Multiple Regression Equation Research Articles

Government Expenditure, Human Development Index and Regional Inequality in Indonesia

This study was conducted with the aim of measuring and analyzing the effect of regional government spending on regional inequality through the Human Development Index in Indonesia. This analysis was conducted using the Williamson index as an indicator of regional inequality. This study uses variables of human development spending, infrastructure spending, economic spending, human development index and regional inequality. The research method used is the statistical panel data approach of 34 provinces in Indonesia in 2017-2023 with the Multiple Regression Equation model with AMOS program path analysis. The test results in this study state that directly the variables of human development spending, infrastructure spending, economic spending, and the human development index have a significant effect on regional inequality in Indonesia. Indirectly, the variables of human development spending and economic spending have a significant effect on regional inequality in Indonesia through the human development index, while the variable of infrastructure spending does not affect regional inequality through the human development index.

The Effects of money supply on Economic Growth of Laos

This research aims to analyze the effect of money supply volume on the economic growth of Lao PDR. Using secondary data annually from 2012–2022. which uses the multiple regression equation (multiple regression) with the method of least squares (OLS) to estimate the coefficients of dependent variables and independent variables, starting from checking the properties of the unit root data using the Augmented Dick Fuller test statistic method to test the data. Through checking the properties of the model of external factors that affect the amount of money supply (M2), it was found that the coefficient of decision Adjusted R Square = 0.9931, which means that the independent variables Gross National Income (GNI), Exchange Rate (Exr), and Consumer Price Index (CPI) can explain the variable according to the amount of money (M2) determined up to 99.31%; the rest depends on other factors that are not included in the study. Therefore, the model of external factors that affect the amount of money M2 can be used and depends on at least one factor identified. Also, by bringing the data to test the effect of the independent variable on the dependent variable in the long term and in the short term, ECM(-1) found that the model that simulates the effect of the amount of money M2 on the economic growth of the Lao PDR in the long-term equilibrium state (co-integration) and in the short-term equilibrium state (Error Correction Model: ECM) is not applicable and does not depend on the identified factors. But it will have a long-term effect, which, through checking the properties of the model from a long-term perspective, found that the coefficient of decision Adjusted R Square = 0.6877, which means that the independent variable M2 money can explain the variable according to the economic growth rate determined in the long term up to 68.77%. The change in the money M2 in the short term does not affect the economic growth rate of the Lao PDR, but the change in the money M2 in the long term will definitely affect the economic growth rate.

THE EFFECT OF AI-DRIVEN INVENTORY MANAGEMENT SYSTEMS ON HEALTHCARE OUTCOMES AND SUPPLY CHAIN PERFORMANCE: A DATA-DRIVEN ANALYSIS

There has been much focus toward increasing integration of AI in healthcare generally, with a specific focus on inventory management systems in particular. As more hospitals in the United States public and private side face increasing costs and concerns over cost and length of operation all systems supporting inventory must be made most efficient and AI system offers the US hospitals the opportunity to maintain accurate inventory and hence improve the quality of patient care. But the literature review presents a small number of studies that focus on the effects of these systems as the means to improve healthcare results and organization. In this study, it is proposed that improving inventory management by actioning artificial intelligence in healthcare organizations will result in enhanced patient care and improved inventory accuracy, cost reduction and supply chain efficiency in the United States of America’s healthcare institutions. A cross-sectional, quantitative survey was administrated to 200 healthcare personnel and supply chain managers from different healthcare organizations based on hospitals, clinics and specialty medical centers of the USA. Sample design: Participants were purposefully chosen, who had prior exposure to the AI-induced systems or are aware about workflow of such systems. Respondents’ data was gathered via an online questionnaire and analyzed using SPSS: descriptive analysis, chi-square test, one-way ANOVA, correlation analysis, multiple regression analysis was used to establish the relationships of the study variables: AI systems, healthcare outcomes and supply chain performance. The results showed that the use of AI in inventory management systems enhances its accuracy in ordering order itself and overall patient care. Separate studies showed that where the management of a facility has deeper insight of the AI system then there will be more operations worked on to improve efficiency or cut costs. Adoption of these pathways among smaller providers including clinics and long-term care facilities was difficult, especially due to cost constraints. An analysis of the multiple regression equation showed that knowledge of AI improved the projection of potential healthcare outcome, creating greater worth and also increases the performance of supply chain. The outcomes of using AI based inventory management systems in the United States seem to have a lot of advantages that may help improve the efficacy of operations in the healthcare system. Issues such as funding and skills needed for implementation, especially in small healthcare organization, continue to slow the uptake of the technology. These existing problems may be solved if more focused investments in the development of comprehensive AI training or systematic solutions are given to enable the healthcare industry to reach the full potential of the application of artificial intelligence.

Predictors of organizational citizenship behavior among government personnel: A structural equation modeling approach

The study aimed to develop a causal paradigm of organizational citizenship behavior among government personnel in the SOCCSKSARGEN Region. 400 employees were selected through stratified balanced sampling. Data were collected using survey questionnaires and analyzed through the mean, Pearson r, multiple regression, and structural equation modeling. The study determined that quality of work life, employee silence, and political skill statistically correlate with organizational citizenship behavior. The study inferred the predictive capacity of the exogenous latent variables in influencing the organizational citizenship behavior of LGU employees. There were five generated models of organizational citizenship behavior, and model five was determined as the best-fit structural model. The model retained five quality indicators of work life, namely compensation and rewards, training and development, relationships and cooperation, organizational culture and climate, and work environment. Prosocial silence and acquiescent silence remained as indicators of employee silence. Political skill was narrowed to three indicators, comprising social astuteness, interpersonal influence, and networking ability. Finally, altruism and civic virtue emerged as the strongest indicators of organizational citizenship behavior.

Effect of Vegetation Growth, Agricultural Irrigation and Climatic Variability on Streamflow in Wujiang, China

Vegetation restoration and farmland irrigation are important environmental factors affecting the water cycle process in basins. Analyzing the impact of vegetation restoration and farmland irrigation on runoff is an international frontier and hot topic in current research, which is crucial for the management and protection of water resources, especially for the ecological protection and high-quality development of basins. Based on the normalized difference vegetation index (NDVI), effective irrigated area (EIA), and meteorology and hydrology data for Wujiang River (WJR), this research aims to quantitatively calculate the influence degree of vegetation recovery, agricultural irrigation, and climatic variability on discharge alteration in WJR. First, Mann–Kendall and Pettitt approaches were used for recognizing the mutation year of streamflow data at Wulong station from 1982 to 2015. Then, a corrective Budyko model was built by constructing multiple linear regression equations for the NDVI, climate factors, EIA, and Budyko parameters. Finally, the corrective Budyko model was adopted to reveal how vegetation restoration, agri-cultural irrigation, and climate variation influence discharge alteration in WJR. The results showed the following: (1) Both runoff depth (R) and rainfall (Pr) exhibited a non-significant de-clining tendency, while potential evapotranspiration (ET0) demonstrated a non-significant in-creasing tendency. The NDVI and EIA both demonstrated a notable upward tendency (p<0.01). (2) The mutation year of discharge in WJR was 2004. (3) The underlying surface parameters ω have a strong correlation with vegetation, agricultural irrigation, and climate factors. (4) The contribution of rainfall (Pr), potential evapotranspiration (ET0), NDVI, EIA, and human activities to runoff depth in WJR were 57.34%, 24.67%, -11.75%, 11.71%, and 18.02%, respectively. This re-search is helpful for elucidating the effects of ecological construction measures and agricultural irrigation on streamflow in WJR, and offers great scientific significance and practical value for understanding the evolution mechanism of water circulation and for managing regional water resources.

Research on the Development Trend of New Energy Electric Vehicles in China based on Mathematical Model

As the global economic development moves closer to the direction of green sustainability, the international automobile market pays more and more attention to the development of new energy vehicles. New energy electric vehicle (NEV), as a kind of new energy vehicles, has developed rapidly in recent years due to its characteristics of low pollution, low energy consumption, peak power consumption and adjustment capacity, and has been welcomed by global consumers and governments. The Chinese government conforms to the development of The Times and actively promotes the development of its industry. This paper aims to study the development trend of China's new energy electric vehicles in China, through the establishment of multiple linear regression equation model analysis of different factors of new energy electric vehicles, and using the stochastic comparison model compared the influence of new energy electric vehicles on the traditional energy vehicle industry, and establish the influence factor model, analyze the international policy influence on the development of new energy electric vehicles, in the whole research process to test and the advantages and disadvantages analysis.

Predictors of self-care behaviors in hypertensive patients based on the protection motivation theory

IntroductionGlobally, hypertension is a significant public health concern and a leading cause of premature death. Since this disease is incurable, it is necessary to promote patients’ self-care behaviors to control it. The goal of this research was to identify the elements that influence self-care actions in individuals with hypertension, utilizing the Protection Motivation Theory (PMT) framework.MethodsA study was carried out in Omidiyeh, located in southern Iran, using a cross-sectional design, involving 397 adults with hypertension (198 females and 199 males) who were selected from comprehensive health centers using a stratified random sampling technique. Data was gathered through a demographic data collection form and a questionnaire created by the researcher, which was based on the PMT. Data were assessed utilizing SPSS 25 and AMOS 24 software, and various statistical tests including one-way ANOVA, Pearson correlation, multiple regression, independent t-test, and structural equation modeling were conducted.FindingsThe variables of intrinsic reward (r = 0.182), extrinsic reward (r = 0.288), and response cost (r = 0.126) showed a significant negative correlation with self-care behaviors. On the other hand, perceived susceptibility (r = 0.212), perceived severity (r = 0.110), self-efficacy (r = 0.555), and response efficacy (r = 0.424) demonstrated a significant positive correlation with self-care behaviors. Specifically, self-efficacy and response efficacy were the most powerful predictors of self-care behaviors, respectively.ConclusionThe results showed that several factors can forecast self-care behaviors in patients with hypertension. Researchers are recommended to incorporate PMT in educational interventions and concentrate on self-efficacy and response efficacy constructs.

НЕКОТОРЫЕ МОМЕНТЫ СОВЕРШЕНСТВОВАНИЯ ПРОГНОЗИРОВАНИЯ ОБЪЕМА ПОЛОВОДЬЯ В УСЛОВИЯХ ОГРАНИЧЕННЫХ ГИДРОМЕТЕОРОЛОГИЧЕСКИХ ДАННЫХ

The article deals with methods of improving the forecasting of the volume of spring flood runoff. The main objects of the study are the Oba, Ulbi and Dresvyanka rivers, which are lateral tributaries of the Shulba reservoir. These rivers belong to the type of rivers with spring-summer flooding and extended flow for more than 2...3 months. The aim of the study is to develop a methodology for flow forecasting based on the relationship between snow accumulation and river flow in spring and summer periods. The results of the study of hydrological and meteorological factors affecting runoff formation are presented, and equations for forecasting runoff during spring and summer floods and its duration are derived. The obtained multiple regression equations demonstrated high accuracy in modelling flood volumes, with correlation coefficients between observed and predicted data ranging from 0,79 to 0,91. According to S/σ calculations, the results obtained indicate good reproducibility of the observed flood volumes, with values ranging from 0,46 to 0,53.

Analisis Faktor Eksternal dan Internal terhadap Perilaku Konsumtif Mahasiswa Jurusan Ilmu Pengetahuan Sosial

The purpose of this study is to evaluate the influence of internal and external factors on consumptive behavior. This problem is focused on the high level of consumptive behavior among students caused by factors that come from outside and within the individual. The research method used is descriptive research with a quantitative approach. The sample was taken using the Purposive Random Sampling technique as many as 271 students of the Department of Social Sciences. The data collection method uses questionnaires, observations, and documentation from students as well as references from journals, books, and other relevant sources. Descriptive statistical analysis, classical assumption test, multiple regression analysis, partial test (t-test), simultaneous test (f-test), and determination coefficient test (R2) were used to analyze the research data. The results showed that there was a multiple regression equation showing that Y = 5.026 + 0.438X1 + 0.267X2. Based on the hypothesis test, the significance value (Sig) of 0.000 is greater than the probability of 0.05, so it can be concluded that the external factor (X1) and internal factor (X2) have a significant and positive influence on the consumptive behavior (Y) of students of the Department of Social Sciences.

The Creation of Value-added on Wood Carving Handicraft in Luang Prabang city, Luang Prabang Province

The study aims to investigate: the purchasing behavior of customers in Luang Prabang district and province, the perspectives of entrepreneurs and consumers who purchase goods to enhance the value of wood carving craft products in Luang Prabang district and province and Adding value to wood carving craft items that influences customers' decisions to buy in Luang Prabang district and province. employing interviews as a method to gather information from 3 merchants of wood carving craft products and 385 respondents' questionnaires. The frequency, percentage, average, standard deviation, and multiple regression equation. The study's findings revealed: Customer behavior in choosing to buy wood carving craft products as a gift or souvenir, ordering them from the store, and recognizing that information from online advertising media influences his decision to buy. Products that the customer may choose from include wood carving handicrafts for other people's daily lives, low-image elephant products, fish and gold fish products, high-image elephant products, and horse products. The product has the same features as before, but price is a deciding factor when making a purchase, so there's no need to mark the product with a purchase value of between 500,001 and 1,000,000 kip and require a cash payment. Entrepreneurs' thoughts on adding value to handcrafted wood-carving goods. Three Entrepreneurs believe that adding value to wood carving craft goods is important in six areas: designing the product, using wood as a raw material, branding, design, service, and production. Customers' perceptions of the entrepreneur's ability to create added value across six dimensions are influenced in part by the following factors: product quality; service quality; design and production; and modeling. Three factors creating additional value in terms of products, design, and production were involved in the manufacture of wood carving craft products that influence customers' purchase decisions.

The link between fear of disease progression and health promotion behaviors in patients with obstructive sleep apnea hypoventilation syndrome (OSAHS): the mediating role of social support

ObjectiveThis study aims to assess the levels of social support, fear of disease progression (FOP) and health promotion behaviors in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), and to examine the associations between FOP, social support, and health promotion behaviors in OSAHS patients, with a focus on exploring the mediating role of social support. The findings aim to provide insights for enhancing health promotion behaviors among OSAHS patients in China and to offer a theoretical foundation for healthcare professionals in devising intervention strategies to promote health behaviors in individuals with OSAHS.MethodsThis cross-sectional study included 307 patients diagnosed with OSAHS in Jinzhou City, Liaoning province. The survey instruments utilized included the Demographic Characteristics Questionnaire, Social Support Rating Scale (SSRS), Fear of Disease Progression Scale (Fop-Q-SF), and Health-Promoting Lifestyle Scale (HPLP II). Pearson correlation analysis was employed to assess correlations, while multiple linear regression and structural equation modeling were utilized to explore potential mediation effects.ResultsIn the OSAHS patient population, FOP (r=-0.55, p<0.001) and social support (r = 0.60, p<0.001) were found to be significantly correlated with health promotion behaviors. In the mediated effects model, social support was identified as a partial mediator in the association between FOP and health promotion behaviors among OSAHS patients, accounting for 59.00% of the total effect.ConclusionFOP demonstrates a direct relationship with health promotion behaviors in patients with OSAHS, with social support playing a mediating role in this connection. Healthcare professionals are advised to underscore the significance of social support in promoting the health of OSAHS patients to mitigate FOP and consequently improve health promotion behaviors.

Исследование факторов, определяющих динамику и качество прогнозирования потребительских цен на рыбную продукцию

Using information of the Territorial body of the Federal State Statistics Service for the Primorsky Territory (Primorskstat), the dynamics of consumer price indices for fishery products in the country as a whole, the Far Eastern Federal District and the Primorsky Territory for 2002-2023, and the dynamics of prices by type of fish products in the Primorsky Territory for 2013-2023 were studied. It is concluded that the cost of fishery products has been steadily increasing throughout the analyzed period, and ahead of the inflation rate. The most intensive increase in the cost of processed fish, cut, more valuable in terms of nutritional characteristics. To determine the reasons for the constant growth, a correlation and regression analysis of the consumer price index for fishery products with other related indicators was carried out. Multicollinear factors were identified and excluded (consumer price index for food, per capita cash income of the population, fish consumption in the Primorsky Territory, industrial production index for the type of activity “Fishing and fish farming”). According to the criteria used in functional analysis, the multiple regression equation compiled using the remaining factors (inflation level and producer price index for fish products) was found to be untenable. As a result of excluding the factor “inflation level”, which had a less close connection with the performance indicator, a pair correlation model was obtained that describes the dependence of the consumer price index on the producer price index of fish products. The synthesized model is recognized as practically significant, and therefore can be used to obtain quantitative estimates of the consumer price index for fish products when forecasting.

Design and testing of a microwave Doppler-based yield estimation system for machine-picked seed cotton

Abstract Accurately obtaining crop yields is an important part of the precision agriculture technology system; with the increased mechanisation of cotton planting and harvesting, it has become particularly important to accurately obtain seed cotton yield data. In order to achieve accurate estimation of machine-picked seed cotton yield, this paper designs a machine-picked seed cotton yield estimation system based on microwave Doppler principle. Based on LabVIEW software, the signal acquisition circuit is designed; the power spectrum of the echo signal is estimated; the signal echo power is obtained; the relationship model between echo power and seed cotton mass is established by using multiple regression equations; and the reliability of the model is verified by using statistical methods. By adjusting the rotational speed of the fan, the wind speed at the inlet of the cotton pipeline was 10 m s−1, 15 m s−1 and 20 m s−1, and the yield estimation tests were carried out at the three wind speeds, and the data showed that the average absolute percentage errors of the estimation at the three wind speeds were 7.36%, 7.72% and 8.17%, and the mean squared errors were 2.699, 4.938 and 4.026, respectively. The test results show that the accuracy of seed cotton mass estimation under the three wind speeds basically meets the needs of the yield measurement system, and the systematic error of seed cotton yield estimation is the smallest when the wind speed is 10 m s−1.

Design and Testing of Key Components for a Multi-Stage Crushing Device for High-Moisture Corn Ears Based on the Discrete Element Method

To improve the crushing efficiency and crushing pass rate of high-moisture corn ears (HMCEs), a multi-stage crushing scheme is proposed in this paper. A two-stage crushing device for HMCEs is designed, and the ear crushing process is analyzed. Firstly, a simulation model for HMCEs was established in EDEM software (2018), and the accuracy of the model was verified by the shear test. Subsequently, single-factor simulation experiments were conducted, with the crushing rate serving as the evaluation index. The optimal working parameter ranges for the HMCE device were identified as a primary crushing roller speed of 1200–1600 revolutions per minute (r/min), a secondary crushing roller clearance of 1.5–2.5 mm, and a secondary crushing roller speed of 2750–3750 r/min. A Box–Behnken experiment was conducted to establish a multiple regression equation. With the objective of maximizing the qualified crushing pass rate, the optimal combination of parameters was revealed: a primary crushing roller speed of 1500 r/min, a secondary crushing roller clearance of 2.5 mm, and a secondary crushing roller speed of 3280 r/min. The pass rate of corn cob crushing in the simulation test was 98.2%. The physical tests, using the optimized parameter combination, yielded a qualified crushing rate of 97.5%, which deviates by 0.7% from the simulation results, satisfying the requirement of a qualified crushing rate exceeding 95%. The experimental outcomes validate the rationality of the proposed crushing scheme and the accuracy of the model, providing a theoretical foundation for subsequent research endeavors.

Trends and factors influencing the development of international tourism

The purpose of the article is to determine the return on investment produced by identifying modern trends in the development of international tourism and identifying factors affecting the development of tourism in the Republic of Kazakhstan. In order to achieve the objectives a certain number of regions and places of visitor accommodation were considered, investment in the tourism industry was analysed, and the results of a dynamic model with a distributed lag were obtained for the selected regions. Tourism development is an urgent topic, tourism is a source of income for millions of people working in this field, therefore, increasing the pace of development of the tourism sector is discussed in the Agendas of international organizations and in the annual Messages of the Head of State to the people of Kazakhstan. To determine the strategy for the development of the tourism services market, the factors influencing it were identified and a statistical analysis was carried out. Multiple correlation and regression analysis is one of the methods by which one can investigate the dependence of one variable on several variable factors. And for this purpose, significant factors were selected, and a multiple linear regression equation was constructed. The authors carried out a correlation and regression analysis using the example of regions, and also analyzed the obtained parameters of the multiple linear regression equation. In conclusion, the obtained regression equations in general and the coefficients are statistically significant and reliable at an appropriate level of significance. It allows us to draw conclusions based on the analysis of the obtained parameters of the multiple linear regression equation for the republic.

Relationship of Chest Girth, Body Length, and Sex with Body Weight and Average Daily Gain in Batur Lambs

This study aimed to investigate the relationships between chest girth, body length, and sex with body weight and average daily gain (ADG) in Batur lambs. The study sample consisted of 12 male and 8 female Batur lambs, aged 5 to 12 months, with an average body weight of 23.97 kg. Data were collected through a survey study using a random sampling method. The variables measured included chest girth, body length, sex, body weight, and ADG. The results demonstrated that: (1) the regression equation for chest girth on body weight was Y= -50.47 + 1.02CG (r = 0.91, R2 = 0.82); (2) the regression equation for body length on body weight was Y= -64.19 + 1.58BL (r = 0.88, R2 = 0.78); (3) the multiple regression equation for chest girth and body length on body weight was Y = -62.08 + 0.65CG + 0.69BL (r = 0.93, R2 = 0.85). These results indicate a very strong positive correlation between chest girth, body length, and body weight, suggesting that body weight in Batur lambs can be accurately predicted using these variables. However, ADG was not significantly correlated with chest girth or body length due to a low coefficient of determination.

PENERAPAN REGRESI LINEAR UNTUK PREDIKSI SUHU BADAN SAPI MENGGUNAKAN DATA SMART TERNAK DARI PT TELKOM INDONESIA: STUDI KASUS DI DESA PENGENGAT

Abstract West Nusa Tenggara (NTB) is one of the regions in Indonesia with significant potential in the field of livestock farming. In the context of the agricultural industry, livestock is considered a valuable asset, and monitoring their health is crucial for improving livestock productivity. This research aims to determine the relationship between the body temperature of cattle (Y) and environmental factors, namely air humidity (), environmental temperature (), and barometric pressure (), using smart livestock data provided by PT Telkom. The study was conducted in Pengengat Village, NTB, involving correlation and regression analysis. The analysis results indicate a significant positive correlation between the body temperature of cattle and environmental temperature () and a negative correlation with humidity (). Additionally, there is a strong positive correlation between the body temperature of cattle and barometric pressure (). The multiple linear regression equation obtained is as follows: = –26.816842418191094 + -0,0014707 + 0.39772325 + 0.04927779. Model testing shows excellent prediction quality with low Mean Squared Error (MSE) values (0.3849 for training and 0.4623 for testing). Furthermore, the high R-squared (R^2) values (0.7283 for training and 0.7110 for testing) indicate that the linear regression model can predict the body temperature of cattle based on the analyzed environmental factors. Keywords: Cattle Body Temperature, Smart Livestock Data, Prediction, Machine Learning, Linear Regression

Pengaruh Harga dan Promosi Terhadap Loyalitas Konsumen Pengguna Aplikasi Grab-Food di Yogyakarta

This research aims to determine the influence of price variables and promotion variables on consumer loyalty among Grab-Food users in Yogyakarta. The variables used in this study consist of independent variables, namely price (X1) and promotion (X2), as well as the dependent variable, which is consumer loyalty (Y). The data was analyzed using descriptive analysis, instrument quality testing (validity and reliability tests), and hypothesis testing through multiple linear regression (t-test and R² test), with a total of 100 respondents. The results of the analysis using the multiple linear regression equation show that price has a significant effect on consumer loyalty among Grab-Food users in Yogyakarta. This means that price significantly influences consumer loyalty and has a simultaneous significant effect on consumer loyalty. Consumer loyalty of Grab-Food users in Yogyakarta is influenced by price (X1) and promotion (X2), while the remaining factors are influenced by other variables not included in this study.

A structural modeling approach to determine the effects of orthorexia nervosa and social appearance anxiety on healthy lifestyle behaviors in young adults

Aims: The aim of this study is to examine the relationship between orthorexia nervosa (ON) and social appearance anxiety (SAA) and to investigate the effects of these variables on healthy lifestyle behaviors (HLB) in young adults aged 18-30 years. Methods: A total of 408 young adult male and female volunteers, with a mean age of 21.7±2.95 years, participated in this study. A structural model was proposed to examine the effects of SAA and ON on HLB. To assess how well the study variables represented the underlying components, we first evaluated a confirmatory measurement model. Subsequently, we analyzed a structural model that allowed for the examination of multiple regression equations involving several dependent variables. Results: SAA had a negative influence on HLB (std β=-0.673; t-value=-8.425), a significant negative relationship between ON, HLB was also promoted (std β=-0.554; t-value=-9.388). It was determined that there is a positive correlation between SAA, ON which is statistically significant (correlation coefficient=0.830; t-value=10.244). Conclusion: It is concluded that the individuals with high social appearance anxiety do not adopt healthy lifestyle behaviors and are more prone to orthorexia nervosa.

Examining Digital Talent Acquisition and Recruitment Strategies to Enhance Economic Sustainable Development Goals (SDGS) Of SME’S in China's Internet Industry

Objective: This paper aims to study the impact of digital recruitment tools on the economic and sustainable development goals (SDGs) of SMEs in China. Theoretical Framework: Based on human resource management theory, innovation diffusion theory and resource-based theory, experiments are designed to verify the proposed hypothesis. Method: We select Chinese Internet SMEs with similar backgrounds as experimental subjects, and collect key information through online questionnaires. Then, multiple linear regression equations are used for data analysis. Recruitment cost, recruitment cycle and new employee performance are set as dependent variables respectively, and the relationship between independent variables: whether an enterprise uses digital recruitment tools, and the number of digital recruitment tools used is explored. Results and Discussion: The results show that the three groups of regression models all show a significant correlation between the independent variable and the dependent variable. The use of digital recruitment software in recruitment will significantly reduce the recruitment cost and cycle, and the performance of new employees will be significantly improved, and the more digital recruitment software is used, the more obvious the effect will be. Originality\Value: This finding provides empirical support for small and medium-sized enterprises in China's Internet industry to use digital tools for human resource management and has important practical significance. The research suggests that businesses should use digital hiring tools for enhanced talent acquisition and a competitive edge boost.