Customer Actions Research Articles

Design and implementation of a scalable IoT-based real-time environmental monitoring and alarm system: an experimental study

Digital transformation is crucial for organizations to survive, be competitive, and grow in the modern age. IoT is the key to enabling this transformation by connecting devices and optimizing processes. This paper presents the design and implementation of a generic IoT-based real-time environmental monitoring and alarm system. The platform is validated by applying it to a manufacturing plant scenario, where various sensors simulate industrial conditions. Scalable message distribution systems such as MQTT and Apache Kafka facilitate reliable data transmission. A microservice architecture is constructed for the backend services to ensure uninterrupted and high throughput services in the application domain. Instead of deploying a real WSN, traffic generation services were chosen to minimize costs, provide greater control and flexibility, and facilitate faster, scalable testing in a controlled environment. The platform also features an integrated alarm system with an event definition module, which allows users to define custom action rules. This flexible, scalable, and resilient architecture can be used across a wide range of application domains that require digital transformation. The experimental study demonstrates the platform's capabilities and great potential for broader IoT applications.

Digital Marketing Strategies and their Impact on Consumer Behavior

The advent of digital marketing has greatly altered customer behavior by altering the way in which companies communicate with their target audiences. The effects of different digital marketing methods on customer actions are discussed in this article. Content marketing, social media marketing, SEO, email marketing, and influencer marketing are some of the key tactics that are addressed. Research shows that these tactics increase customer involvement, familiarity with the brand, and ultimately, sales. The goal of content marketing is to increase customer trust and loyalty by providing them with high-quality, relevant material. In order to increase brand awareness and customer engagement, social media marketers use sites like Twitter, Instagram, and Facebook to build tailored and interactive campaigns. With search engine optimization (SEO), companies may increase their online presence and more effectively reach their target audience. One of the most effective ways to reach out to customers, build rapport, and increase sales is through email marketing. Influencer marketing leverages preexisting trust networks to sell items in a genuine way by teaming up with people who have large online followings. Not only do these tactics affect the whole buyer experience, from awareness to post-purchase assessment, but they also significantly affect consumers' views and preferences. The study highlights the need of comprehending customer behavior and psychology in order to customize digital marketing campaigns successfully. It also stresses how important it is for companies to keep up with new technology and trends if they want to be competitive. Businesses may improve marketing strategies, get insights into consumer preferences, and boost customer happiness by employing data analytics. The report finds that the most effective way to influence customer behavior and achieve corporate goals is through an integrated approach that combines numerous digital marketing methods.

Detection of Bus Driver Mobile Phone Usage Using Kolmogorov-Arnold Networks

This research introduces a new approach for detecting mobile phone use by drivers, exploiting the capabilities of Kolmogorov-Arnold Networks (KAN) to improve road safety and comply with regulations prohibiting phone use while driving. To address the lack of available data for this specific task, a unique dataset was constructed consisting of images of bus drivers in two scenarios: driving without phone interaction and driving while on a phone call. This dataset provides the basis for the current research. Different KAN-based networks were developed for custom action recognition tailored to the nuanced task of identifying drivers holding phones. The system’s performance was evaluated against convolutional neural network-based solutions, and differences in accuracy and robustness were observed. The aim was to propose an appropriate solution for professional Driver Monitoring Systems (DMS) in research and development and to investigate the efficiency of KAN solutions for this specific sub-task. The implications of this work extend beyond enforcement, providing a foundational technology for automating monitoring and improving safety protocols in the commercial and public transport sectors. In conclusion, this study demonstrates the efficacy of KAN network layers in neural network designs for driver monitoring applications.

Forecasting Customer Actions: Exploring Machine Learning Techniques for Behavior Analysis and Prediction

Modern modelling of today’s consumer behaviour is highly dependent on data mining which mines customers’ data in order to answer specific questions in time. This complexity and uncertainty relating to the consumer be-haviour makes it difficult for us to predict. Therefore, selection of appropri-ate methods is very important. Predictive models can be designed so as to identify specific group or individual actions and thus making them useful marketing tools. However, many models are overly simplistic for they leave out some of the essential factors hence leading to inaccurate forecasting out-comes. Consequently, instead of concentrating on customer behaviour-firm capital structure relationships only a strong association rule mining model should be created using online store data that effectively estimates customer response. Knowing their business clients is crucial when it comes to profitability by aligning the company’s earnings with their concerns. Artificial intelligence (AI) optimizes product location by clustering techniques targeting the right customers. It examines customer behaviour and buying trends utilizing Kaggle’s client membership card records such as Customer ID, age, gender, annual income, spending score among others. This includes basic market analytics based on demographic information like age or income split into groups, however big data superiority over traditional methods cannot be underestimated including machine learning applications such as Customer Seg-mentation Automated Methodology (CSAM), k-means clustering for user segmentation among others. This approach facilitates focused advertising campaigns and development and launch of new products aimed at particular consumer sections.

Paradoxes, challenges, and opportunities in the context of ethical customer experience management

AbstractThis article examines paradoxes, challenges, and opportunities in the context of ethical customer experience management. Central to this discussion are different stakeholders such as firms, customers, policymakers, regulators, and society at large, as well as key issues associated with them. Firms aim to design and manage compelling experiences using customer data but face challenges balancing cost, customization, ethical concerns, and fairness in customer experience management. Customers should be encouraged to reflect on whether to share personal information for customized experiences, as well as on how far their responsibility goes in ensuring critically informed decision‐making. Policymakers and regulators must integrate the views of various stakeholders into policies and guidelines that promote a healthy society. Importantly, the digitization of customer experience has given rise to unprecedented ethical concerns related to the prospect of ‘programming’ these experiences. The shrinking temporal and spatial distance between stimuli, customer action, data footprint, and tracking poses significant reflections and dilemmas for both customers and firms. In this context, it is essential to determine ethical reflections and guidelines for customer experience design and management. We argue that firms should involve customers in assessing the implications of their experiences, while policymakers should facilitate the involvement of all stakeholders (including firms, customers, regulators, academics, and NGOs) to create an ongoing, healthy dialogue. This dialogue should guide a continuing debate aimed at evaluating the implications of specific actions in customer experience management.

CUSTOMER PERCEPTION TOWARDS DIGITAL TRANSACTION

This study explores the aspects that impact customers' perspectives and actions about digital transactions, specifically in this dynamic economy. The research draws from a comprehensive literature review to synthesize important aspects pertaining to user experience, trust, perceived risk, convenience, socio-demographic factors, innovative drives, regulatory environment, cultural effects, and future trends. The results show that ease and efficiency are the most important factors in driving adoption, even while people still worry about the security of digital transactions and the perceived risks associated with them. Trust also appears as an important component, and it is significantly influenced by user experience and the reputation of service providers. Consumer sentiments are already complicated, and socio-demographic factors like age, income, and digital literacy further complicate matters. A number of factors come into play, including cultural norms, legislative frameworks, emerging technologies, and social shifts towards cashless economies. Legislators and practitioners alike can benefit from the study's findings on how to increase trust, decrease risks, and promote widespread usage of digital transactions. User experience, ease, security, trust, demography, innovation, regulatory environment, cultural effects, and future trends are some of the words and ideas used to define various parts of the digital transaction. Keywords:-Digital, Transaction, Customer, Perception

ONLINE ADVERTISING AND IT’S IMPACT ON CONSUMER BUYING BEHAVIOUR

Keeping in mind the ever-changing landscape of digital marketing, this study investigates the impact of online advertisements on customers' buying behaviors. The proliferation of online platforms and the increasing number of internet users present an opportunity for businesses to advertise to individuals. The fundamental objective of this research is to figure out how online advertising affects customers' emotions, decisions, and actions. This study synthesises existing literature, empirical data, and theoretical frameworks to investigate the many ways via which online advertising influences consumer behavior. The significance of targeted advertising in identifying and appealing to certain consumer demographics, the persuasive tactics employed by online ads, and the influence of social media endorsements and user-generated material on intents to purchase are all major aspects that are being investigated. The study's key findings indicate that online advertising significantly influences customers throughout the entire buying process, from first awareness and consideration through purchase and post-purchase actions. Customer engagement and brand memory can be enhanced through personalized and immersive advertising experiences made possible by the interactive nature of web platforms. Online content can be made more credible and trustworthy by adding social proof and peer endorsements. This, in turn, influences consumers' purchasing decisions. The ramifications of these results should be carefully considered by marketers and advertisers who wish to make the most of their advertising budgets and make the most of their online advertising strategies. By studying what makes people react to internet ads, businesses may refine their messaging, creative content, and targeting strategies. Because of this, they will be able to connect with their intended consumers and achieve their goals. Keywords: online advertising, consumer buying behavior, digital marketing, persuasive techniques, targeted advertising, social media endorsements, consumer engagement, purchase intentions.

“CUSTOMER PERCEPTION TOWARDS DIGITAL TRANSACTION’’

This study explores the aspects that impact customers' perspectives and actions about internet purchases in this dynamic economy. From a comprehensive literature review, this research synthesises crucial themes such as user experience, trust, perceived risk, convenience, socio-demographic factors, regulatory environment, cultural effects, and future developments. Results show that efficiency and convenience, rather than worries about security, are the main motivators for people to use digital transactions. Trust appears to be a crucial component as well, and it is heavily influenced by user experience and the reputation of service providers. Consumer attitudes are made more complicated by a number of socio-demographic criteria, including age, income, and digital literacy. Elements such as social shifts away from cash-based economies, technological advancements, legal frameworks, and cultural norms all have a role. Legislators and practitioners alike can utilize the study's findings to better understand how to increase the reliability, security, and adoption of digital transactions. Digital transactions are characterized by a myriad of ideas and phrases, such as user experience, ease, security, trust, demography, innovation, regulatory environment, cultural effects, and future trends. Keywords:-Digital, Transactin, Customer , Perception

Advancements in GitHub Automation and Workflow: A Comprehensive Exploration

The landscape of software development has been significantly transformed by the integration of automation and streamlined workflows, with GitHub emerging as a pivotal platform in this evolution. This research paper delves into the multifaceted realm of GitHub automation and workflow, investigating key aspects that contribute to enhanced efficiency, collaboration, and code quality in modern development practices. The paper begins by exploring the utilization of GitHub Actions for establishing robust Continuous Integration and Continuous Deployment (CI/CD) pipelines. Best practices for designing reliable workflows are discussed, along with insights into customizing workflows to suit diverse project requirements through the use of custom actions and templates. The automation of code reviews is scrutinized, with a focus on tools and practices that facilitate objective and efficient evaluation of code quality. The paper also investigates GitHub's role in security automation, covering strategies for vulnerability detection and integration of security checks into the development lifecycle. Collaborative workflows on GitHub, including branch management, pull requests, and code reviews, are explored as pivotal components fostering teamwork. The integration of GitHub into DevOps practices is analyzed, showcasing its synergy with popular DevOps tools and platforms. As a forward-looking endeavor, the research paper concludes by examining emerging trends and technologies in GitHub automation. By encapsulating a comprehensive overview of the current state and future trajectories of GitHub automation and workflow, this paper aims to contribute to the collective understanding of best practices, challenges, and opportunities in contemporary software development.

Green growth in e-commerce : Environmental management and customer actions

The global e-commerce industry has been experiencing rapid growth, especially post COVID-19 pandemic, which has significantly transformed market structures and consumer shopping behaviour. The waste generated and the plastic pollution due to e-commerce is increasing tremendously post pandemic. The surge in e-commerce has contributed to an alarming increase in packaging waste, aggravating the plastic pollution crisis. Research indicates that traditional in-store shopping can substantially reduce CO2 emissions highlighting the environmental impact of online shopping. In response to these challenges, many e-commerce companies are implementing green technologies and sustainable practices to mitigate environmental impacts. However, individual customers also play a crucial role in promoting sustainability in online shopping. By making informed choices, consumers can significantly reduce their carbon footprint and contribute to a more sustainable future. This research explores effective strategies for managing environmental impacts and integrating green technologies in e-commerce operations, emphasizing the collaborative efforts of companies and customers to achieve sustainability. The research reveals that though majority of customers are concerned about environment and sustainability; while shopping they aim to satisfy their immediate needs. Customers have shown willingness to opt for the strategies like concession on returning the packaging, discounted price for less packaging and bulk purchases in a single order if introduced and implemented effectively by e-commerce companies.

B2B Customer Engagement Customer Behaviour Forecast Application

The paper presents the "Casper Customer Behavior Forecast Application" a creative project focused on the creation of an advanced decision support software system. The purpose of this system is to closely observe, fully analyze, and precisely predict the behaviors of Casper Business-to-Business (B2B) clients. Our research study implements an innovative methodology by focusing on the enhancement of client interactions through the utilization of RESTful services. Our objective is to reimagine the future of customer relationship management by analyzing and forecasting client behavior in the business-to-business (B2B) setting. The project involves a comprehensive methodology that incorporates extensive study, complex software design, and careful data analysis. In more detail, it involves extensive monitoring of consecutive RESTful interactions carried out by business-to-business consumers across a period of time. By leveraging the capabilities of modern machine learning and deep learning algorithms, our objective is to develop prediction models that establish novel benchmarks within the industry. The methodology employed in this study includes the development of labeled behavioral datasets and the utilization of a supervised machine learning framework. The evaluation of model performance will be performed systematically using a range of metrics, such as F-Score and Accuracy, in order to establish the model's robustness and reliability in making accurate predictions. The expected outcomes of this project have the potential to bring about significant changes. Primarily, the integration of machine learning and deep learning algorithms will provide our company with an important amount of knowledge. Our organization will obtain an innovative software solution that possesses the capability to precisely forecast the future actions of B2B customers. This results in enhanced inventory management and a significant reduction in client waiting periods, resulting in increased levels of customer satisfaction. Additionally, this study has the potential to make significant additions to the global academic body of knowledge in the domains of machine learning and deep learning. In simple terms, the project titled "Casper Customer Behavior Forecast Application" embodies an innovative and academic effort aimed at enhancing customer engagement and predicting customer behavior within the context of business-to-business (B2B) interactions. This research project carries considerable importance, both in its potential to reinvent the field of customer relationship management and its ability to contribute to the global academic conversation on machine learning and deep learning.

Work Behavior Analysis Of Customer Attitude Through The State Of The Working Environment At Kopiman Coffe Shop

Employee work behaviour is greatly influenced by various kinds of customer attitudes and actions, both positive and negative. In general, a positive attitude from customers will provide indirect encouragement to improve performance. This research aims to analyze how employees' work behaviour relates to various customer attitudes, both positive and negative. This research method uses qualitative methods. The collection technique for this research was interviews with cafe managers and employees. The data obtained is based on information from management and various existing literature. The results of this research refer to how much customer attitudes influence how employees behave. Employees carry out duties and obligations according to the manager's direction. Of course, some of the directions that have been given could have gone better due to customers' attitude in the work environment. Thus, partially, there will be a significant influence between work behaviour and customer attitudes in the work environment.

Exploring nonstandard service frontline situations

THE AIMS OF THE PAPER Jaycustomer behavior often increases the unpredictability of the service to an unmanageable extent. A co-creation based development can be presumed in the attitude of the consumer and of the frontline staff. The primary goal of this research was to investigate the dynamics of interactions between customers and frontline staff, particularly in nonstandard situations. This study aimed to understand how 'jaycustomer' behavior, which includes unpredictable and challenging customer actions, impacts these interactions and the overall service process. METHODOLOGY To explore the above, the study employed a quasi-secondary data collection method (narratives, netnography). Frontline conflicts were simulated based on a mystery shopping scenario. Mystery shoppers were instructed to combine two theoretical jaycustomer types – the Belligerent and the Egocentric Edgar (the selfish) – in their approach. These shoppers were tasked with observing various aspects of frontline staff behavior, including reactions, communication, gestures, and problem-solving intentions. MOST IMPORTANT RESULTS The simulations led to the identification of distinct behavioral patterns on both sides of the customer-staff interaction. The study highlighted specific jaycustomer oriented patterns and the frontline staff's response strategies. Furthermore, essential human resource management skills in dealing with such jaycustomer scenarios were identified, providing insights into both customer and employee behavior in challenging service situations. RECOMMENDATIONS The findings of this research offer significant implications for service management and marketing theory, particularly in developing a consensual model for managing frontline processes. Additionally, the study suggests that reviewing and aligning staff training content with these research findings could be a fruitful direction for enhancing human resource management practices in customer service environments. An expert interview conducted post-experiment further affirmed special tasks for frontline management to handle such jaycustomer situations effectively.

INOVASI SERVICE BLUEPRINT PADA ERA PANDEMI COVID-19: STUDI KASUS DESA WISATA CARANGSARI

Consistency of service quality was a strategic factor in achieving sustainable tourism. This study aimed to design a service blueprint for tourist products in the Carangsari village. There were 4 (four) service blueprints intended as prototypes for the tugek mask dance, trekking, kecak dance, and accommodation services. The service blueprint was a flow chart of activities from the four tourist products the Carangsari tourist village offered. In the service blueprint protype there were 2 (two) factors studied: the sequence of tourist activities and the function of the service providers involved in the Carangsari tourist village. For the order of tourist activities, the design of a service blueprint was adjusted for each tourist product offered. The service provider function included 5 (five) functions applicable to all tourism products: physical evidence, customer actions, public service actions, back staff actions, and support and system actions. The head of the Carangsari tourism village, representing the tourism stakeholders of the Carangsari tourism village, expressed his appreciation for the design of the prototype of the service blueprint to provide consistency in the quality of service for tourists. The prototype of the service blueprint should be updated by the dynamics of the tourism industry. Due to time limitations, impacts and results of service blueprint prototype applications for four tourism products offered by Carangsari Tourism Village have not been evaluated. Conducting another research program to evaluate the effectiveness of service blueprint prototype applications is strongly suggested.

Analysis of the impact of content marketing on the consciousness of generation Z

The aim of the article is to analyze the impact of content marketing on the minds of Generation Z. This knowledge will contribute to positive product perception and purchase decisions. With an understanding of the unique characteristics, preferences, and values of Generation Z, marketers will be able to develop effective content marketing strategies that will resonate with this influential consumer segment. Traditional marketing methods that have worked well for previous generations may not resonate with Generation Z. They are constantly surrounded by advertising and have developed a natural immunity to traditional ads. This presents a challenge for marketers who must find innovative and engaging ways to capture the attention and change the mindset of Generation Z. One of the approaches that has gained considerable popularity in recent years is content marketing. Content marketing involves the creation and distribution of valuable and relevant content to attract target audiences and drive profitable customer actions. This approach is well aligned with the preferences and behavior of Generation Z, which demands personalized and meaningful interaction with brands. Analyses results. The study results showed that Generation Z is actively engaging in content marketing, and originality is a key factor in grabbing their attention and driving engagement. Video content turned out to be the most favorite format, and content marketing has a significant impact on Generation Z's purchasing decisions. The positive correlation between perceived authenticity and engagement underscores the importance of creating authentic and transparent content that aligns with Generation Z's values. Marketers should prioritize personalization and interactivity in their content marketing strategies, brands should build trust, provide relevant content, and use platforms that resonate with Generation Z's digital habits, such as YouTube and TikTok. Conclusions and directions for further research. Overall, this study contributes to the understanding of influencing the mindset of Generation Z through content marketing. By recognizing the preferences of Generation Z and emphasizing the use of engaging content formats, marketers can effectively connect with this generation and promote positive perceptions and purchase decisions. In a rapidly evolving digital environment, understanding how to effectively engage and influence Generation Z is crucial for brands looking to capture the attention and loyalty of this influential demographic. As technology continues to evolve, future research may examine the impact of new technologies such as augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) on the effectiveness of content marketing for Generation Z. Understanding how these technologies can be integrated into content strategies to create immersive and personalized experiences would be valuable for marketers.

Fashioning sustainability: Navigating the perceptions and attitudes in fashion design

Trends, elegance, and rapid consumption have long been connected to the fashion business, creating serious environmental and societal problems. Fashion designers are essential in guiding the industry toward a more sustainable future as the necessity for sustainable practices becomes more and more obvious. The purpose of this research was to investigate more about fashion designers' opinions on sustainable fashion (SF) and the challenges they face when trying to put sustainable design (SD) concepts into practice. Fifty fashion designers were selected to take part in an exploratory study and were interviewed throughout five sessions. The research's major findings show that it's challenging to include SD in the fashion design (FD) process for several internal and external factors. Internal issues include a lack of consensus and knowledge regarding SD, as well as projected trade-offs with other aspects of design goals, including aesthetic styles, budgetary limits, and current trends in fashion. Some examples of such external barriers include the intricacy of sustainability issues, the impression of insufficient customer demand, the existence of deficiencies in customer views and actions regarding the purchase of sustainable collection ranges, and the absence of either monetary or legal incentives for businesses to employ SD methods. The results of this study add to our knowledge of the perspectives and attitudes that fashion designers have regarding sustainability, providing insightful information for those who are involved in the industry, policymakers, and educators. This research aims to enable the incorporation of sustainable practices into fashion design, eventually guiding the industry toward a more responsible and sustainable future by addressing the issues and offering practical solutions.

Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social Media

In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results.

Perilaku Nasabah terhadap Adanya Layanan Anjungan Tunai Mandiri (ATM) di LPD Desa Adat Beraban, Kecamatan Kediri, Kabupaten Tabanan

Customer Behavior Against the Presence of Automated Teller Machines (ATM) at Local Credit Institution (LPD) of Beraban Village, Kediri District, Tabanan Regency
 Local Credit Institutions (LPD) are non-bank financial institutions organized by local governments and are autonomous and oriented towards strengthening the economic and socio-cultural life of indigenous peoples in Bali. Increasing profits and progress made in LPD make it always carry out the latest innovations in products to be marketed. Technological developments are becoming very global with regard to the efficiency and ability of banks to earn non-interest income in the future. One of the information technology innovations that play a very important role in the banking world is ATM services. With the ATM service in the LPD, it will certainly make it easier for people to make transactions. The existence of this ATM service, of course, has to look at how the community will behave towards the latest innovations from the LPD. Behaviors are specific actions directed at some target object. While the desire to behave is a proposition that connects itself with future actions. This study aims to analyze customer behavior towards the existence of ATM services in terms of three indicators, namely the level of customer knowledge, customer attitudes, and customer actions. The data used in this study are primary data and secondary data. Primary data collection was obtained directly by researchers through observation, interviews and filling out questionnaires submitted to customers at the Beraban Village LPD as many as 100 respondents with a stratified random sampling technique, namely respondents were grouped into three groups based on customer balances. The overall analysis carried out is for the variable knowledge either or know about ATM. For the variable of customer attitudes towards ATM. are in the good category, meaning that customers have a very good attitude towards the existence of ATM. In the action variable, the customer already has a very good action on the existence of an ATM. From the results of a thorough analysis obtained, it is stated that customer behavior regarding the emergence of new innovations created by LPD to assist customer transactions in the form of ATM is good, where customers use and receive many benefits from the available ATM.

Enhancing Robustness of Viewpoint Changes in 3D Skeleton-Based Human Action Recognition

Previous research on 3D skeleton-based human action recognition has frequently relied on a sequence-wise viewpoint normalization process, which adjusts the view directions of all segmented action sequences. This type of approach typically demonstrates robustness against variations in viewpoint found in short-term videos, a characteristic commonly encountered in public datasets. However, our preliminary investigation of complex action sequences, such as discussions or smoking, reveals its limitations in capturing the intricacies of such actions. To address these view-dependency issues, we propose a straightforward, yet effective, sequence-wise augmentation technique. This strategy enhances the robustness of action recognition models, particularly against changes in viewing direction that mainly occur within the horizontal plane (azimuth) by rotating human key points around either the z-axis or the spine vector, effectively creating variations in viewing directions. We scrutinize the robustness of this approach against real-world viewpoint variations through extensive empirical studies on multiple public datasets, including an additional set of custom action sequences. Despite the simplicity of our approach, our experimental results consistently yield improved action recognition accuracies. Compared to the sequence-wise viewpoint normalization method used with advanced deep learning models like Conv1D, LSTM, and Transformer, our approach showed a relative increase in accuracy of 34.42% for the z-axis and 10.86% for the spine vector.

WEB APPLICATION METHOD "3D EMOTION COMPOSITION IN SUPERVISION" APPROBATION

The global pandemic has accelerated the development of the digital environment, face-to-face work is transferred online, so it is important to create tools adapted to the digital environment. The emergency of Covid-19 has raised the demand for online supervision. In supervision sessions, it is important to talk about experiencing emotions in a professional environment. Latvia lacks digital methods for working with emotions in supervision. The aim of the study was to adapt the 3DEC method, to create it as a web application for use in remote supervision and to approbate the web application "3D emotion composition in supervision" in individual online supervisions. The original method 3DEC is intended for the evaluation of emotional experience in the practice of art therapists (Lejstrauta, 2021; Vanadziņa, 2021). The theoretical basis of the 3DEC method is integrative and includes the following principles: visual representation of emotions in a visible symbolic form; play and play space (Winnicot, 2007); distancing as one of the cognitive emotion regulation strategies (Powers LaBar, 2019). 3DEC is also based on the Adaptive Coping with Emotions Model (ACE Model) (Berking Whitley, 2014). The adapted method “3D composition of emotions in supervision” (3DECS) is intended for work in online supervision with the aim to promote awareness of emotions, their understanding and self–reflection. Using distancing and view of other perspectives, the method helps the client to reflect and become aware of emotional experiences, as well as provides an educational function about the meaning of emotions. The 3DECS method develops emotional awareness and adaptive emotion regulation skills. Main results: 1) the web application has been tested and is designed to work with emotions in supervision; 2) the data of the approbation stage show that the web application is sufficiently simple and intuitive; 3) the web application is secure because no information about the customer and the customer's actions in the tool is stored; 4) there are no restrictions on the use of the web application in relation to the duration of the cooperation between the supervisor and the supervision client.