Relative Importance Of Factors Research Articles

Overseas Market Expansion Strategy of the Global Electronic Components Company Based on the AHP Analysis of Factors in Technology, Organization, and Environment Context: A Case of Samsung Electro-Mechanics

The overseas market expansion strategy is important for achieving competitive advantage and sustainable growth of global electronic component companies. Although the global electronic component market has grown rapidly recently, research focusing on the innovation strategy of global electronic component companies’ expansion into overseas markets is scarce. This paper defines the key factors that influence the success of a global electronic component company’s overseas market expansion strategy based on the Technology, Organization, and Environment (TOE) framework and quantitatively identifies the relative importance of factors at the technology, organization, and environmental levels through Analytic Hierarchy Process (AHP) analysis. As a case study, we analyze Samsung Electro-Mechanics, which has grown as a global electronic components company through an overseas market expansion strategy in recent decades. As a result of the analysis, among the three top factors defined as key factors, the technology factor was evaluated as the most important factor, and among the subfactors of the technology factor, “R&D Availability”, and “Production Availability” were analyzed as the most important influencing factors. These analysis results suggest that global electronic component companies can achieve successful results when they pursue overseas market expansion strategies by prioritizing technology development and focusing on growth strategies suited to the market environment. This study is meaningful as an academic study focusing on the overseas market expansion strategies of global electronic component companies and makes a practical contribution by providing management implications that can be taken by electronic component companies seeking to expand overseas markets.

Factors that affect GP referral of a child with intellectual disability for a mild illness: A discrete choice experiment.

General practitioners (GP) report multiple challenges when treating individuals with intellectual disabilities which may influence referral rates. The study aimed to establish factors that influence GP's decision-making when referring a child with intellectual disabilities to the emergency department. Discrete choice experiments (DCEs) are increasingly used in health research to further understand complex decision making. A DCE was designed to assess the relative importance of factors that may influence a GP's (N = 157) decision to refer. A random parameters model indicated that perceived limited parental capacity to manage an illness was the most important factor in the decision to refer a child to the ED, followed by a repeat visit, a referral request from the parent, and a Friday afternoon appointment. Understanding the factors that influence referral is important for service improvement and to strengthen primary care provision for this population and their families.

Parent's preferences for unscheduled paediatric healthcare: A discrete choice experiment.

Unscheduled healthcare is a key component of healthcare delivery and makes up a significant proportion of healthcare access, with children being particularly high users of unscheduled healthcare. Understanding the relative importance of factors that influence this behaviour and decision-making is fundamental to ensuring the system is best designed to meet the needs of users and foster appropriate cost-effective usage of health system resources. The aim of the study was to identify the parent's preferences for unscheduled healthcare for a common mild childhood illness. A discrete choice experiment (DCE) was developed to identify the preferences of parents accessing unscheduled healthcare for their children. Data were collected from parents in Ireland (N = 458) to elicit preferences across five attributes: timeliness, appointment type, healthcare professional attended, telephone guidance before attending and cost. Using a random parameters logit model, all attributes were statistically significant, cost (β = -5.064, 95% confidence interval, CI [-5.60, -4.53]), same-day (β = 1.386, 95% CI [1.19, 1.58]) or next-day access (β = 0.857, 95% CI [0.73, 0.98]), coupled with care by their own general practitioner (β = 0.748, 95% CI [0.61, 0.89]), identified as the strongest preferences of parents accessing unscheduled healthcare for their children. The results have implications for policy development and implementation initiatives that seek to improve unscheduled health services as understanding how parents use these services can maximise their effectiveness. The development of the DCE included a qualitative research component to ensure that the content accurately reflected parents experiences when seeking healthcare. Before data collection, a pilot test was carried out with the target population to gather their views on the survey.

Modeling pedestrian behavior at urban signalised intersections using statistical-ANN hybrid approach – Case study of New Delhi

Traffic crashes at intersections are major hazard all over the world. The victims of road crashes are mostly pedestrians. A large share of accidents can be related to unsafe crossing behavior by pedestrians. The accidents are mostly fatal for pedestrians especially if the unsafe act is signal violation. This study primarily focused on cognizing factors which are significantly affecting the signal violation behavior of pedestrians’ at urban signalised intersections. Data is collected at 11 signalised intersections in New Delhi, India using video recording technique. Significant variables are identified using conventional statistics. The variables are then used as input neuron for the Artificial Neural Network (ANN) for modeling signal violation behavior. Sensitivity analysis is performed to identify the relative importance of factors related to the pedestrian signal violation. Host of different factors such as pedestrian demographic, behavioral attributes, crossing state and mobile usage are notably influencing signal violations. Pedestrian crossing speed, crossing path and waiting time are the top three predictors of violation behavior. The accuracy of ANN to predict signal violation behaviour is found to be about 85% and is considerably higher than conventional binary logistic regression (BLR) model. The area under the receiver operating characteristic (ROC) curve (AUC) is found to be 0.753, suggesting good model performance. The results suggest that ANN is a robust alternative to conventional regression, for studying and modeling pedestrian behavior. The findings from this study would assist engineers and policy makers to take proactive measures for designing pedestrian friendly facilities to reduce signal violations. This would ultimately improve pedestrian safety at urban signalised intersections.

Patient Preferences Regarding Surgical Treatment Methods for Symptomatic Uterine Fibroids

Study ObjectiveThe purpose of this study is to rank the factors that are most and least important to patients with symptomatic uterine fibroids when considering surgical treatment options.Materials and MethodsUsing a best worst scaling (BWS) preference elicitation approach, participants completed an online survey to rank factors associated with fibroid surgical treatments. Survey content was based on a literature review and included the following factors: symptom relief; surgical complications; repeat treatment; recovery time; cosmetic effects; risk of spreading undiagnosed cancer; sexual outcomes; maintenance of child-bearing; continuation of menses; unpredictable menses; and location of procedure. Participants completed 11 BWS tasks. For each task, we presented participants with a subset of 5 factors from the possible 11, and participants chose the most important and least important factor. Participants’ responses were analyzed using conditional logistic regression to determine the relative importance of factors. Patient priorities were further explored via age and race.Results285 respondents with symptomatic uterine fibroids (69 physician-confirmed and 216 self-reported) who had not undergone prior surgical treatment completed the survey. Respondents were enrolled from two clinical sites (clinical site cohort) and an online consumer panel (panel cohort). Both cohorts identified symptom relief, cancer risk, repeat treatment and complications as the most important factors in selecting surgical treatment options and location of procedure, return to normal activities after surgery, and cosmetic effects like presence of a scar after the surgical treatment as the least important factors. Of note, younger women (≤ 40) placed greater importance on the ability to have children after the procedure.ConclusionInformation regarding the factors most and least important to patients with symptomatic uterine fibroids might inform development and regulatory evaluation of new technologies and procedures. Study results may be useful in efforts to develop a set of outcomes to include in future fibroids clinical studies.

Factors for Fractional CIO engagement success

As small- to medium-sized enterprises struggle with IT management, an increasing number appoint Fractional CIOs, i.e., CIOs that work part-time for multiple organizations simultaneously. We use Q-methodology to empirically evaluate the relative importance of factors for Fractional CIO engagement success and find that trust, top management team support, and the Fractional CIO's integrity are most important. Further, we identify three groups of Fractional CIOs: Strategic IT Advisors, Full-ownership CIOs, and Change Agents. Our results guide organizations when hiring and working with Fractional CIOs and provide the basis for further research on Fractional CIO archetypes.

Evaluating Drivers of the Patient Experience Triangle: Stress, Anxiety, and Frustration.

Patient experience is a widely used indicator for assessing the quality-of-care process during a patient's journey in hospital. However, the literature rarely discusses three components: patient stress, anxiety, and frustration. Furthermore, little is known about what drives each component during hospital visits. In order to explore this, we utilized data from a patient experience survey, including patient- and provider-related determinants, that was administered at a local hospital in Abu Dhabi, UAE. A machine-learning-based random forest (RF) algorithm, along with its embedded importance analysis function feature, was used to explore and rank the drivers of patient stress, anxiety, and frustration throughout two stages of the patient journey: registration and consultation. The attribute 'age' was identified as the primary patient-related determinant driving patient stress, anxiety, and frustration throughout the registration and consultation stages. In the registration stage, 'total time taken for registration' was the key driver of patient stress, whereas 'courtesy demonstrated by the registration staff in meeting your needs' was the key driver of anxiety and frustration. In the consultation step, 'waiting time to see the doctor/physician' was the key driver of both patient stress and frustration, whereas 'the doctor/physician was able to explain your symptoms using language that was easy to understand' was the main driver of anxiety. The RF algorithm provided valuable insights, showing the relative importance of factors affecting patient stress, anxiety, and frustration throughout the registration and consultation stages. Healthcare managers can utilize and allocate resources to improve the overall patient experience during hospital visits based on the importance of patient- and provider-related determinants.

Interpretable machine learning-based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data.

We aimed to identify and understand risk and protective factors for suicide among South Korean females by linking survey and social media data and using interpretable machine learning approaches. We collected a wide range of potential factors including the material, psychosocial, and behavioral data from a detailed survey, which we then linked to data from social media. In addition, we adopted interpretable machine learning approaches to (1) predict the suicide risk, (2) explain the relative importance of factors and their interactions regarding suicide, and (3) understand individual differences affecting suicide risk. The best-performing machine learning model achieved an AUC of 0.737. Adverse childhood experiences, social connectedness, and mean positive sentiment score of social media posts were the three risk factors that had a monotonic or unimodal relationship with suicide, and satisfaction with life, narcissistic self-presentation, and number of close friends on social media were the three protective factors that had a monotonic or unimodal relationship with suicide. We also found several meaningful interactions between specific psychiatric symptoms and narcissistic self-presentation. Our findings can help governmental organizations to better assess female suicide risk in South Korea and develop more informed and customized suicide prevention strategies.

Predictive modelling of deliberate self-harm and suicide attempts in young people accessing primary care: a machine learning analysis of a longitudinal study

PurposeMachine learning (ML) has shown promise in modelling future self-harm but is yet to be applied to key questions facing clinical services. In a cohort of young people accessing primary mental health care, this study aimed to establish (1) the performance of models predicting deliberate self-harm (DSH) compared to suicide attempt (SA), (2) the performance of models predicting new-onset or repeat behaviour, and (3) the relative importance of factors predicting these outcomes.Methods802 young people aged 12–25 years attending primary mental health services had detailed social and clinical assessments at baseline and 509 completed 12-month follow-up. Four ML algorithms, as well as logistic regression, were applied to build four distinct models.ResultsThe mean performance of models predicting SA (AUC: 0.82) performed better than the models predicting DSH (AUC: 0.72), with mean positive predictive values (PPV) approximately twice that of the prevalence (SA prevalence 14%, PPV: 0.32, DSH prevalence 22%, PPV: 0.40). All ML models outperformed standard logistic regression. The most frequently selected variable in both models was a history of DSH via cutting.ConclusionHistory of DSH and clinical symptoms of common mental disorders, rather than social and demographic factors, were the most important variables in modelling future behaviour. The performance of models predicting outcomes in key sub-cohorts, those with new-onset or repetition of DSH or SA during follow-up, was poor. These findings may indicate that the performance of models of future DSH or SA may depend on knowledge of the individual’s recent history of either behaviour.

Assessing the importance of predictors of adherence to a digital self‑management intervention for osteoarthritis

ObjectiveTreatment adherence is suggested to be associated with greater improvement in patient outcomes. Despite the growing use of digital therapeutics in osteoarthritis management, there is limited evidence of person-level factors influencing adherence to these interventions in real-world settings. We aimed to determine the relative importance of factors influencing adherence to a digital self-management intervention for hip/knee osteoarthritis.MethodsWe obtained data from people participating in a digital OA treatment, known as Joint Academy, between January 2019 and September 2021. We collected data on the participants’ adherence, defined as the percentage of completed activities (exercises, lessons, and quizzes), at 3 (n = 14,610)- and 12-month (n = 2682) follow-up. We used dominance and relative weight analyses to assess the relative importance of sociodemographic (age, sex, place of residence, education, year of enrolment), lifestyle (body mass index, physical activity), general health (comorbidity, overall health, activity impairment, anxiety/depression), and osteoarthritis-related (index joint, fear of moving, walking difficulties, pain, physical function, wish for surgery, Patient Acceptable Symptom State) factors, measured at baseline, in explaining variations in adherence. We used bootstrap (1000 replications) to compute 95% confidence intervals.ResultsMean (SD) adherences at 3 and 12 months were 86.3% (16.1) and 84.1% (16.7), with 75.1% and 70.4% of participants reporting an adherence ≥ 80%, respectively. The predictors included in the study explained only 5.6% (95% CI 5.1, 6.6) and 8.1% (7.3, 11.6) of variations in 3- and 12-month adherences, respectively. Sociodemographic factors were the most important predictors explaining more variations than other factors altogether. Among single factors, age with a nonlinear relationship with adherence, was the most important predictor explaining 2.3% (95% CI 1.9, 2.8) and 3.7% (2.4, 5.3) of variations in 3- and 12-month adherences, respectively.ConclusionPerson-level factors could only modestly explain the variations in adherence with sociodemographic characteristics, mainly age, accounting for the greatest portion of this explained variance.

Geographical shifts in the successional dynamics of inland dune shrub communities.

Species' environmental requirements and large-scale spatial and evolutionary processes determine the structure and composition of local communities. However, ecological interactions also have major effects on community assembly at landscape and local scales. We evaluate whether two xerophytic shrub communities occurring in SW Portugal follow constrained ecological assembly dynamics throughout large geographical extents, or their composition is rather driven by species' individualistic responses to environmental and macroecological constraints. Inland dune xerophytic shrub communities were characterized in 95 plots. Then, we described the main gradients of vegetation composition and assessed the relevance of biotic interactions. We also characterized the habitat suitability of the dominant species, Stauracanthus genistoides, and Ulex australis, to map the potential distribution of the xerophytic shrub communities. Finally, we examined the relationships between the vegetation gradients and a broad set of explanatory variables to identify the relative importance of each factor driving changes in community composition. We found that xerophytic shrubs follow uniform successional patterns throughout the whole geographical area studied, but each community responds differently to the main environmental gradients in each region. Soil organic matter is the main determinant of community variations in the northern region, Setúbal Peninsula, whereas aridity is so in the South/South-Western region. In contrast, in the central region, Comporta, the variation between S. genistoides and U. australis communities is explained mainly by aridity and temperature seasonality, followed by the individualistic responses of the dominant species and soil organic matter. Overall, these results indicate that, the relative importance of the main factors causing community-level responses varies according to regional processes and the suitability of the environmental conditions for the dominant species in these communities. These responses are also determined by intrinsic community mechanisms that result in a high degree of similarity in the gradient-driven community stages in different regions.

Assessing microbial communities across the fine root landscape.

Root-associated microbial communities are increasingly recognized as key contributors to plant function, but we still understand little about the factors driving interactions between microbes and individual roots. To have thorough understanding of this system, we must consider the immense functional and spatial heterogeneity of roots within complex root systems. In this Viewpoint, we describe a prototype study in which we map and analyze root-associated microbial communities throughout a woody root system. Our efforts demonstrate how spatially explicit sampling yields unique insights into the relative importance of factors structuring root-associated microbial communities.

Relative Importance of Well-Being Determinants in Atlantic Canadian Families During the Pandemic.

Framed by the socio-ecological model of well-being, we examined the relative importance of factors contributing to three dimensions of well-being (child, parent, and family) during the COVID-19 pandemic. A sample of 536 participants from the Atlantic provinces of Canada answered a cross-sectional survey in 2021, covering experiences during the pandemic (eg, changes in family life and well-being). Well-being was assessed with 3 single-item measures on positive change in the life of children, parents, and families during the pandemic. This study involved 21 predictor variables (eg, change in time spent on various family activities). Using multiple regression and measures of relative importance based on the Lindeman, Merenda and Gold (lmg) method, we identified the variables most important to predicting well-being. Twenty-one predictors accounted for 21% of the variance in child well-being, 25% in parent well-being, and 36% in family well-being. Well-being at all 3 levels (child, parent, and family) shared the same top predictor (family closeness). The top 6 predictors of well-being at each level were related to leisure (eg, play) and time-use (eg, to prepare meals, engage in self-care, and rest). The effect sizes were smaller for child well-being than at the parent or family level, suggesting there may be important predictors of child well-being not accounted for in these analyses. This study may inform family-level programing and policy that seeks to promote well-being for children and their families.

Analysis of Relative Importance of Factors Affecting the Chance of Admission of Applying to Graduate Students

The conceptualization of statistical model: the initial characteristic of linear regression revealed by Karl Pearson lay the foundation for the exploration of Sir Francis Galton. The article discusses the concept of the linear regression model and R-square and focuses on the relationship between the chance of admission and GRE/TOFEL score by the simple linear regression between independent variables and dependent variables. The collected statistical data from Kaggle about 386 students ’ scores in an American university is fitting the linear regression. This concentrates on the evaluation of the simple linear regression and the discovery of coverage for drawbacks or solutions for the promotion of the accuracy of the relationship. In the demonstrating experiments, collected TOFEL/ GRE scores of 375 graduate students in college indicated new observation that the existence of positively related between two items by R-square while testifying model possess drawbacks and limitations in a degree. The exploration of solutions to offset the impact of these errors and omissions will be concentrated in the following.

Assessing the Relative Importance of Climatic and Hydrological Factors in Controlling Sap Flow Rates for a Riparian Mixed Stand

Evapotranspiration by phreatophytes in riparian zones makes up a large component of the water balance. However, our understanding of the relative importance of controlling factors such as climatic conditions, species type, depth to groundwater and distance to surface water in riparian zones remains a significant knowledge gap. A field experiment was conducted in an irrigated catchment in North Queensland, Australia, to investigate the factors controlling evapotranspiration by groundwater dependent trees. The sap flow of four tree species was measured, along with soil moisture, groundwater levels and local climatic conditions. The relative influence of species, hydrologic and climate factors, and measured variables were investigated with two non-parametric methods: random forest and Principal Component Analysis (PCA). Field monitoring data revealed differences in sap flow rates and diurnal sap flow trends between species. Distance from surface water explained the most variance in sap flow rates, followed by depth to groundwater and species, based on random forest modeling. The sap flow rates for some of the Eucalyptus tessellaris trees at this site reduced as groundwater levels declined. Overall, results demonstrate the value that can be gained from applying non-parametric methods, such as random forest and PCA, to investigate the relative importance of the factors influencing evapotranspiration.

Predicting the Sustained Implementation of an Evidence-Based Parenting Program: A Structural Equation Modelling Approach

PurposeSustained implementation is required for evidence-based parenting programs to promote children and their families’ wellbeing at the societal level. Previous literature has examined the role of a range of different factors in enhancing sustainability. However, the inter-relationship between, and the relative importance of different factors remain largely unknown. The overall aim of this study is to identify predictors of sustained program use, the relative importance of factors, and potential mediation pathways.MethodsWe surveyed 1202 practitioners who were trained in at least one variant of the Triple P-Positive Parenting Program, at least one and half years before data collection. The present data were linked with data collected during professional training. We first examined the independent effect of each factor on sustained program use, then, developed and evaluated a structural equation model of sustained program use.ResultsThe structural equation model explained a considerable amount of variance in sustained program use, with seven positive predictors and one negative predictor identified. Organisational support was identified as a key facilitator, which was not only positively linked with other facilitators but also had an independent positive effect. Perceived usefulness of the program was the most important practitioner-level facilitator, which might be contributed by both research-based evidence and practice-based evidence. Practitioners’ self-regulation in program delivery impacted sustained use by influencing other factors such as perceived usefulness of the program.ConclusionThe findings provided insight into factors influencing the sustainability of evidence-based parenting programs which could be used to inform future implementation practice.

How Do Landscape Heterogeneity, Community Structure, and Topographical Factors Contribute to the Plant Diversity of Urban Remnant Vegetation at Different Scales?

In highly fragmented urban areas, plant diversity of remnant vegetation may depend not only on community structure and topographical factors, but also on landscape heterogeneity. Different buffer radius settings can affect the relative importance of these factors to plant diversity. The aim of this study was to examine the relative importance of landscape heterogeneity, community structure, and topographical factors on plant diversity under different buffer radii in biodiversity hotspots. We established 48 plots of remnant vegetation in Guangzhou city, one of the biodiversity hotspots. A buffer radius of 500 m, 1000 m, and 2000 m was established around the center of each sample plot, and 17 landscape heterogeneity indices in each buffer were calculated by FRAGSTATS 4.2 software. Combined with the community structure and topographical factors, the impact factors of plant diversity under different buffer radii were analyzed by multiple regression analysis. We found the following: (1) The combined explanatory power of the three factors accounted for 43% of the species diversity indices and 62% of the richness index at its peak. The three impact factors rarely act independently and usually create comprehensive cumulative effects. (2) Scale does matter in urban landscape studies. At a 500 m buffer radius, community structure combined with road disturbance indices was strongly related to diversity indices in herb and shrub layers. The stand age was negatively correlated with the tree-layer richness index. As the scale increased, the diversity indices and richness index in the three layers decreased or increased under the influence of comprehensive factors. (3) The richness index in the herb layer was more responsive to impact factors than other biodiversity indices. Except for the herb layer, the interpretation of landscape heterogeneity for each plant diversity index was more stable than that for the other two factors. Road disturbance indices, combined with the other six landscape pattern metrics, can well indicate species diversity and richness. We suggest that the vegetation area of remnant patches within a radius of 500-2000 m should be appropriately increased to protect plant diversity, and the negative effects of road disturbance should also be considered.

A Cross-Sectional, Mixed-Methods Analysis to Identify the Relative Importance of Factors Students and Providers Evaluate When Making a Job Choice.

Little is known about the relative importance of factors that contribute to job choices among health care providers. A convenience sample of 173 health care providers (N=134) and physician assistant students (N=39) completed a cross-sectional survey. Participants rated the importance of sixteen job- (e.g., work environment), community- (e.g., recreational opportunities), and personal-related factors (e.g., children) and one open-ended item. The highest rated item, on average, was an opportunity to make a difference in patient quality of life (mean (M)=4.57, standard deviation (SD)=0.63) while the lowest rated item was wealth/prestige of living in a certain area (M=2.43, SD=1.05). The average importance rating was similar across types of providers and between providers and students. While personal interest to positively affect patients' quality of life was rated as the most important factor, almost all the studied items were rated as important or very important by the majority of participants.

Effects of species richness and nutrient availability on the invasibility of experimental microalgal microcosms

The relationship between biological diversity and community invasibility is a central theme in ecological research. It is often assumed that such relationship is negative at small scales but positive at large scales, with a shift of the major driving factors from biotic to abiotic ones such as resource availability in spatial scale. However, the relative importance of factors driving diversity-invasibility relationships does not necessarily depend on spatial scale per se, and existing studies often test the effects of biological diversity and resource availability on community invasibility in isolation, but rarely consider their simultaneous effects on community susceptibility to invasions. The present study assembled freshwater microalgal communities (including Closterium libellula , Cosmarium sportella , Selenastrum capricornutum , Scenedesmus quadricauda and Actinastrum hantzschii) , and tested how community invasibility was affected by the individual and interactive effects of microalgal richness and nutrient availability. The results showed that the invasion success of the model invader, Pediastrum integrum , decreased with an increase in resident richness, but increased with an increase in nutrient availability. Also, there was an interactive effect of microalgal richness and nutrient availability on community invasibility. Nutrient-enriched microcosms with the lowest microalgal richness were most invasible, whereas non-enriched microcosms, regardless of whether they received nutrient enrichment or not, were most resistant to invasion challenge. A track of nitrogen concentrations in growth medium further showed that high-richness communities were associated with higher nitrogen removal efficiency than low-richness communities. Therefore, strong biotic resistance of high-diversity communities to invasions can be a general ecological pattern found in both macro- and microorganisms, and a resource-based approach can help explain diversity-invasibility relationships and elucidate the mechanisms that give rise to community invasibility.

사회적기업의 ESG 평가요인 연구

Purpose: This study aims to investigate the relative importance of ESG evaluation factors of social enterprises byapplying ESG evaluation factors to social enterprises in securing local social values. In previous studies, ESG-relatedevaluations of various industries are being conducted, and ESG evaluation is becoming important for mid-sized andsmall-sized companies in the future, so it is a task to be performed. This study is a study on the relative determinantsof evaluation factors to determine ESG evaluation factors of social enterprises. Research design, data, and methodology: Lack of unified guidelines, there are confusions in evaluation of ESG institutionsresults may are in evaluation of the same corporate due to differences in evaluation factors and methods. This has anegative effect on a company’s ESG evaluation. Fortunately, the government set up K-ESG guidelines in December2021. In this study, most of the social enterprises were in the form of SMEs, Thus the evaluation factors of K-ESGsmall and medium-sized enterprises were applied, and the Social Value Index (SVI) of the Social Enterprise PromotionAgency was used. The survey area and subjects were surveyed through a survey of related entrepreneurs and expertsof social enterprises in Gangwon-do, and the AHP analysis method was used to find out the relative importance offactors. Results: The conclusion of this study is as follows. First, specific systematization was realized by approaching the ESGevaluation criteria of social enterprises at the hierarchical level of the Triple Bottom Line (TBL). Second, the ESGevaluation criteria of social enterprises with social value as their management goals were quantified through AHPanalysis, and importance ranking were determined. Third, ESG evaluation criteria were established and presentedthrough analysis by experts in the field of social enterprises. Implications: ESG demands due to the importance of continuous management will also affect social enterprises, and astrategic system is needed to establish and promote social value-based evaluation standards pursued by socialenterprises through the establishment of ESG evaluation standards.