Qualitative Factors Research Articles

The Impact of a Local Well-Being Philosophy on Revisit Intentions to Bali Among Digital Nomads

The level of success in tourism is gauged by several metrics; however, the most widely used is the level of tourist arrivals. However, this research answered the call for greater investigation of the impacts of qualitative factors and intangible cultural–heritage assets on destination performance. The primary research purpose was to analyze the effect of implementing a local well-being philosophy (Tri Hita Karana) on tourist revisit intentions for Bali and the mediation of destination quality and destination image. A research model was developed to examine the relationships among local wisdom (TKH), destination quality, destination image, and revisit intentions. Data were collected via a survey of 520 digital nomadic tourists and analyzed using SmartPLS 4. The results indicated that the implementation of THK positively and significantly affected revisit intentions, destination image, and destination quality. Destination image and destination quality had positive and significant effects on revisit intentions, and destination image and destination quality also significantly mediated the effect of THK implementation on revisit intentions. The findings suggested that implementing local wisdom values such as THK in the management of a destination makes visitors feel more favorably about the quality and image of the destination and they have the intention to revisit.

문자중계와 몬테카를로 시뮬레이션 기반 배구 경기 승패 예측에 관한 연구

With advancements in science and technology, a vast amount of data from various media is being generated in the field of sports. Applying statistical methods to extract meaningful information from this data has become essential. This study aimed to predict volleyball match outcomes by performing Monte Carlo simulations based on live text commentary data from the 2023-2024 women's professional volleyball season. By generating the necessary variables for the simulation from the text commentary data and conducting 10,000 iterations of Monte Carlo simulations, the study confirmed that the simulations accurately mirrored actual match outcomes. In particular, when one team demonstrated overwhelming performance, the accuracy of set score predictions was notably high, and the simulation results demonstrated the potential for developing customized strategies. However, limitations were observed in predicting outcomes in specific scenarios, such as when a team with a high total score loses 3-2. To address these limitations, future work should focus on more precisely modeling the complex interactions between various situational and variable factors by converting qualitative factors, which have been challenging to quantify, into objective indicators for integration into the model.

A Novel Deep Learning Model for Stock Prediction Integrating Historical Prices, Core Volatility Trends and Financial News

Accurate stock price forecasting is crucial for investors and analysts aiming to make informed decisions and mitigate risks in financial markets. While deep learning models have proven effective in identifying complex patterns in quantitative data, they often struggle to account for non-quantitative factors such as corporate events, market sentiment, and regulatory changes that can cause unpredictable stock price movements. To address this challenge, an ensemble model is introduced that integrates historical price data, financial news, and stock fundamentals to predict next-day stock prices. The model combines one-dimensional Convolutional Neural Networks (1D CNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs), applied independently to each data stream and concurrently as a unified ensemble, to capture both short-term and long-term market behaviors. NVIDIA (NVDA) stock was selected for testing due to its high volatility and rapid price fluctuations, presenting a challenging case for predictive modeling. Using data spanning from April 2014 to March 2024, the model achieved a coefficient of determination (R²) of 0.983 and a mean absolute error (MAE) of $12.72. These results suggest that the ensemble approach effectively captures both quantitative and qualitative factors, making it a promising tool for improving stock price prediction in volatile market conditions.

News Sentiment and Liquidity Risk Forecasting: Insights from Iranian Banks

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank’s short-term resilience to liquidity shocks. Despite limited prior research, particularly in anticipating upcoming positions of bank liquidity risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the liquidity coverage ratio (LCR) under Basel III reforms, focusing on its direction (up, down, stable) rather than on exact values, thus distinguishing itself from previous studies. The research objectively explores the influence of external signals, particularly news sentiment, on liquidity prediction, through novel data augmentation, supported by empirical research, as qualitative factors to build a model predicting LCR positions using AI techniques such as deep and convolutional neural networks. Focused on a semi-private Islamic bank in Iran incorporating 4,288,829 Persian economic news articles from 2004 to 2020, this study compared various AI algorithms. It revealed that real-time news content offers valuable insights into impending changes in LCR, particularly in Islamic banks with elevated liquidity risks, achieving a predictive accuracy of 88.6%. This discovery underscores the importance of complementing traditional qualitative metrics with contemporary news sentiments as a signal, particularly when traditional measures require time-consuming data preparation, offering a promising avenue for risk managers seeking more robust liquidity risk forecasts.

Decoding Stock Price Movements: How Net Profit Margin and Debt-to-Equity Ratio Drive Value, with Earnings per Share as the Game-Changer

This research investigates the relationships between Net Profit Margin (NPM), Debt-to-Equity Ratio (DER), Earnings per Share (EPS), and stock prices within the context of the Indonesian stock market. Utilizing a quantitative analysis approach, the study employs panel data regression to assess the statistical significance of these financial metrics on stock valuations. The findings reveal that NPM has a meaningful and statistically significant positive impact on stock prices, indicating that higher profit margins are associated with increased investor confidence and market valuation. Conversely, the results show that DER does not significantly influence stock prices, suggesting that investors may perceive high debt levels as a risk that diminishes stock value. Furthermore, the study finds that EPS does not moderate the relationship between NPM and stock prices, nor does it strengthen the effect of DER on stock valuations. These conclusions contribute to understanding financial determinants in stock price movements, emphasizing the importance of profit margins while highlighting the limited role of leverage and earnings per share in this context. The research provides recommendations for investors and corporate managers and suggests avenues for future studies, including broader geographic comparisons and integrating qualitative factors into stock valuation analysis.

Integrating Quantitative Data and Qualitative Insights to Understand 30-Day Readmission Rates: A Mixed-Methods Study.

The rate of patients readmitted to hospitals within 30 days of discharge is a critical indicator of healthcare quality. This study explored the factors contributing to 30-day hospital readmission rates nationally and at Arrowhead Regional Medical Center (ARMC) through a mixed-methods research design. Quantitative analysis utilized data from the Centers for Medicare & Medicaid Services (CMS) database, focusing on patient demographics, principal diagnoses, length of stay, and hospital characteristics. Multivariate regression and descriptive statistics were employed to identify predictors of 30-day readmission. The qualitative analysis sought to understand the specific medical conditions and patient profiles linked to higher readmission rates. The findings revealed that older age, specific principal diagnoses (e.g., heart failure, pneumonia, chronic obstructive pulmonary disease (COPD)), and longer initial hospital stays were associated with an increased likelihood of 30-day readmission. Gender disparities and hospital size/type also influenced readmission rates. These results provide valuable insights into the complex interplay of individual patient characteristics and hospital attributes in driving readmissions. The study's mixed-methods approach yielded a comprehensive understanding of the quantitative patterns and qualitative factors contributing to 30-day hospital readmission rates, offering important implications for healthcare quality improvement initiatives.

Subjectivity of visual assessments in FOCUS kinetics and acceptability of first-order fits for regulatory modelling

The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.

The use of a clip prior to neoadjuvant chemotherapy for breast cancer with microcalcifications may not always be required.

Neoadjuvant chemotherapy is now a common first line therapy for breast cancer. International guidelines recommend placement of a clip before commencement of therapy to assist with localizing the tumor bed in the event of excellent response-this takes up time and resources. The microcalcifications associated usually persist after chemotherapy and could serve as an alternative marker. We investigated to determine prognostic criteria to avoid the need for a marker clip before neoadjuvant chemotherapy for breast tumors associated with microcalcifications. We performed a 7year single-center bi-site retrospective analytical observational study of 88 women with calcified breast carcinoma treated by neoadjuvant chemotherapy at our bi-site institution between September 2015 and September 2022. This study includied two groups (clip-free tumor localization vs. clip-free tumor non-localization), and investigating quantitative and qualitative predictive factors. The clip-free tumor localization after neoadjuvant chemotherapy was defined by the visibility of residual calcifications on both views of the pre-operative mammogram on the day of or the day prior to surgery. The mean age of the 88 women included in our population was 52.8years (± 12.7years standard deviation). Of the 90 tumors with microcalcifications, 64 carcinomas (71.1%) were localizable with no marker clip after neoadjuvant chemotherapy. The main predictive factors significantly associated with clip-free tumor localization were number of calcifications > 10 (P < 0.0001), grade 2 tumor (P = 0.003) with a probability of locating tumor after neoadjuvant chemotherapy of 97.9%, 95% CI [95.6; 99.0]. More than 10 microcalcifications in a grade 2 breast tumor at the initial diagnosis may obviate the need for a marker clip.

Predicting post-surgical complications using renal scoring systems.

Current surgical approaches for renal malignancies primarily rely on qualitative factors such as patient preferences, surgeon experience, and hospital capabilities. Applying a quantitative method for consistent and reliable assessment of renal lesions would significantly enhance surgical decision-making and facilitate data comparison. Nephrometry scoring (NS) systems systematically evaluate and describe renal tumors based on their anatomical features. These scoring systems, including R.E.N.A.L., PADUA, MAP scores, C-index, CSA, and T-index, aim to predict surgical complications by evaluating anatomical and patient-specific factors. In this review paper, we explore the components and methodologies of these scoring systems, compare their effectiveness and limitations, and discuss their application in advancing patient care and optimizing surgical outcomes.

Comprehensive Analysis of Football Player Market Valuation: Integrating Performance Metrics and Marketability Factors

In the fast-paced world of professional football, valuing players involves numerous factors. This paper explores the determinants of football players' market values using a mixed-methods approach. Quantitative data from sources like Opta Sports, Transfermarkt, and the CIES Football Observatory, along with qualitative insights from semi-structured interviews with industry experts, were analyzed. The quantitative analysis found that goals scored and social media followers significantly predict player market value, with a strong social media presence amplifying the impact of goals. Non-linear relationships revealed diminishing returns on market value as social media followers increase. Qualitative insights highlighted the importance of media presence, cultural impact, and digital engagement in shaping player valuations. Players frequently featured in media, seen as cultural icons, or active on digital platforms tend to have higher market values. A comparison with historical data showed a growing emphasis on marketability and digital presence. The study concludes that integrating quantitative performance metrics with qualitative marketability factors is crucial for accurate player valuation. This holistic approach offers valuable insights for clubs, agents, and policymakers, enhancing decision-making in player transfers and contract negotiations. The research contributes to sports management and economics by providing a comprehensive framework for managing player assets in professional football.

The effects of qualitative factors on landslide magnitude and typology in the homogenous geomorphological context of the Prerif unit, Morocco

The effects of qualitative factors on landslide magnitude and typology in the homogenous geomorphological context of the Prerif unit, Morocco

Estimating the impact range of circular economy actions for buildings on basic material demand in the European Union: A combination of qualitative and quantitative approaches

A circular economy in buildings can contribute to reduce the demand for emission-intensive basic materials and thus, to achieve the climate targets of the European Union. However, quantifying the impact on material demand is challenged by the unpredictability of the future. We combined qualitative and quantitative approaches to improve the understanding of such future developments. In detail, expert interviews were conducted to identify influencing factors and to collect estimates for modelling prospective material demand. The results indicate that the adaption is driven by existing and new policies, climate change as well as demographics. This is contrasted by barriers, such as the economic non-viability, the limited availability of substitutes, and the mismatch of supply and demand as well as building regulations. Moreover, the type of influencing factor reflects the maturity of individual strategies related to a circular economy. The estimated impact of circular economy actions on annual material demand for buildings varies between −42 % and +25 % in 2050 compared to a reference development. However, there are not only differences between but also within individual actions. In brief, the maturity of the circular economy actions is reflected by the range of impact on material demand. The novel combination of the qualitative influencing factors and the quantitative modelling results reveals an interrelationship between the drivers and barriers and the impact on material demand. This underlines that the prospective exploitation of these potentials can be actively influenced by adapting the policy framework conditions. This is particularly relevant for less renowned circular economy strategies and actions that are characterized by high potential and high unpredictability. The presented results lay the foundation for a more systematic consideration of material reduction potentials related to a circular economy in prospective scenarios of buildings in the European Union.

Capacity augmentation strategy in α-Fe2O3 nanoparticles through multifaceted structural and electrochemical analyses

Hematite (α-Fe₂O₃) has garnered significant research interest for supercapacitor applications, but capacity fading and limited cyclic life remain serious challenges. In this context, the present research provides a comprehensive study of the factors that significantly impact charge kinetics within the electrode and at the solid/electrolyte interface. By integrating structural and electrochemical analyses, this study offers an effective strategy to enhance the capacity of α-Fe₂O₃ nanoparticles through rigorous scientific analysis and identification of various qualitative and quantitative factors that influence charge storage capacity. A simple one-step co-precipitation method was adopted to synthesize porous α-Fe₂O₃ nanoparticles. The quantitative analysis of charge kinetics demonstrated the domination of diffusion-controlled charge storage as the main contributor to the total charge storage. The α-Fe₂O₃ nanoparticles-based electrode delivered a high energy density of 24.6 Wh kg⁻1 at an impressive power density of 478.7 W kg⁻1. Additionally, it displayed a capacity retention of 79 % over 10,000 cycle operations. The choice of electrode material and the superior porous nanoarchitecture have proven to be advantageous for high-performance supercapacitor applications, offering fast electron/ion transport.

The Relationship Between Body Mass Index (BMI) and Pulmonary Function Among Healthcare Workers in Mosul City/Iraq

The study was designed to evaluate if there is a relationship between Body Mass Index (BMI) and pulmonary function. This highlights the mechanisms by which obesity and overweight status could affect lung health, through parameters such as Forced Expiratory Volume in 1 Second (FEV1%). To determine pulmonary function, spirometry is the main method. It defines the severity of pulmonary function impairment according to FEV1% as normal to mild: average ≥70%, moderate 40-69%, and severe ≤39%. Interestingly, the review even points out that a form of malnutrition can also cause pulmonary function decline. This is why nutritional status should be one consideration in clinical assessments for some types of diseases. Reflecting the fact that obesity is an important contributing factor to respiratory health. It is stated in the document that obesity results from some unwanted changes which can cause the development of smaller lung volumes and also reduce total capacity even less sucking capacities (compared to non-obese people). It also considers waist circumference and additional waist-to-hip ratio as potentially better predictors of pulmonary function than BMI only, because body fat distribution is not reflected by the presence or absence of obesity. The research likewise links this eating pattern to the worldwide rise in obesity and suggests a correlation with myriad health issues such as asthma. This indicates that mechanical consequences of increased body mass could exacerbate airway remodeling, predisposing to subclinical airway dysfunction. Second, it emphasizes the extra burden of breathing experienced by obese patients that may render them short of breath and possibly wheezy. In conclusion, whilst BMI remains a pragmatic measurement to reflect body fat content adequately and therefore pulmonary implications of obesity on lung physiology upstream from Acute exacerbations are also influenced not just by the quantitative trajectory of adiposity (BMI) but qualitative factors related to body distribution including abdominal girth as well as overall nutritional status.

Evaluation of emotion classification schemes in social media text: an annotation-based approach

BackgroundEmotion analysis of social media texts is an innovative method for gaining insight into the mental state of the public and understanding social phenomena. However, emotion is a complex psychological phenomenon, and there are various emotion classification schemes. Which one is suitable for textual emotion analysis?MethodsWe proposed a framework for evaluating emotion classification schemes based on manual annotation experiments. Considering both the quality and efficiency of emotion analysis, we identified five criteria, which are solidity, coverage, agreement, compactness, and distinction. Qualitative and quantitative factors were synthesized using the AHP, where quantitative metrics were derived from annotation experiments. Applying this framework, 2848 Sina Weibo posts related to public events were used to evaluate the five emotion schemes: SemEval’s four emotions, Ekman’s six basic emotions, ancient China’s Seven Emotions, Plutchik’s eight primary emotions, and GoEmotions’ 27 emotions.ResultsThe AHP evaluation result shows that Ekman’s scheme had the highest score. The multi-dimensional scaling (MDS) analysis shows that Ekman, Plutchik, and the Seven Emotions are relatively similar. We analyzed Ekman’s six basic emotions in relation to the emotion categories of the other schemes. The correspondence analysis shows that the Seven Emotions’ joy aligns with Ekman’s happiness, love demonstrates a significant correlation with happiness, but desire is not significantly correlated with any emotion. Compared to Ekman, Plutchik has two more positive emotions: trust and anticipation. Trust is somewhat associated with happiness, but anticipation is weakly associated with happiness. Each emotion of Ekman’s corresponds to several similar emotions in GoEmotions. However, some emotions in GoEmotions are not clearly related to Ekman’s, such as approval, love, pride, amusement, etc.ConclusionEkman’s scheme performs best under the evaluation framework. However, it lacks sufficient positive emotion categories for the corpus.

Construction of balanced sliced orthogonal arrays

Balanced sliced orthogonal arrays (BSOAs) are useful for designing sliced computer experiments with qualitative and quantitative factors or multiple models and nested computer experiments with two codes of different levels of accuracy. In this article, we propose a systematic method to construct BSOAs by making use of the A + B design structure and its modifications. A general method for construction of asymmetric BSOAs is also presented based on an orthogonal array with orthogonal partition. These methods are flexible for strength and factors. Numerous new symmetric and asymmetric BSOAs including a lot of ones with high strength can thus be constructed. Compared with the existing BSOAs, the constructed BSOAs have more number of columns. Moreover, these methods can also be used to construct BSOAs whose strength is different from the strength of their slices. Some selective BSOAs are tabulated for practical uses.

Examining the efficacy of biological filters in the removal of agricultural pesticides and nutrient elements from agricultural drainage water

The high water consumption in agriculture has led to an obvious water crisis in this sector, and the use of unconventional water sources, especially agricultural drains, is considered necessary. For this purpose, the present study was carried out to evaluate the efficiency of biological filters with different types of substrates for treating agricultural wastewater in Khuzestan province, located in the south of Iran, to use receptive resources and reuse them in agriculture. Next, the efficiency of four types of biological filters for treating agricultural drainage water with different retention times was evaluated. Sawdust, cotton stalks, wheat straw, stubble, and rice husk were used as filters. Qualitative factors included agricultural pesticides (Atrazine, Randup, Paraquat, and 2, 4-D) and nutrients (nitrate, nitrogen, phosphate, and phosphorus). By examining the trend of increasing the retention time and the corresponding removal percentage, it was observed that the retention time has a direct relationship with the amount of removal efficiency of nutrients and agricultural toxins. As the residence time increases, the average amount of nutrient compounds in different filters decreases, and their removal percentage increases. The highest removal percentage of nitrate, total nitrogen, phosphate, and total phosphorus was 74.03, 71.66, 57.97, and 61.85% in the sawdust filter and was assigned to 10 days. The highest percentage of removal of Atrazine, Tofudi, Paraquat, and Roundup toxins with a removal efficiency of 91.73, 84.27, 89.81, and 88.46% was also observed in the treatment of sawdust for 10 days. The sawdust filter showed a good performance in removing the parameters of agricultural toxins and nutrient compounds in a retention time of 10 days compared to other filters and retention times. As a general result, the sawdust filter can be cited as a reliable substrate with acceptable efficiency compared to other filters.

School conditions and principal practices promoting instructional data use in high-stakes accountability systems

This study explores how school environments and principal behaviors enhance the use of data within Chile's high-stakes accountability systems. Emphasizing the importance of Data-Driven Decision Making (DDDM) for improving educational outcomes, this mixed-methods research analyzed data employment for accountability, improvement, and instructional purposes. Findings show that schools prioritize data use for enhancing instruction and school improvement over mere compliance with external accountability. Key leadership behaviors, notably instructional leadership, are crucial in promoting effective data utilization. The study identifies both minimal and organizational conditions as critical enablers for data use, although their impact varies with the type of data employed. The results advocate for a balanced approach that considers both quantitative and qualitative factors to optimize data use in educational settings. Future research should delve deeper into specific instructional practices and the factors that influence teacher decision-making to further improve data-driven instruction.

Identifying Patterns among Tourism-Oriented Online Communities on Facebook

The proliferation of social media has transformed how people engage in communication and community building, with platforms like Facebook becoming central to connecting individuals with shared interests. Despite the extensive formation of tourism-oriented online communities on these platforms, there is a notable lack of comprehensive studies examining their structural and managerial dynamics. This study addresses this gap by systematically analyzing fifty international tourism-focused Facebook communities to develop a novel typology based on the nature and type of information shared. The research identifies significant variations in community sizes, engagement levels, and management structures, highlighting that only 6% of these communities qualify as large, with membership exceeding one million. Contrary to common assumptions, a direct link between community size and engagement was not found, with qualitative factors like community purpose and content type being more influential. A notable correlation was observed between the number of administrators and moderators and the member count, emphasizing the importance of effective community governance. The study’s findings contribute to a deeper theoretical understanding of online community dynamics and offer practical implications for tourism marketers and community managers aiming to optimize engagement strategies on social media platforms. The research sets a foundation for future exploration of the interplay between virtual community management and tourism-related discourse.

Blinded by the Data: When Analytics Overshadows Judgment

This article discusses the potential risks that can emerge when organizations overly on data-driven decision making by focusing excessively on quantitative analysis and metrics to the exclusion of important qualitative human factors. The article explores common pitfalls such as leaders losing sight of broader strategic perspectives and higher-level business drivers when immersed in granular data points, organizational chaos and paralysis resulting from constant A/B testing without clear objectives, over-quantification discounting important but difficult to measure "soft" elements like culture, talent and leadership that profoundly shape performance, and overconfidence in the perceived objectivity of models overlooks inherent biases. The article advocates balancing quantitative and qualitative lenses to gain a more textured understanding of complex dynamics by supplementing metrics with perspectives from ethnography and stakeholder input to provide needed context as numbers alone do not replace prudent application of human judgment and experience, and for data-driven initiatives to achieve tangible impacts, analytics must inform innovation and change management rather than replace continuous improvement processes, such that with moderation and integration of multiple viewpoints, data can empower rather than control strategic decision making.