ chances of finding dating partners simultaneously with a certain degree of bias against unpopular users. Online dating platforms can use our theoretical results to understand user behavior and our predictive model to improve their recommendation systems (e.g., by selecting a set of users leading to the highest probabilities of matching or other revenue-generating interactions). Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0132 .

Predictive Prompts with Joint Training of Large Language Models for Explainable Recommendation

Large language models have recently gained popularity in various applications due to their ability to generate natural text for complex tasks. Recommendation systems, one of the frequently studied research topics, can be further improved using the capabilities of large language models to track and understand user behaviors and preferences. In this research, we aim to build reliable and transparent recommendation system by generating human-readable explanations to help users obtain better insights into the recommended items and gain more trust. We propose a learning scheme to jointly train the rating prediction task and explanation generation task. The rating prediction task learns the predictive representation from the input of user and item vectors. Subsequently, inspired by the recent success of prompt engineering, these predictive representations are served as predictive prompts, which are soft embeddings, to elicit and steer any knowledge behind language models for the explanation generation task. Empirical studies show that the proposed approach achieves competitive results compared with other existing baselines on the public English TripAdvisor dataset of explainable recommendations.

Assessment of users' behavior in Lithuanian online communities.

Online communities are gaining importance in modern society by actively structuring public opinion and initiating discussions about various socio-economical issues. As information and communication technologies advance, the online communities are confronted with novel technological and societal hurdles (the spread of misinformation, lack of active participation). To boost their efficacy and productivity, it's crucial to enhance our understanding of user behavior, communication avenues and potential future trends of development. Online platforms serve a function beyond simply sharing information or knowledge; they act as influential social networks affecting various societal sectors, including politics, culture and the economy. There is a need to recognize online communities not as static entities, but as dynamic, evolving systems of collective intelligence. A representative quantitative study was carried out between 1 to 30 of October 2022 through direct, in-person interviews conducted at the respondent's residence (known as an Omnibus survey). The sample of respondents is representative of the entire population of Lithuania regarding essential socio-demographic characteristics. By thoroughly analyzing data gathered from a comprehensive quantitative study, the study raises awareness of the issues surrounding online communities, while also shedding light on social networking behavior within these virtual spaces. Despite the multitude of challenges inherent to virtual communication, there remains a significant knowledge gap in comprehending general user behavior within these online communities. The current research aims to bridge this gap by investigating user behavior in Lithuanian online communities.

Factors Influencing the Intention to Choose Transportation Applications in Bangkok, Thailand

This research delves into the factors that influence individuals' choices when selecting transportation applications in the bustling urban environment of Bangkok, Thailand. The study focuses on perceived usefulness, perceived ease of use, perceived efficiency, and system availability as key determinants shaping users' intentions. Employing a quantitative methodology, the primary data collection method involved the distribution of questionnaires to a sample comprising 400 active users of transportation applications in Bangkok. Convenience sampling was utilized to select participants. The study rigorously tested hypotheses and conducted detailed data analysis using analytical software and statistical techniques. The results have convincingly shown that perceived usefulness, perceived ease of use, perceived efficiency, and system availability collectively hold substantial sway over individuals' intentions when it comes to the adoption of transportation apps in this dynamic urban setting. These findings contribute significantly to a deeper understanding of user behavior and preferences within the realm of modern mobility solutions.

Research and design for improved packaging of food during daily use outside refrigerated environment

Food waste has a huge impact on economic losses and climate. One of the leading problems is due to lack of effective refrigeration. Transitioning to alternative cooling technologies, such as passive cooling, can offer a potentially more sustainable solution. Therefore, the research investigates how food can be kept cold without relying on a refrigerator or electrical components utilizing the combination of insulation materials and cooling fluid. Using design thinking as a methodology, first of all a user survey was conducted during the study to understand user behavior regarding the storage of chilled food and to define the target audience. Subsequently, an exploratory study was performed to determine the optimal placement of cooling elements to a cooling box for extended cooling. Further, material research was conducted for the inner and outer layer of the cooling container, considering temperature resistance and insulation effects. In parallel with this, an examination was carried out to identify a cooling fluid with efficient cooling capacity and low environmental impact. After implementing this method, it was discovered that the most efficient approach involved placing an ice cooling element at the bottom and around the side walls of a cooling container. Further, the material PLA was demonstrated as suitable to use for cold environments. In consideration of previous research and the application of cooling media in commercial settings, salt solutions were capable of storing food below 4.4 °C. Consequently, the decision was made to proceed with a 20% NaCl solution mixed with water. Ultimately, a final cooling element was designed that fits into the standard IKEA +365 containers measuring 15 x 15 x 7 cm. The cooling capacity of this product was tested and maintained a temperature below 4.4°C for an average of 1.17 hours. This study contributes to existing literature by providing more insights in how passive refrigeration can be aligned with the needs of end-users in order to design both practical and user-fit solutions. However, further research is required regarding the practical implications; to improve insulation, address condensation issues and evaluate compatibility with dishwashers.

“Your Protection is in Your Hands Only”: User Awareness and Adoption of Privacy and Security Practices in Five Majority World Countries

Technology companies frequently develop and launch features to protect privacy, reduce user harm, and increase security. But adoption is inconsistent at best. Businesses might increase awareness through prominent placement on user interfaces or in-product promotions, and drive adoption through changes to default user settings, or by guiding users to tutorials that encourage them to engage with a new feature. These solutions can be effective but fail to capture key factors that inform use and adoption – particularly in the majority world. In this study, we conducted qualitative interviews with 24 participants in five majority world countries across three continents: the Philippines, Brazil, India, Egypt, and Nigeria (N = 120). We assess the awareness and use of privacy and security features related to internet usage generally, and internet-connected mobile devices specifically. Participants often cited worries about being victimized by financial scammers, and highlighted how frequent sharing of devices, a common behavior, resulted in consistent concerns about privacy. We identify barriers and concerns around use and adoption for users in these regions and offer a holistic analytical framework that can be leveraged by actors in the space to better understand user behaviors and attitudes, and design accordingly.

The Antecedents of Mobile-Assisted Language Learning Applications Continuance Intention

The purpose of the research is to analyze the factors that influence the continuance intention to use Mobile-Assisted Language Learning (MALL) applications in the context of language courses in Indonesia. The study aims to understand the key factors that contribute to users' intention to continue using MALL applications, particularly in the context of the COVID-19 pandemic and the shift towards online and remote language learning. The research used the expectation-confirmation model and self-determination theory. Moreover, the research utilized a mixed-methods approach to achieve its objectives. A quantitative approach was employed, involving a survey with 445 respondents who had experience using MALL applications. The survey data was then analyzed using covariance-based structural equation modeling. Additionally, qualitative research was conducted through interviews with 17 respondents to gain a deeper understanding of rejected hypotheses and gather qualitative insights. The most important results of the research include identifying the factors that influence the continuance intention to use MALL applications. The study found that users' perceived usefulness of the application, their satisfaction with its usage, and their self-regulation ability significantly influence their intention to continue using MALL. These findings highlight the importance of these factors in shaping users' decision to persist with MALL applications for language learning. By understanding the factors that influence users' intention to continue using MALL, educational institutions and application providers can improve their offerings and tailor them to meet the needs and expectations of learners. This research advances the e-learning area by shedding light on the factors that influence the continuance intention to use MALL applications. By uncovering the importance of perceived usefulness, satisfaction, and self-regulation ability, the study contributes to the understanding of user behavior and decision-making in the context of e-learning. The findings can inform the design and implementation of MALL applications, leading to more engaging and effective language learning experiences in online and remote settings. This research contributes to the advancement of e-learning practices by providing evidence-based insights into the factors that drive user intention and adoption of technology in language learning.

Promoting data use through understanding user behaviors: A model for human open government data interaction

AbstractRecent dramatic increases in the ability to generate, collect, and use datasets have inspired numerous academic and policy discussions regarding the emerging field of human data interaction (HDI). Given the challenges in interacting with open government data (OGD) and the existing research gap in this field, our study intends to explore HDI in the OGD domain and investigate ways HDI can further contribute to OGD promotion. Building upon two existing behavioral models, we proposed an initial conceptual model for OGD interaction, then using this model, conducted two studies to empirically examine users' behaviors when interacting with OGD. Ultimately, we refined this model for OGD interaction and invited three experts to validate it to enhance its understandability, comprehensiveness, and reasonableness. This comprehensive model for human OGD interaction will contribute to the theoretical work of the HDI field as well as the practical design of OGD platforms and data literacy education.