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Search Results (571)

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21 pages, 2763 KiB  
Article
Predicting Environmental Social and Governance Scores: Applying Machine Learning Models to French Companies
by Sina Belkhiria, Azhaar Lajmi and Siwar Sayed
J. Risk Financial Manag. 2025, 18(8), 413; https://doi.org/10.3390/jrfm18080413 - 26 Jul 2025
Viewed by 360
Abstract
The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and [...] Read more.
The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and Support Vector Regression (SVR) were employed to provide more accurate and reliable assessments, thus informing the ESG rating attribution process. The results obtained highlight the excellent performance of the Random Forest method in predicting ESG scores from company financial variables. In addition, the approach identified specific financial variables (operating income, market capitalisation, enterprise value, etc.) that act as powerful predictors of companies’ ESG scores. This modelling approach offers a robust tool for predicting companies’ ESG scores from financial data, which can be valuable for investors and decision-makers wishing to assess and understand the impact of financial variables on corporate sustainability. Also, this allows sustainability investors to diversify their portfolios by including companies that are not currently rated by ESG rating agencies, that do not produce sustainability reports, as well as newly listed companies. It also gives companies the opportunity to identify areas where improvements are needed to enhance their ESG performance. Finally, it facilitates access to ESG ratings for interested external stakeholders. Our study focuses on using advances in artificial intelligence, exploring machine learning techniques to develop a reliable predictive model of ESG scores, which is proving to be an original and promising area of research. Full article
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26 pages, 1254 KiB  
Article
Cheese Analogues, an Alternative to Dietary Restrictions and Choices: The Current Scenario and Future
by Ingrid Leal, Paulo Correia, Marina Lima, Bruna Machado and Carolina de Souza
Foods 2025, 14(14), 2522; https://doi.org/10.3390/foods14142522 - 18 Jul 2025
Viewed by 446
Abstract
The increasing demand for plant-based cheese alternatives reflects a shift toward healthier and more sustainable food choices. This study aimed to map technological trends, formulation strategies, and major challenges in the development of plant-based cheese analogues through a systematic review of the scientific [...] Read more.
The increasing demand for plant-based cheese alternatives reflects a shift toward healthier and more sustainable food choices. This study aimed to map technological trends, formulation strategies, and major challenges in the development of plant-based cheese analogues through a systematic review of the scientific literature and patents. Following the PRISMA protocol, searches were conducted in ScienceDirect and Lens.org between December 2024 and January 2025 using keywords related to cheese analogues. A total of 1553 scientific articles and 155 patents were initially retrieved. After applying inclusion and exclusion criteria, 88 articles and 66 patents were selected for detailed analysis. The results show a growing interest in this field since 2020, peaking in 2024. Data from 2025 may be limited due to the search period. Keywords were clustered into three main areas: (1) Formulation and Composition, (2) Texture and Processing, and (3) Food Safety and Consumer Acceptance. The United States leads in patent registrations (59). Valio Company and Cargill were the most active assignees, with nine and eight patents, respectively. This study highlights the importance of integrating food science and technology to improve the quality, sensory attributes, and market competitiveness of plant-based cheese analogues compared to traditional dairy products. Full article
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24 pages, 4383 KiB  
Article
Predicting Employee Attrition: XAI-Powered Models for Managerial Decision-Making
by İrem Tanyıldızı Baydili and Burak Tasci
Systems 2025, 13(7), 583; https://doi.org/10.3390/systems13070583 - 15 Jul 2025
Viewed by 574
Abstract
Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an [...] Read more.
Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article
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25 pages, 809 KiB  
Article
Measuring Airline Performance: An Integrated Balanced Scorecard-Based MEREC-CoCoSo Model
by Melik Ertuğrul and Eylül Özdarak
Sustainability 2025, 17(13), 5826; https://doi.org/10.3390/su17135826 - 25 Jun 2025
Viewed by 821
Abstract
The assessment of company performance requires a holistic approach, encompassing both financial and non-financial metrics. Accordingly, we develop a comprehensive airline performance evaluation model utilizing the Balanced Scorecard (BSC)-based multi-criteria decision-making (MCDM) framework. Based on contingency theory, we use 30 Key Performance Indicators [...] Read more.
The assessment of company performance requires a holistic approach, encompassing both financial and non-financial metrics. Accordingly, we develop a comprehensive airline performance evaluation model utilizing the Balanced Scorecard (BSC)-based multi-criteria decision-making (MCDM) framework. Based on contingency theory, we use 30 Key Performance Indicators (KPIs) derived from the literature and develop a novel performance model by combining the BSC framework with the Method based on the Removal Effects of Criteria (MEREC) for KPI weighting and the Combined Compromise Solution (CoCoSo) for ranking. The focus on Turkish Airlines, serving as a comparative benchmark, over the period 2020–2023 reveals that while financial KPIs hold the greatest weight, non-financial KPIs have the most significant impact on performance. The lowest performance is recorded in 2020, most probably attributable to the COVID-19 pandemic, followed by a remarkable recovery in 2021. We offer a methodological contribution for managers, decision-makers, and scholars—an objective, data-driven tool to assess airline performance. Furthermore, we furnish policymakers with tangible data for more effective industrial incentives and convenient regulatory strategies. In contrast to most of the literature emphasizing financial indicators and subjective weighting approaches that might yield biased rankings, we suggest a novel integrated performance evaluation model tailored for the airline industry. Full article
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25 pages, 2394 KiB  
Article
Enhancing Safety Performance in UK Metal Manufacturing: A Revised Framework to Reduce Fatal Accidents
by Alexandra Eggleston, Shoaib Sarfraz, Konstantinos Salonitis, Sumit Gupta, Hana Trollman and Sandeep Jagtap
Safety 2025, 11(2), 59; https://doi.org/10.3390/safety11020059 - 18 Jun 2025
Viewed by 575
Abstract
Fatal accidents in UK’s manufacturing sector are expected to remain the same or increase in coming years. This paper has tried to combat this issue by adapting and further developing a previously defined Safety Framework for the Paint Sector, to evaluate the safety [...] Read more.
Fatal accidents in UK’s manufacturing sector are expected to remain the same or increase in coming years. This paper has tried to combat this issue by adapting and further developing a previously defined Safety Framework for the Paint Sector, to evaluate the safety performance of a metal manufacturing facility. To achieve this, the original Safety Framework was updated to align with the current British safety legislation outlined by the British Standards Institution. The framework was based on a three-level multi-attribute value theory (MAVT). Upon reviewing BSI 45001, the Safety Framework was founded upon the concept of Deming’s Plan, Do, Check, Act (PDCA) which is the foundation for the original framework, therefore, the first-level attributes remained consistent. The 13 attributes of the second level and 36 attributes of the third level were derived from the literature review and updated to relevant legislation. To develop the Safety Framework, the Delphi method was used. This included interviews that were conducted with employees and managers from either a Safety or Engineering background. The second part of the paper involved the improvement of the Safety Framework, based on the interview feedback. The main findings of the study revealed that the final Safety Framework has been deemed relevant for the Metal Manufacturing Sector by Industry Suitably Qualified and Experienced Personnel (SQEP). The majority of Interviewees deemed the Safety Framework to have a clear layout and easy to understand. The interviews and final Safety Framework suggested the importance of a company’s emphasis on employee welfare and health, in order to reduce accidents in the workplace. The originality of this paper lies in its application and validation of a sector-specific safety framework, contributing to the body of knowledge by offering a replicable methodology for adapting safety frameworks to other manufacturing sectors. Full article
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19 pages, 1015 KiB  
Article
Cloud Platform Selection Using Extended Multi-Attribute Decision-Making Methods with Interval Type-2 Fuzzy Sets
by Ivana Spasenić, Danijela Tadić, Milan Čabarkapa, Dragan Marinković and Nikola Komatina
Axioms 2025, 14(6), 469; https://doi.org/10.3390/axioms14060469 - 16 Jun 2025
Viewed by 420
Abstract
The selection of an appropriate cloud platform represents a highly important strategic decision for any IT company. In pursuit of business optimization, cost reduction, improved reliability, and enhanced market competitiveness, selecting the most suitable cloud platform has become a major practical challenge. This [...] Read more.
The selection of an appropriate cloud platform represents a highly important strategic decision for any IT company. In pursuit of business optimization, cost reduction, improved reliability, and enhanced market competitiveness, selecting the most suitable cloud platform has become a major practical challenge. This paper proposes a novel two-stage multi-attribute decision-making (MADM) model, enhanced through the use of interval type-2 fuzzy sets (IT2FMADM). This was demonstrated through a case study in an IT company based in Serbia. In the first stage, three experts from the company were surveyed to assess the relative importance of the attributes, and their evaluations were aggregated using the fuzzy harmonic mean operator. As a result, unified fuzzy weight vectors were obtained. In the second stage, two MADM methods extended with interval type-2 fuzzy sets, namely COmplex PRoportional Assessment (IT2FCOPRAS) and Evaluation based on Distance from Average Solution (IT2FEDAS), were applied to support the selection of the most suitable cloud platform. Each platform was evaluated by decision-makers (DMs), who reached a consensus in their assessments, supported by data from company records. A comparative analysis of the results revealed that different methods may produce varying rankings of alternatives, particularly when the alternatives are objectively similar in their characteristics. Nevertheless, the proposed model can serve as a highly useful decision-support tool for company management. Full article
(This article belongs to the Special Issue Advances in Fuzzy Logic and Computational Intelligence)
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21 pages, 4051 KiB  
Article
Optimizing Parcel Locker Selection in Campus Last-Mile Logistics: A Path Planning Model Integrating Spatial–Temporal Behavior Analysis and Kernel Density Estimation
by Hongbin Zhang, Peiqun Lin and Liang Zou
Appl. Sci. 2025, 15(12), 6607; https://doi.org/10.3390/app15126607 - 12 Jun 2025
Viewed by 583
Abstract
The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and [...] Read more.
The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application. Full article
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17 pages, 18004 KiB  
Article
Implicit Prioritization of Life Insurance Coverage: A Study of Policyholder Preferences in a Danish Pension Company
by Julie Bjørner Søe
Risks 2025, 13(6), 103; https://doi.org/10.3390/risks13060103 - 26 May 2025
Viewed by 461
Abstract
This study evaluates the utility derived by policyholders in a Danish pension company, from their life insurance coverages. We quantify the relative importance policyholders assign to their existing coverages versus a hypothetical complete coverage scenario, thereby measuring the implicit priority of their current [...] Read more.
This study evaluates the utility derived by policyholders in a Danish pension company, from their life insurance coverages. We quantify the relative importance policyholders assign to their existing coverages versus a hypothetical complete coverage scenario, thereby measuring the implicit priority of their current coverage. By analyzing these implicit priorities based on individual attributes such as age, financial situation, and company agreement limitations, we gain a comprehensive understanding of policyholders’ evaluations of their current life insurance coverage. Utilizing a continuous-time life cycle model, we optimize consumption and life insurance decisions during the accumulation phase, applying well-established theoretical findings to actual data. Our analysis identifies trends, outliers, and insights that can inform potential improvements in life insurance coverage. This tool aims to assist policyholders in prioritizing their coverage according to their life situations and provides a foundation for advisory dialogues and product development. Full article
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23 pages, 297 KiB  
Article
Green Washing, Green Bond Issuance, and the Pricing of Carbon Risk: Evidence from A-Share Listed Companies
by Zhenyu Zhu, Yixiang Tian, Xiaoying Zhao and Huiling Huang
Sustainability 2025, 17(11), 4788; https://doi.org/10.3390/su17114788 - 23 May 2025
Viewed by 976
Abstract
As global climate change intensifies and carbon emission policies become increasingly stringent, carbon risk has emerged as a crucial factor influencing corporate operations and financial markets. Based on data from A-share listed companies in China from 2009 to 2022, this paper empirically examines [...] Read more.
As global climate change intensifies and carbon emission policies become increasingly stringent, carbon risk has emerged as a crucial factor influencing corporate operations and financial markets. Based on data from A-share listed companies in China from 2009 to 2022, this paper empirically examines the pricing mechanism of carbon risk in the Chinese capital market and explores how different corporate signaling behaviors affect the carbon risk premium. The findings reveal the following: (1) Carbon risk exhibits a significant positive premium (annualized at about 1.33% per standard deviation), which remains robust over longer time windows and after replacing the measurement variables. (2) Heterogeneity analysis shows that the carbon risk premium is not significant in high-energy-consuming industries or before the signing of the Paris Agreement, possibly due to changes in investor expectations and increased green awareness. Additionally, a significant difference in the carbon risk premium exists between brown and green stocks, reflecting a “labeling effect” of green attributes. (3) Issuing green bonds, as an active corporate signaling behavior, effectively mitigates the carbon risk premium, indicating that market investors highly recognize and favor firms that actively convey green signals. (4) A “greenwashing” indicator constructed from textual analysis of environmental information disclosure suggests that greenwashing leads to a mispricing of the carbon risk premium. Companies that issue false green signals—publicly committing to environmental protection but failing to implement corresponding emission reduction measures—may mislead investors and create adverse selection problems. Finally, this paper provides recommendations for corporate carbon risk management and policy formulation, offering insights for both research and practice in the field. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
33 pages, 1925 KiB  
Systematic Review
Impression Management Tactics in the Chairperson’s Statement: A Systematic Literature Review and Avenues for Future Research
by Masibulele Phesa, Mabutho Sibanda, Frank Ranganai Matenda and Zamanguni Gumede
J. Risk Financial Manag. 2025, 18(5), 270; https://doi.org/10.3390/jrfm18050270 - 16 May 2025
Viewed by 836
Abstract
The chairperson’s statement (CS) has evolved into a key component of corporate reporting, offering an authoritative, high-level summary of a company’s activities, initiatives, operations, financial performance, and achievements over the preceding financial year, along with insights into future outlooks. Recognised for its informative [...] Read more.
The chairperson’s statement (CS) has evolved into a key component of corporate reporting, offering an authoritative, high-level summary of a company’s activities, initiatives, operations, financial performance, and achievements over the preceding financial year, along with insights into future outlooks. Recognised for its informative value, the CS is consistently ranked by stakeholders as the most read and most influential section of the integrated report. Despite its importance, the CS is also a platform where corporate management often engages in impression management (IM) to portray a biased and overly positive image of the company. This study conducted a systematic literature review to examine the IM tactics employed within the CS. Based on the findings, an integrative conceptual framework was developed. Identified IM tactics include readability, textual characteristics, the influence of culture, legal systems and capital markets, paratext and intertextuality, the tone of language, forward-looking statements, retrospective sense-making, ambiguous language, the use of photographs and graphs, impersonalisation and evaluative language, and self-serving attributions. The results highlight that the study of IM strategies in CSs represents a rich and relevant research domain that warrants deeper exploration. Given its qualitative complexity and underexplored dimensions, this area offers several promising avenues for future investigation. Full article
(This article belongs to the Special Issue Financial Management)
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22 pages, 353 KiB  
Article
Towards a Sustainable Construction Industry: A Fuzzy Synthetic Evaluation of Critical Barriers to Entry and the Retention of Women in the South African Construction Industry
by Olugbenga Timo Oladinrin, Abimbola Windapo, João Alencastro, Muhammad Qasim Rana, Christiana Ekpo and Lekan Damilola Ojo
Sustainability 2025, 17(10), 4500; https://doi.org/10.3390/su17104500 - 15 May 2025
Viewed by 484
Abstract
Over the past few decades, numerous efforts have been made to increase the proportion of women in the construction industry, coupled with various calls for legislation and rules to prohibit gender discrimination. Despite these efforts, minimal progress has been noticed in the construction [...] Read more.
Over the past few decades, numerous efforts have been made to increase the proportion of women in the construction industry, coupled with various calls for legislation and rules to prohibit gender discrimination. Despite these efforts, minimal progress has been noticed in the construction industry. While recruitment remains crucial, the current culture in construction reveals a knowledge gap in recruitment and retention in employment—a concept known as a ‘leaky pipeline’. Lack of awareness of career options and the challenges of working in a male-dominated, occasionally discriminatory workplace are some of the significant barriers to attracting and keeping women in the construction industry. Much of the research in South Africa shows that most construction companies employed few women but only in lower secretarial and administrative positions. Therefore, this study investigated the barriers facing women’s entry and retention in construction-related employment in South Africa using fuzzy synthetic evaluation (FSE) to understand and prioritise the barriers. Data were collected through the administration of online and paper-based questionnaires. The results of the analysis show that the barriers in the order of criticality include support and empowerment issues (SEs), educational/academic-related barriers (ABs), barriers from professional conditions and work attributes (BPs), social perception and gender stereotype barriers (SPs), professional perceptions and gender bias (PP), and individual confidence/interest/awareness/circumstance-related barriers (IBs), respectively. Based on the findings of the study, several recommendations, including on-the-job tutoring and flexible work arrangements, amongst others, were provided. Full article
23 pages, 2394 KiB  
Article
Diverse Counterfactual Explanations (DiCE) Role in Improving Sales and e-Commerce Strategies
by Simona-Vasilica Oprea and Adela Bâra
J. Theor. Appl. Electron. Commer. Res. 2025, 20(2), 96; https://doi.org/10.3390/jtaer20020096 - 8 May 2025
Viewed by 866
Abstract
Pricing strategy is a critical challenge in e-commerce, where businesses must balance competitive pricing with profitability. Traditional pricing models rely on historical data and statistical methods but often lack interpretability and adaptability. In this study, we propose a novel approach that leverages Diverse [...] Read more.
Pricing strategy is a critical challenge in e-commerce, where businesses must balance competitive pricing with profitability. Traditional pricing models rely on historical data and statistical methods but often lack interpretability and adaptability. In this study, we propose a novel approach that leverages Diverse Counterfactual Explanations (DiCE) to enhance pricing strategies for mobile phones. Unlike previous research that applied counterfactual analysis in customer segmentation, energy forecasting, and retail pricing, our method directly integrates explainability into product-level pricing decisions. Our approach identifies actionable product features, such as improved hardware specifications, that can be modified to increase the predicted price. By generating counterfactual explanations, we provide insights into how businesses can optimize product attributes to maximize revenue while maintaining transparency in pricing decisions. This framework bridges explainable AI with pricing strategies, allowing companies to justify price points and improve market positioning dynamically. Furthermore, we identify other features that could lead to the same price goal. The linear regression model achieved an R2 score of 96.15% on the test set, along with a mean absolute error (MAE) of 108.31 and mean absolute percentage error (MAPE) of 5.43%, indicating strong predictive performance. Through DiCE, the model identified actionable modifications (e.g., increasing front camera resolution and battery capacity) that effectively raise predicted prices by 15–20%. This insight is particularly valuable for product design and pricing optimization. The model provided a ranking of features based on their impact on price increases, revealing that front camera and battery capacity are more influential than RAM in driving pricing changes. Full article
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41 pages, 4581 KiB  
Article
An Integrated New Product Development Evaluation Model in an Interval Type-2 Fuzzy Environment for PLM Strategy Setting
by Sanja Puzović, Jasmina Vesić Vasović, Danijela Tadić and Dragan D. Milanović
Appl. Sci. 2025, 15(9), 5025; https://doi.org/10.3390/app15095025 - 30 Apr 2025
Viewed by 616
Abstract
Product Lifecycle Management (PLM) provides a paradigmatic model that enables companies to operate more effectively in the face of shorter product lifecycles, global networking, and increasing complexity. However, despite strengthening the PLM initiative, companies still struggle to implement this concept. The limited results [...] Read more.
Product Lifecycle Management (PLM) provides a paradigmatic model that enables companies to operate more effectively in the face of shorter product lifecycles, global networking, and increasing complexity. However, despite strengthening the PLM initiative, companies still struggle to implement this concept. The limited results of current PLM implementations often stem from a lack of unique indicators or consistent methodologies that help companies prioritize their implementation efforts. This article proposes an approach to set a PLM strategy, focusing on enhancing company innovation potential by introducing a structured methodology capable of (i) capturing latent needs based on the normative-contingent New Product Development (NPD) evaluation model and (ii) quantifying the influence of various PLM functional aspects on NPD capability. The proposed methodology is based on the Quality Function Deployment (QFD) method, modified to overcome the limitations of the conventional approach, employing the Analytic Hierarchy Process (AHP) for prioritizing request attributes and the Evaluation based on Distance from Average Solution (EDAS) method for quality attribute importance ranking. Motivated by the arbitrary and vague nature of the decision-making environment in the PLM implementation projects, which introduces uncertainties that could be effectively managed by fuzzy logic, the study introduces Interval Type-2 Fuzzy Sets (IT2FSs) to minimize ambiguity and inconsistency in expressing and modeling preferences. The main study contribution pertains to generating quantitative and objective guidelines for adequately grounding a PLM strategy from the perspective of enhancing the company’s innovation potential. The findings of this study ultimately contribute to establishing an optimal model of the PLM concept implementation process, tailored to specific company requirements. Finally, an empirical case study demonstrates the effectiveness and practicality of the proposed approach. Full article
(This article belongs to the Special Issue Applications of Fuzzy Systems and Fuzzy Decision Making)
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13 pages, 680 KiB  
Article
Consumer Acceptance and Perceived Sensory Characteristics of Commercial Vegan Mayonnaise
by Juyoun Lee and Kyunghee Kim
Foods 2025, 14(9), 1542; https://doi.org/10.3390/foods14091542 - 28 Apr 2025
Viewed by 831
Abstract
This study aims to investigate the sensory characteristics of commercially available vegan mayonnaise using the Check-All-That-Apply (CATA) methodology and to determine the acceptability factors influencing consumer purchase intention. Six mayonnaise samples were evaluated by 112 consumers: one conventional mayonnaise and five commercially available [...] Read more.
This study aims to investigate the sensory characteristics of commercially available vegan mayonnaise using the Check-All-That-Apply (CATA) methodology and to determine the acceptability factors influencing consumer purchase intention. Six mayonnaise samples were evaluated by 112 consumers: one conventional mayonnaise and five commercially available vegan mayonnaises (labeled OGM, VVM, EBM, VM, SM, and OVM). Except for fatty flavor, rancid odor, artificial flavor, mouthcoating, melting, and mouthfeel, 15 characteristics (yellowness, glossiness, slimness, thickness, smoothness, beany odor, lemon aroma, nutty flavor, sourness, saltiness, sweetness, savory flavor, off-flavor, goes well with vegetables, and spreads well on crackers) were significantly different among 6 samples (p < 0.001). Across all evaluation attributes, OGM and VM had the highest acceptance, with no significant differences between the two samples except for overall taste. The VM was the only vegan mayonnaise that produced results similar to those of OGM, which is regular mayonnaise. The results of the study suggest that vegan mayonnaise can be a substitute for regular mayonnaise. We hope that this research will provide data that can be used as a basis for developing vegan mayonnaise products that meet the needs of consumers and food companies. Full article
(This article belongs to the Section Plant Foods)
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25 pages, 2468 KiB  
Article
Integrated Logistics Management Through ERP System: A Case Study in an Emerging Regional Market
by Juan Gabriel França Canon, Robério José Rogério dos Santos, Victor Diogho Heuer de Carvalho, Madson Bruno da Silva Monte and Thiago Lima de Barros
Logistics 2025, 9(2), 59; https://doi.org/10.3390/logistics9020059 - 27 Apr 2025
Cited by 1 | Viewed by 4194
Abstract
Background: Logistics and supply chain management are crucial in modern commerce, impacting global competition, and both can directly benefit by using enterprise resource planning (ERP) systems. This case study examines key success factors behind a significant operational transformation in a company in [...] Read more.
Background: Logistics and supply chain management are crucial in modern commerce, impacting global competition, and both can directly benefit by using enterprise resource planning (ERP) systems. This case study examines key success factors behind a significant operational transformation in a company in the countryside of Alagoas, Brazil. From this context, two research questions emerge: (a) What are the main success factors that drove a significant operational transformation in logistics and supply chain management, and how did these factors impact the company’s growth? (b) How does digital transformation and adopting an ERP impact the company’s logistics activities? Methods: Data were collected through on-site observations, interviews with supervisors and a manager, and analysis of company-provided documentation. Results: The study identified key processes, stakeholders, and practices, focusing on critical success factors, mission-critical processes, and the integration of core and support functions. Notable changes were observed through key logistics performance indicators, tracking the evolution from pre-implementation to post-implementation and revealing their impact on the company’s growth. Conclusions: Improved decision making between departments significantly enhanced performance and growth. The analyzed company’s success can be attributed to a process-oriented approach, digital transformation in logistics, and investment in information technology. Full article
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