This research was conducted with the intention of investigating and analyzing the factors that influence the views that consumers have of advertising on social media platforms. The goal of this study is to look at the many ways that new media ads affect consumers’ purchasing behavior. An evaluation of the validity and reliability of the measures has been carried out with the assistance of confirmatory factor analysis. In addition, the quantitative research approach makes use of both simple random sampling and statistical sampling. The information was gathered via the use of a questionnaire that was issued to fans of new media. Using a Likert scale with five points, the questionnaire’s questions were evaluated to ensure that they were appropriately worded. The total sample size that is employed is 359. The purchase behavior of consumers of new media has been evaluated based on five variables, including the ability to attract attention, provide amusement, establish legitimacy, emphasize creative character qualities, and evoke emotional appeal. The objective of this study paper is to investigate the impact that advertisements broadcast via new media have on consumers’ decision-making processes regarding the acquisition of goods and services. The research’s findings show that when consumers are weighing their options for purchase, advertisements having the largest impact on their purchasing decisions in new media. With the goal of offering important insights into the new media advertising industry, the author seeks to link these results with pertinent ideas from the theoretical framework.

The coronavirus pandemic has reinforced the need for sustainable, smart tourism and local travel, with rural destinations gaining in their popularity and leading to increased potential of smart rural tourism. However, these processes need adjustments to the current trends, incorporating new transformative business concepts and marketing approaches. In this paper we provide real life examples of new marketing approaches, together with new business models within the context of the use of new digital technologies. Via hermeneutic research approach, consisting of the secondary analysis of the addressed subject of smart rural tourism in adversity of the COVID-19 and 6 semi-structured interviews, the importance of technology is underscored in transforming rural tourism to smart rural tourist destinations. The respondents in the interview section were chosen based on their direct involvement in the presented examples and geographical location, i.e. France, Slovenia and Spain, where presented research examples were developed, concretely within European programmes, i.e. Interreg, Horizon and Rural Development Programme (RDP). Interviews were taking place between 2022 and 2023 in person, email or via Zoom. This two-phased study demonstrates that technology is important in transforming rural tourism to smart tourist destinations and that it ushers new approaches that seem particularly useful in applying to rural areas, creating a rural digital innovation ecosystem, which acts as s heuristic rural tourist model that fosters new types of tourism, i.e. smart rural tourism.

Vietnam has experienced an impressive period of economic growth since implementing an export-oriented economic policy. Vietnam’s international economic integration is deepening, and the output of the export sector has been continuously improved with a double-digit growth rate in recent years, especially in Ho Chi Minh City. Hence, the purpose of this paper is to study the impact of trade liberalization on export intensity of Vietnamese exporters as well as the moderating role of the location. In this study, data was collected from 80 exporters listing in Vietnam stock markets from 2007 to 2022. Further, regression test was carried out by applying GMM model. The results show that trade liberalization outcomes have a positive impact on export intensity. We, however, do not find enough evidence of the moderating effect of the location factor. These findings support Resource-based View theory, and trade liberalization policy. The findings imply that Vietnam government should continue to implement trade liberalization policy to support export sector growth.

The discourse on advocacy planning involving actors has not explicitly addressed the question of who the actor advocate planner is and how an actor can become an advocate planner. This paper attempts to exploring the actor advocate planner in the context of Regional Splits as, employing social network analysis as a research tool. This research employs an exploratory, mixed-methods approach, predominantly qualitative in nature. The initial phase entailed the investigation and examination of qualitative data through the acquisition of information from interviews with key stakeholders involved in Regional Splits, including communities, non-governmental organizations (NGOs), governmental entities, and political parties. The subsequent phase utilized quantitative techniques derived from the findings of the qualitative analysis, which were then analysis into the Gephi application. The findings indicate that the Regional Splits the Presidium Community represents civil society and political parties serve as crucial advocate planners, facilitating connections between disparate actors and promoting Regional Splits through political parties.

There is a growing trend among elderly people to live alone and this trend is expected to increase in the future. Social isolation and limited support can have a negative impact on the physical and mental well-being of older adults. The increasing life expectancy and expanding geriatric population necessitate the development of innovative solutions to support their health, independence, and autonomy. This article addresses the key challenges and issues confronting the elderly and analyzes various IoT technologies and solutions proposed to enhance their lives. Smart home technologies improve the quality of life and enable older adults to live independently in their own homes while their adult children are at work. This article presents a smart home model for the elderly in Kazakhstan, based on their needs, concerns, and financial capabilities. The proposed prototype will be developed using an accessible, open-source intelligent system that includes health monitoring, medication adherence monitoring, alerting family members in case of falls or deteriorating health indicators, and video surveillance. Another advantage of this system is the automation of processes such as automatic lighting control, voice command functionality, home security, and climate control. Preliminary testing of the hardware model shows promising results, with plans for continuous improvement and evaluation as it is deployed. Key criteria for its implementation include affordability, accessibility, and feasibility. Based on Kazakhstan’s unique socio-cultural and economic context, this paper proposes a sophisticated smart home model tailored to the specific needs and financial capabilities of elderly Kazakhs.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.