Sample Selection Process Research Articles

Market Reactions to Economic, Social, and Environmental Contributions Post-COVID-19 a Study on ESG-Based Index Constituents

A company's value in relation to its economic, social, and environmental performance is measured by market reactions, which are represented by anomalous returns. As evidenced by abnormal returns, corporate performance that promotes sustainability can boost investor confidence in the stock market. Using abnormal returns as a stand-in, this study attempts to examine how market reactions are affected by economic and social contributions per share as well as economic contributions per share. The research sample consists of companies that have consistently been constituents of the SEG index, which includes SRI-Kehati, ESG Sector Leaders IDX KEHATI, and ESG Quality 45 IDX KEHATI, as well as companies in other indices such as LQ45, IDX80, and Kompas1000, from 2021 to 2023. Purposive sampling was used in the sample selection process, and 119 businesses were chosen to serve as the study subjects. Using Eviews, multiple linear regression is the analytical method employed. The analysis concludes that economic contributions have an impact on market reactions, but social and environmental contributions per share do not significantly affect them. The study's managerial implications highlight how crucial it is for managers to creatively convey to the public the company's environmental and social accomplishments in quantifiable financial terms. The value, purpose, and advantages of the company's donations are highlighted by this method, which also makes it easier for the public to accept and comprehend corporate social contributions.

ANNE: Adaptive Nearest Neighbours and Eigenvector-based sample selection for robust learning with noisy labels

An important stage of most state-of-the-art (SOTA) noisy-label learning methods consists of a sample selection procedure that classifies samples from the noisy-label training set into noisy-label or clean-label subsets. The process of sample selection typically consists of one of the two approaches: loss-based sampling, where high-loss samples are considered to have noisy labels, or feature-based sampling, where samples from the same class tend to cluster together in the feature space and noisy-label samples are identified as anomalies within those clusters. Empirically, loss-based sampling is robust to a wide range of noise rates, while feature-based sampling tends to work effectively in particular scenarios, e.g., the filtering of noisy instances via their eigenvectors (FINE) sampling exhibits greater robustness in scenarios with low noise rates, and the K nearest neighbour (KNN) sampling mitigates better high noise-rate problems. This paper introduces the Adaptive Nearest Neighbours and Eigenvector-based (ANNE) sample selection methodology, a novel approach that integrates loss-based sampling with the feature-based sampling methods FINE and Adaptive KNN to optimize performance across a wide range of noise rate scenarios. ANNE achieves this integration by first partitioning the training set into high-loss and low-loss sub-groups using loss-based sampling. Subsequently, within the low-loss subset, sample selection is performed using FINE, while the high-loss subset employs Adaptive KNN for effective sample selection. We integrate ANNE into the noisy-label learning state of the art (SOTA) method SSR+, and test it on CIFAR-10/-100 (with symmetric, asymmetric and instance-dependent noise), Webvision and ANIMAL-10, where our method shows better accuracy than the SOTA in most experiments, with a competitive training time. The code is available at https://github.com/filipe-research/anne.

Support Vector Machines With Uncertainty Option and Incremental Sampling for Kriging

ABSTRACTThis paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring.

LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment

Abstract With the rapid development of aerospace and unmanned aerial vehicles, using neural networks for object detection in optical remote sensing images (O-RSI) has encountered heightened challenges. The optical remote sensing images have the characteristics of complex geometric scenes, dense groups of objects, and significant multi-scale variations of objects; researchers need to use more complex models to achieve higher accuracy. However, this complexity also brings challenges to the application of lightweight scenes. Therefore, to cope with the trade-off challenge between model complexity and detection accuracy, we propose a lightweight network model LRSDet in this study. The model integrates local and global information processing mechanisms and introduces a fast positive sample assignment strategy to adapt to resource-constrained embedded and mobile platforms. By constructing a lightweight feature extraction network and a lightweight path aggregation network and incorporating the ESM-Attention module, the feature extraction capability of the model in complex remote sensing scenarios is significantly improved. In addition, the application of a dynamic soft threshold strategy further optimizes the positive sample selection process and improves the detection efficiency of the model. Experimental on the O-RSI datasets DIOR, NWPU VHR-10, and RSOD, while analyzing model real-time performance on aerial video and embedded devices, outperforming other state-of-the-art methods.

The Effect of Tax Planning and Leverage on Firm Value with Firm Size as a Moderating Variable

This study aims to examine the influence of tax planning and leverage on firm value using firm size as a moderating variable. The model employed in this study is a descriptive quantitative research design using secondary data. The population in this study includes consumer goods sector companies listed on the Indonesia Stock Exchange from 2018-2023. The sample selection process used a purposive sampling method, resulting in 156 observations from 26 companies selected as samples. Hypothesis testing is conducted using a moderation analysis model. The results of the study indicate that tax planning affects firm value and leverage affects firm value. Firm size as a moderating variable is capable of moderating the influence of tax planning and leverage on firm value.

Potrzeby osób z niepełnosprawnościami na poziomie lokalnym na przykładzie powiatu trzebnickiego

Getting to know the needs of people with disabilities might truly contribute to the improvement of their quality of life. The aim of the study discussed in this article is the hierarchisation of the needs of people with disabilities at the local level, on the example of Trzebnica poviat, and determining whether there are differences between their order of needs and that indicated at the regional level. The study was carried out from June to September 2020 among people with disabilities aged 15 and over divided into two samples: one consisting of 50 inhabitants of Trzebnica poviat and the other of 188 inhabitants of Dolnośląskie (Lower Silesian) Voivodship. Quota sampling in terms of selected socio-economic features, i.e. the respondents’ gender, place of residence and degree of disability was adopted in the study. Information about the surveyed population obtained from the National Population and Housing Census 2011 and non-governmental organisations was used in the sample selection process. People with disabilities inhabiting Trzebnica poviat identified bathroom modernisation as well as obtaining medicine and care products as their most urgent needs. They pointed to the absence of lifts in residential buildings, obstacles to travelling independently by means of public transport, lack of information about available training, poor prospects for obtaining resources for pursuing their interests and poor condition of roads as the most nagging everyday problems. The comparison of the order of needs of people with disabilities from the Trzebnica poviat and that of people with disabilities from the whole Dolnośląskie Voivodship shows there are differences. However, diagnosing the needs of people with disabilities at a local level and the subsequent implementation of relevant changes by local governments might improve the quality of life of people with disabilities in the whole region.

The Effect of Current Ratio and Total Assets Turnover on Return on Assets at PT Merck Indonesia Tbk for the Period of 2016-2023

For the years 2016–2023, this study set out to analyse PT MERCK Tbk's Return On Assets (ROA) and how the Current Ratio and Total Asset Turnover impacted it. A descriptive associative technique is used in the study procedure, which is quantitative in nature. Secondary data extracted from PT MERCK Tbk's financial filings constitute the study's population. The quarterly financial statements of PT MERCK Tbk from 2016 to 2023 were used as the basis for the sample selection process, which used non-probability sampling using purposive sampling techniques. Multiple linear regression, t-tests, F-tests, coefficients of determination, and Pearson product-moment correlation analyses were used to assess the impact analysis. While the Total Asset Turnover (TATO) did not have any partial effect on Return On Assets (ROA), the Current Ratio did, according to the studies performed in this research. When used in conjunction, the Current Ratio and Total Asset Turnover (TATO) do not affect Return on Assets (ROA)

Pengaruh pembiayaan Mudharabah,Murabahah dan Musyarakah Terhadap Bank Umum Syariah dengan NPF sebagai Variabel Moderasi

The research conducted analyzes the impact of Mudharabah, Murabahah and Musyarakah financing on the profitability of Islamic commercial banks during the period from 2016 to 2020. In this analysis, NPF was used as a moderating variable. Quantitative methods were adopted to analyze monthly time series data from 2016 to 2020, which was accessed via the OJK statistical database. The sample selection process was purposive sampling, referring to certain criteria, with a total of 120 samples examined over the five years. This research uses multiple linear regression analysis method which is conducted through SPSS application to process the data. This research helps to highlight factor affecting the Financial Perfomance of Islamic Banks. The findings of this research can provide valuable information to decision makers in Islamic banks to design more optimal financing strategies and improve credit risk management through a deeper Understanding the relationship between financing and profitability.. The results of this analysis show a significant positive influence between Mudharabah, Murabahah and Musyarakah financing on profitability, which mainly affects ROA in Sharia commercial banks. Although NPF does not significantly moderate the relationship between Mudharabah and ROA, this research reveals the important role of NPF in moderating the impact of Murabahah and Musyarakah financing on ROA.

Dynamic weighted residual ensemble learning for hyperspectral image classification driven by features and samples

Dynamic ensemble selection has emerged as a promising approach for hyperspectral image classification. However, selecting relevant features and informative samples remains a pressing challenge. To address this issue, we introduce two novel dynamic residual ensemble learning methods. The first proposed method is called multi-features driven dynamic weighted residuals ensemble learning (MF-DWRL). This method leverages various combinations of features to construct classifier pools that incorporate feature differences. The K-Nearest Neighbors algorithm is employed to establish the region of competence (RoC) in the dynamic ensemble selection process. By assessing the performance of the RoC, the feature sets that yield the highest classification accuracy are identified as the optimal feature combinations. Additionally, the classification accuracy is utilized as prior information to guide the residual adjustments of each classifier. The second method, known as features and samples double-driven dynamic weighted residual ensemble learning (FS-DWRL), further enhances the performance of the ensemble. This approach not only considers the selection of feature combinations but also takes into account the informative samples. By jointly optimizing the feature and sample selection processes, FS-DWRL achieves superior classification accuracy compared to existing state-of-the-art methods. To evaluate the effectiveness of the proposed methods, three hyperspectral datasets from China—WHU-Hi-HanChuan, WHU-Hi-LongKou, and WHU-Hi-HongHu—are used for classification experiments. For these datasets, the proposed methods achieve the highest classification accuracies of 90.57 %, 98.77 %, and 91.08 %, respectively. The MF-DWRL and FS-DWRL methods exhibit significant improvements in classification accuracy.

THE IMPACT OF PHET-ASSISTED EXPERIMENTAL LEARNING METHODS ON STUDENTS' SCIENCE PROCESS SKILLS IN SCIENCE TOPICS

The focus of this study is to find out how the PhET-assisted experiential learning technique affects students' Science Process Skills on energy-related material. A quantitative technique was applied in the research design, which was one group pretest posttest. This study will take place in SMP N 1 Sugio Lamongan. Purposive sampling was used in the sample selection process. There were 181 students in this study. The research included 66 students divided into two groups, class A and class B. However, data could not be gathered for 5 kids due to school activities, limiting the sample to 61 students. The insight gathering approach involves cognitive tests and observation of performance tests, followed by hypothesis evaluation with a paired simple t-test. The findings of this investigation show that learning using the PhET-assisted experimental approach to learning encounters a substantial impact on students' Science Process Skills in energy change material, with an average cognitive pretest score of 40.03, a posttest result of 78.95, and an N-gain value of 0.64, and obtained data from performance test observations with a pretest value of 35.65, a posttest result of 84.42, and an N-gain value of 0.75. The integration of experiential learning techniques aided by PhET has an impact on students' Science Process Skills, as evidenced by pretest posttest outcomes that have increased to the moderate level.

Cardiomyopathy Classification System

Cardiomyopathy encompasses various types and refers to a condition affecting the heart muscle, which impairs the organ's normal pumping function. When diagnosing cardiomyopathy, it is crucial to identify the specific type of the disease. This paper outlines the benefits and implications of a new classification system for cardiomyopathy, highlighting its potential to improve diagnosis and treatment, as well as its impact on research and clinical trials. The methodology section details the research design, data collection methods, and sample selection process. The data analysis section explains the techniques used for data processing and statistical analysis. Finally, the evaluation and validation section describes the expert review process and the testing andvalidation methods applied. This research adopts an interpretive paradigm, which emphasizes the importance of individual experience in understanding social realities. The statistical analysis assessed the diagnostic capability of genetic testing across genders using ROC curves, revealing strong performance for both males (AUC=.856, p < .001) and females (AUC=.835, p < .001), surpassing pure chance. A one-way ANOVA test was conducted to examine age differences among four genetic patient groups. The assumption of homogeneity of variance was satisfied, as indicated by Levene's test for equality of variances, F(3,156) = 1.277, p > .05. Significant age differences were found among the groups, F(3,156) = 5.537, p < .005. An evidence-based classification system would enhance diagnostic accuracy, treatment efficacy, and research quality, thereby improving outcomes for patients with cardiomyopathy.

Pengaruh Gaya Hidup terhadap Pengelolaan Keuangan Mahasiswa

This research aims to determine the influence of lifestyle on student financial management. The method in this research uses quantitative methods. The sample selection process can use a simple random sampling technique, namely taking samples from each population randomly without paying attention to the strata in the population. The type of data used is primary data. The data analysis technique uses simple linear regression, validity test, reliability test, classical assumption test (normality test), hypothesis test (t test) and coefficient of determination (r-square). The results of the research prove that lifestyle has a positive and significant effect on student financial management.The better a person's lifestyle, the better their financial management will be.

Analisis Pengaruh Current Ratio (CR), Debt to Equity Ratio (DER) dan Total Asset Turn Over (Tato) terhadap Return on Assets (ROA) pada PT Unilever Indonesia Tbk Periode 2013-2023

This research aims to determine the influence of Current Ratio, Debt to Equity Ratio (DER) and Total Asset Turn Over (TATO) partially or simultaneously on Return On Assets (ROA). The method in this research uses quantitative methods. The sample selection process can involve purposive sampling techniques, namely determining samples using certain criteria. The financial reports studied for the 2013-2023 period are the financial data of PT Unilever Indonesia Tbk. The type of data used is secondary data. Data analysis techniques use multiple linear regression, classical assumption tests (normality test, multicollinearity test, heteroscedasticity test, and autocorrelation test), hypothesis testing (t test and F test) and coefficient of determination (r-square). The results of the research prove that partially the Current Ratio (CR) and Debt to Equity Ratio (DER) have no effect on Return On Assets (ROA), while Total Asset Turn Over (TATO) has a significant effect on Return On Assets (ROA). Simultaneously Current Ratio (CR), Debt to Equity Ratio (DER) and Total Asset Turn Over (TATO) have a significant effect on Return On Assets (ROA).

Curriculum-guided dynamic division strategy for graph contrastive learning

Contrastive learning is a commonly used framework in the field of graph self-supervised learning, where models are trained by bringing positive samples closer together and pushing negative samples apart. Most existing graph contrastive learning models divide all nodes into positive and negative samples, which leads to the selection of some meaningless samples and reduces the model’s performance. Additionally, there is a significant disparity in the ratio between positive and negative samples, with an excessive number of negative samples introducing noise. Therefore, we propose a novel dynamic sampling strategy that selects more meaningful samples from the perspectives of structure and features and we incorporate an iteration-based sample selection process into the model training to enhance its performance. Furthermore, we introduce a curriculum learning training method based on the principle of starting from easy to difficult. Sample training for each iteration is treated as a task, enabling the rapid capture of relevant and meaningful sample information. Extensive experiments have been conducted to validate the superior performance of our model across nine real-world datasets.

DAMPAK REAL EARNINGS MANAGEMENT TERHADAP NILAI PERUSAHAAN DENGAN KEPEMILIKAN INSTITUSIONAL SEBAGAI MODERATING VARIABLE PADA PERUSAHAAN MANUFAKTUR SUB SEKTOR MAKANAN DAN MINUMAN YANG TERDAFTAR PADA BURSA EFEK INDONESIA PERIODE 2015–2019

The research has a purpose to determine and analyze the impact of real earnings management (REM) on firm value and institutional ownership in moderating the impact of real earnings management (REM) on firm value in food and beverage sub-sector manufacturing companies listed on the Indonesia Stock Exchange for the period 2015-2019. The population in this research are a manufacturing companies in food and beverage sub-sector, that obtain sample of 9 companies after through the sample selection process using the purposive sampling method. The result of this research indicate that real earnings management (REM) proxied by abnormal CFO, abnormal production cost, and abnormal discretionary expenses has a negative effect on firm value, institutional ownership is able to moderate the effect of real earnings management (REM) proxied by abnormal CFO, abnormal production costs, and abnormal discretionary expenses on firm value. The implication of the result of this reseach are that company management, the investor, and the academics expected to consider the practice of real earnings management (REM) in order to degrade the firm value.

Pandemic Pivot: Unraveling the Digital Fashion Frontier on Shopee

During the COVID-19 pandemic, there has been a major shift in people's activities and behaviors, which are moving towards the digital world. This helps people in their daily lives, both as buyers and business people in various industries, including the fashion industry. The fashion industry itself is a sector of multinational companies that have a high-net-worth value, whose focus is mainly on designing, producing, and marketing clothes to the public. Online shopping digital platforms such as Shopee are one of the main choices for people in making shopping transactions today. The purpose of this study was to find out online consumer reviews, social media advertisement, and influencer endorsements can influence people's online purchase intention for fashion stores on the Shopee platform. The research used a non-probability sampling approach, specifically choosing the convenience sampling technique during the sample selection process. The number of samples used in the study was 130 respondents, and data collection was conducted by distributing questionnaires through Google Forms. The data analysis was conducted using the Structural Equation Modelling (SEM) program SmartPLS 4.0. The research shows that social media ads on Shopee in Jabodetabek significantly impact online purchase intentions. Influencer engagement also influences intentions positively, although not statistically significant. Online consumer reviews and purchase intention variables share similarities, both influenced by external factors. Businesses should tailor advertising strategies for optimal performance and leverage social media effectively to thrive in e-commerce.

Using practitioners’ voices in developing a business rescue practitioner expert profile

The study explored how business rescue (BR) practices can inform the development of an occupation profile needed to lay a foundation for developing business rescue practitioners’ occupation-specific qualifications. There are 11 professional organisations whose members qualify to serve as business rescue experts. The relevant occupation cannot have more than one occupation expert profile. The study employed qualitative approaches that entailed semi-structured interviews with 20 informants (12 business rescue practitioners, four short skills development programme (SSDP) managers, and 4 SSDP facilitators) and qualitative document content analysis of 30 court cases settled on the BR. The 12 business rescue practitioners (BRP) were randomly selected. However, other informants were accessed through a purposeful sample selection process. The field findings show that the BRP occupation in South Africa is a regulated practice area without an occupation expert profile. The BR practices have not been incorporated in a qualification framework registered on the Qualification Framework (NQF) governed by the South African Qualifications Authority (SAQA). Development of pipeline talent is limited, and the monitoring of SSDPs encounters limitations without an occupation expert profile and an occupation-specific qualification. The BR practices can be categorised into 11 tasks linked to practitioner training disciplines. The existing SSDPs provide an important mechanism for continuing professional development. However, the contents should be linked to BR practices and an occupation profile embracing the BRP role as an interim managing director in a business rescue process. The uniqueness of this article resides in its documentation of BR practices generated from multiple data sources that should be used to develop an occupation expert profile that reflects BRP’s capability. The setting of the BR process requires experts to operate in the capability realm. Methods: The study employed qualitative approaches that entailed semi-structured interviews with 20 informants (12 business rescue practitioners, four short skills development programme (SSDP) managers, and 4 SSDP facilitators) and qualitative document content analysis of 30 court cases settled on the BR. The 12 business rescue practitioners (BRP) were randomly selected. However, other informants were accessed through a purposeful sample selection process. Results: The field findings show that the BRP occupation in South Africa is a regulated practice area without an occupation expert profile. The BR practices have not been incorporated in a qualification framework registered on the Qualification Framework (NQF) governed by the South African Qualifications Authority(SAQA). Development of pipeline talent is limited, and the monitoring of SSDPs encounters limitations without an occupation expert profile and an occupation-specific qualification. The BR practices can be categorised into 11 tasks linked to practitioner training disciplines. Practical implications: The existing SSDPs provide an important mechanism for continuing professional development. However, the contents should be linked to BR practices and an occupation profile embracing the BRP role as an interim managing director in a business rescue process. Originality value: The uniqueness of this article resides in its documentation of BR practices generated from multiple data sources that should be used to develop an occupation expert profile that reflects BRP’s capability. The setting of BR process requires experts to operate in the capability realm.Keywords: business, expert, occupation, practitioner, practice, profile, rescue, voices.

Towards a sustainable future: a comprehensive review of Green Sukuk

PurposeGreen sukuk (GS) is an emerging financial tool that has gained momentum in recent years owing to increased attention being given to Islamic finance, socially responsible investing (SRI) and sustainability agendas. Yet, GS studies are fragmented, dispersed and lack comprehensive reviews. As a response to this gap in academia, this paper aims to synthesize the knowledge on GS into thematic clusters, providing a more comprehensive understanding of the subject and offering guidelines for future research.Design/methodology/approachThis study implemented a systematic literature review approach to analyse studies on GS that were published prior to and including June 2023. The PRISMA 2020 protocol was used in the sample selection process. A total of 62 peer-reviewed journal articles from six databases were identified and categorized into various themes.FindingsThe results suggest that previous research has predominantly focused on the areas of GS advantages, drivers, market development and potential sectors, along with challenges and recommendations to improve the market. However, it was found that some other aspects, including GS pricing, performance and purchasing intention, require further research attention. The analysis also indicated that the use of theories in the GS context was limited, with only five theories employed in just four out of the 62 articles examined. Moreover, this paper’s findings revealed that the studies employing quantitative and empirical analysis methods were limited to four articles. Geographically, most of the studies were conducted in Indonesia and Malaysia, while other countries with high-potential markets (e.g. GCC) had limited GS practices and studies.Practical implicationsThe results of this study have several practical implications. For investors, a review of GS will provide greater insight into the understanding of the GS market, helping them make better investment decisions. For policymakers, this paper empowers them with the knowledge to make informed decisions regarding GS markets by highlighting key recommendations identified in the literature. Finally, the proposed guidelines can be used in future research.Originality/valueWhile Green Bonds have received significant attention, there is a dearth of research on GS and those that exist are fragmented. A systematic literature review is necessary to identify knowledge gaps for future research.

How does a parent’s knowledge influence the knowledge bridging of joint ventures?

Previous research has underscored the significance of the equity investment relationship in facilitating knowledge transfers between a joint venture and its parent company. Successful knowledge bridging in joint ventures plays a crucial role in shaping organizational learning and innovation performance outcomes. The empirical evidence on the effectiveness of knowledge transfers, however, presents a mixed picture. Through a rigorous sample selection process, we collect panel data on 366 joint venture-year observations across four industries during an 18-year period to empirically test the proposed hypotheses. In this research, we contend that the ability of joint ventures to act as knowledge bridges depends on the diversity of the parent’s knowledge and the technological similarity between the joint venture and the parent, especially in the context of internal knowledge development. An examination of bridging performance in the relationship between joint ventures and parent companies indicates that both parental knowledge diversity and joint venture – parent technology similarity positively impact the knowledge acquired through the joint venture – parent equity relationship. Furthermore, when the joint venture strongly depends on internal knowledge, the positive relationship among parental knowledge diversity, joint venture – parent technology similarity, and joint venture knowledge bridging becomes more pronounced. Our findings shed light on how the specific features of a parent company contribute to the knowledge learning bridged by a joint venture and underscore the role of equity investments in joint venture firms’ innovation.

The Efficacy of Music Therapy Programs on the Development of Social Communication in Children with Autism Spectrum Disorder: A Systematic Review

Autism spectrum disorder (ASD) in children is characterized by difficulties in social communication and restricted repetitive behavior patterns. Music therapy appears to have beneficial effects in the area of social interaction and communication. The aim of this systematic literature review is to investigate the effectiveness of music therapy programs on the development of social communication among preschool- and school-aged children with ASD. For this purpose, a detailed study of the relevant literature that has been published in the last decade in peer-reviewed scientific journals was conducted. Web of Science, Springer Link, PsyINFO, and ERIC databases were searched, and according to the eligibility and exclusion criteria, 12 studies were finally selected from the 167 initially found. From the analysis of the results, the following categories were defined to contribute to enhancing social communication: (a) improvisational music therapy; (b) joint attention and eye contact; (c) therapeutic relationship, synchronization, and attunement; (d) imitation; (e) understanding and interaction; (f) family-centered music therapy; and (g) language and socioemotional adjustment. The results of the review were mixed, as music therapy appeared to have a positive effect on improving the social communication of children with ASD, but a general conclusion could not be drawn about the degree of its effectiveness compared to the standard treatments that children usually receive. In the future, further research in this field, especially on the improvisational music Therapy technique, is recommended. Moreover, an increased focus on the development of a common methodological practice regarding the sample selection process and measurement tools is suggested.