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Factors influencing green, environmentally-friendly consumer behaviour

  • Suhaeni Suhaeni , Eliana Wulandari , Arjon Turnip and Yosini Deliana EMAIL logo
Published/Copyright: May 29, 2024
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Open Agriculture
From the journal Open Agriculture Volume 9 Issue 1

Abstract

Excessive consumption of non-green products contributes to increasing levels of environmental damage. One effort to overcome this problem is to influence consumer behavior in a more environment-friendly direction. Therefore, it is necessary to identify factors that influence environment-green consumer behavior. The main aim of this research is to build a more comprehensive consumer behavior model inductively using artificial intelligence algorithms. This research aims to identify factors based on questionnaire instruments and interviews as data collection methods. Questionnaires were distributed to the public in the province of West Java, Indonesia, where only 253 respondents gave valid answers. This research measures stated behavior and not actual behavior. This research focuses on variables that influence environment-green consumer behavior, including environmental knowledge, environmental concern, health concern (HC), attitude toward behavior (ATB), subjective norm (SN), perceived price (PP), perceived value and quality, and green consumer behavior (GCB). All variables were validated using the partial least square-structural equation model method using SmartPLS 4.0 software. Furthermore, the validated variables were modeled and classified using the support vector machine (SVM) method. The test results show that all variables, both direct and indirect, have a positive and significant influence on environment-green consumer behavior, with a significant level of p < 0.05 and t > 1.96. The SVM modeling predictions reach a high level of accuracy of about 96%. This means that the variables ATB, SN, and PVC have a direct effect on GCB. Additionally, most respondents fell into the “less environment-green behavior” classification, indicating there is a space for improvement in promoting environment-green choices.

Keywords: environment-green; green product; green consumer; SEM; SVM

1 Introduction

Climate change and global warming have caused various environmental problems, such as reduced natural resources; pollution of air, water, and land; and loss of biodiversity [1,2]. One of the causes of these environmental problems is human activity and the negative impact of the current mega-trend of modern civilization. In recent decades, unprecedented growth in consumption has led to the decline of natural resources and environmental degradation [3]. Lifestyles that tend to be less environment-green contribute to negative environmental phenomena [4]. Lifestyles that are not environment-green also influence the adoption of unsustainable consumption patterns. Based on research findings conducted by Goyal et al. [5] and Bulut et al. [6], consumption patterns driven by socio-economic desires rather than true needs are currently driving a shift toward environment-green and sustainable consumption models.

The lifestyle and consumption patterns of today’s modern consumers are not particularly altruistic, but their behavior is often irrational. Today’s consumers often buy products based solely on impulse, without considering their impact on the environment. In today’s economic growth, excessive consumption patterns have caused severe environmental damage in several countries, both developed and developing countries [7,8]. A more concerning problem is food loss and waste, commonly known as food waste (i.e., consumers contribute about a quarter of the greenhouse gas emissions associated with food production, amounting to 6% of total global emissions [9]). This percentage does not include losses in the agricultural sector during the production and harvesting process; in other words, the actual impact is likely to be higher.

One way to mitigate environmental damage is to foster green consumer behavior [10,11]. Therefore, green consumption patterns need to be encouraged. Previous research has explored the topic of green consumer behavior using conventional methods [12]. The reason underlying the importance of changing consumer behavior is that the strength of their preferences greatly influences green production and marketing. Furthermore, developing consumer behavior is also important in overcoming environmental problems and finding sustainable solutions. When consumers have awareness and concern for the environment, this will encourage them to adopt environment-green products in their daily lives. This change in behavior has occurred in several developed and developing countries, including Indonesia, causing increased demand for environment-green products (green products) [13]. In response to this demand, organic farming businesses have experienced significant growth, albeit with high risks, especially in the production of horticultural agricultural products [14].

Most research on green consumers is usually focused on Western countries. Studies regarding environment-green consumer behavior in Asian countries, especially in developing countries such as Indonesia, are still limited. To increase the significance of the study, literature that emphasizes research involving non-Western participants may be useful [15]. Therefore, this research is expected to fill this gap. Based on information from various literature sources, there are different interpretations regarding green consumer behavior. Some of them only focus on the intention to purchase the product, its characteristics, and the characteristics of green consumers. In some cases, it is stated that the research results acknowledge the environmental impact of consumer choices. Shmasi et al. [16] have analyzed the determining factors of consumer intention to purchase green products. Consumers’ environmental concerns (ECs) and awareness have a significant influence on their intention to purchase environment-green products [16]. The quality of green products also remains the most influential factor in purchasing intentions compared to other factors such as trust, price, availability, curiosity, and consumer effectiveness [17].

It is important to identify factors that can increase the tendency to consume green products in society, such as offering green products with desirable attributes. For example, price plays an important role for certain consumer segments [18]. Signaling theory predicts that green products that are clearly recognized have signal value for consumers. These benefits can lead to advantages in social interactions – additional benefits that can increase consumers’ willingness to pay, thereby addressing the attitude–behavior gap [19,20,21]. Moreover, experts must provide comprehensive information regarding the importance of environment-green products to consumers. Products must be equipped with information, branding, and green labels, because these elements greatly influence consumer decision-making [22,23]. When these requirements are met, consumers will actively seek out environment-green products which in turn changes green consumer behavior [24,25].

Based on findings in previous research, researchers often adopt the theory of planned behavior (TPB) in building conceptual models of green consumer behavior [26]. Variables in the TPB consist of attitudes, subjective norms (SNs), and perceived behavioral control. This model has been widely applied in various cases to analyze pro-environmental behavior [27,28,29]. However, the TPB theory has limitations because this theory mainly focuses on non-altruistic (selfish) behavior, which is only based on personal interest motives [30]. In this study, EC variables in the form of actions derived from personal norms (i.e., from the fundamental theory of the norm activation model [NAM] [31]) were added to compensate for non-altruistic factors. The NAM theory was proposed to explain consumer psychological processes related to altruistic behavior (non-egoism) [32]. It also refers to consumers’ emotional responses, such as concerns about environmental issues [33]. Altruistic behavior implies that individuals provide benefits to others even though they often do not receive the benefits directly. In addition, this research also investigates the role of environmental knowledge (EK), where when EK becomes deeper, consumers gain a better understanding of problems and solutions. In this way, individuals will indirectly play an active role in protecting the environment.

Therefore, this study aims to provide a more comprehensive understanding of the factors that influence consumer behavior by including additional variables beyond the TPB model, including sociodemographic factors such as age, gender, education level, occupation, ethnicity, marital status, level of income, number of family members, and job category [34]. This research is also expected to bring novelty and contribute to the research focus on green consumer behavior.

In previous studies, questionnaires distributed to respondents were usually only evaluated using statistical analysis software such as SPSS or the partial least square-structural equation model (PLS-SEM) method. However, these methods are still very limited in terms of variable testing and predictive modeling. Therefore, an analysis method with higher adaptability and a more accurate prediction model [35] using an artificial intelligence approach [36,37,38], namely support vector machine (SVM) [39], was introduced. Several studies have explored the application of classification using the SVM method in various scientific fields. For example, an evaluation study was conducted to examine the impact of startup technology innovation and customer relationship management on customer participation, value co-creation, and consumer purchasing behavior [40]. The SVM method has also been proven to be able to provide very accurate prediction results in various other studies [41,42,43]. However, the use of the SVM method to classify environment-green consumer behavior regarding related variables is still very rare. Based on those descriptions, this research aims to identify factors that can influence green consumer behavior. In addition, this research proposes the use of two approaches: PLS SEM and machine learning (i.e., SVM) in evaluating and classifying factors influencing green consumer behavior with much higher accuracy.

2 Methodology

This research was conducted in West Java Province, Indonesia, with a number of districts that have environmental pollution with high dangerous impacts (Central Statistics Agency, 2021), and it is believed that the majority of the population has non-green behavior. The data used in this research are primary and secondary data. Primary data were obtained from green consumers, while secondary data were taken from literature reviews of journals, related agencies or institutions, and other supporting sources.

The data collection technique was carried out using a survey approach while distributing questionnaires. The limitation of measuring green consumer behavior is that it is obtained only based on the opinions expressed by respondents in the questionnaire, without directly observing consumers’ actions when purchasing products. The questionnaire was designed in simple language, based on field study information, as well as literature reviews on similar research topics so that respondents could easily understand the questions. There are 20 indicators used to measure the following: EK, EC, health concern (HC), attitude toward behavior (ATB) [23], SNs, perceived price (PP), perceived value and quality (PVQ), and green consumer behavior (GCB). The data are then measured using a Likert scale of 1 to 5, in which 1 means strongly disagree, 2 means disagree, 3 means unsure, 4 means agree, and 5 means strongly agree. The 5-point rating scale provides easy assessment for respondents and makes it easier for researchers to organize and interpret data.

The research sample consisted of consumers who had purchased green products in the last 6 months. The sampling method used was simple random sampling. Before distributing the questionnaire, a pre-test was carried out to test the validity and reliability of the instrument used. From the pre-test results, it was found that there were several question indicators that were less reliable and therefore needed to be removed or corrected. A total of 300 respondents were initially asked to fill out a questionnaire for 1 month; however, only 253 provided valid responses. The survey was conducted using online and offline methods. Online questionnaires were prepared using Google forms and distributed via email and WhatsApp, while paper-based questionnaires were distributed for offline completion. During data collection, several obstacles were found in accessing respondents, namely, some respondents were not willing to fill out the questionnaire because they were busy, some respondents did not understand green products, and others are limited in using Google forms.

The data analysis methods used in this research include the PLS-SEM technique [44] using SmartPLS4.0 software and machine learning for modeling and classification. PLS-SEM was chosen because it can handle complex models, accommodate abnormal data, assess structural indicators, and facilitate theory development [45]. The SEM analysis consists of several stages: (1) evaluation of the measurement model, including analysis of construct validity and reliability; (2) assessment of discriminant validity; (3) structural model assessment; and (4) hypothesis testing. There are 13 hypotheses in this research, as seen in Figure 1. In the measurement model, various metrics are evaluated based on the internal consistency assessment of composite reliability (CR), Cronbach’s alpha for indicator reliability, average variance extraction (AVE) for convergent validity, and discriminant validity. CR and AVE values are used to test validity and reliability, with values greater than 0.50 for all constructs.

Figure 1 Relationship between variables.
Figure 1

Relationship between variables.

The collected data were modeled and classified using the SVM method. The SVM method developed by Cortes and Vapnik in 1995 is a widely used machine learning technique for data classification problems [46,47]. The SVM has the ability to make more accurate classifications than other classifiers [48,49]. The underlying concept of SVM data classification is to obtain an optimal hyperplane separator that can linearly separate the classification problems [50,51]. Given a sample set S = { { ( x i , y i ) i = 1 n | x i ∈ R N , y i ∈ { − 1,1 } , i = 1,2 , … , l } } , , where x i represents a data sample and y i represents a category sample. If the hyperplane equation reaches the optimal separation plane standard, the samples can be properly separated within the maximum class interval. The solution for the optimal separation plane is converted into an objective function and its constraints

(1) min 1 2 ‖ w ‖ 2 s . t . y i ( w x i + b ) ≥ 1 ,

where w represents the weight vector and b represents the bias vector. To ensure classification accuracy, a relaxing factor ξ ≥ 0 , i = 1,2 , … , n , is introduced, so the optimization problem is rewritten as follows:

(2) min 1 2 ‖ w ‖ 2 + c ∑ i = 1 n ξ i ( ξ i ≥ 0 ) s . t . y i ( w x i + b ) ≥ 1 − ξ i c ≥ 0 , ( i = 1 , 2 , … n )

where c represents the penalty factor. The difference between algorithm complexity and classification accuracy can be minimized by adjusting the penalty factor. Thus, the SVM classification problem is described as a multiple optimization problem

(3) max ( a ) = ∑ i = 1 n a i − 1 2 ∑ i , j = 1 n a 1 a i y i y i K ( x i x j ) s . t . ∑ i = 1 n a i y i ( 0 ≤ a i ≤ c ; i = 1 , 2 , … , n ) ,

where K ( x i x j ) represents a kernel function and meets K ( x i x j ) = 〈 φ t ( x i ) . φ ( x j ) 〉 note that 〈 · 〉 is the inner product of the operation. Thus, the final classification result of the data sample can be evaluated by the decision f ( x ) , as shown in equation (4)

(4) f ( x ) = sign ( ∑ i = 1 n a i y i 〈 φ T ( x j ) · φ ( x j ) 〉 + b ) = sign ∑ i = 1 n a i y i 〈 K ( x i , x j ) 〉 + b .

The kernel functions used generally have linear, polynomial, Gaussian, and radial basis functions (RBF). Because RBF can approximate any nonlinear function, in this study, RBF was chosen as the kernel function of the SVM classifier, as defined in formula (5), where g denotes the kernel parameter that affects the performance of SVM classification

(5) K ( x i , x j ) = exp − ‖ x i − x j ‖ 2 2 g 2 .

The SVM algorithm represents each data sample as a point in an n-dimensional space on a scatter plot. The values of each data attribute determine the coordinate points on the plot. The algorithm then classifies the different data points by drawing a straight line. The SVM is a robust algorithm widely used for data classification and verifying model accuracy [52]. In this study, SVM was employed to validate predictive models and classify green consumer behavior based on variables previously tested using the PLS-SEM approach. The classified data were then clustered according to the specified range. Clustering of each subject was performed by considering the maximum value, minimum value, range, and intervals, resulting in five classifications: Strongly Green Behavior (97–105), Moderately Green Behavior (89–97), Less Green Behavior (81–89), Not Green Behavior (73–81), and Strongly Not Green Behavior (65–73). Microsoft Excel compiled the green consumer data, and the model was developed using MATLAB 2021.

3 Results and discussion

3.1 Demographic result

Based on the demographic analysis, most of the respondents were female, about 69.6% of the total sample, while 30.4% were male. In terms of age distribution, the most of respondents were younger than 25 years old (52.6%), followed by the age groups of 35–45 years (21.3%), 25–35 years (18.6%), and older than 45 years old (7.5%). In the educational background, it was found that 114 respondents (45.1%) held a bachelor’s degree, and 83 respondents (32.8%) graduated from senior and vocational high school. Additionally, 42 (16.6%) and 14 (5.5%) respondents had a master’s and doctoral degree, respectively.

The characteristics of respondents also show that education has a significant influence on green consumer behavior. Based on income, less than 1,000,000 is 26.1%, 1,000,000–2,500,000 is 16.2%, 2,500,000–5,000,000 is 25.7%, 5,000,000–7,500,000 is 12, 3%, and more than 7,500,000 as much as 19.8%. This information shows that income level does not have a significant effect on green consumer behavior. Even though the income level is relatively low, respondents’ awareness and tendency toward green consumer behavior remains high. Regarding marital status, the data show that 147 respondents (58,1%) are not married, while 106 respondents (41.9%) are married. This shows that the majority of green consumers are unmarried individuals, but the difference is not very significant (Table 1).

3.2 Measurement model (construct validity and reliability analysis)

This measurement model was utilized to analyze the validity and reliability of the constructs. Validity and reliability tests were conducted to measure the validity and reliability of the variables proposed by researchers [53]. Cronbach’s alpha and CR were employed to evaluate construct reliability. Any loading factor values less than 0.708 were omitted [54]. In this study, the lowest loading factor value was 0.709, indicating that no items needed to be omitted. Meanwhile, the CR values in this study ranged from 0.806 to 0.913. According to Nunnally and Berntein (1994) [55], CR values above 0.70 are considered acceptable. However, Cronbach’s alpha value ranged from 0.542 to 0.809, with a value below the recommended cut-off value (0.70) for the EC item, which was 0.542. Nonetheless, these items were overall reliable, as the CR value exceeded 0.70. Construct validity could be measured using convergent and discriminant validities. Convergent validity was calculated using the AVE, with an AVE value above 0.50 (50%) considered acceptable [44,56]. The analysis results indicate that the lowest AVE value was 0.597, fulfilling the requirements of reliable convergent validity.

3.3 Assessment of discriminant validity

The heterotrait–monotrait ratio (HTMT) was employed to measure discriminant validity [57]. The finding estimated the correlation between two latent variables. A maximum HTMT limit value of 0.90 indicates the formation of discriminant validity, while a value more than 0.90 suggests a lack of discriminant validity. Based on the calculation results, the research HTMT value was lower than their threshold of 0.90, indicating that the discriminant validity was achieved. This means that the variance shared by each measuring item was higher within its own construct than other variable items. According to Hair et al. [54], the HTMT is a recommended approach for assessing discriminant validity compared to Fornell–Larcker and cross-loading methods. In addition to HTMT, the Fornell–Larcker approach can also be used to determine discriminant validity [56]. This approach examines whether the AVE square root value indicated by the diagonal line must have a more significant correlation between constructs. The analysis showed that the square root values of AVE in each variable were more significant than the correlation values between the constructs, indicating the fulfillment of discriminant validity.

Table 1

Demographic characteristics

Sample characteristics Sample characteristics Frequency Percentage
Gender Male 77 30.4
Female 176 69.6
Age Less than 25 133 52.6
25–35 47 18.6
36–45 54 21.3
More than 45 19 7.5
Degree High school 83 32.8
Bachelor’s degree 114 45.1
Master’s degree 42 16.6
Doctoral degree 14 5.5
Income per month (in IDR) Less than 1,000,000 66 26.1
1,000,000–2,500,000 41 16.2
2,500,001–5,000,000 65 25.7
5,000,001–7,500,000 31 12.3
More than 7,500 50 19.8
Marital status Unmarried 147 58.1
Married 106 41.9

3.4 Structural model assessment

The initial step in testing the structural model was a multicollinear examination between variables using the inner variance inflated factor (VIF). According to the criterion, if the VIF value was less than or equal to 3 (≤3.3), it indicated the absence of multicollinearity in the model [58]. Table 2 displays all the VIF values, which were found to be less than or equal to ≤3.3, indicating that the multicollinearity did not occur.

Table 2

Structural model results estimation

Relationship Original sample (O) Sample mean (M) Standard deviation (STDEV) T Statistics P Values R 2 f 2 VIF Q 2
ATB 0.416 0.234
GCB 0.370 0.218
PVQ 0.330 0.215
SN 0.266 0.211
ATB → GCB 0.320 0.326 0.066 4.848 0.000 0.107 1.510
EC → ATB 0.189 0.189 0.054 3.533 0.000 0.051 1.198
EC → PVQ 0.165 0.163 0.064 2.576 0.010 0.035 1.167
EK → ATB 0.185 0.189 0.055 3.338 0.001 0.051 1.141
EK → SN 0.140 0.140 0.059 2.362 0.019 0.024 1.112
HC → ATB 0.390 0.394 0.060 6.461 0.000 0.197 1.325
HC → PVQ 0.291 0.292 0.059 4.907 0.000 0.099 1.279
HC → SN 0.354 0.356 0.062 5.687 0.000 0.140 1.221
PP → ATB 0.136 0.136 0.058 2.334 0.020 0.027 1.171
PP → PVQ 0.309 0.309 0.059 5.237 0.000 0.123 1.159
PP → SN 0.185 0.185 0.067 2.780 0.006 0.040 1.164
PVQ → GCB 0.214 0.212 0.064 3.367 0.001 0.060 1.207
SN → GCB 0.231 0.232 0.058 4.016 0.000 0.057 1.495

The next testing stage in assessing the structural model utilized a 5.000 iteration bootstrapping procedure. This test was used to evaluate the significance of indicators and path coefficients [59]. The value of R 2 indicated the coefficient of determination. According to Hair et al. [54], the value of R 2 was categorized into three categories: (a) 0.25 (weak), (b) 0.5 (moderate), and (c) 0.75 (substantial). Based on analysis results, the R 2 was found to be 0.370 (Figure 2). This indicates that the model can explain 37% of the total variance of green consumer behavior. Therefore, there are still other factors beyond the scope of this study that influence green consumer behavior.

Figure 2 Structural model results, path coefficients (t-values, with the level of significance) and R-square values.
Figure 2

Structural model results, path coefficients (t-values, with the level of significance) and R-square values.

The f 2 value was used to measure the effect size of each path in the model, following the criteria of 0.02 (small effect), 0.15 (medium effect), and 0.35 (substantial effect) [54]. In this study, the variables that demonstrated a medium effect were HC on ATB (0.197). The remaining variables demonstrated a small effect. The Q 2 value was employed to evaluate the structural model [54]. The analysis results showed that all Q 2 values above zero indicated the model’s acceptance as having predictive power.

Table 2 presents the results of the 13 formulated hypotheses. All relationships demonstrated a t-statistic > 1.96 and a significant level of p < 0.05. The predictors are EK on ATB (H1) (β = 0.320, p < 0.05), EK on SN (H2) (β = 0.140, p < 0.05), HC on ATB (H3) (β = 0.390, p < 0.05), HC on SN (H4) (β = 0.354, p < 0.05), HC on PVQ (H5) (β = 0.291, p < 0.05), EC on ATB (H6) (β = 0.189, p < 0.05), EC on PVQ (H7) (β = 0.165, p < 0.05), PP on ATB (H8) (β = 0.136, p < 0.05), PP on SN (H9) (β = 0.185, p < 0.05), PP on PVQ (H10) (β = 0.309, p < 0.05), ATB on GCB (H11) (β = 0.320, p < 0.05), SN on GCB (H12) (β = 0.231, p < 0.05), and PVQ on GCB (H13) (β = 0.214, p < 0.05).

The pathway model analysis revealed that ATB, SNs, and PVQ serve as mediators between EK, health consciousness (HC), EC, and perceived prices (PP) and their influence on green consumer behavior (GCB). The significance of the indirect effects was confirmed using bootstrap analysis. The results indicate that the relationship between EK and GCB is mediated by ATB and SN. Similarly, HC to GCB is mediated by ATB, SN, and PVQ. Additionally, the relationship between PP and GCB is mediated by ATB, SN, and PVQ. All of these mediating relationships were found to have p-values <0.05, indicating their significance. However, EC to GCB is mediated by ATB but cannot be mediated by PVQ (p-values >0.05), indicating a lack of significance. The results of the research conducted by Shamsi et al. [16] show that EC and self-awareness are reasons for purchasing green products.

This study investigated the factors influencing green consumer behavior by employing a modified version of the TPB model. A sample of 253 was taken in this survey. Based on the model’s measurement structure analysis, the model is considered suitable for predicting green consumer behavior. All the model-forming variables used in the study demonstrated good data quality and supported the model hypothesis that they have a positive influence on green consumer behavior. Our result is reliable with research by Hossain et al. [60]. A positive attitude toward green products is a significant predictor of green purchasing behavior among adolescents [22].

This study revealed the significant impact of ATB, SNs, and PVQ on green consumer behavior. These variables played essential roles in shaping the behavior of consumers toward green products. Specifically, the variable of attitude in this study, defined as the positive sentiments that respondents hold toward green products, emerges as the most influential factor compared to SNs and perceived values and qualities.

This finding can be attributed to the fact that green behavior is related to the respondents’ educational level [61]. Most of the respondents having an undergraduate degree reflects their tendency to make informed decisions when purchasing green products. Furthermore, female respondents (69.6%) exhibited a more positive attitude toward green or eco-green products than males. This finding aligns with the research conducted by Witek and Kuźniar [62]. The cultural context in Indonesia, where women typically handle household shopping, contributes to women’s greater likelihood of purchasing green products and having green behavior. However, the research conducted by Dangelico et al. [63] on green consumers in Italy states that about 58.63% of women are the ones who mostly purchase green products.

Female respondents who were young, highly educated, and unmarried exhibited a positive attitude toward the idea of consuming green products. Specifically, young consumers positively viewed the consumption of green products, leading to a positive impact on the environment, which is consistent with the study by Shamsi et al. [16]. Respondents’ incomes are quite varied, indicating that green consumer behavior is not solely based on their monthly income levels. Respondents purchase green products based on their ECs and the benefits derived from these green products. These research findings align with a study by Zeynalova and Namazova [24]. Additionally, the benefits obtained from green products provide advantages in social interactions. According to signal theory, environment-green products can offer tangible benefits. These benefits act as incentives for consumers to pay a higher price for green products. They can also offset the drawback of the relatively high price of green products [21]. However, this research has limitations, as it only restricts the measurement of stated behavior, not observed behavior. Thus, researchers can only analyze the opinions expressed by respondents in the distributed questionnaires. Subsequent research could utilize methods that involve observing actual behavior.

3.5 SVM algorithm

The green consumer behavior prediction model using the SVM algorithm achieved an accuracy of 96% in the dataset. The dataset was classified into five classes of behavior: strongly green behavior, moderately green, less green, not green, and strongly not green. In addition, two types of differences were considered: correct and incorrect classifications. Figure 3 displays the scatter plot that serves as an initial representation of the numeric values of two variables. The plot utilized colored dots to illustrate the differences in each class. The comparison of variables in Figure 3 shows a comparison between the ATB and GCB variables. The “less green behavior” class is dominant compared to other prediction models.

Figure 3 Scatter plot.
Figure 3

Scatter plot.

Figure 4 illustrates the findings of the confusion matrix, with predicted classes (output classes) in rows and actual classes in columns. The true positive rates (TPR) and false negative rates (FNR) were analyzed to determine the classification in each class. The TPR and FNR help identify areas of good and poor performance. Good and poor performance were marked in blue and orange, respectively. Based on the SVM classification results, most of the respondents were categorized in the “less green behavior” class with a score of (81–89). There were 131 respondents categorized in the “less green behavior” class with a TPR value of 99.2% and an FNR of 0.8%. Additionally, 56 respondents were in the “moderately green behavior” class with a TPR value of 94.6% and an FNR of 5.6%. Thirty-seven respondents were classified in the “not green behavior” class, with a TPR value of 91.9% and an FNR of 8.1%. Twenty-two respondents were in the “strongly green behavior” class, with a TPR value of 95.5% and an FNR of 4.5%. Finally, seven respondents were classified in the “strongly not green behavior,” with a TPR value of 71.4% and an FNR of 26.6%.

Figure 4 Confusion matrices.
Figure 4

Confusion matrices.

After assessing the accuracy of each algorithm, a performance test of the dataset model was conducted using K-fold cross-validation, which aimed to evaluate the classification system’s performance with a fold number of 50. The training data were utilized for testing through K-fold cross-validation, while the test data served for model validation. The dataset was randomly divided into multiple K-partitions. A test was carried out using the area under curve (AUC) on the receiver operating characteristic to ensure the dataset’s accuracy. The AUC represents a square-shaped field with values ranging between 0 and 1. The resulting point is displayed on the curve in areas (0.0) and (1.1). If AUC < 0.5, it indicates that the evaluated model has minimal success based on the accuracy value exhibited in the prediction model [64]. Figure 5 displays one of the AUC values in the “less green behavior” class. The AUC values in the other models are similar, with a classification value of 1.00 in each model, indicating high accuracy for each class.

Figure 5 ROC curve.
Figure 5

ROC curve.

Based on the SVM model testing results, the majority of respondents were categorized as having “less green behavior.” This finding can be attributed to a lack of education and socialization regarding green and environment-green products, which means many respondents do not fully comprehend these products. In addition, the high prices of green products also played a role in limiting green behavior, particularly among respondents with low-income backgrounds, who had to consider their purchases of green products carefully. Nonetheless, most respondents had a positive attitude toward being green consumers, indicating their belief in the potential to contribute to a better environment. Using machine learning, businesses can make more informed, efficient, and accurate decisions. The results of this study provide valuable insights into green consumer behavior, which are helpful and can significantly assist green product businesses in implementing their business strategies.

4 Conclusion

ATB, SNs, and Perceived Values and Quality are important factors that shape green consumer behavior. Moreover, EK, EC, HC, and PP are important factors that indirectly influence green consumer behavior. The study concluded that two methods could be used to predict green consumer behavior models: PLS-SEM for identifying significant factors and SVM for creating a predictive model. The PLS-SEM test results indicated that all variables, both directly and indirectly, had a positive and significant influence on green consumer behavior, with a significant level of p < 0.05 and t > 1.96. Additionally, the predictive modeling of green consumer behavior using SVM achieved a high accuracy level of 96%. Subsequently, the variables examined in this study notably impact green consumer behavior. The use of this approach has higher adaptation and accuracy capabilities compared to other approaches.

This research makes a significant contribution by creating a framework that encompasses environmental contextual factors. However, the TPB theory is insufficient to describe the relevant factors for green consumer behavior. There is a need for additional factors to be included. A suggestion for further research is to integrate the TPB theory with the NAM theory. While this study already used personal norms (one of the variables in the NAM theory) with its action being EC. It would be even more comprehensive to incorporate all the variables found in the NAM theory. Furthermore, it could also integrate additional factors that have the potential to influence green consumer behavior, such as variables found in the theory of consumption values.

The use of artificial intelligence is highly recommended, especially for processing very large data, so that predictions of green consumer behavior become more accurate. Currently, research using AI in the analysis of green consumer behavior is still very limited. These findings assist managers and policymakers understand the underlying psychology of targeted consumers. Policymakers can formulate an operational marketing approach for the market, especially in Indonesia, by realizing that consumer behavior is an important aspect of green marketing. They can investigate and provide solutions to barriers that hinder green consumer behavior.

Acknowledgements

The authors thank the Rector of Universitas Padjadjaran for providing research facilities and thank the field assistants of Universitas Singaperbangsa Karawang during the activity.

  1. Funding information: This research was financed by Pusat Layanan Pembiayaan Pendidikan (Puslapdik) and Lembaga Pengelola Dana Pendidikan (LPDP).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. SS: conceptualization, study execution and writing process with the contribution of all co-authors. EW: methodology, AT: model development, and YD: reviewing and editing process.

  3. Conflict of interest: Arjon Turnip, who is the co-author of this article, was Guest Editor of Open Agriculture. This fact did not affect the peer-review process.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-06-26
Revised: 2023-10-02
Accepted: 2024-02-05
Published Online: 2024-05-29

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
https://doi.org/10.1515/opag-2022-0269
Keywords for this article
environment-green; green product; green consumer; SEM; SVM
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Supplementation of P-solubilizing purple nonsulfur bacteria, Rhodopseudomonas palustris improved soil fertility, P nutrient, growth, and yield of Cucumis melo L.
  3. Yield gap variation in rice cultivation in Indonesia
  4. Effects of co-inoculation of indole-3-acetic acid- and ammonia-producing bacteria on plant growth and nutrition, soil elements, and the relationships of soil microbiomes with soil physicochemical parameters
  5. Impact of mulching and planting time on spring-wheat (Triticum aestivum) growth: A combined field experiment and empirical modeling approach
  6. Morphological diversity, correlation studies, and multiple-traits selection for yield and yield components of local cowpea varieties
  7. Participatory on-farm evaluation of new orange-fleshed sweetpotato varieties in Southern Ethiopia
  8. Yield performance and stability analysis of three cultivars of Gayo Arabica coffee across six different environments
  9. Biology of Spodoptera frugiperda (Lepidoptera: Noctuidae) on different types of plants feeds: Potency as a pest on various agricultural plants
  10. Antidiabetic activity of methanolic extract of Hibiscus sabdariffa Linn. fruit in alloxan-induced Swiss albino diabetic mice
  11. Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance
  12. Nicotinamide as a biostimulant improves soybean growth and yield
  13. Farmer’s willingness to accept the sustainable zoning-based organic farming development plan: A lesson from Sleman District, Indonesia
  14. Uncovering hidden determinants of millennial farmers’ intentions in running conservation agriculture: An application of the Norm Activation Model
  15. Mediating role of leadership and group capital between human capital component and sustainability of horticultural agribusiness institutions in Indonesia
  16. Biochar technology to increase cassava crop productivity: A study of sustainable agriculture on degraded land
  17. Effect of struvite on the growth of green beans on Mars and Moon regolith simulants
  18. UrbanAgriKG: A knowledge graph on urban agriculture and its embeddings
  19. Provision of loans and credit by cocoa buyers under non-price competition: Cocoa beans market in Ghana
  20. Effectiveness of micro-dosing of lime on selected chemical properties of soil in Banja District, North West, Ethiopia
  21. Effect of weather, nitrogen fertilizer, and biostimulators on the root size and yield components of Hordeum vulgare
  22. Effects of selected biostimulants on qualitative and quantitative parameters of nine cultivars of the genus Capsicum spp.
  23. Growth, yield, and secondary metabolite responses of three shallot cultivars at different watering intervals
  24. Design of drainage channel for effective use of land on fully mechanized sugarcane plantations: A case study at Bone Sugarcane Plantation
  25. Technical feasibility and economic benefit of combined shallot seedlings techniques in Indonesia
  26. Control of Meloidogyne javanica in banana by endophytic bacteria
  27. Comparison of important quality components of red-flesh kiwifruit (Actinidia chinensis) in different locations
  28. Efficiency of rice farming in flood-prone areas of East Java, Indonesia
  29. Comparative analysis of alpine agritourism in Trentino, Tyrol, and South Tyrol: Regional variations and prospects
  30. Detection of Fusarium spp. infection in potato (Solanum tuberosum L.) during postharvest storage through visible–near-infrared and shortwave–near-infrared reflectance spectroscopy
  31. Forage yield, seed, and forage qualitative traits evaluation by determining the optimal forage harvesting stage in dual-purpose cultivation in safflower varieties (Carthamus tinctorius L.)
  32. The influence of tourism on the development of urban space: Comparison in Hanoi, Danang, and Ho Chi Minh City
  33. Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia
  34. The role of organic rice farm income on farmer household welfare: Evidence from Yogyakarta, Indonesia
  35. Exploring innovative food in a developing country: Edible insects as a sustainable option
  36. Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia
  37. Factors influencing green, environmentally-friendly consumer behaviour
  38. Factors affecting coffee farmers’ access to financial institutions: The case of Bandung Regency, Indonesia
  39. Morphological and yield trait-based evaluation and selection of chili (Capsicum annuum L.) genotypes suitable for both summer and winter seasons
  40. Sustainability analysis and decision-making strategy for swamp buffalo (Bubalus bubalis carabauesis) conservation in Jambi Province, Indonesia
  41. Understanding factors affecting rice purchasing decisions in Indonesia: Does rice brand matter?
  42. An implementation of an extended theory of planned behavior to investigate consumer behavior on hygiene sanitation-certified livestock food products
  43. Information technology adoption in Indonesia’s small-scale dairy farms
  44. Draft genome of a biological control agent against Bipolaris sorokiniana, the causal phytopathogen of spot blotch in wheat (Triticum turgidum L. subsp. durum): Bacillus inaquosorum TSO22
  45. Assessment of the recurrent mutagenesis efficacy of sesame crosses followed by isolation and evaluation of promising genetic resources for use in future breeding programs
  46. Fostering cocoa industry resilience: A collaborative approach to managing farm gate price fluctuations in West Sulawesi, Indonesia
  47. Field investigation of component failures for selected farm machinery used in small rice farming operations
  48. Near-infrared technology in agriculture: Rapid, simultaneous, and non-destructive determination of inner quality parameters on intact coffee beans
  49. The synergistic application of sucrose and various LED light exposures to enhance the in vitro growth of Stevia rebaudiana (Bertoni)
  50. Weather index-based agricultural insurance for flower farmers: Willingness to pay, sales, and profitability perspectives
  51. Meta-analysis of dietary Bacillus spp. on serum biochemical and antioxidant status and egg quality of laying hens
  52. Biochemical characterization of trypsin from Indonesian skipjack tuna (Katsuwonus pelamis) viscera
  53. Determination of C-factor for conventional cultivation and soil conservation technique used in hop gardens
  54. Empowering farmers: Unveiling the economic impacts of contract farming on red chilli farmers’ income in Magelang District, Indonesia
  55. Evaluating salt tolerance in fodder crops: A field experiment in the dry land
  56. Labor productivity of lowland rice (Oryza sativa L.) farmers in Central Java Province, Indonesia
  57. Cropping systems and production assessment in southern Myanmar: Informing strategic interventions
  58. The effect of biostimulants and red mud on the growth and yield of shallots in post-unlicensed gold mining soil
  59. Effects of dietary Adansonia digitata L. (baobab) seed meal on growth performance and carcass characteristics of broiler chickens: A systematic review and meta-analysis
  60. Analysis and structural characterization of the vid-pisco market
  61. Pseudomonas fluorescens SP007s enhances defense responses against the soybean bacterial pustule caused by Xanthomonas axonopodis pv. glycines
  62. A brief investigation on the prospective of co-composted biochar as a fertilizer for Zucchini plants cultivated in arid sandy soil
  63. Supply chain efficiency of red chilies in the production center of Sleman Indonesia based on performance measurement system
  64. Investment development path for developed economies: Is agriculture different?
  65. Power relations among actors in laying hen business in Indonesia: A MACTOR analysis
  66. High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought
  67. Converting compression ignition engine to dual-fuel (diesel + CNG) engine and experimentally investigating its performance and emissions
  68. Structuration, risk management, and institutional dynamics in resolving palm oil conflicts
  69. Spacing strategies for enhancing drought resilience and yield in maize agriculture
  70. Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types
  71. Investigating Spodoptera spp. diversity, percentage of attack, and control strategies in the West Java, Indonesia, corn cultivation
  72. Yield stability of biofertilizer treatments to soybean in the rainy season based on the GGE biplot
  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
  85. Physical and textural properties and sensory acceptability of wheat bread partially incorporated with unripe non-commercial banana cultivars
  86. Cereibacter sphaeroides ST16 and ST26 were used to solubilize insoluble P forms to improve P uptake, growth, and yield of rice in acidic and extreme saline soil
  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
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