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Article

Does Knowledge of Wine Affect Consumers’ Wine Purchase Behavior in Restaurants? An Application of Extended Theory of Planned Behavior (ETPB)

by
Ji-in Yang
1 and
Jinkyung Choi
2,*
1
Department of Culinary, Graduate School of Global Culinary, Woosong University, Daejeon 34606, Korea
2
Department of Food Science and Nutrition, Pukyong National University, Busan 48513, Korea
*
Author to whom correspondence should be addressed.
Beverages 2022, 8(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/beverages8010011
Submission received: 8 December 2021 / Revised: 10 January 2022 / Accepted: 21 January 2022 / Published: 14 February 2022
Versions Notes

Abstract

:
Wine and dine-in restaurants have been nonspecial for consumers and increasingly popular. This study investigated consumers’ wine purchase behaviors by applying the extended theory of planned behavior (ETPB) and consumer knowledge. This study used a survey method to collect the data. The study measured wine consumption behaviors and understanding. We divided respondents based on their subjective and objective knowledge of wine and then added an extended variable to the model. We analyzed the data with descriptive analysis, correlations, exploratory factor analysis, ANOVA, and multiple regression. Results of exploratory factor analysis extracted attitude, subjective norm, perceived behavioral control (PBC), and subjective knowledge. Results of regression showed that attitude, PBC, and subjective and objective knowledge affected consumers’ behavioral intention to purchase wine at a restaurant while the subjective norm showed no significance. This study applied the extended theory of planned behavior to analyze consumers’ subjective and objective knowledge in investigating their wine consumption behaviors. Results of this study suggest that consumers’ consumption of wine at restaurants is not a special occasion. The consumer’s attitude toward wine significantly impacted their decision to purchase wine at a restaurant. In addition, subjective knowledge showed more impact on behavioral intention than objective knowledge.

1. Introduction

Wine consumption in Europe is 57% of global wine consumption [1], while wine consumption in Asia is a new market [2]. Increased eating out and interest in gastronomy affected wine consumption expansion [3,4]. In Korea, eating out trends are changing, increasing spending for eating out. In addition, increasing national income and women joining the workforce affected wine consumption from high-end to universal alcoholic beverages. Moreover, food service establishments implement various ideas while grappling with fierce competition for consumers’ attention in a changing market. Wine, as ‘healthy alcohol,’ gained popularity through the concept of wine and food pairing, which boosts food taste by adding healthy sides of wine. In a restaurant, food has been a center of the products offered to consumers and now wine is what consumers look for besides food when ordering.
Previously, the theory of planned behavior (TPB) [5], developed from the theory of reasoned action (TRA) [6], was a widely known theoretical model explaining consumer behaviors. The TRA explained an individual’s behavior by two variables: attitude and subjective norm. The basis of the variables is a person’s rationality; however, in reality, many uncontrollable variables, such as monetary value, time opportunity, resources, etc., affect consumer behaviors other than attitude and subjective norm [7]. Hence, the model added perceived behavioral control (PBC), internal control elements such as a person’s intention or self-confidence, and external control elements such as time or monetary value [8]. The TPB model is well-known in the social science discipline. It has proven validity; however, the inconsistency with the TPB model required complementation [9]. Recently, researchers raised issues with the model’s limitation in explaining consumer behaviors [10]. To make up for weak areas of TPB, researchers suggested supplementation of additional variables or changing of directions of variables in the model resulting in the extended theory of planned behavior (ETPB).
The purpose of this study is to measure consumers’ wine purchase intention using ETPB. Therefore, this study added knowledge as an extended variable of the ETPB along with attitude, subjective norm, and perceived behavioral control.

2. Literature Review

2.1. Extended Theory of Planned Behavior

Ajzen [5] suggested additional variables to the TPB model or adjusted directions in the model to allow more power to the model explanation. Researchers added perceived risks and uncertainty variables to the model and showed that these two variables negatively affected consumers’ travel intention [11]. Another study added more variables to the TPB, and compared the TPB and ETPB [12]. The comparisons showed that the TPB model explanation increased from 70% to 75% using the ETPB, demonstrating that additional variables give more power to the model. Other studies also showed this pattern in which ETPB explained 27.9% of consumer behavioral intention [13], while the TPB model explained 15% of consumer behavioral intention. Food neophobia [14] and curiosity and perceived usability [15] showed the strength of ETPB.

2.2. Attitude

Attitude is a relatively more potent factor than the subjective norm or other external factors affecting behavioral intention [5]. In general, we expect individuals to evaluate the results of their specific actions favorably. Therefore, the person takes a positive attitude toward their action, finally bringing about their anticipation and involvement in the activity [5,16]. For example, consumers perceive themselves as intelligent and elegant when ordering wine at a restaurant, and the perception influences wine purchase [17]. In other words, a positive attitude toward a particular activity enhances the activity. Therefore, attitude predicts consumers’ behavioral intention better than the subjective norm [18]. Researchers also found that attitude was the most significant predictor of consumers’ wine purchase intention [19]. In addition, attitude was the strongest variable predicting consumers’ food purchase intention among other variables such as subjective norm and PBC [14]. Hence the hypothesis of this study is as follows.
Hypothesis 1 (H1).
Attitude Significantly Affects Behavioral Intention.

2.3. Subjective Norm

Subjective norm is how the person accepts others’ opinions toward individual performance, and the acceptance can be affected by the reference group with which one is involved [5]. Subjective norm relates to supporting opinions about one’s actions of important people such as family, friends, or colleagues [20]. Hiram et al. [14] found a significance between subjective norm and purchase behavioral intention in food neophobia, suggesting an influence of family or friends’ opinions on new food consumption. We can differentiate subjective norms by culture, which shows personal opinion is more important in individualistic cultures, whereas collectivist cultures find people paying more attention to others’ views [21]. For example, Chinese behavioral intention to travel overseas was affected more by their subjective norm than attitude when comparing Americans, suggesting Asians have a more collectivist culture [22]. Aref et al. [13] found that subjective norms have a more significant influence on high school students in junk food consumption behaviors resulting from adolescents being strongly affected by reference groups in their age. In other words, the subjective norm is affected by culture and age. In general, ordering wine at a restaurant can also be affected by others who are visiting the restaurant. Hence, the subjective norm is an important predictor variable in estimating behavioral intention. Therefore, this study postulated the following.
Hypothesis 2 (H2).
Subjective Norm Significantly Affects Behavioral Intention.

2.4. Perceived Behavioral Control

PBC is a perceived subjective difficulty in performing an action [23], and the behavior has external and internal control elements [8]. For example, if an individual can control other aspects of one’s behavior, measuring PBC is unnecessary [24]. If one perceives difficulty carrying out a particular action, PBC can strongly predict one’s behavioral intention. Therefore, the less the effect of PBC, the more positive impact of the behavioral intention [5]. PBC may directly or indirectly affect one’s behaviors through behavioral intention [23]. In other words, the bigger perceived self-confidence, resources, or opportunity, the more positive behavioral intention or behaviors [25]. However, PBC did not affect behavioral intention in selecting a low-calorie menu for healthy eating. This instance suggested that PBC can be associated with behavioral intention differently [26]. Sparks [27] showed that attitudes toward previous wine tourism experience, subjective norm, PBC, and food and wine involvement affected consumers’ wine tourism places’ visit intention. Furthermore, Sparks’ study validated the ETPB model and claimed PBC was the strongest predictor of behavioral intention. However, researchers still considered PBC a vital predictor of behavioral intention [22]. Especially, they considered monetary value prohibiting behaviors. Therefore, PBC is an important predictor of behavioral intention in eating out occasions. This study hypothesizes as following.
Hypothesis 3 (H3).
PBC Significantly Influences Behavioral Intention.

2.5. Knowledge

Knowledge is subjective knowledge stored in one’s memory. Objective knowledge is one’s perceived knowledge level and confidence [28]. Therefore, measuring knowledge requires examining subjective and objective knowledge. We evaluate objective knowledge by others or pervasive knowledge known as truth. On the other hand, we measure subjective knowledge through personal evaluation. Therefore, objective knowledge is more appropriate when assessing knowledge. However, consumers’ preferences for a product or service is more important than other estimations; therefore, subjective knowledge may be more effective when considering one’s behaviors [29]. Furthermore, knowledge plays a crucial role in processing information [30]. For example, when one agrees with processed information, one promptly evaluates the information. At the same time, they disagree with the information provided based on their knowledge. Therefore, we need another step to analyze the data [30]. However, one with a low knowledge level would depend on external information to decide [31].
A study found that one with higher self-efficacy and knowledge of computers had higher behavioral intention to use a computer [32]. Knowledge was a moderator between attitude and intention to pay shipping fees in a study of halal product transportation [33]. Consumers evaluate the product with their previous experience and information received according to their knowledge. Therefore, knowledge critically influences consumers’ behavioral intention, and it is an essential predictor of consumer behavioral intention. Consumption of wine, especially, requires previous experiences and knowledge [34]; knowledge can predict consumer behavioral intention to purchase wine at a restaurant. According to Dodd et al. [28], consumers may believe their knowledge is sufficient to purchase wine even though their knowledge is low objectively. Therefore, there is a gap between subjective and objective knowledge and these two variables should be examined individually. Hence this study postulated the following hypotheses:
Hypothesis 4 (H4).
Knowledge significantly impacts behavioral intention.
Hypothesis 4 (H4-1).
Subjective knowledge significantly impacts behavioral intention.
Hypothesis 4 (H4-2).
Objective knowledge significantly impacts behavioral intention.

2.6. Behavioral Intention

In behavioral theory, a behavioral intention is one’s belief to act after forming an attitude toward a certain subject or situation [35]. Especially in marketing, behavioral intention is a concept where consumers’ attitudes and subjective norms affect behaviors [5]. In this study, wine consumers are those who purchase/consume wine. Consumers’ behavioral intention is either positive or negative. Positive behavioral intention affects re-visitation or repurchasing the product, whereas negative behavioral intention affects secession or not purchasing the product [36]. Furthermore, behavioral intention is related to positive word of mouth and purchase intention [37]. Therefore, behavioral intention takes a role as an intention based on word of mouth, recommendation, or repurchase of the product [38]. Previous studies showed that behavioral intention is a dependent variable that plays a vital role in explaining the prediction of consumer behavior.

3. Method

3.1. Participants

This study adopted a quantitative method to collect data. We surveyed citizens of the Republic of Korea over two weeks using a self-administered online survey. Participants were aged 20 years and over and had wine experience at a restaurant six months before the study. We gathered 310 responses but excluded six unusable responses, leaving 304 surveys for further analyses.

3.2. Measurement Items

The questionnaire was composed of questions related to ETPB and demographic characteristics. We adapted ETPB measurement items from previous studies on a five-point Likert scale (1, not at all to 5, very much). We used four items from previous studies [5,39] to measure attitude, such as ‘I consider the consumption of wine positively.’ We also used four items from previous studies [5,39] to measure the subjective norm, such as ‘I can afford wine with food at a restaurant.’ Again, adapting from Ajzen [5], we measured four PCB items. We used five items from previous studies [40,41] to measure subjective knowledge, such as ‘I am knowledgeable about selecting a wine when dining at a restaurant.’ We measured objective knowledge with five items adapted from [42], coded ‘1’ for correct answers and ‘0’ for incorrect answers. We recoded correct answers. We measured behavioral intention using four items from [43]. Example statements include: “I intend to visit a restaurant to have wine with food”, “I will try to visit a restaurant to enjoy wine with food”, “I will regularly visit a restaurant to have wine with food in the future”, and “I will recommend others to visit a restaurant to enjoy wine with food”.

3.3. Analysis

We analyzed the data using IBM SPSS (ver. 25.0, IBM Corp., Armonk, NY, USA). We ran exploratory factor analysis on 17 items with the maximum likelihood method using varimax rotation. We also conducted a Cronbach’s alpha and correlation analysis to verify the validity and reliability of the measurement items. We used an independent t-test to compare demographic characteristics on respondents’ subjective and objective knowledge. Lastly, we ran a multiple regression to verify the effect of the independent variables on behavioral intention.

4. Results

4.1. Profiles of the Respondents

We received an almost even split between men and women: 52.3% male and 47.7% female. The respondents’ ages ranges were highest in the 40–49 group (48.7%), followed by 30–39 (31.9%), 50–59 (10.9%), 20–29 (4.9%), and 60–69 (3.6%). More than half of the respondents were married (69.4%), followed by single (28.9%), and ‘other’ status (1.6%). Most of the respondents were college students or graduated (63.8%), followed by graduate school students or graduated (26%), and high school or less (10.2%). The respondents’ average monthly incomes were between 2,000,000–3,999,999 won (35.9%), 4,000,000–5,999,999 won (28.9%), 6,000,000 won and over (22.4%), and less than 2,000,000 won (12.8%).

4.2. Results of Exploratory Factor Analysis

Table 1 shows the results of exploratory factor analysis. We extracted a total of 17 items from four constructs with satisfactory reliability. Four extracted constructs were F1, subjective knowledge (Cronbach’s alpha = 0.945); F2, attitude (Cronbach’s alpha = 0.881); F3, PCB (Cronbach’s alpha = 0.713); and F4, subjective norm (Cronbach’s alpha = 0.862). Cronbach’s alpha for behavioral intention was 0.936. Results of correlation indicated satisfactory validity (Table 2) except for correlations between objective knowledge and subjective knowledge and between objective knowledge and PBC, which showed less significant correlations. All factors were significant at p = 0.01.

4.3. Comparisons of Demographic Characteristics on Subjective and Objective Knowledge

We ran an independent t-test and ANOVA to compare subjective knowledge and objective knowledge by demographic characteristics (Table 3). Gender showed significant differences in subjective knowledge (t = 4.413, p < 0.001) while we found no significant differences in objective knowledge. Men (M = 3.173, SD = 0.899) had higher subjective knowledge than women (M = 2.721, SD = 0.884). Education showed significance in subjective knowledge (F = 10.639, p < 0.001); however, we found no differences in objective knowledge. Respondents who were college students or graduates (M = 2.991, SD = 0.874) and graduate school students or graduates (M = 3.139, SD = 0.986) were significantly different from respondents who had a high school education or below (M = 2.283, SD = 0.722). Age did not have any difference on either subjective or objective knowledge. Average monthly income showed significance on subjective knowledge (F = 9.905, p < 0.001). Average monthly income with less than 2,000,000 won (M = 2.430, SD = 0.842) was significantly different from average monthly income with 4,000,000–5,999,999 won (M = 3.020, SD = 0.927) and average monthly income with 6,000,000 won and over (M = 3.352, SD = 0.805). In addition, income with between 2,000,000–3,999,999 won was different from income with between 6,000,000 won and over.

4.4. Results of Regression Analysis

We found regression on behavioral intention in five independent variables: attitude, subjective norm, PCB, and subjective knowledge and objective knowledge (Table 4). Results of regression showed attitude (t = 8.379, p < 0.001), PCB (t = 2.369, p < 0.05), subjective knowledge (t = 6.262, p < 0.001), and objective knowledge (t = 2.435, p < 0.05) significantly affected behavioral intention. However, subjective norm did not influence behavioral intention. Hence, these results support hypotheses 1, 3, and 4.

5. Discussion

This study measured consumers’ behavioral intention to purchase wine at a restaurant using ETPB. We added knowledge as an extended variable to the theory along with attitude, subjective norm, and behavioral intention and measured subjective and objective knowledge. Results show no significant differences of objective knowledge on demographic characteristics. Only subjective knowledge showed significant differences in gender, education, and average monthly income. Subjective knowledge is an individual’s belief in their ability to understand the material. Consumers’ can be swayed in their belief in a product rather than objective knowledge [28,29]. They may rely on their previous experiences and beliefs to purchase wine [34]. This experience contrasts with the required objective knowledge one needs when using computers [32].
This study showed that the mean value of objective knowledge of respondents was above the median of the scale while subjective knowledge varied. Although objective knowledge showed no significant differences among demographics characteristics, subjective knowledge had significant differences in gender, education, and average monthly income. According to Thompson and Barrett [17], wine purchase is affected by the perception of intelligence and elegance in drinking wine. The individual’s previous experience with wine and dining or accumulated memory results in this perception. This perception then becomes their subjective knowledge aligning with the notion that behavior is affected by external and internal control elements [8]. Therefore, many food-service establishments try to offer satisfactory services to consumers that contain physical products and anything that might influence their experiences in line with the marketing principle that focuses on not changing consumers’ minds but offering what they want. For example, wine and food suggestions in a menu would generate more profits than food-only menus at restaurants since beverages have a much higher profit margin. Therefore, good wine and food pairing suggestions by a sommelier or wait staff could enhance consumers’ experience with wine.
Results of regression analysis suggested that attitude, PCB, subjective knowledge, and objective knowledge influenced behavioral intention to purchase wine at a restaurant. Attitude was the strongest predictor of behavioral intention [5,14,19], followed by subjective knowledge. Attitude forms after an individual’s experience on a specific subject or situation. This study suggests that attitude may be very much related to subjective knowledge. Overall, attitude is affected by one’s belief, emotion, and psychological status, and, therefore, further research related to these areas is needed.
PCB and objective knowledge also influence behavioral intention, while the subjective norm was not a predictor of behavioral intention. Unlike findings of other studies [13,14,21], the subjective norm was not the predictor of the behavioral intention in this study. This study suggests and agrees with the current phenomenon of ‘for oneself’ such as myself is the most crucial existence for me; hence, I do what I want. Thus, the consumption of wine at a restaurant is not an occasion in which we need others’ agreement. The Republic of Korea is still within the circle of collectivist culture. However, drinking wine at a restaurant has become an ordinary scene; even women do not feel that they need social agreement [44]. Therefore, marketing to women should add another revenue stream for restaurants. However, women’s subjective knowledge was much lower than men’s; hence boosting their wine experience would help consumers’ attitudes toward wine and increase subjective knowledge.
While this study provided valuable results, it also has limitations. First, the study’s participants had wine and dining experiences at a restaurant before the survey. Hence, future studies might want to include expectations from various consumers when it comes to restaurant dining and wine. Second, we added knowledge as the extended variable in ETPB. However, researchers could consider many other variables in a similar study, such as consumer emotions, occasions, involvement, value for health, etc. For example, a fine dining restaurant may employ an expert sommelier who could positively influence the buying behavior of customers and educate them in the selection of wines to pair with food; hence, contributing to increased revenue. Although this study did not intend to measure restaurant typology, service, location, décor, pricing and perception (hedonic and utilitarian), etc., it is worthwhile including them to make the study more inclusive for future studies. Lastly, we suggest including geographic and cultural differences in future research.

6. Conclusions

This study investigated whether knowledge, attitude, subjective norm, and PBC predict consumers’ behavioral intention to order wine at a restaurant. We measured two types of knowledge in this study: objective and subjective. Subjective knowledge differed by respondents’ gender, education, and average monthly income, while demographic characteristics did not impact objective knowledge. Subjective knowledge and attitude were stronger predictors of behavioral intention than PBC and objective knowledge to have wine with food at a restaurant. Wine is an attractive and practical beverage to bring consumers to visit restaurants and increase sales. Therefore, understanding consumers’ attitudes should be an antecedent of behavioral studies. Of several factors, researchers should investigate knowledge and related elements to understand consumers’ behaviors.

Author Contributions

Conceptualization, J.-i.Y. and J.C.; methodology, J.C.; software, J.C.; validation, J.-i.Y. and J.C.; formal analysis, J.C.; investigation, J.-i.Y.; resources, J.-i.Y.; data curation, J.-i.Y. and J.C.; writing—original draft preparation, J.-i.Y.; writing—review and editing, J.C.; visualization, J.C.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Pukyong National University Research Fund in 2021 (CD20210990).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Woosong University (1041549-200107-SB-84).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Results of Exploratory Factor Analysis.
Table 1. Results of Exploratory Factor Analysis.
SKAttitudePCBSN
MeasurementLoadings
I considered consumption of wine positively 0.794
Dining food with wine is worth 0.836
Dining food with wine is beneficial 0.836
Dining food with wine is needful 0.703
My friends and colleagues will agree if I have wine with food 0.843
My friends and colleagues will positively think If I have wine with food 0.783
My friends and colleagues will understand if I have wine with food 0.732
People I consider to be important to me will agree if I have wine with food 0.753
If I want, I can have wine with food 0.728
I have many opportunities to have wine with food at a restaurant 0.543
I can afford wine with food at a restaurant 0.724
I have time to have wine with food at a restaurant 0.651
I am knowledgeable to select a wine when I dine at a restaurant0.887
I have knowledge to enjoy wine with food at a restaurant0.880
I know etiquettes to have wine with food 0.791
I am knowledgeable how to select a good wine0.845
I am knowledgeable enough to explain about the wine to others when having wine with food at a restaurant0.875
Eigen value4.3223.0482.8262.069
Cronbach’s alpha0.9450.8810.7130.862
KMO = 0.902 p < 0.001.
Table
Table 2. Correlations among variables.
Table 2. Correlations among variables.
AttitudeSNPBCSKOKBI
Attitude1
SN0.567 **1
PBC0.387 **0.459 **1
SK0.491 **0.371 **0.526 **1
OK0.187 **0.0470.1010.169 **1
BI0.664 **0.464 **0.472 **0.608 **0.239 **1
** Significance at 0.01 (two tailed).
Table
Table 3. Comparisons of knowledge among demographic characteristics.
Table 3. Comparisons of knowledge among demographic characteristics.
Demographic CharacteristicsSubjective KnowledgeObjective Knowledge
Mean ± SD
GenderMan3.173 ± 0.8993.635 ± 0.977
Woman2.721 ± 0.8843.482 ± 1.093
t-value4.413 ***1.284
EducationHighschool or less2.283 ± 0.722 a3.354 ± 1.050
College students or graduated2.991 ± 0.874 b3.567 ± 1.017
Graduate school students or graduated3.139 ± 0.986 b3.632 ± 1.076
F-value10.639 ***0.807
Age20–293.293 ± 0.7853.533 ± 0.915
30–393.004 ± 0.8613.587 ± 1.115
40–492.936 ± 0.9623.567 ± 1.037
50–592.751 ± 0.9433.424 ± 0.902
60 and over3.000 ± 0.9033.727 ± 0.904
F-value1.0020.233
Average Monthly income (1000 won)Less than 20002.430 ± 0.842 ad3.461 ± 1.274
2000 and less than 40002.849 ± 0.901 cde3.550 ± 1.013
4000 and less than 60003.020 ± 0.927 be3.511 ± 0.934
6000 and over3.352 ± 0.805 b3.705 ± 1.051
F-value9.905 ***0.632
Mean scores demoted by the same letter are not significantly different from each other *** p < 0.001.
Table
Table 4. Results or regression analysis.
Table 4. Results or regression analysis.
ModelBSEBetat-Value
Constant−0.5630.262 −2.147 *
Attitude0.5280.0630.4218.379 ***
SN0.0800.0710.0551.130
PCB0.1540.0640.1142.369 *
SK0.3030.0480.3046.292 ***
OK0.0840.0350.0952.435 *
R = 0.753 R2 = 0.567, Adjusted R2 = 0.560, F = 77.994, p = 0.000, * p < 0.05, *** p < 0.001.
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Yang, J.-i.; Choi, J. Does Knowledge of Wine Affect Consumers’ Wine Purchase Behavior in Restaurants? An Application of Extended Theory of Planned Behavior (ETPB). Beverages 2022, 8, 11. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages8010011

AMA Style

Yang J-i, Choi J. Does Knowledge of Wine Affect Consumers’ Wine Purchase Behavior in Restaurants? An Application of Extended Theory of Planned Behavior (ETPB). Beverages. 2022; 8(1):11. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages8010011

Chicago/Turabian Style

Yang, Ji-in, and Jinkyung Choi. 2022. "Does Knowledge of Wine Affect Consumers’ Wine Purchase Behavior in Restaurants? An Application of Extended Theory of Planned Behavior (ETPB)" Beverages 8, no. 1: 11. https://0-doi-org.brum.beds.ac.uk/10.3390/beverages8010011

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