https://orcid.org/0000-0003-3535-9657
Figures
Abstract
Samgyeopsal has become a widely popular cuisine in the Philippines since 2014. The rise of Samgyeopsal is evident worldwide as it is available in countries such as the United States, Northern, and Southern Asia. This study aimed to explore the intention to eat Samgyeopsal during the COVID-19 pandemic utilizing structural equation modeling and random forest classifier. With a total of 1014 responses collected online, the result showed that utilitarian and hedonic motivation, Korean influence, and attitude led to very high actual behavior in east Samgyeopsal in the Philippines. Moreover, subjective norm, perceived behavioral control, and intention led to significant results influencing intention to actual behavior. Lastly, the COVID-19 safety protocol showed the least significant result. This study is the first study that evaluated the intention of consumers to eat Samgyeopsal in the Philippines during the COVID-19 pandemic. The results of this study would be beneficial to Korean BBQ restaurateurs and the further development of their marketing strategies even in other countries. Finally, the model construct of this study can be extended and applied in evaluating the consumers’ eating intention toward other varieties of food or cuisines worldwide.
Citation: Ong AKS, Prasetyo YT, Manguray ARB, Moral EJMG, Maun ALM, Diaz JGF, et al. (2023) Evaluating factors influencing customers’ intention to eat Korean cuisine “Samgyeopsal” in the Philippines: A structural equation model forest classifier approach. PLoS ONE 18(5): e0286077. https://doi.org/10.1371/journal.pone.0286077
Editor: Charles Odilichukwu R. Okpala, Wroclaw University of Environmental and Life Sciences: Uniwersytet Przyrodniczy we Wroclawiu, POLAND
Received: November 5, 2022; Accepted: May 9, 2023; Published: May 19, 2023
Copyright: © 2023 Ong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data is available online in this link: 10.6084/m9.figshare.22491274.
Funding: We would like to mention that this research was funded by Mapúa University Directed Research for Innovation and Value Enhancement (DRIVE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The Korean wave has had a great impact on the Philippines. It is also known as “Hallyu—wave or flow of Korea” where Korean films, Korean pop music (K-pop), and even Korean dramas have become popular [1, 2]. The Philippines had the highest growth rate of Hallyu enthusiasts among 113 nations [2]. As an impact, the media popularization of the Korean wave has increased its influence, even on the cuisines [3]. Eventually, the Filipinos’ consumption of these media has taken their level of interest in Korean cuisine [4].
Korean cuisine such as Korean barbeque (Korean BBQ), commonly referred to by Filipinos as Samgyeopsal, has rapidly become a popular social and dining activity in the Philippines [5–7]. According to a 2020 survey conducted by the Korea Trade-Investment Promotion Agency (KOTRA) in Manila, the number of Korean restaurants in the Philippines has climbed by 81.2 percent from 2014 to 2018. Consequently, all-you-can-eat buffets such as Korean BBQ restaurants are highly popular in the Philippines as they give great offers to consumers who are looking to eat as much as possible at an affordable price. Moreover, numerous restaurateurs have started franchises that market Samgyeopsal in different varieties.
Fig 1 represents the top 10 Korean barbecue restaurants in the Philippines and their respective number of branches. Based on this figure, it can be observed that Romantic Baboy and Samgyupsalamat are the two restaurants that have the most number of branches in the Philippines. They have a combined number of 131 branches in the Philippines while the other restaurants range from 1 to 7. Ultimately, the figure below shows that the top 10 Korean BBQ restaurants currently have a total of 159 branches all over the Philippines [5–7].
Various studies on meat linked with Samgyeopsal will help provide better information on how research on meat or Korean barbecue was conducted. Nam et al. [8] studied Samgyeopsal relating to East Asian meat products and consumption habits. Their results showed that East Asia’s dynamic environment, religion, history, and main food staples have contributed to a lack of meat consumption culture. Recently, due to the globalization of the food business and the countries’ rapid economic expansion, the amount of meat produced and consumed in Eastern countries has increased dramatically. However, the study of Nam et al. [8] only focused on three Northeast Asian countries: China, Japan, and Korea, and two Southeast Asian countries, Vietnam and Thailand. Both Vietnam and Thailand have traditionally shared comparable environmental and cultural contacts but have their own distinct cuisine cultures.
Cultural influences lead to the variation in the practices of preparation and in the consumption of food and cuisine [9]. It influences shapes the fundamental structure of food attitudes [10]. The consumers’ experiences, beliefs, and values are shaped by culture, which is connected to attitudes, emotions, social norms, intentions, and behavior [11]. The impact of culture in correlation with food choice or consumption has been tackled by different studies [3, 10, 12] and is commonly measured using the Theory of Planned Behavior.
The current times changed the behavior of people [13, 14]. It was explained in the different studies how reassessment of behavioral factors should be considered. To which, their study implemented the utilization of TPB, SERVQUAL 5 dimension, and even the social exchange theory. The separate analysis made provided justification for the change in human behavior due to the influence that happened during the strict lockdown, health fear, and perception of people [15]. As well as their study focusing on the transportation sector, restaurants have also been influenced due to changes in the behavioral aspects of people. Thus, the need to evaluate behavioral intention through TPB is needed to further close the gap of changes that happened during the COVID-19 pandemic [16]. In accordance, the lack of study focusing on Samgyeopsal despite its popularity should be evaluated to provide managerial implications and suggestions for restaurants in the near end of the COVID-19 pandemic such as in the Philippines.
The Theory of Planned Behavior (TPB) is a commonly used expectancy-value model of attitude-behavior interactions that has been successful in predicting a range of behaviors to some extent [17–20]. According to the TPB, attitudes, subjective norm, and perceived behavioral control predicts behavioral intention which then affects the actual conduct of a person [21]. The model of the TPB is beneficial in predicting the behavioral intentions of individuals to predict consumers’ purchase intentions [18]. Several studies have used the TPB to determine the intention and measure human behavior [22–24]. However, only a few researchers have used the theory to investigate a consumer’s satisfaction and intention for eating meals [25, 26].
Hoeksma et al. [27] utilized an extended TPB to predict consumers’ willingness to purchase mobile slaughter unit (MSU) meat. Their results showed that consumers who decided to buy MSU meat have a stronger intention in buying. However, the study only covered pork and beef meat. Charlton et al. [28] considered a Visual Analogue Scale (VAS) rating for hunger and satiety over a set time. Pork, beef, and chicken were test meals used in this study. Their result showed that all three types of meats had similar effects on eating behavior. However, the study only covered three (3) meals to represent the differences in the release of intestinal hormones associated with appetite and hunger. Thus, studies on Samgyeopsal have been underexplored despite the consequent impact and rise of popularity with the Korean wave on Filipinos eating Korean food, particularly Samgyeopsal.
This study aimed to evaluate the varying factors influencing consumers’ intention to eat Samgyeopsal in the Philippines by extending the Theory of Planned Behavior. Various factors such as hedonic motivation, utilitarian, Korean influence, attitude, subjective norms, perceived behavioral control, COVID-19 safety protocols, intention, and actual behavior were analyzed using the Structural Equation Modelling (SEM) and Random Forest Classifier approach. This is considered the first study to analyze the different factors influencing consumers’ intention to eat Samgyeopsal in the Philippines during the COVID-19 delta variant pandemic. The results of this study would be beneficial to Korean BBQ restaurateurs and the further development of their marketing strategies since Samgyeopsal is a business that has been growing exponentially, even in other countries. Finally, the model construct of this study can be extended and applied in evaluating the consumers’ eating intention toward other varieties of food or cuisines.
2. Conceptual framework
This study considered the extended Theory of Planned Behavior (TPB) to evaluate factors affecting the influence of consumers’ intention to eat Samgyeopsal in the Philippines during the COVID-19 delta variant. As seen in Fig 2, there were 10 hypotheses created for this study that included different latent such as hedonic motivation, utilitarian, Korean influence, attitude, subjective norms, perceived behavioral control, COVID-19 safety protocols, intention, and actual behavior.
Hedonic motivation refers to a person’s willingness to engage in acts that improve positive experiences (pleasant or nice) while also reducing bad experiences [29]. The link between beliefs and attitudes is analogous to hedonic motivation [30]. Hedonic motivation has been discovered to have a significant impact on cognitive-emotional attitudes about purchasing intentions. Moon et al. [31] presented in their study how consumers’ perceptions of hedonic attributes are important and are considered a positive predictor of cognitive and affective attitude. Ong et al. [22] also presented how hedonic motivation had a positive significant direct effect on attitude of consumers. Thus, it was hypothesized that:
- H1. Hedonic motivation had a significant direct positive effect on attitude.
The utilitarian aspect of a consumer’s attitude toward an action relates to the behavior’s utility, value, and wisdom in the consumer’s eyes [32]. Attitude is directly affected by the Utilitarian motivation [33, 34]. Prior studies found that utilitarian traits are concerned with more cognitive components of attitude when shopping or purchasing (e.g., convenience, economic value for money, or time savings) [35–37]. Similarly, Ong et al. [22] showed how utilitarian motivation had a significant effect on consumers’ attitude when purchasing. Hence, it was hypothesized that:
- H2. Utilitarian had a significant direct positive effect on attitude.
Consumers’ attitudes are positively influenced by the attachment and affinity features of Korean trending dramas [38]. With that, Lee et al. [39] stated that consumption of Korean items would ultimately lead to a better understanding of Korean culture and appreciation for Korea. This demonstrates that people’s understanding and acceptance of culture are highly influenced by their level of contact with the environment, whether directly or indirectly. In addition, media plays a vital role in learning to embrace the culture of a foreign place through experience [39]. Thus, it was hypothesized that:
- H3. Korean influence had a significant direct positive effect on attitude.
- H4. Korean influence had a significant direct positive effect on subjective norms.
- H5. Korean influence had a significant direct positive effect on perceived behavioral control.
Attitude is an antecedent of intention, as well as the degree to which an individual has a previous valuation of possible behavior concerning any purchasing situation [17]. TPB specifies that attitudes, subjective norms, and perceived behavioral control are the essential components of behavioral intent and actual health-related behavior [40]. The subjective assessment of the risks and advantages of the expected outcome, as well as the attitude about the chance that the behavior will have the desired effect that subsequently influence behavioral intentions [41].
Lau et al. [42] mentioned that the variables of behavioral intention are directly correlated to the factors derived from the TPB. In addition, Ong et al. [43] presented how attitude is one of the highest factors influencing Filipinos that affects their intention. According to Latimer and Martin-Ginis [44], perceptions of others’ views may influence intention, depending on how much the individual fears being judged. In the context of the TPB, it is plausible that fear of being judged badly by others moderates the path from social standards to fast food intents [44]. In addition, Research suggests that if consumers possess high perceived behavioral control towards purchasing organic food products, positive behavioral intentions will also be high [45–47]. Thus, it was hypothesized that:
- H6. Attitude had a significant direct positive effect on intention.
- H7. Subjective norms had a significant direct positive effect on intention.
- H8. Perceived behavioral control had a significant direct positive effect on intention.
- H9. Intention had a significant direct positive effect on actual behavior.
Restaurants, where cluster infections are easily spread, have been a source of concern during the pandemic. Droplets, airborne particles, and person-to-person contact can disseminate COVID-19 and someone with the coronavirus infecting the environment and anything in it can easily cause its transmission [48]. Because of this, the people have become cautious and thus, influenced their intention to dine out and eat. To promote safe dining out, the government announced that restaurants must implement social distancing rules and set safety guidelines [7]. To reduce the risk of infection, close physical contact and food sharing in restaurants should be avoided. Other precautionary measures that restaurants can take to make customers feel safer and reduce physical and psychological risks include hand sanitizer, contactless payment, segregated dining areas, and separate restrooms. Hence, it was hypothesized that:
- H10. COVID-19 safety protocols had a significant direct positive effect on intention.
As control variables, this study also considered demographic factors such as age, frequency of consumption, and monthly allowance/salary which may affect the actual behavior for consumption as presented among several studies [21, 29, 31, 34]. Based on the described presentation of the different studies, demographic characteristics may play a significant role in the actual behavior of consumers from a different perspective. In addition, the presentation of age, income or allowances, and frequency had presented a great factor among consumption or actual behavior on a positive or negative decision [22, 30]. Therefore, the following were hypothesized:
- H11. Age as a control variable had a significant direct positive effect on actual behavior.
- H12. Monthly Allowance/Salary as a control variable had a significant direct positive effect on actual behavior.
- H13. Frequency of consumption as a control variable had a significant direct positive effect on actual behavior.
3. Methodology
3.1 Participants
The study employed a self-administered online cross-sectional survey using Google forms. Due to the COVID-19 pandemic, the survey was distributed online via social media platforms [49]. This study used a convenience sampling approach, and respondents were asked to sign a consent form before answering the study’s questionnaire. As shown in Table 1, a total of 1,014 people voluntarily responded to the survey. This study underwent verification by Mapua University Research Ethics Committees. Informed consent was obtained from all participants prior to the data collection in accordance with Data Privacy Act or Republic Act No. 10173 in the Philippines. Utilizing the Yamane Taro equation, the Philippines’ total population is 62.6 million. At 95% confidence, 399 or roughly 400 respondents would suffice the generalizability [50]. The current study had 1014 respondents, which may be confidently a representation of the consumers.
The demographics are made up of 433 males, 557 females, and 24 other genders with the majority belonging from the 15 to 24 years old age bracket (85.40%). Furthermore, approximately 80.87% receive a monthly salary/allowance of <15,000 PHP, and eat Samgyeopsal once a month (68.74%). Most of the respondents came from National Capital Region (45.86%). The result is supported by Gallinera et al. [51] who indicated that the majority of the consumers of Samgyeopsal in the Philippines have low budgets and are between 15–35 years old.
3.2 Questionnaire
Table 2 presents the constructs that were used to create this study’s questionnaire that can assess the various factors influencing consumers’ intentions to eat Samgyeopsal or Korean BBQ in the Philippines. Following the study of Ong et al. [22, 43], a 5-point Likert scale was used to measure all of the latent constructs using Structural Equation Modelling (SEM). The questionnaire consisted of eleven sections: (1) Consent of the Respondents (2) Demographic Information (gender, age, monthly salary/allowance, and health insurance). (3) Hedonic Motivation, (4) Utilitarian, (5) Korean Influence, (6) Attitude, (7) Subjective Norms, (8) Perceived Behavioral Control, (9) COVID-19 Safety Protocols, (10) Intention, and (11) Actual Behavior. A total of 54 questions were developed for this study and adapted from different studies. Prior to the distribution and apart from the approval of the Ethics Committee, the questionnaire was subjected to preliminary assessment considering 150 respondents. The overall results showed acceptable findings (Cronbach’s alpha > 0.70, Hair [52]). To which, the full questionnaire was utilized for distribution and data collection.
3.3 Structural equation modeling
Structural Equation Modeling (SEM) is a combination of statistical techniques for calculating and analyzing the correlations between observable and latent variables. It investigates linear causal links between variables while taking measurement error into account [70]. The correlations between grandparenting practices, food insecurity, and childhood depression were investigated using SEM [71]. Cunha et al. [72] conducted a study on the differences between observed and self-reported food safety practices by using SEM since it compensates for the model’s complexity and may corroborate the distinction between observed and self-reported behaviors. SEM investigates the possible mechanisms through which information and perceptions influence behavior [73]. Hence, this study utilized SEM to evaluate the factors that influence consumers’ intention to eat Samgyeopsal in the Philippines by utilizing the extended theory of planned behavior.
3.4 Random Forest Classifier
Random Forest Classifier (RFC) is a type of decision tree that produces higher accuracy in classifying factors [74]. This type of algorithm has several advantages such as easy interpretation and data processing. Moreover, the interpretation of the result is also considered to be uncomplicated [75]. Different studies regarding human behavior utilized RFC to classify the different factors considered [74, 76, 77]. Compared to other machine learning tools, the RFC provides a higher accuracy rate and easier optimization, processing, and analysis. It was also established that RFC shows only the highly significant factors affecting a subject matter. German et al. [50] explained that mostly, RFC can suffice as support for SEM analysis since it provides insight into the significant latent variables relating to the dependent variables which may be significant due to the relative indirect effects but may not be evident.
Similarly, German et al. [50] showed the accuracy rates of different machine learning tools. To which, neural networks may provide better accuracy rates, but would have higher standard deviation compared to RFC which shows the consistent output. In addition, Chen et al. [74] expounded on the efficiency compared to other advanced machine learning tools that may also provide higher accuracy rates, but are more complicated those present almost similar findings. Thus, this study considered the utilization of RFC to predict factors that significantly affect the actual behaviour of consumers in eating Samgyeopsal in the Philippines.
Before running the RFC, data cleaning using correlation analysis was done for the 1014 responses collected. Only significant values (p-value < 0.05) and high correlation (> 0.20) were considered for the 54,756 data. Following this was data aggregation and data normalization. In utilizing RFC, different training and testing ratio (60:40, 70:30, 80:20, 90:10) were utilized. To which, criterion (gini and entropy) and splitter (best and random) were utilized following the study of Yang and Zhou [77]. A total of 9,600 runs were done for the optimization process using Python 3.8.
4. Results
Fig 3 represents the initial SEM for consumers’ intention to eat Samgyeopsal in the Philippines. Utilizing AMOS 25 for a factor-based model, the indicators were visually inspected for values below the 0.5 threshold. According to Ong et al. [49] and Hair [52], the removal of non-significant latent and indicators below 0.5 may be done to increase the model fit. As seen in Fig 3, SN2 and AB3 were below 0.5, therefore, were removed.
From the initial SEM, the latent were all significant. Fig 4 represents the final SEM for consumers’ intention to eat Samgyeopsal in the Philippines with significant latent variables and indicators. Presented in Table 3 are the descriptive statistics of the factor loading. It could be seen that the final factor loading had values greater than 0.50.
Table 4 represents the parameters for model fit. The threshold for the acceptable range was adopted from different studies [78, 79]. The minimum cut-off value is greater than 0.80 for IFI, TLI, CFI, GFI, and AGFI [78] and less than 0.07 for RMSEA [79]. From which, the different values were greater than 0.80 and RMSEA presented 0.058. Ong et al. [22] and Hair [52] stated that when the model fit parameters were within range, then the SEM is considered acceptable.
The composite reliability (CR), average variance extracted (AVE), and Cronbach’s alpha are presented in Table 5 for the test of the internal validity and reliability of the constructs. Hair [52] stated that the values of CR and Cronbach’s alpha should be greater than 0.70, while the AVE should be greater than 0.50 to present validity and reliability among the constructs. Lastly, setting an acceptable Variance Inflation Factor (VIF) value of 5, all the variables were seen to be below the threshold which presents further acceptability. As seen in Table 5, all the latent possessed values greater than the threshold which indicates internal validity and reliability.
The Common Method Bias (CMB) was run using SPSS 25. The CMB utilizing Harman’s Single Factor should be below 50% to determine any bias among the constructs [43]. From the results, it was seen that the CMB had a 35.505% value, which is considered acceptable. Moreover, Garger et al. [80] that when CMB is not present, no single source bias would be evident. Lastly, Table 6 presents the direct, indirect, and total effects of the model. It could be seen that all hypotheses are significant.
The summary of 9,600 runs of RFC results is presented in Table 7. Analyzing using Analysis of Variance, the 92% accuracy of prediction from ‘gini’ criterion and ‘best’ splitter showed the optimum result. Similar to the results of Yang and Zhou [77]. Utilizing the 80:20 training and testing ratio, the result showed a 0.00 standard deviation. High accuracy of 92% is considered to be acceptable and could be utilized for classification on predicting the actual behavior to eat Samgyeopsal in the Philippines [74].
Fig 5 presents the Optimum Decision Tree by Random Forest Classifier. It could be deduced that utilitarian motivation (X1) predicts the actual behavior of east Samgyeopsal in the Philippines. X1 would lead to hedonic motivation (X0) which would consider X1 or Korean influence (X2). If X1 was satisfied, it would also consider attitude which would lead to very high actual behavior to eat Samgyeopsal in the Philippines. If X2 would be considered, it will lead to high actual behavior to eat Samgyeopsal in the Philippines. This dictates that both utilitarian and hedonic motivation together with Korean influence and attitude predicts high to very high actual behavior to eat Samgyeopsal in the Philippines during the COVID-19 pandemic.
5. Discussion
Over the past years, Samgyeopsal has been one of the most popular social and dining activities in the Philippines. In this study, the SEM tool and RFC were utilized to examine the causal relationships between hedonic motivation (HM), utilitarian (U), Korean influence (KI), attitude (A), subjective norms (SN), perceived behavioral control (PBC), COVID-19 safety protocols (CSP), intention (I), and actual behavior (AB) in eating Samgyeopsal in the Philippines. An online survey was administered for this study with a total of 1,014 respondents.
The SEM results, it indicated that A had the highest significant direct effect on I (β = 0.722; p = 0.002). Based on the indicators, it was seen that Samgyeopsal restaurants were the primary choice among consumers, eating at Samgyeopsal lifted a person’s mood, there is a sense of relief, and there was an overall great experience when dining. Dunn et al. [68] suggested that positive moods and emotions may be orthogonal to their negative counterparts. This may be a critical factor to consider when recognizing that people may experience uncertainty within the affective component of their attitudes towards meals. This supports the significance of A to I. Emotions when eating Samgyeopsal were identified as major factors that influenced the intention to eat Samgyeopsal. This is found to be consistent with previous research suggesting that people’s attitudes have a major impact on their behavioral intentions [81].
Second, HM was found to have a significant effect on A (β = 0.633 and p = 0.001). Several key indicators under HM include feeling better, feeling enjoyment, pleasure, excitement, and happiness after eating Samgyeopsal which led to a positive A. These findings were supported by Ettis and Haddad [82] who also mentioned that HM is the key toenhancing the positive A. Similarly, Schuitema et al. [83] also highlighted the importance of HM towards positive A. It shows that positive A is linked to a higher expectation of hedonic incentives, according to other supporting studies.
Subsequently, UM was also found to have a significant effect on A (β = 0.695 and p = 0.003). After consuming Samgyeopsal, several signs related to UM, such as satisfying the needs, accomplishment, and functionality, were discovered to be crucial markers that contributed to a positive attitude. Ong et al. [22] backed up these findings, saying that UM is the key to improving one’s good attitude. Similarly, Aderonke [84] emphasized the significance of utility towards A. It demonstrates that A and perceived utility have a consistent and significant relationship with each other.
Fourth, I was found to have a significant direct effect to AB (β = 0.716; p = 0.003). Multiple factors under I such as continuing to eat Samgyeopsal, wanting to eat Samgyeopsal in the future, and recommending Samgyeopsal to others were identified as key indicators that resulted in positive AB. These findings were supported by Prasetyo et al. [85] who mentioned that people’s willingness to follow leads to healthy behavior. In addition, Eren and Gauld [86] specified the importance of I since it was found to be the only significant predictor of AB.
Fifth, KI had a significant direct effect on SN (β = 0.408; p = 0.001) and PBC (β: 0.190; p = 0.002). Indicators such as the influence of Korean trendy dramas, pop music, and Korean culture emphasized that the consumption of these media certainly influences a customer’s decision-making process. Moreover, KI shows to have a significant indirect effect to I (β = 0.229, p = 0.002) and AB (β = 0.164, p = 0.002). This finding suggests that KI is one of the most important considerations for I and AB. Thus, PBC (β = 0.229, p = 0.002), SN (β = 0.229, p = 0.002), and A (β = 0.229, p = 0.002) also led to having an indirect significant effect to AB.
Similar to the RFC results, HM, UV, A, and KI presented as significant factors that influenced actual behavior to eat Samgyeopsal in the Philippines. It could be deferred that the feeling of happiness, enjoyment, and functionality of eating Samgyeopsal would lead to a positive AB. Moreover, the influence of Korean pop, culture, and drama led to positive AB. These influences would have a positive attitude among consumers when it comes to the overall great experience, leaving them to continuously consume Samgyeopsal despite the COVID-19 pandemic.
Consequently, the SEM results also showed that SN had a significant direct effect on I (β = 0.312; p = 0.003). This indicates that the increasing trend of eating Samgyeopsal and the preference of the people surrounding them greatly impact their overall intention to eat Samgyeopsal as well. These findings have been corroborated by Jain [87] wherein it was discussed how SN and PBC were positively linked to luxury purchase intentions. Likewise, the results in the study of Ong et al. [7] showed that SN is the key motivator of purchase intention in relation to foods.
It was also found that PBC had significant effects on I (β = 0.154; p = 0.002). It implies that factors such as perceived confidence and financial capability are key predictors which lead to one’s definite intention to eat Samgyeopsal. The findings were consistent with previous research, which found that Koreans make extensive efforts to experience Yak-sun food, emphasizing that PBC is a significant and the strongest predictor of consumers’ behavioral intention to buy Yak-sun food [88]. Accordingly, Ong et al. [7] highlighted the significance of PBC as a more significant predictor of behavioral intention than any other construct.
Lastly, using the demographic factors as latent variables, only the frequency of Samgyeopsal consumption was seen to be significant. Other demographic factors were insignificant (p-value > 0.05). The frequency showed a significant direct effect on AB (β = 0.136; p = 0.001). Di Crosta et al. [89] explained that the frequency of consumption, especially in the pandemic era negates purchasing intention and the actual behavior of consumers. Most of which spend only on necessities. However, their results also explained that the consumption of unnecessary products would still depend on their economic capabilities. Rodrigues et al. [90] presented how demographic factors affect impulse consumption behavior, but when it comes to food consumption such as Samgyeopsal, this study has presented that age and monthly salary/allowance had no significant differences. From the descriptive statistics results, most of the consumers eat Samgyeopsal at least once (68.7%), twice (21.3%), or thrice (6.50%) a week. Despite the constant consumption, Liu [91] expounded on a possible cutback on indulgence. Thus, marketers may strategize on this effect by changing up schemes for Samgyeopsal consumption to reduce distaste for eating among consumers.
5.1 Recommendation
The extended TPB utilized in this study considered factors such as hedonic and utilitarian motivation, COVID-19 safety protocol, and Korean influence. Based on the results, all the latent considered were deemed to be significant. This study presents that the cultural influence and motivations have a significant effect on consumers’ intention to eat in Samgyeopsal restaurants. The framework utilized may be considered in other cuisine and restaurant that have been influenced by other cultures. In addition, the utilization of RFC showed high accuracy of classification to predict factors that greatly influence different latent. Following the discussion of Fan et al. [92], some factors may have lower significance due to the causal relationship present in the SEM. Thus, integrating the method with machine learning algorithm such as RFC could be done to determine the factors that greatly contribute to the relationship considered.
5.2 Practical implication
With the exponential increase of Samgyeopsal restaurants in the Philippines and with the surge of branches opening, this study was able to deduce that hedonic and utilitarian motivation, attitude, and Korean influence led to very high actual behavior to eat in Samgyeopsal restaurants in the Philippines. The feeling of happiness and enjoyment, pleasant environment, and functionality should be taken into consideration by restaurateurs. The setting of ambiance to Korean culture, playing of Korean music, and continuous offering of unlimited food at an affordable price may be capitalized on. Business strategies could be created with these indicators to promote the consumption of Samgyeopsal. Moreover, creating promotions for mass events and gatherings may be done right after the COVID-19 pandemic to enhance profitability and marketing.
5.3 Limitations
Despite the strength of the findings, there are still limitations being considered in this study. First, the study considered collecting the responses through a self-administered survey online. Further results and factors may be evident if an interview was conducted along with the survey. Second, this study was conducted during the COVID-19 pandemic. The results may be different when the respondents were able to consume Samgyeopsal with no restrictions set by the pandemic. In addition, another perspective of the findings would be evident if the distribution of demographic characteristics were identified. Due to the strict lockdown implementation, the study was only able to collect the data utilizing an online survey. Lastly, further classification using conjoint analysis and K-Means clustering for market segmentation may be done to classify the respondents of this study. To which, business strategies and target consumers may be highlighted.
6. Conclusion
The increase of Samgyeopsal restaurants has become evident in the past years with a lot of branches opening in different areas of the Philippines. This study aimed to determine the factors of affection intention to eat Samgyeopsal in the Philippines during the COVID-19 pandemic. Specifically, this study considered extending the TPB and analyzed using SEM and RFC to classify the different factors affecting actual behavior to consume Samgyeopsal.
Based on the RFC results, hedonic and utilitarian motivation, Korean influence, and attitude led to very high actual behavior. Moreover, SEM also indicated that the constructs of TPB and the COVID-19 safety protocol were significant factors that affected the actual behavior to eat Samgyeopsal in the Philippines. Business strategies may be created based on the indicators presented in the result of this study. The results of this study would be beneficial to Korean BBQ restaurateurs and the further development of their marketing strategies even in other countries. Finally, the model construct of this study can be extended and applied in evaluating the consumers’ eating intentions toward other varieties of food or cuisines worldwide.
References
- 1. Ravina M. Introduction: conceptualizing the Korean wave. Southeast Review of Asian Studies. 2009;31:3–10.
- 2. Ha J-Y. Hallyu in and for Asia. Kritika Kultura. 2017;28:55–62.
- 3. Joven A. Hansik and Hallyu: An Analysis of the Filipino Appropriation of Korean Cuisine as a Function of Imagining Korean Culture. 2015.
- 4. Arpon YLG. Chasing the Korean Dream: ‘Koreanovelas’ Fever is Sweeping the Philippines. Korea Herald. 2008.
- 5. Fabian. Korean barbecue—getting a taste of Seoul: Asia Exchange; 2018 [cited 2021 September 26]. Available from: https://asiaexchange.org/blogs/what-is-korean-barbecue/.
- 6. Argarin M. Samgyeopsal (삼겹살): Why it is a Popular Korean dish in the Philippines: PinoySeoul.com; 2021 [cited 2021 September 26]. Available from: https://www.pinoyseoul.com/2021/03/samgyeopsal-why-it-is-popular-korean.html?m=1.
- 7. Ong AK, Prasetyo YT, Esteller AJ, Bruno JE, Lagorza KC, Oli LE, et al. Consumer Preference Analysis on the attributes of Samgyeopsal Korean cuisine and its market segmentation: Integrating conjoint analysis and K-means clustering. Plos One. 2023;18. opeblo23. pmid:36795718
- 8. Nam KC, Jo C, Lee M. Meat products and consumption culture in the east. Meat Science. 2010;86(1):95–102. pmid:20510536
- 9. Mutsikiwa M, Basera CH. The influence of socio-cultural variables on consumers’ perception of halal food products: A case of masvingo urban, Zimbabwe. International Journal of Business and Management. 2012;7(20). https://doi.org/10.5539/ijbm.v7n20p112. mb12.
- 10. Cervellon MC, Dubé L. Cultural influences in the origins of food likings and dislikes. Food Quality and Preference. 2005;16(5):455–60. https://doi.org/10.1016/j.foodqual.2004.09.002. cd05.
- 11. Schiffman LG, Kanuk LL, Wisenblit J. Consumer behavior: global edition: Pearson education Upper Saddle River, NJ; 2010.
- 12. Enriquez JP, Archila-Godinez JC. Social and cultural influences on food choices: A review. Critical Reviews in Food Science and Nutrition. 2022;62(13):3698–704. pmid:33427479
- 13. Cahigas MM, Prasetyo YT, Persada SF, Ong AK, Nadlifatin R. Understanding the perceived behavior of public utility bus passengers during the era of covid-19 pandemic in the Philippines: Application of social exchange theory and theory of planned behavior. Research in Transportation Business & Management. 2022;45:10084. https://doi.org/10.1016/j.rtbm.2022.100840. cppon22.
- 14. Chuenyindee T, Ong AK, Ramos JP, Prasetyo YT, Nadlifatin R, Kurata YB, et al. Public Utility Vehicle Service Quality and customer satisfaction in the Philippines during the COVID-19 pandemic. Utilities Policy. 2022;75:101336. corpnks22. pmid:35035116
- 15. Cucchiarini V, Caravona L, Macchi L, Perlino FL, Viale R. Behavioral changes after the COVID-19 lockdown in Italy. Frontiers in Psychology. 2021;12. ccmpv21. pmid:33776842
- 16. Suh B, Kang S, Moon H. Estimating the impact of COVID-19 pandemic on customers’ dining-out activities in South Korea. Sustainability. 2022;14(15):9408. https://doi.org/10.3390/su14159408. skm22.
- 17. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991;50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-t. a91.
- 18. Ajzen I. Constructing a theory of planned behavior questionnaire. 2006.
- 19. Conner M, Sparks P. The theory of planned behaviour and health behaviours. 1996.
- 20. Godin G, Kok G. The theory of planned behavior: a review of its applications to health-related behaviors. American journal of health promotion. 1996;11(2):87–98. pmid:10163601
- 21. Fila SA, Smith C. Applying the theory of planned behavior to healthy eating behaviors in urban Native American youth. International journal of behavioral nutrition and physical activity. 2006;3(1):1–10. pmid:16734903
- 22. Ong AK, Cleofas MA, Prasetyo YT, Chuenyindee T, Young MN, Diaz JF, et al. Consumer behavior in clothing industry and its relationship with open innovation dynamics during the COVID-19 pandemic. Journal of Open Innovation: Technology, Market, and Complexity. 2021;7(4):211. ocpcydnr21.
- 23. Krishnadas R, Renganathan R. WITHDRAWN: Examining the theory of planned behaviour in determining the farmers intention to purchase tractors. Elsevier; 2021.
- 24. Savari M, Gharechaee H. Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. Journal of Cleaner Production. 2020;263:121512.
- 25. Bagozzi RP, Wong N, Abe S, Bergami M. Cultural and situational contingencies and the theory of reasoned action: Application to fast food restaurant consumption. Journal of consumer psychology. 2000;9(2):97–106.
- 26. Mahon D, Cowan C, McCarthy M. The role of attitudes, subjective norm, perceived control and habit in the consumption of ready meals and takeaways in Great Britain. Food Quality and Preference. 2006;17(6):474–81.
- 27. Hoeksma DL, Gerritzen MA, Lokhorst AM, Poortvliet PM. An extended theory of planned behavior to predict consumers’ willingness to buy mobile slaughter unit meat. Meat science. 2017;128:15–23. pmid:28167401
- 28. Charlton S, Cyna AM, Middleton P, Griffiths JD. Perioperative transversus abdominis plane (TAP) blocks for analgesia after abdominal surgery. Cochrane Database of Systematic Reviews. 2010;(12). pmid:21154380
- 29. Kaczmarek LD. Hedonic motivation. Encyclopedia of Personality and Individual Differences. 2017:1–3. https://doi.org/10.1007/978-3-319-28099-8_524-1. k17.
- 30. Hirschman EC, Holbrook MB. Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing. 1982;46(3):92. https://doi.org/10.2307/1251707. hh82.
- 31. Moon MA, Khalid MJ, Awan HM, Attiq S, Rasool H, Kiran M. Consumer’s perceptions of website’s utilitarian and hedonic attributes and online purchase intentions: A cognitive–affective attitude approach. Spanish Journal of Marketing—ESIC. 2017;21(2):73–88. https://doi.org/10.1016/j.sjme.2017.07.001. mkaar.
- 32. Ahtola OT. Hedonic and utilitarian aspects of consumer behavior: An attitudinal perspective. ACR North American Advances. 1985.
- 33.
Novela S, Sihombing YO, Caroline E, Octavia R. The effects of hedonic and utilitarian motivation toward online purchase intention with attitude as intervening variable. International Conference on Information Management and Technology (ICIMTech). 2020:75–80.
- 34. Natesan P, Pookulangara S. Examining consumers’ channel-migration intention utilizing theory of planned behavior: A multigroup analysis. International Journal of Electronic Commerce. 2010;1(2):97–116.
- 35. Jarvenpaa SL, Todd PA. Consumer reactions to electronic shopping on the World Wide Web. International Journal of Electronic Commerce. 1996;1(2):59–88. https://doi.org/10.1080/10864415.1996.11518283. jt96.
- 36. Teo TSH. Demographic and motivation variables associated with internet usage activities. Internet Research. 2001;11(2):125–37. https://doi.org/10.1108/10662240110695089. t01.
- 37. Zeithaml VA. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing. 1988;52(3):2. https://doi.org/10.2307/1251446. z88.
- 38. Chae H, Park JH, Ko E. The effect of attributes of Korean trendy drama on consumer attitude, national image and consumer acceptance intention for sustainable Hallyu culture. Journal of Global Fashion marketing. 2019;11(1):18–36. https://doi.org/10.1080/20932685.2019.1680305. cpk19.
- 39. Lee JW. Mass communication effects of the `Hallyu’: Impacts of the Chinese’s use of Korean cultural products upon the attitudes towards Korea and intention to buy Korean products. Korean Journal of Journalism & Communication Studies. 2003;47(5):5–35. l03.
- 40. Kam JA, Matsunaga M, Hecht ML, Ndiaye K. Extending the theory of planned behavior to predict alcohol, tobacco, and marijuana use among youth of Mexican heritage. Prevention Science. 2009;10(1):41–53. pmid:18985451
- 41. LaMorte WW. The Theory of Planned Behavior 2019 [cited 2021 September 24]. Available from: https://sphweb.bumc.bu.edu/otlt/mph-modules/sb/behavioralchangetheories/BehavioralChangeTheories3.html.
- 42. Lau JT, Yeung NC, Choi K, Cheng MY, Tsui H, Griffiths S. Factors in association with acceptability of A/H1N1 vaccination during the influenza A/H1N1 pandemic phase in the Hong Kong general population. Vaccine. 2010;28(29):4632–7. pmid:20457289
- 43. Ong AK, Prasetyo YT, Lagura FC, Ramos RN, Sigua KM, Villas JA, et al. Factors affecting intention to prepare for mitigation of The big one earthquake in the Philippines: Integrating protection motivation theory and extended theory of planned behavior. International Journal of Disaster Risk Reduction. 2021;63:102467. https://doi.org/10.1016/j.ijdrr.2021.102467. oplrsvydpr21.
- 44. Latimer AE, Martin Ginis KA. The importance of subjective norms for people who care what others think of them. Psychology & Health. 2005;20(1):53–62.
- 45. Al-Swidi A, Mohammed Rafiul Huque S, Haroon Hafeez M, Noor Mohd Shariff M. The role of subjective norms in theory of planned behavior in the context of organic food consumption. British Food Journal. 2014;116(10):1561–80. https://doi.org/10.1108/bfj-05-2013-0105. ahs14.
- 46. Yadav R, Pathak GS. Intention to purchase organic food among young consumers: Evidences from a developing nation. Appetite. 2016;96:122–8. pmid:26386300
- 47. Padel S, Foster C. Exploring the gap between attitudes and behaviour: Understanding why consumers buy or do not buy organic food. British food journal. 2005;107(8):606–25.
- 48.
Agency EP. Indoor Air and Coronavirus (COVID-19): Environmental Protection Agency; n.d. [cited 2021 october 1]. Available from: https://www.epa.gov/coronavirus/indoor-air-and-coronavirus-covid-19?fbclid=IwAR2q6z0QqxZ-ebzxswbgmcX9lrfMNFRhiwbrh31PcHqKQBgsylA_2KodUKw.
- 49. Ong AK, Prasetyo YT, Salazar JM, Erfe JJ, Abella AA, Young MN, et al. Investigating the acceptance of the reopening Bataan Nuclear Power Plant: Integrating Protection Motivation Theory and extended theory of planned behavior. Nuclear Engineering and Technology. 2021. https://doi.org/10.1016/j.net.2021.08.032. opseaycnn21.
- 50. German JD, Ong AK, Perwira Redi AA, Robas KP. Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach. Heliyon. 2022;8(11). gorar22. pmid:36349283
- 51. Gallinera Corpuz, Gocotano Demdam. 맛있어요: The popularity of Samgyeopsal in the Philippines: STEM 12 HEALTH 2; 2019 [cited 2021 December 25]. Available from: https://education238214257.wordpress.com/2019/09/15/%EB%A7%9B%EC%9E%88%EC%96%B4%EC%9A%94-the-popularity-of-samgyeopsal-in-the-philippines/.
- 52. Hair JF. Multivariate Data Analysis: A Global Perspective: Pearson; 2010.
- 53. Taquet M, Quoidbach J, de Montjoye YA, Desseilles M, Gross JJ. Hedonism and the choice of everyday activities. Proceedings of the National Academy of Sciences. 2016;113(35):9769–73. tqdd16. pmid:27528666
- 54. Lam T, Hsu CHC. Predicting behavioral intention of choosing a travel destination. Tourism Management. 2006;27(4):589–99. https://doi.org/10.1016/j.tourman.2005.02.003. lh06.
- 55. López FJ, García CP, Abad JC, Ardura IR. Hedonic motivations in online consumption behaviour. International Journal of Business Environment. 2016;8(2):121. https://doi.org/10.1504/ijbe.2016.076628. lgaa16.
- 56. Dhar R, Wertenbroch K. Consumer choice between hedonic and utilitarian goods. Journal of Marketing Research. 2000;37(1):60–71. https://doi.org/10.1509/jmkr.37.1.60.18718. dw00.
- 57. Childers TL, Carr CL, Peck J, Carson S. Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing. 2001;77(4):511–35. ccp.
- 58. Izquierdo-Yusta A, Gomez-Canto CM, Pelegrin-Borondo J, Martinez-Ruiz MP. Consumers’ behaviour in fast-food restaurants: a food value perspective from Spain. British Food Journal. 2019;121(2):386–99.
- 59. Jang G, Paik WK. Korean Wave as tool for Korea’s New Cultural Diplomacy. Advances in Applied Sociology. 2012;02(03). https://doi.org/10.4236/aasoci.2012.23026. jp12.
- 60. Jang W, Kim IK, Cho KJ, Song JE. Multi-Dimensional Dynamics of Hallyu in the East-Asian Region. 2012.
- 61. Huang X, Ge J. Electric Vehicle Development in Beijing: An analysis of consumer purchase intention. Journal of Cleaner Production. 2019;216:361–72. https://doi.org/10.1016/j.jclepro.2019.01.231. hg19.
- 62. Wang YH, Lin LH. Why Consumers Go to All-You-Can-Eat Buffets?. Journal of Business & Economic Policy. 2017;4(4). https://doi.org/https://jbepnet.com/journals/Vol_4_No_4_December_2017/10.pdf. wl17.
- 63. Choe JYJ, Kim SS. Effects of tourists’ local food consumption value on attitude, food destination image, and behavioral intention. International journal of hospitality management. 2018;71:1–10.
- 64. Youn H, Xu JB, Kim JH. Consumers’ perceptions, attitudes and behavioral intentions regarding the symbolic consumption of auspiciously named foods. International Journal of Hospitality Management. 2021;98:103024. https://doi.org/10.1016/j.ijhm.2021.103024. yxk21.
- 65. Kim YJ, Kim HS. A Study on the Consumption Value of Customer Using the Extended Theory of Planned Behavior: Focused on Upscale Korean Restaurant. Culinary Science & Hospitality Research. 2021;27(2). https://doi.org/https://doi.org/10.20878/cshr.2021.27.2.004. kk21.
- 66. Administration FaD. Best practices for food retailers during COVID-19 pandemic. U.S.: Food and Drug Administration; 2020 [cited 2021 September 10]. Available from: https://www.fda.gov/food/food-safety-during-emergencies/best-practices-retail-food-stores-restaurants-and-food-pick-updelivery-services-during-covid-19.
- 67. Sanew C. Food delivery and carryout safety during the COVID-19 pandemic: National Safety Council; 2020 [cited 2021 September 21]. Available from: https://www.nsc.org/safety-first-blog/food-delivery-and-carryout-safety-during-the-covid-19-pandemic.
- 68. Dunn KI, Mohr P, Wilson CJ, Wittert GA. Determinants of fast-food consumption. an application of the theory of planned behaviour. Appetite. 2011;57(2):349–57. dmww11. pmid:21683749
- 69. Coşkun A, Yetkin Özbük RM. What influences consumer food waste behavior in restaurants? an application of the extended theory of planned behavior. Waste Management. 2020;117:170–8. c20. pmid:32861079
- 70. Beran TN, Violato C. Structural equation modeling in medical research: A Primer. BMC Research Notes. 2010;3(1). bv10. pmid:20969789
- 71. Yang F, Liu X. Grandparenting styles, childhood food insecurity, and depression among Chinese rural left-behind children: A structural equation model. Children and Youth Services Review. 2020;119:105648. https://doi.org/10.1016/j.childyouth.2020.105648. yl20.
- 72. Cunha DT, de Rosso VV, Pereira MB, Stedefeldt E. The differences between observed and self-reported food safety practices: A study with food handlers using structural equation modeling. Food Research International. 2019;125:108637. cdps19. pmid:31554110
- 73. Wang CW, Chen DR. Associations of sugar-sweetened beverage knowledge, self-efficacy, and perceived benefits and barriers with sugar-sweetened beverage consumption in adolescents: A structural equation modeling approach. Appetite. 2022;168:105663. wc22. pmid:34455023
- 74. Chen J, Li Q, Wang H, Deng M. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China. International Journal of Environmental Research and Public Health. 2019;17(1):49. clwd19. pmid:31861677
- 75. Carranza EJM, Laborte AG. Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm. Ore Geology Reviews. 2014;71:777–87. https://doi.org/10.1016/j.oregeorev.2014.08.010. cl14.
- 76. Srinivsan N, Rajendran V. Decision Trees for Nuclear Power Plant Performance Optimization 2021 [cited 2021 October 18]. Available from: https://www.researchgate.net/publication/351992810_Decision_Trees_for_Nuclear_Power_Plant_Performance_Optimization.
- 77. Yang W, Zhou S. Using decision tree analysis to identify the determinants of residents’ CO2 emissions from different types of trips: A case study of Guangzhou, China. Journal of Cleaner Production. 2020;277:124071. https://doi.org/10.1016/j.jclepro.2020.124071. yz20.
- 78. Gefen D, Straub D, Boudreau M. Structural equation modeling and regression: guidelines for research practice. Commun Assoc Inf Syst. 2000;4. http://dx. gsb00.
- 79. Steiger JH. Understanding the limitations of global fit assessment in structural equation modeling. Personality and Individual differences. 2007;42(5):893–8.
- 80. Garger J, Jacques PH, Gastle BW, Connolly CM. Threats of common method variance in student assessment of Instruction Instruments. Higher Education Evaluation and Development. 2019;13(1):2–17. https://doi.org/10.1108/heed-05-2018-0012. gjgc19.
- 81. Karki ST, Hubacek K. Developing a conceptual framework for the attitude–intention–behaviour links driving illegal resource extraction in Bardia National Park, Nepal. Ecological Economics. 2015;117:129–39.
- 82. Ettis SA, Haddad MM. Utilitarian and hedonic customer benefits of e-insurance: A look at the role of gender differences. International Journal of E-Business Research (IJEBR). 2019;15(1):109–26.
- 83. Schuitema G, Anable J, Skippon S, Kinnear N. The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice. 2013;48:39–49.
- 84. Aderonke AA. An Empirical Investigation of the Level of Users’ Acceptance of E-Banking in Nigeria. Journal of Internet Banking and Commerce. 2010;15(1):1.
- 85. Prasetyo YT, Tanto H, Mariyanto M, Hanjaya C, Young MN, Persada SF, et al. Factors affecting customer satisfaction and loyalty in online food delivery service during the COVID-19 pandemic: Its relation with open innovation. Journal of Open Innovation: Technology, Market and Complexity. 2021;7(1):76. https://doi.org/10.3390/joitmc7010076. ptmhypmr21.
- 86. Eren H, Gauld C. Smartphone use among young drivers: Applying an extended Theory of Planned Behaviour to predict young drivers’ intention and engagement in concealed responding. Accident Analysis & Prevention. 2022;164:106474. pmid:34781172
- 87. Jain S. Assessing the moderating effect of subjective norm on luxury purchase intention: A study of Gen Y consumers in India. International Journal of Retail & Distribution Management. 2020;48(5):517–36.
- 88. Lim HR, An S. Intention to purchase wellbeing food among Korean consumers: An application of the theory of planned behavior. Food Quality and Preference. 2021;88:104101. la21. pmid:33071469
- 89. Crosta Di, Adolfo Irene Ceccato, Marchetti Daniela, Pasquale La Malva Roberta Maiella, et al. “Psychological Factors and Consumer Behavior during the COVID-19 Pandemic.” PLOS ONE 16, no. 8 (2021). pmid:34398916
- 90. Rodrigues Rosa Isabel, Lopes Paula, and Varela Miguel. “Factors Affecting Impulse Buying Behavior of Consumers.” Frontiers in Psychology 12 (2021). pmid:34149580
- 91. Liu Peggy J. “Frequency versus Intensity: How Thinking of a Frequent Consumption Indulgence as Social versus Solitary Affects Preferences for How to Cut Back.” Journal of Marketing Research 59, no. 3 (2022): 497–516. https://doi.org/10.1177/00222437211055741.
- 92. Fan Y, Chen J, Shirkey G, John R, Wu SR, Park H, et al. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes. 2016;5(1):1–12.