Rating Data Research Articles

Measuring cyclists’ subjective perceptions of the street riding environment using K-means SMOTE-RF model and street view imagery

Cyclists' willingness to ride is usually influenced by their subjective perception of the street riding environment. Measuring this perception is crucial for enhancing residents' willingness to ride. We propose an SSB framework (Public Security, Traffic Safety, Scenic Beauty) to quantify cyclists' subjective perception using street view imagery (SVI) and volunteer rating data. To address the issue of imbalanced class distribution in the volunteer rating data and enhance the model's ability to distinguish between positive and negative perception scenes, we employed a combination of the Kmeans Synthetic Minority Over-Sampling Technique (Kmeans-SMOTE) and the Random Forest (RF) classifier. The Kmeans SMOTE-RF model improved Area Under the Curve (AUC) by 0.327 for public safety, 0.2 for traffic safety, and 0.209 for scenic beauty compared to the RF model. Additionally, we incorporated Shapley Additive Explanations (SHAP) to examine how the visual features of SVI impact cyclists' subjective perception. Trees had a positive impact on all dimensions. Fence and sidewalk were key features for enhancing traffic safety perception, while roads positively affected public security and scenic beauty. These insights support urban planners in understanding the relationship between SVI features and cyclists' perceptions, aiding the design of cyclist-friendly street environments.

Evaluation of covariate effects in item response theory models.

Item response theory (IRT) models are usually the best way to analyze composite or rating scale data. Standard methods to evaluate covariate or treatment effects in IRT models do not allow to identify item-specific effects. Finding subgroups of patients who respond differently to certain items could be very important when designing inclusion or exclusion criteria for clinical trials, and aid in understanding different treatment responses in varying disease manifestations. We present a new method to investigate item-specific effects in IRT models, which is based on inspection of residuals. The method was investigated in a simulation exercise with a model for the Epworth Sleepiness Scale. We also provide a detailed discussion as a guidance on how to build a robust covariate IRT model.

Establishing analytic score profiles for large-scale L2 writing assessment: The case of the CET-4 writing test

This study addresses a critical need in large-scale L2 writing assessment by emphasizing the significance of tailoring assessments to specific teaching and learning contexts. Focusing on the CET-4 writing test in China, the research unfolded in two phases. In Phase I, an empirically-developed analytic rating scale designed for the CET-4 writing test was rigorously validated. Twenty-one raters used this scale to rate 30 essays, and Many-Facets Rasch Model (MFRM) analysis was performed on the rating data. The outcomes demonstrate the scale’s robustness in effectively differentiating examinees’ writing performance, ensuring consistency among raters, and mitigating rater variation at both individual and group level. Phase II extends the research scope by applying the validated scale to score 142 CET-4 writing scripts. Utilizing Hierarchical and K-Means cluster analyses, this phase unveils three distinct score profiles. These findings are significant for both the CET-4 writing test and other L2 large-scale writing assessment. Theoretically, this study introduces a perspective that aims to enhance our understanding of learners’ performance in large-scale L2 writing assessment. Methodologically, this study presents a framework that integrates the validation of the rating scale with the identification of distinct score clusters, thus aiming to provide a more detailed solution for tailoring assessments to specific learning contexts.

Identifying and developing strategies for implementation of a guided internet- and mobile-based infant sleep intervention in well-baby and community mental health clinics using group concept mapping

BackgroundThis study aimed to identify strategies for the implementation of a guided internet- and mobile-based intervention (IMI) for infant sleep problems (“Sleep Well, Little Sweetheart”) in well-baby and community mental health clinics.Study designWe used group concept mapping, a two-phased mixed methods approach, conducted as a two-day workshop in each clinic. We recruited 20 participants from four clinics and collected sorting and rating data for implementation strategies based on the Expert Recommendations for Implementing Change taxonomy and brainstorming sessions. Data were analyzed using descriptive statistics, multidimensional scaling, and hierarchical cluster analysis to create cluster maps, laddergrams, and Go-Zone graphs. Participants were presented with the results and discussed and interpreted the findings at each of the clinics in spring 2022.ResultsParticipants identified 10 clusters of strategies, of which Training, Embedding and Coherence, User Involvement and Participation, and Clinician Support and Implementation Counseling were rated as most important and feasible. Economy and Funding and Interactive and Interdisciplinary Collaboration were rated significantly lower on importance and feasibility compared to many of the clusters (all ps < 0.05). There was a correlation between the importance and feasibility ratings (r =.62, p =.004).ConclusionsThe use of group concept mapping made it possible to efficiently examine well-baby and community clinics’ perspectives on complex issues, and to acquire specific knowledge to allow for the planning and prioritization of strategies for implementation. These results suggest areas of priority for the implementation of IMIs related to infant sleep problems.Trial registrationThe study was pre-registered at Open Science Framework (www.osf.io/emct8).

Customer Satisfaction and Internal Control

ABSTRACT We investigate whether firms’ ineffective internal control over financial reporting is associated with customer satisfaction, measured by product ratings on Amazon.com. Internal control weaknesses will likely corrupt the information environment, compromise coordination, and divert corporate resources to address the control deficiencies. Using a large sample of product rating data from Amazon.com, we find robust and consistent evidence that internal control weaknesses are negatively associated with customer satisfaction. Furthermore, the negative association is more pronounced for environment-level (versus other) internal control weaknesses, noncore (versus core) products, and more (versus less) operationally complex firms. Our findings provide initial evidence that ineffective internal control compromises customer satisfaction. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M11; M41; M42; M48.

Interviewer Ratings of Physical Appearance in a Large-Scale Survey in China

Abstract Interviewer ratings of respondents’ physical appearance have been collected in several major social surveys. While researchers have made good use of such ratings data in substantive studies, empirical evidence on their measurement properties is rather limited. This study evaluates two potential threats to the quality of interviewer ratings of physical appearance: interviewer effects and halo effects. Using data from the China Family Panel Studies, we show large interviewer effects on interviewer ratings of respondents’ physical appearance based on cross-classified models. We also provide possible evidence for halo effects based on high correlations between physical appearance ratings and other theoretically distinct constructs, after controlling for interviewer effects. However, we find support for convergent and discriminant validity of physical appearance ratings when both interviewer effects and halo effects are controlled for. Empirical studies using interviewer observation data should take into account interviewer effects and halo effects when possible or at least discuss their potential impact on the substantive findings.

Enterprise Digital Transformation, Dynamic Capabilities, and ESG Performance

Digital transformation improves the operational efficiency and perceptual capabilities of businesses and creates a foundation for fulfilling ESG responsibilities. The authors use the theories of technological innovation and dynamic capabilities and select a sample of 4054 listed companies with commercial integration of ESG rating data to explore the mechanisms through which digital transformation affects ESG performance. Dynamic capabilities are further divided into innovation changeability (IT), coordination integration capability (CI), and learning absorption capability (LA) to investigate the mediating effects and summarize the causal path of “digital transformation - dynamic capabilities - ESG performance.” The research focuses on the impact of digital technology on ESG performance with the aim of helping businesses utilize technology more effectively to build dual competitive advantages in digitization and ESG.

A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation Systems

The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning data within the rating matrix. Since attackers often inject a significant quantity of poisoning data in a short period to achieve their desired impact, these data may exhibit spatial proximity. In other words, poisoning data may be concentrated in adjacent rows of the rating matrix. This paper capitalizes on the proximity characteristics of poisoning data in the rating matrix and introduces a sampling-based method for detecting data poisoning attacks. First, we designed a rating matrix sampling method specifically for detecting poisoning data. By sampling differences obtained from the original rating matrix, it is possible to infer the presence of poisoning attacks and effectively discard poisoning data. Second, we developed a method for pinpointing malicious data based on the distance of rating vectors. Through distance calculations, we can accurately identify the positions of malicious data. After that, we validated the method on three real-world datasets. The results demonstrate the effectiveness of our method in identifying malicious data within the rating matrix.

Deep learning architecture towards consumer buying behaviour prediction using multitask learning paradigm

A recommendation system serves as a distributed information filter, predicting customer preferences in reviews, ratings, and comments. Analysing customer behaviour aids in understanding needs and predicting intentions. E-commerce tracks product usage and sentiment to provide a personalized network based on consumer preference modelling. The challenge lies in optimizing item selection for suitable consumers to enhance performance. To address this, an imperative is the item recommendation approach for modelling future consumer behaviour. However, traditional machine learning methods often overlook dynamic product recommendations due to evolving user interests and changes in preferences reflected in customer ratings, causing cold-start issues. To overcome these challenges, a comprehensive deep learning approach is introduced. This approach incorporates a deep neural network for consumer preference prediction, utilizing a multi-task learning paradigm to accommodate variations in consumer ratings. The research contribution lies in applying this network to predict consumer preference scores based on latent multimodal information and item characteristics. Initially, the architecture manages changing consumer aspects and preferences by extracting features and latent factors from customer review rating data. These latent factors include customer demographic information and other concealed features that signify preferences based on experiences and behaviours. Extracted latent features are processed using a sentiment analysis model to generate embedding latent features. A finely-tuned deep neural network with hyper-parameter adjustments serves as a prediction network, forming a customer performance-oriented recommendation system. It processes embedded latent features along with associated sentiments to achieve high prediction accuracy, reliability, and latency. The deep learning architecture, enriched with consumer-specific discriminative information, generates an objective function for item recommendations with minimal error, significantly enhancing predictive performance. Empirical experiments on Amazon review datasets validate the proposed model’s performance, showcasing its enhanced effectiveness and scalability in handling substantial data volumes.

Trajectories and predictors of high-occurrence pain flares in ambulatory cancer patients on opioids.

Pain flares have a substantive impact on the quality of life and well-being of patients with cancer. We identified longitudinal trajectories (clusters) of cancer pain flares in ambulatory patients and sociodemographic and clinical predictors of these trajectories. In a prospective cohort study using ecological momentary assessment (mEMA), we collected patient-reported daily pain flare ratings data over 5 months and identified predictors and correlates using validated measures. The mean age of the sample (N = 270) was 60.9 years (SD = 11.2), 64.8% were female, and 32.6% self-identified as African American. Four pain flare clusters were identified. The "high-occurrence" cluster (23% of patients) experienced 5.5 (SD = 5.47) daily flares, whereas low-moderate clusters (77%) reported 2.4 (SD = 2.74) daily flares (P < .000). Those in the high-occurrence cluster reported higher pain scores (P = .000), increased pain-related interference (P = .000), depressive symptoms (P = .023), lower quality of life (P = .001), and reduced pain self-efficacy (P = .006). Notably, 67.2% of those prescribed opioids as needed (PRN only) were in the high-occurrence pain flare cluster, compared with 27.9% with PRN and around-the-clock opioid prescriptions (P = .024). Individual predictors of high-occurrence pain flares were income below $30 000, unemployment, being African American, lower education level, Medicaid insurance, current opioid misuse (COMM), baseline inpatient hospital stay duration, and PRN-only opioid regimen. In the multiple predictor model, lower education level, unemployment, COMM score, extended inpatient duration, and PRN-only opioid regimen remained significant. In ambulatory patients with cancer, high occurrence of pain flares may be mitigated by attention to opioid prescription factors and addressing social determinants of health needs of underserved patients.

ASKAT: Aspect Sentiment Knowledge Graph Attention Network for Recommendation

In modern online life, recommender systems can help us filter unimportant information. Researchers of recommendation algorithms usually utilize historical interaction data to mine potential user preferences. However, most existing methods use rating data to mine user interest preferences, ignoring rich textual information such as reviews. Although some researchers have attempted to combine ratings and reviews for recommendation, we believe the following shortcomings still exist. First, existing methods are overly dependent on the accuracy of external sentiment analysis tools. Second, existing methods do not fully utilize the features extracted from reviews. Further, existing methods focus only on the aspects that users like, while ignoring the aspects that users dislike, and they cannot completely model users’ true preferences. To address the above issues, in this paper, we propose a recommendation model based on the aspect of the sentiment knowledge graph attention network (ASKAT). We first use the improved aspect-based sentiment analysis algorithm to extract aspectual sentiment features from reviews. Then, to overcome the difficulty in underutilizing the information extracted from the comments, we build aspects of sentiment-enhanced collaborative knowledge mapping. After that, we propose a new graph attention network that uses sentiment-aware attention mechanisms to aggregate neighbour information. Finally, our experimental results on three datasets, Movie, Amazon book, and Yelp, show that our model consistently outperforms the baseline model in two recommendation scenarios, click-through-rate prediction and Top-k recommendation. Compared with other models, the method shows significant improvement in both recommendation accuracy and personalised recommendation effectiveness.

Credit rating agencies during credit crunch

AbstractIn this paper, we study whether credit rating agencies (CRAs), as they claim, follow the rating through‐the‐cycle approach as opposed to a pro‐cyclical approach. In particular, we compare the behavior of CRAs during the credit crunch and normal market conditions. Using the credit rating data by S&amp;P, we find that CRAs assign lower credit ratings to firms during credit crunch relative to normal times. Nevertheless, this result does not necessarily imply that CRAs show an excessively pro‐cyclical behavior if credit crunches have a long‐term fundamental impact on firms. Our further investigation reveals that downgrades during a credit crunch will not be reversed over the subsequent 1–5 years, which supports through‐the‐cycle credit rating.

Blockchain-driven framework for financing credit in small and medium-sized real estate enterprises

PurposeThis study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.Design/methodology/approachThe design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.FindingsThe results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.Originality/valueThis study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

경제적 어려움 하에서 ESG와 기업가치의 관계

[Purpose] This study examines the effect of environmental, social, and governance (ESG) performance on corporate value in the face of economic difficulties. If a company’s survival is at stake, we predict that the capital market will focus on increasing the viability of a firm more important than the long-term benefits of ESG engagements.&#x0D; [Methodology] We use sales decline as a proxy for economic hardship. A firm’s sales decline is measured in a variety of ways：relative to the firm’s own sales in the preceding year or relative to the change in aggregate sales of the industry in the concurrent year. Additionally, we use operating profit decline as an alternative indicator of economic hardship. We collect ESG performance (ratings) data from Korea Corporate Governance System (KCGS) from 2011 to 2020.&#x0D; [Findings] We find that the positive value effect of ESG performance is weaker for firms that experience such shocks. Furthermore, during the COVID-19 pandemic, the effect of enhancing the value of ESG performance is not observed. Our results are robust to controlling for potential endogeneity of ESG performance using the two-stage least squares (2SLS) approach.&#x0D; [Implications] Our findings suggest that the beneficial effects of ESG performance on long -term corporate value is attenuated when firms face economic difficulties and severe economic crisis.

경영자 능력이 사업위험에 미치는 영향

[Purpose] This study aims to analyze the impact of managerial abilities on a company’s business risks and examine whether ESG (Environmental, Social, Governance) management has a differential effect. [Methodology]The study conducts an empirical analysis on manufacturing companies listed in the Korean Stock Exchange and KOSDAQ from 2012 to 2022. It utilizes the Earnings Downside Risk (EDR) measure proposed by Konchitchki et al. (2016) as a metric for business risk and assesses Managerial Ability using the method developed by Demerjian et al. (2012a). Additionally, the study employs ESG rating data from the Korea Corporate Governance Service(KCGS). [Findings]The study reveals that ESG management significantly lowers business risk, with superior Managerial Ability playing a crucial role, especially in companies focused on ESG practices. This suggests that effective management skills in ESG－oriented companies are key to reducing business risk. [Implications]The study indicates that effective ESG management in companies can lead to long－term competitiveness and reduced business risk, with skilled managers playing a key role in achieving strategic benefits like cost savings, improved efficiency, and a better corporate image.

기업의 ESG 활동이 이익조정에 미치는 영향

[Purpose]This study emphasizes the importance of ESG (Environmental, Social, and Governance) management, proposing that it provides incentives for companies to achieve transparent and sustainable management. The study aims to analyze the impact of ESG management on corporate earnings management, particularly focusing on the effect of one financial characteristic, the Return on Sales (ROS), and how ESG influences this relationship. [Methodology]The study utilizes ESG rating data publicly disclosed by the Korea ESG Standards Authority from 2015 to 2022, focusing on manufacturing companies with available ESG ratings. The discretionary accruals were measured using the Jones model as the earnings management variable, and ROS was used to reflect financial characteristics. [Findings]The study found that ESG management tends to reduce corporate earnings management activities. Moreover, while companies with higher ROS were found to engage in more earnings management, the interaction between ROS and ESG management, particularly in terms of ESG integration, environmental (S), and governance (G) components, showed a negative relationship with earnings management. This suggests that ESG management contributes to reducing earnings management. [Implications]The study suggests that ESG management plays a crucial role in enhancing financial transparency and ethical standards in corporations. It was observed that companies with higher total sales profitability, when implementing ESG management, tend to show a decrease in earnings management activities. This indicates that ESG management can be a vital tool for enhancing financial soundness and transparency.

The Effect of Lavender Aromatherapy on The Level of Anxiety Level of Laboring Mothers in The Era of New Habits In PMB Bdn. Luh Ayu Koriawati, A.Md.Keb

Anxiety disorders comprise non-psychotic mental disorders, generalized anxiety disorder, phobias, panic attacks, obsessive compulsive disorder, and other disorders that fall under the broader category of "anxiety disorders without other specifications" (American Psychiatric Association, 2013). In general, anxiety disorders are characterized by excessive fear and anxiety, as well as certain behavioral disturbances. While fear is an emotional response to an imminent threat, characterized by acute autonomic system activation, anxiety is also described as the "anticipation of future threats" (Donelli et al., 2019). The design of this study was quasi experimental. This research was conducted at PMB Bdn. Luh Ayu Koriawati, A.Md.Keb with an average maternity population of 90 mothers giving birth at PMB Bdn. Luh Ayu Koriawati, A.Md.Keb. The sample in this study amounted to 30 respondent mothers at PMB Bdn. Luh Ayu Koriawati, A.Md.Keb who met the inclusion criteria. The sampling technique of this research is purposive sampling. Data were collected with the help of an anxiety level questionnaire using the Hamilton Rating Anxiety Scale (HARS) and data analysis was carried out in stages including univariate and bivariate (Wilcoxon Signed Rank Test). Based on the results of the study obtained from 30 respondents (100%), after being given aromatherapy (posttest) there were 18 respondents (60.0%) did not experience anxiety and there were 12 respondents (40.0%) experiencing mild anxiety. The bivariate results show that there is a significant value of 0.000 (p-value &lt;0.05), thus it is concluded that there is a significant effect on the average score of anxiety levels before and after giving lavender aromatherapy at PMB Bdn. Luh Koriawati, S.Tr.Keb

Corporate ESG rating divergence and excess stock returns

ESG rating have progressively become an important reference index within the investment decision-making process. However, divergence might arise among diverse ESG rating agencies when appraising the same company. Such divergence affects investors' perception of a companies' ESG performance, consequently affecting its excess stock returns. This paper delves into the mechanics behind ESG rating divergence on excess stock returns based on the ESG rating data encompassing Shanghai and Shenzhen A-share listed companies in China spanning from 2018 to 2022. The research reveals the following findings: Firstly, ESG rating divergence negatively impact stock excess return rates. Investor sentiment, ESG improvement potential and information transparency play a positive moderating role in the influence of ESG rating divergence on excess stock returns. Secondly, there are heterogeneity characteristics in the impact of ESG rating divergence on excess stock returns. Companies that actively disclose ESG reports, low-carbon companies, companies with QFII, and those with high divergence and high rating experience a greater impact form ESG rating divergence on their excess stock returns. Thirdly, the social rating divergence has the greatest impact on excess stock returns, followed by environmental and governance levels.

How uncertainty can determine corporate ESG performance?

AbstractUsing Sino‐Securities Environmental, social, and governance (ESG) ratings data, we examine how environmental uncertainty affects the ESG performance of Chinese A‐share non‐financial listed firms from 2008 to 2020. Our findings show that environmental uncertainty harms corporate ESG performance. In particular, when environmental uncertainty increases, a firm's ESG score and ESG ratings decline due to factors such as financial constraints and industry competition. We argue that as the environmental risk premium rises, it increases the real options value of postponing sustainable investment for a firm. Consequently, the firms tend to cut down their ESG investment by weighing the long‐term benefits and short‐term direct costs. The value of real options changes with the investment opportunities available to the firms and the financing constraints and competitive pressure changes the size of investment opportunities. We argue that higher financing constraints and industry competition restrict available investment opportunities and dilute the negative impact of environmental uncertainty on corporate ESG performance. These results add to the existing literature investigating the impact of uncertainty on corporate ESG performance and offer insights to regulators and enterprise managers. These results are robust to alternate proxies of ESG performance and alternate regression techniques.

Analysis of pressure perception of internal roads in existing settlements based on random forest/deep learning

The quality of road space has an influential role in human psychological health. Quantifying residents' perceptions of road spatial quality and exploring the relationship between perceptions and constituent elements have become topics of interest to many parties. In this study, based on the high-resolution remote sensing images acquired by Baidu Maps, the road images of built-up settlements in Beijing in three different periods (1949-1977, 1978-1997, and 1998-2022) were semantically segmented. The large-scale rating data and subjective evaluations of residents' spatial perceptions of roads were analyzed using random forest and multiple linear regression methods. The results show that stress, a negative perception, is positively correlated with buildings and cars, and negatively correlated with vegetation, sky, and sidewalks. The composition of elements affecting residents' stress perceptions in communities of different build-up years was consistent, but the degree of influence of each element was ranked differently. This study attempts to summarize the road characteristics and residential advantages of different built-up neighborhoods, and proposes optimization strategies for upgrading road space in old neighborhoods and countermeasure suggestions for future improvement of the human living environment.