PEER-REVIEW ARTICLE PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9768 Predicting Consumer Preferences for Furniture Products on E-commerce Platforms: An Analysis Using Machine Learning and Favorite Listing Data Hüseyin Çardak,a Selahattin Bardak,b,* Timuçin Bardak,a Okan Capraz,a Sultan Ozcetin,a and Samet Kızılırmak c The rapid growth of e-commerce platforms presents unique opportunities to analyze consumer behavior and predict product preferences in the furniture industry. This study explores the use of machine learning techniques to predict consumer choices for furniture products based on favorite listing data from e-commerce platforms. A dataset of 239 furniture products was collected, categorized into three groups: most preferred, moderately preferred, and least preferred. Key attributes, including furniture type, dimensions (width, depth, height), color, material, and price, were analyzed. Machine learning models, specifically Decision Trees and Random Forests, were applied to develop prediction models for these categories. The models were assessed using metrics such as accuracy, precision, sensitivity, and F1-score. Results indicated that the Random Forest model outperformed the Decision Tree, achieving 83% accuracy in predicting preference categories. Feature importance analysis highlighted that price and physical dimensions were the most significant factors influencing consumer preferences. These findings suggest that practical and economic aspects are prioritized over aesthetic features when choosing furniture. The study demonstrates the potential of machine learning in predicting consumer behavior, offering valuable insights for manufacturers and retailers in optimizing product development, inventory management, and marketing strategies. DOI: 10.15376/biores.20.4.9768-9784 Keywords: Furniture industry; E-commerce; Data mining; Prediction Contact information: a: Furniture and Decoration Program, Bartin Vocational School, Bartın University, 74200, Bartın, Turkey; b: Department of Computer Engineering, Faculty of Engineering and Architecture, Sinop University, 57000, Sinop, Turkey; c: Department of Forest Industrial Engineering, Faculty of Forestry, Bartin University, 74200, Bartın, Turkey; *Corresponding author: sbardak@sinop.edu.tr INTRODUCTION The growing influence of the Internet on modern life has led to a significant increase in online shopping. The large amounts of data collected on e-commerce platforms enable management to make informed decisions and gain deeper insights into consumer behavior. In addition, these forecasts enable more accurate financial budgeting, more effective operational planning, and more efficient inventory policies (Sun et al. 2008). Artificial intelligence (AI) has become an essential element of Industry 4.0 and is expected to maintain its importance with the advent of Industry 5.0. At the same time, companies are increasingly integrating AI technologies into all operational areas. This phenomenon reflects the profound impact of AI on industrial processes and business models (Sigov et al. 2022; de Waal et al. 2024). Machine learning (ML) algorithms have emerged as a PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9769 powerful tool for solving complex industry-specific forecasting problems. However, research studies in the furniture industry are still limited. This study fills this gap by uniquely applying ML techniques to big data from e-commerce platforms, focusing on predicting consumer preferences in the furniture sector based explicitly on favorite listing behavior. This approach makes a unique contribution to the field, as it combines the power of ML with rich data obtained from online shopping platforms. Applying ML techniques makes it possible to analyze large amounts of data, identify patterns, and formulate predictions. The use of ML algorithms facilitates the acceleration of innovation by processing large data sets and running simulations (Xue et al. 2024). Random forest (RT) is a widely used supervised ML algorithm. This algorithm can be effectively applied to classification and regression problems. Random forest has gained popularity among researchers and practitioners due to its adaptability to different data sets and high accuracy (Breiman 2001; Lee et al. 2024). Decision tree (DT) is a nonparametric supervised learning technique for classification or regression. It simplifies and visualizes complex decision processes by modeling patterns in the data set in a hierarchical structure. The DT works by repeatedly partitioning the data according to specific characteristics and making the most appropriate distinction at each node. This way, effective results can be achieved in classification and regression problems (Song and Ying 2015; Alakbari et al. 2023). Over the past decade, e-commerce platforms such as Amazon and Alibaba have become indispensable tools for consumers searching for and purchasing products. The success of these platforms can be attributed to their sophisticated ability to collect and analyze consumer behavior data. E-commerce sites use recommendation systems to provide personalized product recommendations to individual users, thereby encouraging consumers to discover and purchase products that match their established shopping habits. This approach has the dual benefit of improving the user experience and increasing sales (Zhang et al. 2020; Zhou et al. 2024). In recent years, there has been a significant increase in research into using ML techniques to predict consumer behavior and future buying trends. These approaches use a variety of algorithms, such as logistic regression, decision trees, artificial neural networks, and support vector machines, to identify patterns in consumer data. The identified patterns are then used to create models that predict consumer preferences (Moro et al. 2014; Liu et al. 2024). To illustrate, the deep learning approach proposed by Gabel and Timoshenko (2022) extracts preference representations based on customers’ purchase history and uses this information to predict future product choices. The furniture industry has complex variables that must be considered when predicting consumer preferences and behavior. Consumers’ furniture choices are influenced not only by the tangible characteristics of the products in question (e.g., dimensions, composition, color) but also by individual aesthetic tendencies and economic conditions. This requires using more sophisticated and accurate analytical tools to accurately predict the number of times furniture products will be added to the favorites list. Applying ML algorithms to extract meaningful insights from large data sets is a promising way to improve such predictions’ accuracy by overcoming traditional methods’ limitations. In this study, the aim was to predict consumers’ preferences for furniture products using data from e-commerce platforms and ML algorithms. Furniture products were classified into three groups based on the number of times they had been added to the favorites list: highly preferred, moderately preferred, and low preferred. This approach is a unique contribution to the field, combining the power of ML with rich data from online shopping platforms. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9770 The study used two major ML algorithms: Decision Trees and Random Forests. These algorithms were chosen based on their proven effectiveness in handling complex, multidimensional data and their ability to provide interpretable results. The study used a comprehensive dataset of 239 furniture products, including variables such as furniture type, dimensions (width, depth, height), color, primary material, and price. Predictive models were built and evaluated using standard classification metrics such as accuracy, precision, sensitivity, and F1 score. In addition, feature importance analysis was performed to determine the relative impact of different factors on consumer preferences. The results of this research have important practical implications for the furniture industry and could transform production planning, inventory management, and marketing strategies. By accurately predicting consumer preferences based on the number of times they are added to a favorites list, companies can optimize their operations, reduce waste, and better meet customer demands. EXPERIMENTAL Methodology Data collection and pre-processing This study utilized a comprehensive dataset of 239 furniture products, meticulously collected from several publicly accessible Turkish e-commerce platforms. Data included furniture type, dimensions, color, material, price, and preference category counts. The data were collected between December 11, 2022, and August 11, 2023. The collected data underwent standard cleaning, missing value handling, and outlier correction. Table 1 shows Sample Furniture Product Data (excerpt). Table 1. Sample Furniture Product Data (excerpt) Furniture Type Width (cm) Depth (cm) Height (cm) Color Main Material Price (TL) Favorite Count TV Unit 120 35 61 Dark Particle Board (PB) 459 60568 TV Unit 150 30 44 Light Particle Board (PB) 838 60072 TV Unit 120 30 40 Light Particle Board (PB) 529 58101 Coffee Table 34 18 49 Dark Particle Board (PB) 289 56247 Data Categorization Using K-means Clustering The K-means clustering algorithm was used to more objectively categorize furniture items based on the number of times they were added to the favorites list. K- means clustering was chosen because of its effectiveness in identifying natural groups in the data. Furniture items were categorized into three groups based on the number of times they were added to the favorites list: most preferred, moderately preferred, and least preferred. To address the recommendation for specific classification criteria, the K-means algorithm identified the following ranges for each category based on the ‘number of times added to favorites list’ attribute: Least Preferred: 0 – 15231 favorite listings Moderately Preferred: 15232 – 33193 favorite listings Most Preferred: 33194 – 60568 favorite listings PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9771 These ranges were determined by the clustering process, which aimed to minimize the within-cluster variance and maximize the between-cluster variance, thereby creating distinct and meaningful groups. The ‘k’ parameter for the K-means algorithm was set to 3, as the aim was to categorize the furniture into three preference levels as per the study’s objective. This categorization was used as the target variable for the prediction models used in this study. Model Building In this study, consumer preferences for furniture products operated as the frequency with which items are added to users’ favorites—using Decision Trees (DT) and Random Forest (RF). These algorithms were selected due to their ability to handle mixed-type, multidimensional data and capture non-linear relationships while retaining interpretability for industry stakeholders. DT provides transparent, rule-based structures that clarify how attributes drive classification, whereas RF—an ensemble that aggregates the predictions of multiple decision trees—improves accuracy and mitigates overfitting relative to a single tree, yielding robust generalization on complex e-commerce data (Breiman 2001). Prior work further supports the use of DT/RF in e-commerce analytics and preference prediction (Haque 2024; Mustakim et al. 2024). Both models were trained on the collected e- commerce dataset containing categorical (e.g., type, color, primary material) and numerical (e.g., width, depth, height, price) features; the evaluation procedure and performance metrics are detailed in the following subsection. The dataset was divided into training and test sets, with 70% allocated for training and the remaining 30% for testing. This 70/30 split is a widely adopted standard in ML, balancing the need for sufficient training data to build robust models and enough testing data to evaluate model performance effectively (Pham et al. 2019). Such a division helps in mitigating overfitting and provides a reliable assessment of the model’s generalization capabilities. Fig. 1. RapidMiner process (software interface screenshot) used to train and evaluate the models PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9772 In the context of furniture preference modeling, this approach aligns with methodologies employed in recent studies, such as Yu et al. (2023), who utilized a similar data partitioning strategy to analyze the relationship between consumer personality traits and preferences for wood furniture product characteristics. The models in this study were trained and evaluated using RapidMiner, a software platform widely recognized for its applicability in scientific research and ML tasks (Gonçalves et al. 2013; Mozaffarinya et al. 2019; Gonçalves et al. 2020; Sher et al. 2022). The process created for applying the models in the RapidMiner program is given in Fig. 1. RapidMiner process (software interface screenshot) was used to train and evaluate the models. To optimize model performance, comprehensive hyperparameter tuning was conducted using grid search methodology through RapidMiner’s Optimize Parameters (Grid) operator. This operator systematically executed the subprocess for all combinations of selected parameter values to identify the optimal configuration that maximized prediction accuracy. Table 2 presents the three most critical hyperparameters for both Random Forest and Decision Tree models, along with their optimal values determined through this grid search process. Table 2. Most Important Hyperparameters for Random Forest and Decision Tree Models Algorithm Parameter Optimal Value Definition Random Forest number_of_trees 21 The number of decision trees in the ensemble Random Forest maximal_depth 0 (unlimited) Maximum depth allowed for each tree in the forest Random Forest use_local_random_seed true Use a local random seed for reproducible results within this operator. Decision Tree maximal_depth 70 Maximum depth of the single decision tree Decision Tree minimal_leaf_size 1 Minimum number of examples required in a leaf node. Decision Tree apply_pruning false Whether to apply post-pruning to the tree Model Evaluation The model’s performance was comprehensively evaluated using accuracy, precision, and sensitivity, which are standard metrics for classification tasks in machine learning, providing a robust assessment of overall correctness, positive prediction reliability, and true positive identification, respectively (Sokolova and Lapalme 2009; Puri et al. 2017; Siering et al. 2018; Szabó et al. 2024). Accuracy was calculated by dividing the total number of correctly classified observations by the total number of observations: "Accuracy" = ("TP" + "TN") / ("TP" + "FP" + "TN" + "FN") (1) Precision is a metric that measures the accuracy of a classifier, i.e., whether a sample classified as belonging to a certain class belongs to that class: "Precision" = "TP" / ("TP" + "FP") (2) PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9773 Sensitivity is defined as the ratio of true positive predictions to the number of positive samples: "Sensitivity" = "TP" / ("TP" + "FN") (3) In Eq. 3, TP is True Positive; TN means True Negative; FP is False Positive; and FN denotes False Negative. These metrics were used to compare the performances of different algorithms and permit them to select the best-performing model. Feature Importance Analysis Following the identification of the Random Forest model as the most robust predictor, a comprehensive feature importance analysis was conducted to quantify the influence of each attribute on consumer preferences for furniture products. This analysis was performed using the ‘Attribute Weights’ functionality inherent to the Random Forest operator in RapidMiner. The calculation of these weights is based on the principle of measuring the total reduction in node impurity (specifically, the Gini impurity criterion) that an attribute provides across all decision trees within the ensemble. For each attribute, its importance score is computed as the sum of the Gini impurity decreases for every node where that attribute was utilized for splitting the data. Consequently, attributes that are frequently selected for splitting and contribute significantly to the homogeneity of child nodes receive higher importance weights. This quantitative approach made it possible to objectively rank the factors influencing consumer choices, revealing the relative impact of attributes such as price, dimensions, and material on product favoritism. This detailed methodology ensures that the findings regarding feature importance are transparent and can be independently verified or applied by other researchers interested in similar analytical approaches. After selecting the best-performing model (Random Forest; accuracy = 83.10%), the next step was to compute the global feature importance in RapidMiner using the Weight by Tree Importance operator. This method returns an ExampleSet with attributes and normalized weights (sum = 1). Each weight equals the total decrease in node impurity contributed by that attribute across all splits in the forest, weighted by the number of samples at each node; improvements are computed with the same splitting criterion as the model (Gini for classification). Only the following features were evaluated: Price, Width, Height, Depth, Color, Primary Material, and Furniture Type. RESULTS AND DISCUSSION This section reports the empirical findings and their implications. Using Random Forest (RF) and Decision Tree (DT), there was an evaluation of the predictive performance with accuracy, precision, sensitivity (recall), and F1-score. As summarized in Table 2, RF consistently outperformed DT: 83.10% overall accuracy for RF versus 73.24% for DT. Class-level F1-scores likewise favored RF (Class 0: 71.43%; Class 1: 70.59%; Class 2: 89.36%) over DT (Class 0: 53.33%; Class 1: 48.28%; Class 2: 83.67%), indicating better generalization across “most,” “moderately,” and “least” preferred categories. These gains are consistent with RF’s ensemble learning, which reduces overfitting relative to a single tree and captures non-linear interactions in mixed-type e-commerce data. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9774 Following model assessment, a feature-importance analysis with the best- performing RF model showed that Price (0.304) and dimensional attributes—Width (0.228), Height (0.188), Depth (0.146)—were the dominant drivers of preference, whereas Color (0.073), Primary Material (0.036), and Furniture Type (0.025) were comparatively less influential. Practically, this implies that consumers prioritize economic and spatial constraints over aesthetic or categorical attributes when selecting furniture online; pricing and size-fit thus emerge as primary levers for product, inventory, and merchandising decisions. Model Performance Comparison Table 3 presents a detailed comparison of the performance metrics for both the RF and DT models when applied to the e-commerce dataset. Table 3. RF vs. DT Performance on the E-commerce Dataset Model Class Precision Recall F1-Score Accuracy Random Forest 0 83.33% 62.50% 71.43% 83.10% 1 70.59% 70.59% 70.59% 2 87.50% 91.30% 89.36% Decision Tree 0 57.14% 50.00% 53.33% 73.24% 1 58.33% 41.18% 48.28% 2 78.85% 89.13% 83.67% Note: 0 = Most preferred furniture, 1 = Moderately preferred furniture, 2 = Least preferred furniture The Random Forest Confusion Matrix (Table 4) and Decision Tree Confusion Matrix (Table 5) demonstrate the detailed performance breakdown. Table 4. Random Forest Confusion Matrix True Most Preferred True Moderately Preferred True Least Preferred Class Precision Pred. Most Preferred 5 1 0 83.33% Pred. Moderately Preferred 1 12 4 70.59% Pred. Least Preferred 2 4 42 87.50% Class Recall 62.50% 70.59% 91.30% Table 5. Decision Tree Confusion Matrix True Most Preferred True Moderately Preferred True Least Preferred Class Precision Pred. Most Preferred 4 1 2 57.14% Pred. Moderately Preferred 2 7 3 58.33% Pred. Least Preferred 2 9 41 78.85% Class Recall 50.00% 41.18% 89.13% PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9775 The RF model demonstrated superior overall performance with an accuracy rate of 83.10%, surpassing the DT model’s accuracy of 73.24%. This suggests that the RF algorithm more effectively captures complex patterns in e-commerce furniture preference data, consistent with recent findings showing the superior predictive capabilities of RF over DT (Helmud et al. 2024). In terms of model strengths and weaknesses, the RF model exhibited consistent performance across all preference categories and demonstrated high accuracy, particularly in identifying the least preferred furniture products. This consistency is attributed to RF’s ensemble learning approach, which mitigates overfitting and enhances generalization by aggregating predictions from multiple decision trees (Kinasih et al. 2025). Conversely, the DT model showed solid performance in determining the least preferred products, while exhibiting lower overall accuracy. However, it encountered difficulties in distinguishing between the most and moderately preferred products. The RF algorithm implemented in this study showcased remarkable success, meeting the performance criteria widely accepted in the literature (Sokolova and Lapalme 2009; Zhu et al. 2010; Luo et al. 2016). Its ability to handle high-dimensional data and provide robust predictions aligns with findings from other recent studies where RF models outperformed even more complex algorithms such as deep learning models in specific contexts, such as predicting furniture prices (Bardak 2023). Feature Importance In this study, the most powerful RF algorithm was used to determine the importance levels of the features that influence the number of favorites of furniture products. The factors analyzed included different characteristics such as height, width, depth, color, primary material, price, and type of furniture. Table 6 shows in detail the important weights of these factors obtained from the e-commerce platforms data using the RF algorithm. These results contribute significantly to a more comprehensive understanding of the factors that shape consumer preferences in the furniture sector and can provide valuable insights to stakeholders in the sector. Table 6. Weights of Factors Based on Furniture Data Obtained through the Random Forest Algorithm Factor Importance Weight Price (TL) 0.304 Furniture Width 0.228 Furniture Height 0.188 Furniture Depth 0.146 Furniture Color 0.073 Furniture Primary Material 0.036 Furniture Type 0.025 The RF algorithm’s analysis of furniture attributes reveals significant insights into consumer preferences in the furniture industry. Price emerges as the dominant factor with the highest importance weight (0.304), indicating strong consumer price sensitivity. This is closely followed by dimensional attributes - width, height, and depth - suggesting that the physical size of furniture plays a critical role in purchase decisions, likely due to space constraints or aesthetic considerations. Color is moderately important, while furniture PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9776 material and type are relatively unimportant. This hierarchy of factors suggests that consumers prioritize practical and economic aspects over aesthetic or categorical features when selecting furniture. This finding aligns with previous research, where price was found to be a decisive factor in furniture purchasing decisions and RF models demonstrated high accuracy in predicting furniture prices (Bardak 2023). Dimensional attributes particularly width, height, and depth also significantly impact consumer decisions. Gudarzi et al. (2022) similarly found that physical dimensions of furniture are critical considerations for consumers, often outweighing other product characteristics in importance. While color holds moderate importance, material and type appear to be less influential factors. Supporting this, Guzel (2020) reported that consumers in Kayseri, Turkey, frequently prioritize affordable and functional composite furniture over more expensive solid wood alternatives. Moreover, Yu et al. (2023) highlighted that consumer personality traits such as extraversion and conscientiousness significantly influence the perceived importance of product features like quality and design, although these factors still ranked below economic and dimensional considerations. This hierarchy of factors suggests that for the furniture products analyzed in this study (coffee tables and TV units), practical and economic aspects such as price and physical dimensions are significant factors influencing consumer preferences. While these findings highlight the importance of practical and economic factors within our specific dataset, it is important to acknowledge that consumer preferences for furniture can be highly nuanced and influenced by various other factors, including aesthetic considerations, specific furniture categories (e.g., functional vs. decorative), and diverse user demographics (e.g., young people, parents). Future research could explore these aspects in more detail by examining a broader range of furniture categories and demographic segments. CONCLUSIONS The aim of this study was to contribute to the existing literature on the application of machine learning in consumer behavior analysis, with a particular focus on the furniture industry. 1. This study examined the potential of machine learning algorithms in predicting consumer preferences for furniture products using e-commerce favoriting data. The research aimed to estimate the number of favorites of furniture products using the capabilities of Decision Trees and Random Forest algorithms. The analysis demonstrated that the RF algorithm exhibited superior performance (accuracy of 83.10%) compared to the DT model (73.24% accuracy). 2. Feature importance analysis using the RF model revealed that price and the physical dimensions of the furniture (width, height and depth) significantly influenced consumer preferences. These findings highlight the importance of practical considerations, such as space and financial constraints, in e-commerce furniture preference decisions. In contrast, factors such as furniture type and material were found to have a relatively limited influence on consumer choice. This suggests that aesthetic or categorical features may be secondary to functional attributes. PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9777 3. These findings may provide valuable insights for furniture manufacturers and retailers, enabling them to optimize product development, inventory management, and marketing strategies based on predictive analysis. 4. The successful application of machine learning techniques in this study demonstrates their potential to contribute to decision-making processes in the furniture industry. As e-commerce platforms continue to generate large amounts of consumer data, the integration of advanced analytics will be critical to driving business success and responding to changing market trends. It should be noted that the present findings are specific to the functional furniture categories examined (coffee tables and TV units) and may not be generalizable to decorative furniture items or different consumer demographic groups. Future studies should investigate how preferences vary across different furniture categories and user segments to provide a more comprehensive understanding of consumer behavior in the furniture industry. ACKNOWLEDGMENTS This study was supported by the Scientific and Technological Research Council (TUBITAK) of Turkey (2209-A BIDEB, Funding Number: 1919B012202575). REFERENCES CITED Alakbari, F. S., Mohyaldinn, M. E., Ayoub, M. A., Salih, A. A., and Abbas, A. H. (2023). “A decision tree model for accurate prediction of sand erosion in elbow geometry,” Heliyon 9(7), article 17639. DOI: 10.1016/j.heliyon.2023.e17639 Bardak, T. (2023). “Predicting prices of case furniture products using web mining techniques,” BioResources 18(4), 7412-7427. 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(2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9780 APPENDIX: Data Table Furniture Type Furniture Width (cm) Furniture Depth (cm) Furniture Height (cm) Furniture Color Main Material Price (TL) Number of Favorites TV Unit 120.00 35.00 61.00 Dark color Particleboard (PB) 459.00 60568 TV Unit 150.00 29.50 44.10 Light color Particleboard (PB) 838.00 60072 TV Unit 120.00 30.00 40.00 Light color Particleboard (PB) 529.00 58101 Coffee Table 34.00 18.00 49.00 Dark color Particleboard (PB) 289.50 56247 Coffee Table 34.00 34.00 49.00 Dark color Particleboard (PB) 289.00 56244 Coffee Table 60.00 44.00 46.00 Light color Particleboard (PB) 529.00 55310 TV Unit 180.00 40.00 45.00 Dark color Particleboard (PB) 2049.00 54332 TV Unit 180.00 40.00 45.00 Dark color Particleboard (PB) 1844.00 53944 TV Unit 160.00 25.50 49.00 Dark color Particleboard (PB) 900.00 47737 TV Unit 160.00 25.50 49.00 Light color Particleboard (PB) 899.00 47610 TV Unit 120.00 30.00 40.00 Dark color Particleboard (PB) 529.00 45772 TV Unit 180.00 35.00 48.60 Dark color Particleboard (PB) 2309.00 44882 TV Unit 120.00 46.00 43.00 Dark color Particleboard (PB) 449.00 44225 TV Unit 180.00 35.00 49.00 Dark color Particleboard (PB) 1965.00 44136 TV Unit 180.00 29.60 45.00 Dark color Particleboard (PB) 989.00 43120 TV Unit 180.00 35.00 54.00 Dark color Particleboard (PB) 1429.00 43114 TV Unit 180.00 35.00 54.00 Dark color Particleboard (PB) 1429.00 43095 TV Unit 180.00 29.60 45.00 Dark color Particleboard (PB) 990.00 42955 TV Unit 180.00 29.60 44.30 Dark color Particleboard (PB) 1750.00 40169 Coffee Table 60.00 44.00 46.00 Light color Particleboard (PB) 529.00 39887 TV Unit 150.00 30.00 35.00 Light color Particleboard (PB) 1240.00 39505 TV Unit 150.00 30.00 35.00 Light color Particleboard (PB) 1239.00 39179 TV Unit 180.00 29.60 44.30 Dark color Particleboard (PB) 1540.00 38606 Coffee Table 33.00 33.00 46.00 Light color Particleboard (PB) 310.00 38283 Coffee Table 45.00 35.00 40.00 Dark color MDF + PB 289.00 38106 Coffee Table 34.00 18.00 49.00 Dark color Medium-density FB 289.00 37230 TV Unit 160.00 30.00 49.00 Dark color Particleboard (PB) 1050.00 36369 TV Unit 160.00 30.00 49.00 Light color Particleboard (PB) 1079.00 36037 TV Unit 180.00 35.00 45.00 Light color Particleboard (PB) 1294.00 33193 TV Unit 184.00 183.00 30.00 Dark color Particleboard (PB) 1900.00 32916 TV Unit 180.00 35.00 135.00 Light color Particleboard (PB) 1295.00 32592 TV Unit 120.00 30.00 40.00 Dark color Particleboard (PB) 560.00 32550 TV Unit 180.00 32.00 30.00 Light color Particleboard (PB) 954.00 32550 Coffee Table 33.50 33.50 46.00 Light color Particleboard (PB) 310.00 32030 TV Unit 180.00 30.00 32.00 Light color Particleboard (PB) 702.00 31954 Coffee Table 40.00 35.00 45.00 Light color Medium-density FB 289.00 31797 Coffee Table 60.00 37.00 46.00 Light color Medium-density FB 529.00 30109 Coffee Table 37.00 18.00 51.00 Light color Medium-density FB 588.00 29676 Coffee Table 33.00 33.00 52.00 Light color Medium-density FB 349.00 29627 TV Unit 138.00 15.00 45.00 Dark color Particleboard (PB) 957.00 29539 TV Unit 138.00 15.00 45.00 Dark color Particleboard (PB) 956.00 29446 PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9781 TV Unit 120.00 30.00 42.00 Light color Particleboard (PB) 619.00 28064 TV Unit 180.00 35.00 54.00 Dark color Particleboard (PB) 2069.00 27824 TV Unit 120.00 30.00 42.00 Light color Particleboard (PB) 610.00 27633 TV Unit 180.00 54.00 54.00 Dark color Particleboard (PB) 2070.00 27544 Coffee Table 33.00 18.00 50.00 Light color Particleboard (PB) 289.00 25005 TV Unit 150.00 31.40 44.40 Light color Particleboard (PB) 945.00 24330 TV Unit 150.00 31.40 44.40 Light color Particleboard (PB) 950.00 24228 TV Unit 150.00 31.40 44.40 Light color Particleboard (PB) 1050.00 24110 TV Unit 138.00 29.50 42.60 Dark color Particleboard (PB) 740.00 23350 TV Unit 180.00 35.00 45.00 Light color Particleboard (PB) 1294.00 23201 TV Unit 140.00 33.00 40.00 Light color Particleboard (PB) 670.00 22560 TV Unit 140.00 33.00 40.00 Light color Particleboard (PB) 669.00 22412 TV Unit 180.00 35.00 48.30 Light color Particleboard (PB) 1362.00 22294 TV Unit 180.00 35.00 48.00 Light color Particleboard (PB) 1143.00 22074 TV Unit 180.00 35.00 48.30 Dark color Particleboard (PB) 2127.00 21141 Coffee Table 34.00 34.00 49.00 Light color Medium-density FB 289.00 21048 TV Unit 138.00 35.00 50.00 Dark color Particleboard (PB) 900.00 21040 TV Unit 150.00 30.00 44.00 Light color Particleboard (PB) 1100.00 20965 TV Unit 150.00 30.00 43.00 Light color Particleboard (PB) 1100.00 20777 Coffee Table 54.00 40.00 75.00 Dark color MDF + PB 791.00 20754 Coffee Table 54.00 40.00 75.00 Dark color Particleboard (PB) 791.49 20750 TV Unit 138.00 35.00 50.00 Dark color Particleboard (PB) 834.00 20680 Coffee Table 33.50 33.50 46.00 Light color Particleboard (PB) 310.00 20657 Coffee Table 33.00 33.00 46.00 Dark color Medium-density FB 575.00 20336 TV Unit 180.00 35.00 48.60 Dark color Particleboard (PB) 1966.00 20134 TV Unit 180.00 35.00 48.00 Dark color Particleboard (PB) 1668.00 19922 TV Unit 180.00 35.00 54.00 Dark color Particleboard (PB) 2019.00 19628 TV Unit 120.00 30.00 50.00 Light color Particleboard (PB) 749.00 19298 Coffee Table 34.00 34.00 49.00 Light color Particleboard (PB) 289.00 19209 TV Unit 180.00 30.00 47.00 Light color Medium-density FB 1589.00 19123 TV Unit 180.00 44.50 44.60 Light color Medium-density FB 1973.00 18683 TV Unit 150.00 29.50 44.10 Dark color Medium-density FB 1019.00 18330 Coffee Table 45.00 18.00 35.00 Light color Medium-density FB 629.00 17621 TV Unit 140.00 30.00 39.00 Light color Particleboard (PB) 830.00 16703 Coffee Table 34.00 34.00 49.00 Dark color Particleboard (PB) 289.00 16562 TV Unit 150.00 35.00 58.00 Light color Medium-density FB 2469.00 16539 TV Unit 120.00 35.00 61.00 Dark color Particleboard (PB) 412.00 16501 Coffee Table 55.00 34.50 51.00 Dark color Medium-density FB 414.00 16342 TV Unit 180.00 29.60 49.00 Light color Particleboard (PB) 915.00 16237 Coffee Table 60.00 41.00 52.00 Dark color Particleboard (PB) 2150.00 16029 TV Unit 180.00 35.00 60.00 Light color Particleboard (PB) 2450.00 15688 TV Unit 120.00 29.50 40.00 Light color Particleboard (PB) 650.00 15537 TV Unit 180.00 35.00 49.00 Light color Particleboard (PB) 1374.00 15471 Coffee Table 60.00 40.00 45.00 Dark color Medium-density FB 806.00 15231 Coffee Table 45.00 35.00 40.00 Light color Medium-density FB 289.00 15062 Coffee Table 34.00 34.00 49.00 Dark color Medium-density FB 289.00 14979 Coffee Table 40.00 35.00 40.00 Dark color Particleboard (PB) 291.00 14843 TV Unit 180.00 35.00 45.00 Light color Particleboard (PB) 1295.00 14601 TV Unit 180.00 35.00 48.30 Dark color Particleboard (PB) 1475.00 14448 Coffee Table 35.00 18.00 51.00 Dark color Particleboard (PB) 399.00 14276 PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9782 TV Unit 180.00 35.00 48.30 Light color Particleboard (PB) 1562.00 14160 Coffee Table 34.00 34.00 49.00 Light color Medium-density FB 289.00 13455 TV Unit 180.00 30.00 50.00 Light color Particleboard (PB) 1100.00 13440 Coffee Table 60.00 60.00 46.00 Light color Medium-density FB 599.00 13401 TV Unit 150.00 30.00 44.00 Dark color Particleboard (PB) 1150.00 13116 Coffee Table 34.00 34.00 49.00 Dark color Medium-density FB 289.00 13012 Coffee Table 40.00 60.00 47.00 Dark color Particleboard (PB) 579.00 12964 TV Unit 180.00 30.00 45.00 Light color Particleboard (PB) 1600.00 12871 TV Unit 180.00 30.00 47.00 Light color Particleboard (PB) 1560.00 12834 Coffee Table 60.00 40.00 51.00 Dark color Medium-density FB 600.00 12705 Coffee Table 40.00 75.00 50.00 Dark color Particleboard (PB) 749.00 12332 TV Unit 120.00 30.00 50.00 Light color Particleboard (PB) 700.00 12229 TV Unit 180.00 35.00 48.30 Light color Particleboard (PB) 1600.00 12220 TV Unit 210.00 36.80 46.80 Light color Particleboard (PB) 3100.00 11920 Coffee Table 33.00 33.00 52.00 Light color Particleboard (PB) 349.00 11812 TV Unit 180.00 35.00 48.30 Light color Particleboard (PB) 2230.00 11760 Coffee Table 65.00 42.00 54.00 Light color Medium-density FB 1604.00 11728 TV Unit 120.00 30.00 45.00 Light color Particleboard (PB) 650.00 11579 Coffee Table 33.00 33.00 46.00 Light color Medium-density FB 310.00 11565 TV Unit 168.00 37.00 51.60 Dark color Particleboard (PB) 3309.00 11488 TV Unit 140.00 50.00 60.00 Dark color Particleboard (PB) 815.00 11460 TV Unit 150.00 30.00 40.60 Dark color Particleboard (PB) 1378.00 11383 Coffee Table 72.50 38.00 48.00 Dark color Particleboard (PB) 835.00 11356 TV Unit 138.00 29.50 42.60 Dark color Particleboard (PB) 740.00 11354 TV Unit 150.00 35.00 53.00 Light color Particleboard (PB) 1189.00 11189 TV Unit 130.00 30.00 40.00 Light color Particleboard (PB) 640.00 11146 TV Unit 140.00 33.00 40.00 Light color Particleboard (PB) 680.00 11127 Coffee Table 31.50 45.00 40.00 Light color Particleboard (PB) 354.00 10932 TV Unit 200.00 29.00 43.00 Light color Particleboard (PB) 1999.00 10867 Coffee Table 35.00 18.00 40.00 Light color Particleboard (PB) 354.00 10748 TV Unit 150.00 30.00 44.00 Light color Particleboard (PB) 1450.00 10553 TV Unit 180.00 35.00 48.30 Dark color Particleboard (PB) 1310.00 10539 TV Unit 160.00 30.00 50.00 Dark color Particleboard (PB) 990.00 10452 TV Unit 180.00 35.00 58.00 Dark color Medium-density FB 2750.00 10356 Coffee Table 60.00 44.00 46.00 Light color Medium-density FB 529.00 10233 TV Unit 140.00 30.00 39.00 Light color Particleboard (PB) 830.00 10145 TV Unit 178.00 34.00 60.00 Dark color Medium-density FB 1230.00 10020 Coffee Table 65.00 45.00 54.00 Light color Medium-density FB 1604.00 10015 Coffee Table 38.00 18.00 51.00 Dark color Medium-density FB 605.00 9849 TV Unit 120.00 30.00 50.00 Dark color Particleboard (PB) 1200.00 9787 TV Unit 140.00 30.00 39.00 Dark color Particleboard (PB) 750.00 9728 TV Unit 180.00 30.00 50.00 Dark color Particleboard (PB) 1100.00 9602 TV Unit 120.00 29.00 46.00 Light color Particleboard (PB) 450.00 9577 TV Unit 180.00 29.60 49.00 Light color Particleboard (PB) 1580.00 9437 TV Unit 138.00 29.50 48.60 Light color Particleboard (PB) 805.00 9402 TV Unit 160.00 30.00 45.00 Dark color Particleboard (PB) 1070.00 9342 Coffee Table 53.00 35.00 52.00 Light color Medium-density FB 459.00 9331 TV Unit 180.00 40.00 35.00 Dark color Medium-density FB 1980.00 9247 TV Unit 180.00 30.00 44.00 Light color Medium-density FB 1530.00 9167 TV Unit 180.00 35.00 42.00 Dark color Medium-density FB 1700.00 9070 PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9783 TV Unit 180.00 35.00 53.00 Dark color Medium-density FB 1370.00 9035 TV Unit 180.00 35.00 48.30 Light color Medium-density FB 1860.00 9028 Coffee Table 35.00 35.00 49.00 Light color Medium-density FB 289.00 9023 TV Unit 120.00 30.00 42.00 Light color Particleboard (PB) 480.00 8890 TV Unit 180.00 35.00 40.00 Light color MDF + PB 3570.00 8873 TV Unit 180.00 44.50 44.60 Dark color Medium-density FB 1980.00 8787 TV Unit 150.00 30.00 30.00 Light color Particleboard (PB) 800.00 8612 TV Unit 150.00 30.00 42.00 Dark color Particleboard (PB) 800.00 8612 Coffee Table 35.00 35.00 51.00 Dark color Medium-density FB 289.00 8534 Coffee Table 34.00 18.00 49.00 Light color Medium-density FB 289.00 8510 TV Unit 143.00 30.00 44.00 Light color MDF + PB 2380.00 8452 TV Unit 180.00 30.00 50.00 Dark color Particleboard (PB) 1100.00 8355 TV Unit 180.00 350.00 48.30 Dark color MDF + PB 2410.00 8204 Coffee Table 60.00 44.00 46.00 Dark color Medium-density FB 529.00 8106 Coffee Table 60.00 37.00 46.00 Light color Particleboard (PB) 529.00 8098 TV Unit 180.00 35.00 48.30 Dark color Particleboard (PB) 1500.00 8042 TV Unit 180.00 45.00 44.00 Light color Particleboard (PB) 915.00 7980 TV Unit 180.00 29.00 45.00 Light color Particleboard (PB) 2200.00 7795 TV Unit 180.00 32.00 42.00 Dark color Particleboard (PB) 820.00 7734 Coffee Table 50.00 37.00 46.00 Dark color Medium-density FB 529.00 7583 TV Unit 180.00 35.00 50.00 Dark color Particleboard (PB) 1170.00 7539 TV Unit 160.00 30.00 49.00 Light color Particleboard (PB) 1170.00 7455 TV Unit 180.00 30.00 47.00 Dark color MDF + PB 1560.00 7438 Coffee Table 34.00 34.00 49.00 Light color Particleboard (PB) 289.00 7382 TV Unit 140.00 33.00 40.00 Light color Particleboard (PB) 680.00 7323 TV Unit 180.00 35.00 54.00 Light color Particleboard (PB) 1160.00 7266 Coffee Table 65.00 42.00 57.00 Dark color Medium-density FB 1699.00 7242 Coffee Table 33.50 33.50 46.00 Dark color Medium-density FB 310.00 7214 TV Unit 180.00 30.00 46.00 Light color Particleboard (PB) 1600.00 7154 Coffee Table 60.00 39.00 52.00 Dark color Particleboard (PB) 615.00 7093 Coffee Table 60.00 44.00 46.00 Dark color Particleboard (PB) 529.00 7038 Coffee Table 62.00 32.00 56.00 Light color Particleboard (PB) 1999.00 6946 TV Unit 140.00 30.00 39.00 Dark color Particleboard (PB) 715.00 6923 TV Unit 180.00 35.00 49.00 Light color MDF + PB 2170.00 6922 Coffee Table 34.00 34.00 49.00 Dark color Medium-density FB 289.00 6904 Coffee Table 60.00 37.00 46.00 Light color Particleboard (PB) 529.00 6849 Coffee Table 33.00 33.00 46.00 Dark color Medium-density FB 310.00 6820 TV Unit 270.00 30.00 29.00 Light color MDF + PB 1600.00 6788 TV Unit 180.00 35.00 45.00 Light color Particleboard (PB) 900.00 6749 TV Unit 120.00 35.00 50.00 Dark color Particleboard (PB) 690.00 6744 TV Unit 180.00 35.00 54.00 Light color MDF + PB 1450.00 6739 TV Unit 180.00 35.00 48.30 Light color MDF + PB 1280.00 6712 Coffee Table 60.00 37.50 51.00 Dark color Medium-density FB 665.00 6591 Coffee Table 60.00 37.00 46.00 Dark color Particleboard (PB) 999.00 6584 Coffee Table 35.00 35.00 40.00 Light color Particleboard (PB) 279.00 6336 Coffee Table 60.00 37.00 46.00 Light color Particleboard (PB) 529.00 6335 Coffee Table 33.00 33.00 46.00 Dark color Medium-density FB 575.00 5661 Coffee Table 35.00 18.00 40.00 Dark color Medium-density FB 309.00 5654 Coffee Table 60.00 44.00 46.00 Dark color Medium-density FB 529.00 5578 Coffee Table 75.00 40.00 50.00 Light color Particleboard (PB) 749.00 5471 PEER-REVIEWED ARTICLE bioresources.cnr.ncsu.edu Çardak et al. (2025). “E-consumer preferences,” BioResources 20(4), 9768-9784. 9784 Coffee Table 73.00 41.00 57.50 Dark color Medium-density FB 2700.00 5465 Coffee Table 60.00 37.00 46.00 Dark color Medium-density FB 529.00 5465 Coffee Table 60.00 37.50 51.00 Light color Medium-density FB 671.00 5454 Coffee Table 42.00 29.00 45.00 Light color Medium-density FB 1604.00 5399 Coffee Table 33.00 33.00 46.00 Dark color Medium-density FB 575.00 5265 Coffee Table 65.00 40.00 52.00 Light color Medium-density FB 1100.00 5259 Coffee Table 60.00 44.00 46.00 Light color Medium-density FB 529.00 5207 Coffee Table 33.00 33.00 52.00 Dark color Particleboard (PB) 349.00 5178 Coffee Table 65.00 42.00 57.00 Light color Medium-density FB 1604.00 5138 Coffee Table 55.00 34.50 51.00 Light color Medium-density FB 414.00 5094 Coffee Table 50.00 35.00 44.00 Light color Medium-density FB 534.00 5016 Coffee Table 40.00 18.00 35.00 Light color Particleboard (PB) 289.00 4921 Coffee Table 31.00 18.00 44.00 Light color Medium-density FB 319.00 4918 Coffee Table 75.00 40.00 54.00 Dark color Medium-density FB 747.00 4904 Coffee Table 75.00 40.00 50.00 Light color Particleboard (PB) 749.00 4330 Coffee Table 60.00 40.00 47.00 Light color Particleboard (PB) 579.00 4181 Coffee Table 33.00 33.00 46.00 Dark color Medium-density FB 310.00 4061 Coffee Table 40.00 30.00 40.00 Light color Medium-density FB 319.00 4035 Coffee Table 60.00 37.50 51.00 Light color Particleboard (PB) 650.00 3914 Coffee Table 65.00 42.00 57.00 Light color Medium-density FB 1604.00 3899 Coffee Table 34.00 34.00 49.00 Light color Medium-density FB 289.00 3889 Coffee Table 60.00 40.00 47.00 Light color Particleboard (PB) 579.00 3773 Coffee Table 60.00 41.00 52.00 Light color Particleboard (PB) 2150.00 3751 Coffee Table 31.50 45.00 40.00 Dark color Medium-density FB 354.00 3724 Coffee Table 33.80 18.00 52.00 Light color Medium-density FB 700.00 3684 Coffee Table 39.00 39.00 55.00 Dark color Particleboard (PB) 1699.00 3678 Coffee Table 40.00 44.00 50.00 Dark color Medium-density FB 1199.00 3669 Coffee Table 66.00 40.50 53.50 Dark color Medium-density FB 1548.00 3662 Coffee Table 34.00 34.00 49.00 Light color Medium-density FB 289.00 3613 Coffee Table 55.00 32.00 58.00 Dark color Medium-density FB 654.00 3606 Coffee Table 60.00 40.00 49.00 Dark color Medium-density FB 810.00 3597 Coffee Table 39.00 39.00 51.00 Dark color Particleboard (PB) 648.00 3580 Coffee Table 61.00 45.00 51.00 Light color Medium-density FB 1388.00 3575 Coffee Table 40.00 35.00 45.00 Light color Medium-density FB 279.00 3561 Coffee Table 108.00 40.00 46.00 Dark color Medium-density FB 649.00 3555 TV Unit 145.00 36.00 50.00 Light color Particleboard (PB) 1000.00 725 TV Unit 150.00 30.00 45.00 Dark color Particleboard (PB) 1654.00 500 TV Unit 180.00 44.00 30.00 Dark color Particleboard (PB) 2550.00 499 TV Unit 150.00 31.00 52.00 Dark color Particleboard (PB) 2049.00 406 TV Unit 180.00 40.00 48.00 Light color Medium-density FB 4134.00 397 TV Unit 180.00 37.00 40.00 Dark color Particleboard (PB) 2790.00 384 TV Unit 180.00 29.00 49.00 Dark color Particleboard (PB) 2140.00 359 TV Unit 180.00 40.00 53.00 Light color Medium-density FB 4163.00 345 TV Unit 180.00 36.00 52.00 Light color Particleboard (PB) 1960.00 285 TV Unit 180.00 30.00 45.00 Light color Particleboard (PB) 2790.00 282 TV Unit 200.00 40.00 42.00 Dark color Medium-density FB 22079.00 218 TV Unit 240.00 35.00 35.00 Light color Medium-density FB 4208.00 189 TV Unit 220.00 40.00 50.00 Light color Medium-density FB 5077.00 183