Personal and Emotional Values Embedded in Thai-Consumers’ Perceptions: Key Factors for the Sustainability of Traditional Confectionery Businesses

Abstract

The confectionery market in Thailand is being overpowered by Western confectioneries. This study proposes and verifies a model of the factors that define consumer behaviour towards traditional Thai confectioneries and consumer willingness to support them. In recent years, there has been a boom in posting various aspects of Thai youth culture, including confectionery, on social networking services (SNS), especially Instagram. In major Thai cities, such as Bangkok, stores are being built with ‘Insta-image’ in mind, strengthening the younger generation’s inclination towards Western confectionery. Under these circumstances, the share of traditional confectioneries, which have long been familiar to Thai people, is declining. Based on survey data collected from 400 consumers in Bangkok, we designed a model to analyse the antecedent factors for consumers’ purchase intention and support behaviour for Thai traditional confectioneries, such as the word-of-mouth (WOM) approach and repeated purchase. Structural equation modelling (SEM) was conducted on the dataset to examine the antecedent factors’ impact on purchase intention and supportive actions. The results revealed that four latent factors, as determining antecedents of purchase intentions, had a significant impact on purchase intentions, resulting in loyalty and word-of-mouth behaviour. Among the determining factors, personal feelings and attachment to traditional confectionery were found to have the greatest impact, surpassing subjective norms. To expand the fan base of Thai traditional confectioneries and to support their businesses, appealing to the value of traditional confectioneries, nostalgic value and long-held Thai personal feelings to support traditional confectionery was found to be an effective marketing strategy for corporates. Such efforts are also meaningful in terms of maintaining the diversity of food culture in the face of increasing Westernisation and a decrease in unique food ingredients and food culture. Furthermore, according to this analysis, willingness to purchase is strongly linked to purchasing behaviour, and the cultivation and securing of loyal customers and their WOM recommendations are important for developing a customer base in the market. WOM recommendations by consumers can activate communication among customers and companies in the market, strengthen the community and stimulate the dissemination of information about traditional confectioneries. This study is expected to be a useful and valuable resource for the development of marketing strategies to ensure the sustainability of traditional confectionery in the Thai sweets market.

1. Introduction

1.1. Background of the Study

This study investigates the case of Thailand’s confectionery sector, which has been competing with Western sweets. Traditional Thai sweets have been part of Thai culture for a long time, and the younger generation still loves traditional sweets. However, similar to other sectors of commodities and food, the spread of Western-style products has increased their share of the markets, and the number of traditional sweets shops and stalls has gradually declined [1]. The current market situation has raised the issue of how to sustain a traditional local confectionery businesses in the competitive market.

1.2. Research Rationale and Aim

This study aims to develop actionable implications for the subject of how to sustain the traditional confectionery sector, which has been under a competitive pressure from Western brands, and, in so doing, this study aimed to examine the behavioural patterns and attitudes of consumers towards sweets in the Thai market. The Thai people are known for their sweet tooth; however, with the recent rise in Western brand confectionaries due to globalisation, the consumption of traditional Thai confectionaries are being overwhelmed by Western confectionaries. In this study, using quantitative research methods, we examined the reasons for the consumption behaviour and consumer preferences of traditional Thai confectionery and attempted to present basic data for the planning of effective marketing strategies.

In recent years, especially in Thai youth culture, there has been a boom in the use of social networking services (SNS), such as Instagram, to communicate about various aspects, including confectionery. In large cities, such as Bangkok, shops are being built with Instagram in mind. It has been pointed out that this situation has spurred the popularity of Western confectionery and that the share of traditional confectionery, which has been familiar to Thai citizens for many years, is declining. In addition, the introduction of measures to revive the traditional confectionery sector, which has been deeply rooted in, and familiar to, Thai society for many years, is awaited [1]. Therefore, the research aims to examine the current Thai consumers selection behaviour between traditional of modern sweets to find key issues to sustain a balanced diversity in the confectionery sector.

In this study, based on survey data collected from 400 consumers in Bangkok, we designed a model to decipher the reasons for the preferences and evaluation aspects of Thai traditional confectionery in the consciousness of modern consumers through structural equation modelling (SEM). An empirical analysis was conducted to propose basic data for the relevant sector.

2. Literature Review

2.1. Purchase Intention and Antecedent Factors

Studying consumer behaviour helps to improve or develop products or services to become more effective. Additionally, the consumer behaviour model by [2] has been used to explain how many factors can influence consumption stimulus and which relevant aspects are affected. It has been found that psychological and social factors are the most dominant factors influencing customers’ willingness to buy [3].

Customers repurchase a product after they have perceived the product’s adequate value and its ability to respond to their needs and fulfil their expectations. Basically, purchase intention is more focused on customers’ attitudes than other tools, such as demographics or economic factors. However, purchase intention concerns feelings and cognition, as it will be difficult to change the attitude if a certain attitude has been established in the consumer. It has been argued that the influence of social connections and reputation has a higher impact on consumers’ attitudes [4]. It is also discussed that customer satisfaction is a factor associated with purchase intention [5]. It acts as a mediator between itself and purchase intention and can occur in purchasing actions.

2.2. Certification Programmes Impact

The theory of reasoned action (TRA) aims to explain the relationship between attitudes and behaviour. It is mainly used to predict how individuals will behave based on their existing attitudes and behavioural intentions and is the analytical model of this research.

The TRA framework is simple and is based on the concept that whether an individual will take a particular action or not is determined by the consequences that would result from taking that action. It was proposed that it has become the basis for much empirical research in social sciences, building on earlier work, such as the persuasion model and attitude theory [6].

Although the relationship between attitude and behaviour (A–B relationship) was suggested earlier, the TRA was proposed with the intention of proving that attitude is an indicator of human behaviour. Later, to resolve the contradiction of the A–B relationship with the theory of planned behaviour (TPB) and the reasoned action approach to behaviour (RAA), the two authors repeatedly extended and verified the model. Subsequently, this model has been used by many scholars as a pillar of empirical analysis, especially in marketing [7].

The main purpose of the TRA is to understand individuals’ behaviour by examining their basic motivations for performing behaviours [8]. The TRA states that a person’s intention to perform a behaviour is the main predictor of whether or not that person will actually perform the behaviour [9]. In other words, in view of this model and the theme of this study, there is a focus on whether the intention to purchase traditional Thai sweets leads to actual purchase behaviour. As the antecedents that define people’s purchase intentions, social and subjective norms surrounding the behaviour of purchasing traditional sweets may also be related to consumers’ actual intentions and behaviours. Thus, the TRA emphasises the relationship between intentions and the consequences of actual behaviour [10]. It was demonstrated through Turkish data that a discrepancy exists between consumers’ intentions regarding corporate social responsibility (CSR) (i.e., intentions that CSR is desirable and that they want to support companies that promote CSR) and their actual behaviour of purchasing products from companies that contribute to CSR [11].

Thus, intention (in this study, the intention to purchase traditional Thai sweets) has important theoretical implications because it is determined by attitudes and subjective norms towards behaviour [12]. In the next section, we will examine the purchase intention derived from the three factors hypothetically prepared by this study. The TRA states that the stronger the intention, the greater the effort to carry out the action and the more likely it is that the action will be carried out. This relationship will be discussed by focusing on the size and significance of the path coefficient between purchase intention and action through quantitative analysis, as described in the next section.

2.3. Antecedent Factors Leading to Purchase Intention

2.3.1. Functional Value

Functional value is the perceived functional utility based on a product feature provided to the customer. In this context, it may become a main point that can explain why customers choose to consume one product over another. It was also discussed that the benefits correlated with price, performance and reliability that lead to a purchase are generally considered for functional value [13]. For instance, proposed nutrition and hedonic factors should be key elements for functional value for food [14], whereas the effect of cause-related marketing on consumers’ food purchase intention was also investigated [15,16]. These studies suggested the potential of impact of a quality certificate, which could indicate the good function value of food.

With regard to the functional aspects of confectionery, as mentioned above, previous studies have already shown that factors such as nutritional value, price and appearance influence purchasing behaviour. Therefore, with regard to the first element of the model for analysis, the latent variable of functional aspects, we referred to the discussions of [17,18] and decided to include functional aspects and attributes that would influence purchasing behaviour in our analysis.

2.3.2. Epistemic Value

Epistemic value, gained from consuming a product that provides a desire for knowledge and induces curiosity, is generally provided through something new. Moreover, the behaviour of looking for a selection appears to be the tendency of innovative purchase. It can, then, be assumed that Thais may be used to consuming traditional Thai desserts; thus, turning to Western desserts’ is becoming another alternative. Epistemic values should be investigated while researching consumption values and behaviour [19,20]: food consumption values and their leading effect on real buying behaviour have been examined. Ethical consumption intentions and real buying choice in the context of organic food seems to be one of the popular topics in the field [21], and it has been suggested consumers’ environmental concerns have an impact on physical buying behaviour as a moderating effect. In line with this discussion, the impact of consumers’ epistemic value on young consumers purchasing behaviour is another popular topic for further study [22].

2.3.3. Personal and Emotional Value

Emotional value is obtained by purchasing a product that induces feelings or affective states [23]. In terms of consumption preferences, this value may arise in both positive and negative ways, such as excitement, loyalty, guilt and fear [13]. The impacts of consumers’ emotional values in purchasing behaviour can be discussed in contrast to consumers’ logical choice behaviour [24,25]: this aspect might have been influenced by the consumers’ mental health with learning experiences. It is also emphasised the food consumption is impacted by consumers’ quality of life, especially in the context of the COVID-19 pandemic [26]. Investigation into relationships between lifestyles and food consumption behaviour with a focus on consumers’ mental condition and well-being seems to have become more popular in the post-COVID-19 pandemic era [27]. the impact of consumption values on consumers’ purchase of organic food has also been discussed, with a focus on consumers’ environmental perceptions [28], and the emphasis on the impact of personal and emotional value in food choice has been discussed to develop actionable implications for industries.

Another element of personal and emotional value in consumer confectionery purchasing behaviour that perhaps should not be overlooked is the country-of-origin (COO) factor. This COO continues to constitute an important discussion point in various studies in consumer choice behaviour; however, the Thai sweets covered by this study are assumed to be traditional confectioneries where all ingredients and manufacturing processes are also carried out in Thailand, and there are no plans to include discussion of COO at this point in time. However, in a discussion highlighting the competition between traditional products and confectionery imports from the West, etc. in an increasingly globalised confectionery market, this issue is considered to have important implications. Therefore, the memorandum here is a reminder to add that the COO element is an important theme in commodity purchasing research [29,30].

2.3.4. Subjective Norm

A subjective norm is ‘an individual’s perception that people who are important to him or her believe that the behaviour in question should be done’ [6]. Subjective norms represent a person’s feelings about the social pressures they feel about a particular behaviour [31].

According to the TRA, people have certain beliefs or normative beliefs about whether a behaviour is acceptable or not [32]. These beliefs shape a person’s perception of a behaviour and determine their intention regarding whether to perform that behaviour or not [15]. For example, if a Thai consumer believes that the social group to which he or she belongs encourages the purchase of domestic traditional sweets, then he or she is more likely to want to engage in that activity. Subjective norms also consider people’s motivations to conform to the views and perceptions of social groups, which vary according to the situation and individual motivations [33]. Various studies have shown influential positive relationship between subjective norms and product purchase intentions [34,35,36,37].

Exploration into the antecedents of consumers’ purchase intentions in emerging countries reveals a focus on sustainability and subjective norms [28,38]. Discussion of buying behaviour in the context of personal perceived values and subjective norms implied the impact of food values in line with consumers’ subjective norm and brand loyalty [39,40]. These studies examined the influencing factors of consumers’ purchasing behaviour from health concerns as a peer-group pressure. Based on these recent discussions, this study extends these findings and analytical model to the purchasing behaviour of traditional sweets in Thailand and examines the role of subjective norms in this context.

2.4. Intention to Behaviour

Another discussion point has been investigating the relation between purchase intention and purchasing behaviour. As we discussed above, the antecedents for consumers’ purchase intention have been studied in the context of various goods and services, whereas examination of influential factors in the context of COVID-19 on ethical-food purchase intentions and analyses of the difference in consumers’ intention–behaviour gap suggested implications for the field of study [41,42,43]. Recent studies discussed the intention and behaviour gap based on the theory of planned behaviour, with a focus on the moderating roles of communication, satisfaction, and trust in the food sector, and, finally, consumer purchasing behaviour of organic food in an emerging market has been discussed, suggesting their purchase intention and purchasing behaviour might transition to another stage in the course of economic development [44].

2.5. Behaviour Has a Significant Impact on Loyalty

The most important factors affecting customer loyalty are customer satisfaction and service quality, both directly and indirectly. In addition, according to these, a strong relationship exists between loyalty and satisfaction for consumers who perceive high value [45,46]. How to build loyalty through perceived values has been explored, with a focus on WOM.

It is not a common approach to discuss the direction from purchasing behaviour to loyalty. While there are various definitions of loyalty, [47] examined antecedents and effects of loyalty on food purchasing in the context of sustainability, and the relationship between local food experiences and behavioural intention via satisfaction was validated [48]. Consumers’ attachment with retail stores has been discussed by examining antecedents and impact on patronage intentions [49]. These recent studies imply that purchasing behaviour and consumers’ loyalty perceptions are interactively related and loyalty is fostered and strengthened by synergistic effects, and should be viewed as a double helix structure, so to speak. Based on this discussion, this study focuses on the relationship between purchase behaviour evoked by intention and loyalty.

2.6. Behaviour Has a Significant Impact on Word of Mouth

To date, communication tools, such as WOM, play a large role in shaping consumers’ buying behaviours and their attitudes [50]. It has been discussed that the degree of effectiveness can depend on the personal characteristics of the WOM spreader [51]. Positive WOM reflects a prospective future consumption; [52,53] investigated drivers of the purchasing behaviour of products with a focus on WOM and consumers’ perceived value of sustainability. The impact of opinion leadership in the information communication age, and, eventually, online WOM has been examined for its impact on consumers’ buying behaviour on the platform of digital apps, which have been another popular topic for researchers [54,55].

Thus, the spreading of information through WOM by existing customers is becoming a key contributor to other customers’ future expectations, which could lead to diversified food choices based on their own perceptions and intentions to compromise their loyalty. Throughout interactions with other loyal customers, each customer will construct their own tendencies and preferences for a variety of sustained confectionery sector.

2.7. Loyalty Has a Significant Impact on Word of Mouth

The final relationship in the model is loyalty and its impact on WOM [56], and it has been discussed that customer loyalty is increased by WOM, especially in the specific context of the time, such as ambiguity and terror; this discussion can be applied to consumer behaviour during the COVID-19 pandemic [57]. The role of brand love with a focus on social-media promotion and E-WOM online shopping space has also been investigated, in line with accumulated knowledge and experiences [58]. The synergetic effect of after-sales service and loyalty, which can be also increased by WOM, results in repurchase behaviour. Therefore, the relationship between loyalty and WOM is included in the analytical model.

2.8. Conceptual Framework and Hypotheses

Globalisation has transformed the food environment in low- and middle-income countries (LMICs), affecting diet and nutrition. However, to date, most food environment assessments and, to a lesser extent, analyses of food choice behaviour have been developed for use in high-income countries, so there has not been a significant body of research modelling informal and formal consumer food choices. The authos orf [59] examined 113 data-collection assessments (methods, tools, and metrics) for eight aspects of the food market environment and whether they are consistent with the market realities in low- and middle-income countries, but they further suggested some more detailed analysis would be needed to analyse the impact on emotional factors embedded in the consumer mind-set, and other psychological aspects.

In addition to assessing multiple aspects of the food market environment, including food quality, ingredient reliability, marketing, and regulation, this study also focused on ensuring the sustainability of diverse food cultures in an LMIC context, to support global diet, nutrition, and health, as well as the diversity and balanced development of the Thai confectionery market as a middle-income country. Factors supporting the development of a diverse and balanced development are examined in the light of consumer awareness data. As a result, the proposed model and measurements are practical guidepost for further discussion of food diversity, market trends, and consumers’ evaluative perspectives, not only in high-income countries but also in low- and middle-income markets [59].

From the discussions, a conceptual model with hypotheses has been developed, as shown in Figure 1, which includes key issues and sustainable elements for Thai traditional confectioneries which have been losing ground.

Table 1. Hypotheses and supported reference.

Number	Hypotheses	Supported Reference
H1a	Functional value has a significant impact on purchase intention.	[13,14,15,16,17,18]
H1b	Epistemic value has a significant impact on purchase intention.	[19,20,21,22]
H1c	Personal & Emotional factor has a significant impact on purchase intention.	[23,24,25,26,27,28]
H1d	Subjective norm has a significant impact on purchase intention.	[6,15,31,32,33,34,35,36,37,38]
H1e	Four inflence factors has been covariance related.	[11,23]
H2	Purchase intention has significant impact on consumers’ behaviour.	[41,42,43,44]
H3a	Behaviour has a significant impact on loyalty.	[45,46,47,48,49]
H3b	Behaviour has a significant impact on word-of-mouth.	[50,51,52,53,54,55]
H4	Loyalty has a significant impact on word of mouth.	[56,57]

presents a summary of the hypotheses built on the relevant academic discussions related to each hypothesis.

3. Methodology

3.1. Data Collection

This study adopts a quantitative approach to examining consumer behaviour in Thailand. Survey data collected from 400 consumers living in Bangkok were analysed using SEM. The survey was conducted online by convenience sampling, aiming at 400. The questionnaire consisted of questions corresponding to the hypotheses in the previous section and the demographic data of the respondents. After data cleaning, 400 samples were finalised.

3.2. Analysis

The data collected were subjected to descriptive statistics, confirmatory factor analysis using the Cronbach’ alpha test to check the reliability of the four antecedent factors corresponding to the analytical model, and correlation tests for each factor, followed by SEM analysis to examine the validity of the model and the relationship between each latent factor.

Notably, the main question was designed with a five-point Likert-type scale (1 = strongly disagree, 5 = strongly agree). SPSS v26 and AMOS v26 were used to analyse the data to test the hypotheses, with practical implications for relevant researchers and practitioners. Through this analytical approach, it was hoped to reveal critical essences to support the competitiveness of traditional confectionery.

3.3. Survey Design

Following a pilot test that was carried out with eight volunteers, some modifications were made to the wording and the questioning style before launching the main survey. The survey was conducted using a web-based online format, which allowed us to collect primary data from Bangkok, Thailand. The questions and corresponding options used in this research were carefully defined based on the key findings from the academic sources discussed in the previous sections, following the model shown in Figure 1. The original survey questions were prepared in English and, thereafter, a bilingual individual translated them into Thai. A second bilingual individual, who had not seen the original questions, subsequently back translated the items into English before a third bilingual individual checked the translations. Throughout this process, any inconsistencies that emerged were reviewed and resolved to finalise the questionnaire (Appendix A). This procedure was carried out to ensure the cultural and language equivalency of the scales being used [60,61].

4. Findings and Analysis

4.1. Data Profile and Descriptive Analysis

The collected data are presented in

Table 2. Sample data attributes.

Profile		Frequency	Percent	Cumulative Percent
Gender
	Female	257	64.3	64.3
	Male	138	34.5	98.8
	No comment	5	1.3	100.0
	Total	400	100.0
Age
	16–20 years	9	2.3	2.3
	21–25 years	66	16.5	18.8
	26–30 years	127	31.8	50.5
	31–35 years	59	14.8	65.3
	36–40 years	52	13.0	78.3
	Over 40 years	87	21.8	100.0
	Total	400	100.0
Education
	Secondary School	5	1.3	1.3
	Diploma Degree	23	5.8	7.0
	Bachelor’s Degree	263	65.8	72.8
	Master’s Degree	104	26.0	98.8
	Doctorate’s Degree	5	1.3	100.0
	Total	400	100.0
Occupation
	Private sector employee	155	38.8	38.8
	State enterprise employee	40	10.0	48.8
	Government employee	48	12.0	60.8
	Self-employed (Business owner)	41	10.3	71.0
	Unemployed	22	5.5	76.5
	Unoccupied (student)	71	17.8	94.3
	Others	23	5.8	100.0
	Total	400	100.0
Monthly income
	Less than 10,000 Baht	44	11.0	11.0
	10,001–15,000 Baht	40	10.0	21.0
	15,001–20,000 Baht	52	13.0	34.0
	20,001–25,000 Baht	60	15.0	49.0
	25,001–30,000 Baht	63	15.8	64.8
	30,001–35,000 Baht	35	8.8	73.5
	Over 35,001 Baht	106	26.5	100.0
	Total	400	100.0

with demographic information.

Table 3. Descriptive analysis of the questions.

Questionnaires	N	Mean	Std. Deviation
PEV1	400	3.63	0.861
PEV2	400	3.65	0.809
PEV3	400	3.13	0.858
PEV4	400	3.11	0.851
PEV5	400	3.70	0.832
SN1	400	3.11	0.977
SN2	400	3.15	0.904
SN3	400	2.96	0.865
SN4	400	3.28	0.910
SN5	400	3.26	0.860
FV1	400	3.69	0.797
FV2	400	3.66	0.752
FV3	400	4.05	0.799
FV4	400	3.87	0.783
FV5	400	4.09	0.943
EV1	400	3.42	0.790
EV2	400	3.48	0.794
EV3	400	2.99	0.758
EV4	400	3.54	0.735
EV5	400	3.33	0.902
EV6	400	3.71	0.959
BH1	400	3.87	0.783
BH2	400	3.46	0.919
LY1	400	3.37	0.874
LY2	400	3.34	0.989
LY3	400	3.89	0.883
WOM1	400	3.30	0.841
WOM2	400	3.55	0.806
WOM3	400	3.54	0.920

shows the descriptive statistics of the observed variables for purchase intention for Thai suites.

4.2. Factor Analysis and Reliability Test

A confirmatory factor analysis was conducted with 21 and 8 questions; the obtained results are shown in

Table 4. Factor analysis (influencing factors).

Observed Variable	Component
	1	2	3	4	Alpha
PEV1	0.777	0.097	0.256	−0.007	0.816
PEV2	0.752	0.160	0.356	0.134
PEV3	0.715	0.256	0.028	0.326
PEV4	0.688	0.103	0.457	0.155
PEV5	0.666	0.399	−0.106	0.261
SN1	0.133	0.825	0.150	0.031	0.829
SN2	0.212	0.790	0.193	0.011
SN3	0.181	0.741	0.031	0.236
SN4	0.109	0.575	0.170	0.469
SN5	0.188	0.518	0.326	0.169
FV1	0.125	0.173	0.715	0.245	0.769
FV2	0.084	0.343	0.671	0.121
FV3	0.461	0.068	0.628	0.362
FV4	0.344	0.208	0.508	0.190
FV5	0.219	0.277	0.464	0.118
EV1	0.118	0.063	0.234	0.825	0.824
EV2	0.205	0.007	0.377	0.736
EV3	0.204	0.388	−0.031	0.622
EV4	0.199	0.223	0.385	0.575
EV5	0.147	0.426	0.211	0.482
EV6	0.123	0.048	0.257	0.150
Sums of squared loadings	4.633	4.086	3.941	3.206
% of Valiance	17.820	15.716	15.159	12.331
Cumulative %	17.820	33.536	48.695	61.026

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 9 iterations.

and

Table 5. Factor analysis (behavioural factors).

Observed Variable	Component
	1	2	3	Alpha
LY1	0.880	0.208	0.090	0.880
LY2	0.802	0.027	0.389
LY3	0.770	0.113	0.335
BH1	0.093	0.849	0.234	0.774
BH2	0.246	0.758	0.086
WOM1	0.154	0.008	0.874	0.708
WOM3	0.357	0.199	0.864
WOM2	0.165	0.444	0.705
Sums of squared loadings	3.483	1.603	1.278
% of Valiance	43.542	20.036	15.981
Cumulative %	43.542	63.577	79.558

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 5 iterations.

. Table 4 demonstrates four factors were generated: personal and emotional value (alpha = 0.816), subjective norm (alpha = 0.829), functional value (alpha = 0.767) and epistemic value (alpha = 0.824). These generated factors were validated using the results of the Cronbach’s alpha test. All alpha values were higher than 0.6, which implies that the attained factors were acceptable [62].

The same procedure of factor analysis was conducted with the right-hand side of the analytical model which generated the three factors demonstrated in Table 5.

Next, we confirmed that the latent factors generated from factor analysis were suitable for the SEM analysis using convergent and discriminant validity tests.

4.3. Convergent and Discriminant Validity Tests

To check the validity of the data set, a convergent and discriminant validity test were conducted. As shown in

Table 6. Convergent and discriminant validity tests (influencing factors).

	N	Mean	SD	CA	CR	AVE	PEV	SN	FV	EV
Personal emotional V	400	3.633	0.613	0.816	0.792	0.560	0.748
Subjective norm	400	3.657	0.702	0.829	0.829	0.618	0.547 **	0.786
Fuctional V	400	4.060	0.714	0.769	0.485	0.320	0.516 **	0.591 **	0.566
Epsitemic V	400	4.105	0.711	0.824	0.578	0.407	0.468 **	0.623 **	0.527 **	0.638

Values bold on the main diagonal are the square rooted of AVEs; SD is standard deviation; CA is Cronbach alpha; CR is Composite reliability; AVE is average variance standard. ** p < 0.01.

, the correlations range from 0.516 to 0.591, which indicates no multi-correlation issues [63]. On the Cronbach alpha test, personal value (0.816), subjective norm (0.829), functional value (0.769) and epistemic value (0.824) are all above 0.7, meaning that the data is assumed to be reliable [62]. Values bolded on the main diagonal are the square root of AVEs >0.5; hence, it is possible to explain these factors [64]. Similarly, the result of behavioural factors’ analysis is shown in

Table 7. Convergent and discriminant validity tests (behavioural factors).

	N	Mean	SD	CA	CR	AVE	BH	LY	WOM
Behaviour	400	3.533	0.893	0.880	0.858	0.670	0.818
Loyalty	400	3.665	0.883	0.774	0.786	0.432	0.396 **	0.657
WOM	400	3.396	0.841	0.708	0.889	0.728	0.255 **	0.584 **	0.853

Values bold on the main diagonal are the square rooted of AVEs; SD is standard deviation; CA is Cronbach alpha; CR is Composite reliability; AVE is average variance standard. ** p < 0.01.

.

These were estimated and are presented in both Table 6 and Table 7. For instance, most of the examined values are greater than the accepted lowest values of CR and AVE, which are 0.7 [65] and 0.5 [66]. Therefore, it can be confirmed that all values have met the relevant requirements, and the overall outcome implies that the constructs are reliable, consistent, and valid [64].

Regarding the discriminant validity test result, if the square root of the AVE of each construct, or the average variance (AV), is found to be greater than the Pearson correlation coefficient of that construct with other constructs, then it can be concluded that discriminant validity is corroborated. We computed AVs and Pearson correlation coefficients as demonstrated in Table 6 and Table 7. It was confirmed that the Cronbach’s alpha of each construct also indicates that each construct is consistent, as the values of the Cronbach’s alpha of each construct are greater than the lowest acceptable value of 0.6 [67]. Constructs so identified are said to be consistent. Correlation analysis is useful for detecting the existence of a covariant among relevant factors that constitute the SEM. As high correlation indicates multicollinearity among variables, the correlation coefficients would ideally not be higher than 0.7 [68]. As shown in Table 6 and Table 7, the factors are confirmed to be suitable for SEM analysis.

From the results of the tests above, the data can be confirmed to be eligible for the next step, which is a SEM analysis.

4.4. SEM Analysis to Test the Factors’ Impact on Purchase Intention

Following the data-cleaning process, the sample size was 400, which resulted in a high goodness of fit for the model. Figure 2 shows that all four latent factors have a significant impact on purchase intention. In addition, the model is compatible with the dataset, as its GFI is high enough (0.921 > 0.90). These scores show that the SEM is an appropriate result.

Other parameters, such as RMSEA (0.0079 < 0.08), AGFI (0.909 > 0.90) and CFI (0.913 > 0.90), imply that this model was appropriately structured for application to the dataset (Hair, 2011). Pass coefficients of the four latent factors to purchase intention are as follows: ‘functional value’ (0.174) is not significant; next are ‘epistemic value’ (0.173 1fix), ‘personal and emotional value’ (p < 0.493 **) and ‘subjective norm (p < 0.343 ***).

It is observed that ‘purchase intention’ to ‘behaviour’ has p < 0.942 ***, which means those relationships are very tight. Additionally, ‘behaviour’ splits into two: the first is ‘loyalty’ (0.937 1fix), and the second is ‘WOM’ (p < 0.936 ***). Consequently, ‘loyalty’ leans towards ‘WOM’ (p < 0.271 ***). As we mentioned in the note that *** means p < 0.001, ** means p < 0.01, and * means p < 0.05, this shows the coefficient value is fixed as 1.

In order to check the goodness of fit judgments obtained here more precisely, attention was paid to SRMR (standardised root mean square residual), an indicator that removes the influence of differences in data units through standardisation. This means that the reported value of RMSEA is very close to the limit of model acceptance, so in the absence of CI for RMSEA and RMSR, it is hard to decide about actual GFI and AGFI is often above the required level for models not fitting the data well. SRMR is easy to consider as a reference guide because it is a standardised indicator, with a value below 0.05 being interpreted as very good fitting level, while it is acceptable when it is lower than 0.1. However, as a highly effective and reasonable value, it is possible to judge the fit to be good if it is less than 0.08 [69]. On this basis, the SRMR of this model, as measured by AMOS, is less than 0.08 (<0.07), which meets this criterion, and, therefore, the fit of this model is judged to be sufficiently high.

Table 8. Result of the SEM analysis.

To		From	Estimate	p
Purchase Intention	<---	Epistemic value	0.173	1 fix
Purchase Intention	<---	Personal & Emortional value	0.493	0.001
Purchase Intention	<---	Functional value	0.174	0.115
Purchase Intention	<---	Subjective Norm	0.343	***
Behaviour	<---	Purchase Intention	0.942	***
Loyalty	<---	Behaviour	0.937	***
WOM	<---	Behaviour	0.936	1 fix
WOM	<---	Loyalty	0.271	***
PEV1	<---	Personal & Emortional value	0.712	***
PEV2	<---	Personal & Emortional value	0.907	1 fix
PEV3	<---	Personal & Emortional value	0.703	***
PEV4	<---	Personal & Emortional value	0.876	***
SN1	<---	Subjective Norm	0.717	1 fix
SN2	<---	Subjective Norm	0.814	***
SN3	<---	Subjective Norm	0.830	***
FV1	<---	Functional value	0.712	1 fix
FV2	<---	Functional value	0.762	***
EV1	<---	Epistemic value	0.755	1 fix
EV2	<---	Epistemic value	0.940	***
BH1	<---	Behaviour	0.763	1 fix
BH2	<---	Behaviour	0.843	***
LY1	<---	Loyalty	0.884	1 fix
LY2	<---	Loyalty	0.805	***
LY3	<---	Loyalty	0.845	***
WOM1	<---	WOM	0.795	1 fix
WOM2	<---	WOM	0.620	***
WOM3	<---	WOM	0.652	***
Covariances			Estimate	p
Epistemic value	<-->	Personal & Emortional value	0.511	***
Epistemic value	<-->	Subjective Norm	0.280	***
Personal & Emortional value	<-->	Subjective Norm	0.474	***
Functional value	<-->	Subjective Norm	0.527	***
Epistemic value	<-->	Functional value	0.609	***
Personal & Emortional value	<-->	Functional value	0.666	***

*** means p < 0.001.

shows that details of the estimate values of each path and the significant impact among latent factors and observed elements.

4.5. Discussion

The results of this study showed that there is a strong relationship between intention and behaviour, and that the top two influencing factors, personal feelings and social norms, have a negative impact on purchasing behaviour. In other words, functional aspects such as nutritional value, palatability, and quality of ingredients, which were assumed to be valid factors, did not have much influence on decision making and purchase behaviour. On the contrary, highly emotional aspects play a role in determining the purchase of traditional confectioneries. This indicates that consumer valorisation and attitudes have a significant impact on the purchase of confectioneries.

In a model in which attitudes dictate behaviour, i.e., attitudinal factors influence the act of purchasing traditional confectionery, this study focused on four factors: three hypothetical factors and one subjective norm. As discussed, subjective norms in the context of the research topic are responses to the expectation that purchasing traditional confections is expected by others and should be continued. Subjective norms have been discussed as an important determinant of behavioural intentions and refer to the perceptions of relevant groups and individuals, such as family, friends, and peers, which influence the performance of one’s behaviour based on social pressure [70]. Based on the results of this study, social pressure, emotional attachment, and a personal “sense of obligation” may, in fact, meaningfully influence the purchase of traditional Tendo confectionery. Attitudes towards traditional sweets can be significant driving forces leading to purchase intention, which urges buying activities in the market.

Similar to this finding, significantly, of the three antecedents prepared, the personal emotional factor shows the greatest impact with regard to traditional Thai-confectionery purchasing behaviour. The respondents of this survey, Thai consumers, have a strong emotional attachment to their personal traditional confectionery: personal memory value and emotional attachment, along with subjective norms. This aspect may provide a valid and important guideline for supporting confectioneries that embody unique and distinctive national culture, for developing effective marketing strategies from the perspective of preserving food and culture diversity amid globalisation and maintaining the prevalence of Western food ingredients and global food culture.

5. Conclusions

5.1. Imlications

Concerning the main objective of this research, which is to investigate the differences in Thai consumers’ perceptions and purchase behaviours regarding traditional Thai and Western sweets, more in-depth analysis needs to be conducted. As mentioned in the introduction, the research results presented here are at the pilot level, and the analytical framework needs to be refined based on the findings of previous studies in the food sector [71].

The tentative analytical model used here also indicates a strong relationship between purchase intention and purchasing behaviour. Furthermore, the results suggest that consumers, along with favourable WOM, show a willingness to stick to traditional confectioneries as repeat customers as a result of evoked purchasing behaviour.

This indicates that the younger generation still respects traditional values and supports a diverse sweet market rather than being solely devoted to Western confectioneries, even after Thai sweets have been overtaken by Western confectioneries in the market.

5.2. Limitations and Further Research Opportunities

As noted above, in the process of substantiating the hypothetical model, this study was combined with the proposal of a scale that would be useful for further analysis. We recognize the need for further development of the research results presented here in order to produce a more robust contribution.

First, we plan to elaborate on the model and re-evaluate the scale to closely examine consumer purchasing behaviour, which may have undergone further transformation after the COVID-19 era. To do so, we need to conduct an empirical study based on a larger data set collected from other markets that are also middle-income countries. Second, the models and measurements tested in this study will be compared using data sets from different markets, including emerging, developing, and developed markets, to discuss the determinants of consumer attitudes and behaviours in each market. Third, the literature review suggests that the nature of the relationships between factors may not be as simple as the research design of the study. For example, the possibility that behaviour and loyalty are interrelated cannot be ruled out. Similarly, if WoM and behaviour influence each other, this bidirectionality should be interpreted in future research agendas. Therefore, it is envisaged that the next stage of the project will be to analyse this issue when conducting a deep dive into consumer behaviour and attitudes based on qualitative methods. Moreover, we are expecting the use of qualitative research methods will help us in gaining a better and more accurate understanding of the analysed phenomena. By doing so, we hope to further strengthen and generalise the output of this research, to be more in-depth and to provide clearer answers to the questions of ‘what’, ‘where’, ‘how’ and ‘why’. The methodology will contribute to further extend the scope of this research and enable the results and findings obtained to be deployed in other market contexts and to provide guidance for more specific business strategies.