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Article

Understanding Purchase Intention of Fair Trade Handicrafts through the Lens of Geographical Indication and Fair Trade Knowledge in a Brand Equity Model

by
Eunmi Lee
and
Li Zhao
*
Textile and Apparel Management, College of Arts and Science, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(1), 49; https://doi.org/10.3390/su16010049
Submission received: 17 September 2023 / Revised: 9 December 2023 / Accepted: 10 December 2023 / Published: 20 December 2023
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Abstract

:
This study aims to investigate the effects of geographical indication (GI) and fair trade knowledge (FTK) on U.S. female consumers’ perceived brand equity and the purchase intention of fair trade apparel handicrafts. Employing a web-based experiment and survey, the study divided participants into groups exposed to brands with and without GI certification marks. A total of 206 responses were collected. A perceived brand equity model was developed, featuring elements such as perceived quality (PQ), brand awareness (BA), brand association (BS), uniqueness (UQ), and willingness to pay a price premium (PP). Structural equation modeling was used to analyze the data and validate the hypotheses. The results revealed that consumers are willing to pay a price premium for products with GI and that FTK positively impacts brand equity. Furthermore, FTK had an interactive effect with GI, enhancing PQ and BS. Increased scores in BS, UQ, and PP were also found to positively affect purchase intention. This study fills a research gap by quantitatively examining the relationship between GI, FTK, brand equity, and purchase intention in the domain of fair trade apparel handicrafts. The findings offer strategic insights for enterprises and organizations in the fair trade handicrafts sector.

1. Introduction

Handicrafts embody the traditions and cultures of countries around the world and have been handed down from generation to generation [1,2]. Being closely associated with people’s lifestyles, history, and conventions, handicrafts are significant means of preserving and inheriting rich traditions and heritages [2]. Moreover, developing countries heavily rely on the export of handicrafts, meaning this sector generates substantial household income and employment [3,4]. Due to these values and benefits, the future development of handicrafts is essential.
However, globalization and the mass production of counterfeit handicrafts manufactured in factories have occupied the market, and genuine handicrafts have been pushed out [5]. The counterfeits are generally of poor quality and have degraded the value of genuine handicrafts [6].
As attention to the dominance of counterfeits has increased, there has been considerable discussion on the preservation and new development directions of traditional knowledge. This includes promoting and protecting artisanal handicrafts in global markets [7]. International institutions, including the World Intellectual Property Organization (WIPO) and UNESCO, have made efforts to integrate the unique traditional and cultural knowledge of handicrafts into the current intellectual property regimes. These efforts aim to discourage counterfeit goods and gradually build up brands that embody the value of handicrafts [8,9]. Verification or audit systems have emerged to ensure that artisans are fairly rewarded and valued for their craftsmanship. The WIPO devised a geographical indication (GI) label that protects traditional craft skills through certification. Established in the early 1990s, fair trade (FT) organizations, such as the World Fair Trade Organization (WFTO) and Fairtrade Labeling Organization (FLO), developed fair trade labels and guarantee systems. These systems ensure the authenticity of traditional handicrafts and empower handicraft producers in developing countries.
Multinational fashion companies with abundant capital power have developed various branding strategies for brands, thereby increasing their market share and inducing consumers. On the contrary, the handicraft sector is short of capital in general. The businesses are operated in small sizes and informally [7,10], so professional branding and marketing to attract consumers has rarely been carried out. Due to growing concerns about the dilution of traditional handicrafts by counterfeit goods, significant measures such as geographical indication (GI) certification [9] and fair trade (FT) campaigns are actively being implemented. These initiatives serve branding purposes and are pivotal in supporting artisanal handicrafts. The GI certification, in particular, is expected to bring about a fundamental change. It aims to elevate the perceived value of artisanal products, setting them apart from mass-produced goods by emphasizing their unique cultural and geographic origins [9]. Similarly, FT campaigns advocate for fair pricing and ethical production practices, enhancing the visibility and appeal of authentic handicrafts.
However, despite these efforts, there is a notable gap in research concerning the actual impact of these initiatives. Studies examining how consumers understand and perceive GI and FT labels are limited. It’s unclear whether these certifications truly influence consumer behavior or if they effectively communicate the value and uniqueness of the artisanal products they represent. This research aims to bridge this gap by exploring consumer perceptions of GI and FT labels and assessing their real-world impact on the artisanal handicraft market.
Generally, handicraft items include clothing, textiles, furniture, accessories, and so on. However, this study focused on textile and clothing handicrafts because they have different characteristics compared to other types of handicrafts. First, in the textile and clothing industries, women are the main producers and consumers [11]. Second, the market is highly saturated and vulnerable to competition with mass producers [12]. Third, frequent detrimental social and environmental practices have happened. Many garment factory workers endure unfair treatment and are exposed to unsafe working conditions [13].
In addition to concentrating on the textile and clothing sector, this study specifically focused on female consumers for the experiment. Extensive research indicates that female consumers exert significantly greater influence on the purchase and consumption of textile and apparel goods compared to other consumer sectors [14]. To investigate the factors and variables affecting the brand equity and purchase intention of fair trade (FT) textile and apparel handicrafts, a comprehensive brand equity model was developed. This model was crafted by conducting extensive research on existing brand equity literature.
The study employed a survey method for data collection, and structural equation modeling was utilized for data analysis. It is anticipated that the findings of this study will reveal the tangible benefits of GI certification for fair trade handicrafts. Additionally, the study aims to provide practical guidance for branding and market improvements of FT handicrafts.

2. Literature Review

2.1. Geographical Indication and Handicrafts

According to the World Trade Organization’s Trade-Related Intellectual Property Rights (TRIPS) agreement in 1994, GI is defined as “an indication which identifies a good as originating in the territory [of a member] where a given quality, reputation, or other characteristic of the good is essentially attributable to its geographical origin” [9]. Therefore, GI-certified products possess a certain geographical origin or contain unique characteristics that come from a specific geographical origin [9].
GI certification has been a valuable asset to handicraft producers, as the certification adds significant value to their creations and differentiates them from mass-produced counterfeit goods [15]. Due to these advantages, the number of handicrafts earning GI certification is on the rise. In India, led by the government, 301 GI registrations have been granted since 25 October 2017, and 610 GI applications have been filed as of January 2018. The proportion of handicrafts in India’s registered GI goods is 64 percent [16].

2.2. Fair Trade Handicrafts

Fair trade (FT) first began in the late 1940s with the sustainable business practices of Ten Thousand Villages and SERRV, aimed at supporting the livelihoods of impoverished and exploited producers in developing countries. At that time, there was growing criticism regarding the exploitative and unfair business practices of multinational companies [17]. Based on long-term and stable partnerships, FT organizations like Ten Thousand Villages and SERRV focused on the payment of a fair price for labor and on empowering producers.
The fair trade slogan, “Trade not Aid,” gained international recognition in 1968 when it was endorsed by the United Nations Conference on Trade and Development [17]. The most widely accepted definition of fair trade was created in 1998 by FINE. FINE is a coalition of four fair trade organizations, FLO, WFTO, Network of European Worldshops (NEWS!), and EFTA. FINE’s fair trade definition is as follows:
“Fair Trade is a trading partnership, based on dialogue, transparency and respect, that seeks greater equity in international trade. It contributes to sustainable development by offering better trading conditions to, and securing the rights of marginalized producers and workers—especially in the South. Fair Trade organizations (backed by consumers) are engaged actively in supporting producers, awareness raising and in campaigning for changes in the rules and practice of conventional international trade” [18].
FINE’s definition encompasses three core elements of fair trade, fairness, trade, and advocacy [19]. Since the FT labeling movement first emerged in the late 1980s, many FT organizations gained FT certification on their products. The certification system served FT products to enter mainstream businesses such as large supermarket chains and boosted sales compared to before [20]. Of course, handicrafts have also been highly certified for fair trade [21], but the FT handicrafts market has shrunken due to mass production and globalization. FT handicrafts are invaluable in that they signify centuries-old heritages and diverse cultures transmitted through generations [21,22]. Therefore, some countries, including the United Kingdom, Netherlands, South Korea, and Japan, are educating students about the value of FT handcrafts in schools [23].

2.3. Theoretical Framework

2.3.1. Boundary Objects and GI

Star and Griesemer [24] (p. 7) state that “boundary objects are objects which are plastic enough to adapt to local needs and the constraints of several parties employing them, yet robust enough to maintain a common identity across sites”. Similarly, Star and Griesemer [24] state that connections and interactions among diverse social worlds occur through standardization in boundary objects theory. The heterogeneous nature of various actors enables each to comprehend social worlds from different perspectives. Certainly, for GI-certified FT textile and clothing handicrafts, boundary objects not only represent the uniqueness and authenticity of the product but also help distinguish the product from counterfeit goods. Moreover, based on boundary object theory, GI certification aids in making informed purchasing decisions by transferring and communicating traditional knowledge of handicrafts to consumers.
The boundary object concept was applied in food label research by investigating consumers’ perceptions of organic and functional foods through the labels attached [25]. In the study, Eden [25], (p. 6) argued that “boundary objects could link not only the worlds of scientists and nonscientists but the worlds of expert producers and lay consumers—worlds that are highly diverse because of the complexity of modern manufacturing systems”. Through GI certification, cultural and traditional knowledge contained in FT handicrafts is simplified and represented, facilitating the transmission and communication of this knowledge to consumers. Thus, even with very little information, consumers can recognize the value of GI-certified handicrafts, as a GI label functions as a proxy for more complex information.

2.3.2. Social Learning Theory and Fair Trade Knowledge

Both Hilgard and Marquis [26] and Domajn and Burkhard [27] state that learning involves permanent behavioral changes occurring when individuals experience various events in their surroundings. According to Hoch and Deighton [28], learning is the process of integrating new data into the beliefs consumers held in the past. Bandura [29] combined behavioral learning theory and cognitive learning theory to develop social learning theory (SLT).
SLT posits that human behavior results from continuous interactions between individuals and their environments [29]. More specifically, Bandura [29] asserts in his SLT that learning occurs not only through direct experience and active engagement but also through observing other people’s behavior or following instructions. Explaining how observing others’ behavior leads to the observer’s behavior change, SLT suggests that observations are more likely to lead to learning when they are unique, positively evaluated, easy to follow, frequent, and notable [29].
From the SLT perspective, people’s consumption behavior can be influenced by observing others’ consumption habits [30]. Therefore, FT education and campaigns, which aim to improve FT knowledge, can lead consumers toward more ethical and sustainable consumption. Extensive research indicates that FT education in schools contributes significantly to students’ learning about global and developmental issues [31]. Moreover, the impact of fair trade education in schools not only encouraged students to consume fair trade products but also prompted supermarket chains to stock more such products [32]. Ultimately, this education significantly increased the accessibility and availability of FT products to ordinary consumers [32].

2.3.3. Dimensions of CBBE for Fair Trade Textile and Clothing Handicraft

Although scholars have different views on the elements of brand equity models, depending on the research purpose, most scholars include brand awareness, brand association (brand image), and perceived quality, in terms of consumer recognition, and brand loyalty, in terms of consumers’ behavior and attitude, as major constituents of brand equity. Accordingly, in order to develop a brand equity model for FT handicrafts, this study first reviewed the most frequently discussed four elements: perceived quality, brand awareness, brand association (brand image), and brand loyalty.
Brand awareness and brand association are significant elements of Consumer Based Brand Equity (CBBE) as both represent the relationship and communication between the company and consumers [33,34]. Brand loyalty is “the attachment that a customer has to a brand” [33] (p. 39) and has the nature of outcome after purchasing a certain brand repeatedly [35]. Therefore, brand loyalty is excluded because a virtual brand for an experiment was created in this study. Uniqueness [36,37] and willingness to pay a price premium [23,38,39] are included in the brand equity model of this study because the two factors constitute the FT product features that consumers perceive. In conclusion, this study developed a brand equity model of FT textile and clothing handicrafts including the five dimensions, perceived quality (PQ), brand awareness (BA), brand association (BS), uniqueness (UQ), and willingness to pay a price premium (PP).

2.3.4. Conceptual Framework

This study adopted boundary objects theory and social learning theory in order to explore the potential relationship between purchase the intention of FT handicrafts and GI and FT knowledge in a CBBE model (See Figure 1).

3. Hypotheses Development

3.1. Effects of GI on CBBE

Consumers use various signifiers, such as physical attributes, to evaluate the product quality and figure out product information; but appearance and tangible features of products tend to be easily copied [40]. In this respect, the certification like a GI is useful to discern and differentiate a genuine GI good from its counterfeit by facilitating consumer recognition. A GI label links consumers and producers with trust [25] and adds value to the products. Consumers recognize that GI-tagged products (such as wines) are of better quality due to their geographical origins [41]. Certifications or labels on goods contribute to increasing trust in the products to consumers [42]. Kim and Kwon [43] conducted a study of GI-tagged agricultural products in South Korea. In the study, consumers who are knowledgeable and familiar with GIs perceived the products as overall superior. Other studies argue that GI tags and official recognitions of historical and traditional importance add value [15]. Through GIs, consumers have more recognition of the indigenous products and pay more attention to the traditional knowledge from geographic origins, thus differentiating GI goods. In the case of FT products, consumers trusted the products more when a label certifying FT products was attached [44], for FT products increase trust in the product to consumers who know the label. As such, the major benefit of GI certification is that it can guarantee product quality to consumers [45].
Based on these perspectives, the first hypothesis is developed as follows:
H1. 
There is a positive relationship between GI certification and consumers’ perceived quality of FT textile and clothing handicrafts.
More and more consumers value Corporate Social Responsibility (CSR) [46]. Corporate Social Responsibility (CSR) refers to the voluntary commitment of businesses to incorporate ethical practices, environmental sustainability, and social welfare into their operations and interactions with stakeholders. It extends beyond legal compliance, emphasizing a holistic approach where companies actively contribute to the betterment of society and the environment [46]. Consumers are willing to buy products from ethical companies and try to make ethical consumption as they believe their consumption will help solve social issues and promote public good and are willing to purchase products from ethical companies to make ethical consumption [47]. A previous study found that CSR also affected brand awareness, positive brand image, and brand association for consumers [48]. Larceneux et al. [49] claimed a “halo effect” that certification branding can foster strong positive associations. Anson [50] also claimed that GI certification can add consumer awareness and loyalty to a brand. The values, preserving cultural identity and traditional knowledge and artisans’ livelihoods, that GI labels drive consumers towards a positive impression of GI-tagged goods.
As a close brand communication between consumers and companies, storytelling is an effective tool. Lundqvist et al. [51] claim that the brand story of a company enhances the positive BS. Consumers perceive the unique storytelling of a brand as a differentiating aspect from others [52]. GI-certified textile and apparel handicrafts have their own stories that include unique historical, cultural, and traditional values, which can be effectively delivered through storytelling.
To sum up, it is assumed that GI certification has a positive impact on BA and BS in terms of the CBBE and, thus, the second and third hypotheses were developed as follows:
H2. 
There is a positive relationship between GI certification and brand awareness of FT textile and clothing handicrafts.
H3. 
There is a positive relationship between GI certification and brand association of FT textile and clothing handicrafts.
Geographically limiting the production of a particular product through GI certification and official recognition of its inherent cultural and traditional significance is instrumental in the increasing rise of the demand for the product in the global market [53]. Thus, it has a role to play in boosting the exports along with commanding premiums in the global market [9]. Consumers are highly concerned about the geographical origin of commodities and expect that goods with a specific origin have certain qualities or values [40]. Anson (2012) argues for unique qualities, the specific characteristics of a region, the perceived uniqueness, and the distinctiveness as motivations for consumers to purchase GI products and claims that this depends largely on the attitudes and preferences of consumers [50]. Uniqueness is a fundamental property of GI products, and a GI mark allows for consumers to reduce product search costs and time [54].
In regard to product price, GIs protect producers from unfair competition and imitation and create a premium price for the product [45], as consumers are willing to pay more for authentic products [40]. Compared to non-GI products, GI products in the EU are priced 2.2 times higher on average [55].
In addition, a survey in 2003 revealed that 40% of consumers in the survey were willing to pay a premium of more than 10% of the product price [56]. Previous studies indicated a willingness to pay a premium for GI goods, although the extent of the premium would vary depending on each product and consumer experience [40].
Therefore, based on the premiums, added values, and uniqueness attached to GIs, the hypotheses were developed as follows:
H4. 
There is a positive relationship between GI certification and the uniqueness of FT textile and clothing handicrafts.
H5. 
There is a positive relationship between GI certification and the willingness to pay a price premium for FT textile and clothing handicrafts.

3.2. Interaction between GI and FTK on CBBE

Based on the SLT perspective, the aims of consumer education, campaigns, and movement in FT are to enhance their knowledge of FT and change their consumer behavior through learning by watching. Extensive surveys and evidence have indicated that FT education brings positive changes in raising awareness of ethical purchasing practices and buying FT products [57]. Moreover, people educated about FT encourage others to engage in the FT movement, purchasing FT products and asking retailers to increase the availability of FT products [32]. Such effects of FT education are further strengthened through word of mouth (WOM). In addition, consumers with a positive attitude toward ethical products are likely to pay more for FT products by adding monetary value to intangible ethical values [58].
Brucks [59] stated that consumer knowledge plays a role in enhancing the problem-solving ability to find, select, and evaluate products. Fair trade knowledge (FTK) is expected to play a role as consumer knowledge. In addition, since GI and FT share values, such as artisan support and empowerment, economic development, and local traditions and culture preservation, awareness of GIs will also be strengthened or weakened depending on the FTK level.
Accordingly, the following four hypotheses are established:
H6. 
The effect of GI certification on perceived quality is moderated by FT knowledge.
H7. 
The effect of GI certification on brand awareness is moderated by FT knowledge.
H8. 
The effect of GI certification on brand association is moderated by FT knowledge.
H9. 
The effect of GI certification on being willing to pay a price premium is moderated by FT knowledge.

3.3. Effects of CBBE on Purchase Intention

Purchase intention (PI) refers to the consumers’ planned future buying behavior and the likelihood that the beliefs and attitudes related to the purchase will be transferred to actual behavior. Many studies regarding the relationship between PI and CBBE of Aaker [60] and Keller [61] have been conducted, and it has been found that PQ, BA, and BS have a positive effect on PI [62]. PP has also been found to have a positive effect on consumer PI for a particular brand [63]. However, UQ does not directly affect PI but rather indirectly affects PI through the mediator, PP [63].
Based on the above discussions, the following hypotheses were derived:
H10. 
There is a positive relationship between perceived quality and the purchase intention of FT textile and clothing handicrafts.
H11. 
There is a positive relationship between brand awareness and the purchase intention of FT textile and clothing handicrafts.
H12. 
There is a positive relationship between brand association and the purchase intention of FT textile and clothing handicrafts.
H13. 
There is a positive relationship between the willingness to pay a price premium and the purchase intention of FT textile and clothing handicrafts.
H14. 
There is a positive relationship between uniqueness and the purchase intention of FT textile and clothing handicrafts via the willingness to pay a price premium.
Figure 2 summarizes the conceptual model for all proposed hypotheses based on the theories.

4. Research Method

A web-based experiment was conducted on the website. In the experiment, therefore, the mock brand, Chandria, was virtually created for both the treatment and control groups. However, the stimulus, a GI certification mark, and its descriptions were presented only to the treatment group. The test participants were asked to look at webpage images of a mock brand inserted into a survey and then asked to answer the survey questionnaire. The survey was divided into four categories: (a) the measurement of brand equity and purchase intention, (b) FTK measurement, (c) the subject’s integrity, and (d) demographics. The questionnaires in the survey to measure brand equity and purchase intention were adopted from previous literature after reviewing comprehensive studies.

4.1. Measurement of Brand Equity and Purchase Intention

Perceived quality (PQ): Four items, except the durability item from Agarwal and Teas’s [64] study, were adopted. The four items on a 7-point Likert scale ranged from “very low” (1) to “very high” (7).
Brand awareness (BA): Yoo et al.’s [65] three BA measurement items were adopted, and the items “I have difficulty imagining X in my mind”, “I am aware of X”, and “I can quickly recall the symbol or logo of X” were excluded. The items rated the BA in terms of “strongly disagree” (1) to “strongly agree” (7) with a 7-point Likert scale.
Brand association (BS): The four items were developed with reference to Tong and Hawley’s [66] study. The 7-Likert scale of items ranged from “strongly disagree” (1) to “strongly agree” (7).
Uniqueness (UQ): Based on Netemeyer et al.’s [63] and Su and Chang’s [67] studies, three items were developed, and the items on the 7-Likert scale ranged from “strongly disagree” (1) to “strongly agree” (7).
Willing to pay a price premium (PP): Three items from Netemeyer et al. [63] and Dwivedi et al.’s [68] study were adopted, and one item of an open-ended question “How much they are willing to pay more” was excluded. The item rated PP in terms of “strongly disagree” (1) to “strongly agree” (7) with the 7-Likert scale.
Purchase intention (PI): Based on Jalilvand et al.’s [62] study, three items were adopted and rated with the 7-Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7).

4.2. Measurement of Fair Trade Knowledge

After reviewing prior studies, 10 items from Poncelet et al. [69] and Pelsmacker and Janssens’ [70] studies were adopted, with some modifications. The original items measured survey respondents’ answers with a 7-Likert scale but, in this study, the options for answers were modified to choose one of the three, “true,” “false,” or “I don’t know,” because the survey participants, with no knowledge of FT, were likely to have difficulty making any choice on the 7-Likert scale questionnaires. Unlike the Likert scale, the method is used for the assessment of consumers’ objective knowledge [71,72].

4.3. Subject’s Integrity Measurement

Some survey or experiment participants neglect to answer questionnaires as their primary motive for participation was gaining financial rewards [73]. Even a small amount of unfaithfulness may distort study results [73], so an item measuring participants’ integrity was developed.
The experiment adopted a post-test-only control group design. Participants first viewed the virtual brand images and stimuli on a website and then completed a survey questionnaire. Figure 3 illustrates the experimental process. After obtaining approval from the University of Missouri Institutional Review Board, data collection commenced in July 2020. Qualtrics, a reputable data collection agency, recruited U.S. female consumers aged 18 years or older as survey participants. Qualtrics randomly selected samples from their panel and sent them a participation link. Participants could only partake in the experiment upon agreeing to do so. Both treatment and control group subjects were required to view the stimuli designated for their group, after which they were allowed to complete the survey questionnaire. To ensure adequate exposure, a timer setting mandated that the treatment and control groups view the stimuli for a minimum of two minutes and one minute, respectively. Additionally, the survey was designed to be completed only if all questions were answered, minimizing the possibility of missing data.
A structural model was developed based on the hypothesized conceptual framework presented in this study. Structural equation modeling (SEM) served as the quantitative statistical method for hypothesis testing. Following the two-step approach proposed by Anderson and Gerbing [74], along with a bootstrapping approach, SEM was employed. SEM’s capacity to manage complex relationships, including latent variables like brand equity and purchase intention, provides a comprehensive analysis of direct and indirect effects, accounting for measurement errors and ensuring the accuracy of findings. Its ability to simultaneously analyze all paths in the hypothesis model, coupled with robust hypothesis testing, allows for an exhaustive evaluation of our theoretical constructs. The method’s flexibility in handling experimental designs and various types of variables renders it a versatile and potent tool for this study, offering deep and credible insights into consumer behavior dynamics in the handicrafts context.
In addition, in order to examine the moderating effect of FTK on the relationship between GI and consumers’ perception of four elements of brand equity (i.e., PQ, BA, BS, and PP), the interaction between GI and FTK was tested. The value of the interaction term was calculated by multiplying a variable of the main effect and a variable of the moderating effect. A mean-centering method was used to solve multicollinearity problems that could occur in the analysis process, and thus, a mean-centered variable was created and named MC_FTK. The mean-centered variable in the study, MC_FTK, was calculated for both the treatment and control groups. In the analysis of the FTK moderating effect, GI was a variable that distinguished the treatment group from the control group, and the control group was coded zero and the treatment group was coded one. As stated above, the interaction term was calculated by multiplying a variable of the main effect and a variable of the moderating effect. Therefore, by multiplying two variables, GI and MC_FTK, a new variable and interaction term, GI*MC_FTK, was created.
Although there is no universal standard for sample size, a smaller sample tends to increase the estimation error. Therefore, at least 150 samples are typically required for SEM analysis [74]. Furthermore, rules-of-thumb suggest that each estimated parameter necessitates five to ten observations [75], and according to Nunnally et al. [76], each variable requires at least 10 cases. Given that this study involved 21 variables, a minimum sample size of 110–210 was deemed appropriate based on previous research guidance. Initially, 122 respondents from the treatment group and 117 from the control group were collected, totaling 239 samples. However, those who provided incorrect responses to the survey integrity measurement question were excluded, resulting in 104 respondents in the treatment group and 102 in the control group. Consequently, the final sample size amounted to 206.

5. Results and Discussions

5.1. Demographic

The demographic information of the survey sample was examined by the frequency of age, education, race, household income, and place of residence. The average age was 50.04 years and 45 respondents (21.8%) were between 60 and 69 years old, followed by 32 respondents (15.5%) were at the age of between 20 and 29, and 32 respondents (15.5%) between 70 and 79. In total, 61 participants had associate degrees (29.6%), 59 were high school graduates (28.6%), and 55 had bachelor’s degrees (26.7%). The majority of the respondents were Caucasians (75.7%) with the number of 156, followed by 28 African Americans (13.6%), and 10 Asians (6.55%). The 69 respondents (33.5%) belonged to an income level of over USD 25,000 and less than USD 50,000, followed by 68 respondents (33.0%) with an income level of over USD 50,000 and less than USD 100,000. Being spread throughout the United States, Florida had the largest number of participants’ residences, followed by New York, Texas, California, New Jersey, Illinois, North Carolina, and Georgia.

5.2. Exploratory Factor Analysis and Assessing Measurement Model

To explore the factorial structure in the data, all 20 items of the instrument were conducted to an exploratory factor analysis (EFA) with maximum likelihood extraction and oblique (direct oblimin) rotation. The number of factors was fixed as six. All items, excluding BS1, were appropriately loaded onto each factor related to PQ, BS, BS, UQ, PP, and PI. However, factor loading of BS1 onto factor six-related BS was as low as −0.194. Whereas, in factor four, which was related to UQ, the factor loading of BS1 was as high as −0.594. Thus, BS1 was removed from the study model. Table 1 shows the results of EFA performance after BS1 removal. Table 2 shows the results of exploratory factor analysis and confirmatory data analysis.
The rotation method is direct oblimin. Factor loadings were from the pattern matrix. ( ) indicates cumulative percentile of variance. Cronbach’s α before BS1 excluding.
The measurement model was made with six latent variables (i.e., PQ, BA, BS, UQ, PP, PI) and 19 measurement items, and CFA was performed. The goodness of fit of the model was acceptable, χ2 (df = 137, N = 206) = 252.953, p < 0.001, CFI = 0.967, GFI = 0.882, TLI = 0.958, RMSEA = 0.064, SRMR = 0.040.
To test the discriminant validity of measurement items, the squared correlation between latent variables and AVE was examined, and the results are shown in Table 3. The discriminant validity was confirmed as AVE values were higher than the squared correlations in the majority of cases [77,78]. However, the correlation coefficient between PP and PI was 0.848. Also, the value of squared correlation was 0.719, which was higher than the value of PP’s AVE, 0.664.

5.3. The Effect of Geographical Indication

The mean difference of latent variables between the treatment group exposed to GI and the control group not exposed to GI was analyzed, and based on these results, the effect of GI on PQ, BA, BS, UQ, and PP was examined. Table 4 shows the results of the latent mean analysis. The goodness of fit of the model was acceptable, χ2 (df = 300, N = 206) = 459.051, p < 0.001, CFI = 0.955, TLI > 0.948, RMSEA = 0.051, SRMR = 0.048.
Among the five latent variables (i.e., PQ, BA, BS, UQ, PP) constituting FT textile and clothing handicraft brand equity, only PP showed a statistically significant difference between the two groups, p = 0.039, latent mean = 0.289. The Cohen’s d value of PP was 0.326, which was confirmed as the medium effect size. Therefore, hypothesis H5 was supported and the rest of the hypotheses (H1, H2, H3, and H4) were rejected.

5.4. Interaction between Geographical Indication and Fair Trade Knowledge

In order to examine the moderating effect of FTK on the relationship between GI and consumers’ perception of four elements of brand equity, the interaction between GI and FTK was tested. The value of the interaction term was calculated by multiplying a variable of the main effect and a variable of the moderating effect. The mean of FTK for the treatment group was 6.971 and for the control group was 6.681.
The goodness of fit of the structural model was acceptable, χ2 (df = 86, N = 206) = 134.714, p < 0.001, CFI = 0.978, TLI = 0.969, RMSEA = 0.053, SRMR = 0.041 (See Table 5).
There are two types of moderators: pure and quasi-moderators. A pure moderator interacts with an independent variable but has a minor relation with a dependent variable [79,80]. A quasi-moderator has interaction with an independent variable, as well as being an independent variable itself [80]. The analysis results confirmed a positive influence of GI*MC_FTK (interaction term) on PQ (p < 0.01, C.R. = 2.700, B = 0.127, β = 0.226) and BS (p < 0.05, C.R. = 2.094, B = 0.098, β = 0.184). In addition, the results showed a positive influence of FTK on both PQ and BS. That being said, FTK is a positive quasi-moderator on the relationship between GI and PQ, as well as between GI and BS. However, the analysis indicated no moderating effect of GI*MC_FTK on BA and PP. Therefore, the two hypotheses, H6 and H8, were supported, while H7 and H9 were rejected.
Figure 4 below indicates the magnitude of the moderation effect (interaction) of GI*MC_FTK on PQ and BS, which was estimated based on unstandardized estimates (B) of GI*MC_FTK, GI, and FTK. The slopes of the graphs below show how much each group’s PQ and BS increased due to the FTK’s moderating effect when FTK increases by one unit.

5.5. Structural Relationship between Fair Trade Handicraft Brand Equity and Purchase Intention

Based on the hypothesized conceptual model indicating female consumers’ perception of FT handicraft brand equity is positively related to PI, a structural model was established and assessed. The goodness of fit of the structural model was acceptable. χ2 (df = 141, N = 206) = 303.612, p < 0.001, CFI = 0.953, TLI = 0.943, RMSEA = 0.075, SRMR = 0.082 (See Table 6).
The results indicated positive influence of BS (p < 0.001, C.R. = 4.906, B= 0.494, β = 0.496), PP (p < 0.001, C.R. = 7.707, B = 0.699, β = 0.633), and UQ (p < 0.001, C.R. = 6.908, B = 0.526, β = 0.572) on PI. The squared multiple correlation (SMC), presenting the explanation power of the UQ and PP relationship, was 0.327. The SMC, presenting the explanation power of FT handicraft brand equity and PI relationship, was 0.801. However, PI had no statistically significant relationship with PQ and BA. Table 7 estimates the confidence intervals of direct and indirect effects of five brand equity latent variables on PI, using the bootstrapping approach. The indirect effect of UQ on PI was statistically significant (p < 0.01, B = 0.368, 95% CI of B [0.252, 0.521], β = 0.362, 95% CI of β [0.271, 0.468]). Following the results, the three hypotheses, H12, H13, and H14, were supported, and H10 and H11 were rejected.

5.6. The Summary of the Results and Emphases

In a comparison of the mean differences, the mean of the control group was set to zero, and the relative mean size of the treatment group was compared. The results showed that, only in PP, the mean of the treatment group was statistically significantly greater than that of the control group. This result supports existing empirical studies conducted several times in the EU region that are willing to pay a price premium for GI products as GI tags protect producers from unfair competition and copies [40,55,56].
The effects of GI on PQ, BA, BS, and UQ were rejected. The potential reasons can be assumed as follows: Firstly, while GI certification inherently guarantees product quality and uniqueness [45], its impact is highly contingent on individual consumer perceptions. Certifications like GI, which may still be relatively unknown to many consumers, often struggle to garner immediate trust [79]. Furthermore, as suggested by various scholars, the two consumer-based brand equity (CBBE) dimensions, BS and BA, share overlapping concepts to some extent [80]. Song [81] highlighted that BA and BS are typically low for unfamiliar brands, suggesting that such brands require an entirely different brand equity model from conventional ones. In this context, especially for lesser-known brands like Chandria, as featured in this study, factoring in brand familiarity before building consumer-based BA and BS might be pivotal.
This study has suggested FTK had positive effects on PQ and BS through interactions with GI. These results explain that FTK, which has been increased through FT education, has direct and indirect impacts on consumers’ decision-making in consumption, enhancing understanding of FT brands, and consequently promoting the sales of FT goods. This assumption is based on several studies demonstrating the influence of consumer knowledge on understanding, exploring, selecting, and evaluating products [59]. It is postulated that the trust and solidarity in FT, strengthened by FTK, provide an indirect impetus to enhance the CBBE, despite Chandria being relatively unknown to consumers [37]. Moreover, these results align with the notion that consumers engage in social learning [29], creating a virtuous cycle where FT consumption begets further FT consumption [31,32].
A structural model was developed to test whether an increase in the five dimensions (i.e., PQ, BA, BS, UQ, and price premium (PP)), constituting the FT handicraft brand equity in this study, leads to an actual increase in consumer purchase intention (PI). The findings indicated that increases in BS [62], UQ, and PP [63] corresponded with increased PI. This aligns with previous research indicating that strengthening CBBE boosts consumer purchase intention. Notably, PP demonstrated a significant positive influence on PI (B = 0.699), supporting Netemeyer et al.’s [63] assertion that consumers’ willingness to pay a price premium correlates with their purchasing behavior. However, PQ and BA showed a negative influence on PI in the structural model, albeit not statistically significant. This suggests that consumers tend to exhibit higher purchase intentions towards familiar brands [82]. Consequently, the unfamiliarity of the virtual brand used in this study likely influenced the relationship between FT handicraft brand equity and PI. For instance, PQ is influenced by factors such as previous knowledge and experience, social background, income level, and more [83]. Unfamiliarity with a specific brand can increase the perceived risk and, consequently, affect PI [84]. In general, FT textile and apparel handicraft brands are likely to be unfamiliar to many consumers.
Figure 5 below demonstrates the 14 hypotheses test results in the study.

6. Conclusions

6.1. Theoretical Contributions

Handicrafts, as high-value-added creations by artisans, hold immense importance due to their roles in preserving culture and heritage and generating employment. This study proposed a brand equity model for fair trade (FT) textile and apparel handicrafts, consisting of five dimensions of customer-based brand equity (CBBE), and explored the relationships between geographical indication (GI), fair trade knowledge (FTK), each brand equity dimension, and purchase intention (PI).
The findings contribute academically and theoretically to areas that have been lacking in various literatures. Firstly, the study proposed dimensions for a brand equity model specifically tailored for FT textile and apparel handicraft brands. Unlike most brand equity studies focusing on established, well-known brands, this study ventured into the relatively unexplored territory of a virtual FT handicraft brand in the textile and apparel sector. Additionally, given that many FT handicraft companies in this sector are small and often unable to afford brand equity research, this study aims to bridge this gap. Drawing on Aaker [35] and Keller’s [61] brand equity theories and models, it investigates how companies with FT and/or GI certification can build brand equity while upholding globalization and sustainability in consumption.
Secondly, the study quantitatively examined the influence of GI and FTK on consumers. While it was hypothesized that GI would positively influence perceived quality (PQ), brand awareness (BA), brand association (BS), and willingness to pay a price premium (PP), statistical analysis showed that only PP was positively influenced by GI. Nevertheless, many studies have demonstrated GI’s positive role in encouraging consumer purchases [9,15,41].
Thirdly, increasing consumer understanding and knowledge of FT can affect not just those with FT-related knowledge, but also influence the consumption behavior of other consumers. This study highlights the importance of FTK as a form of consumer knowledge and supports the need for continuous FT education to strengthen this knowledge.

6.2. Practical Contributions

Due to the lack of marketing resources, such as advertising and public relations, FT handicrafts organizations are in need of a systematic and efficient management strategy. Additionally, the COVID-19 pandemic, a worldwide outbreak in early 2020, has also rapidly increased the management difficulties of small-sized FT handicraft companies in the textile and apparel industry that lack basic physical strength [85]. In this unfavorable market environment, this study guided the direction of future business management by revealing the composition of brand equity from the perspective of female consumers for FT textile and apparel handicrafts brands. In this regard, three practical contributions of the research results are presented as follows:
The first is what brand equity components should be built with priority. The constituent elements of FT handcraft brand equity in the textile and clothing sector, presented in this study, were quantified through statistical analysis of a sample group. Based on the analysis results, these companies will be able to prioritize and have ideas about which brand equity elements to include to target consumers and to reinforce to have a greater influence on consumers’ purchase intention.
Second, in the experiment of this study, the narrative for GI and FT certification was created based on storytelling and demonstrated the possibility of building brand equity with storytelling. A brand story plays a positive role as a communication tool with consumers [51]. It has been confirmed in the cases of Nike’s 30th anniversary of Air Jordan [86], TOMS Shoes [87], and Uber Eats [88] that a good brand story can spread in a short time through social networking services (SNS), which are high-impact media such as Facebook, Instagram, and Twitter, and may increase consumers’ desire to purchase.
Third, promoting fair trade education can result in more consumption of FT textile and clothing handicrafts. Modern consumers want to buy products that have value to them and express these values through using the products [89]. As a variety of socially problematic events, such as environmental destruction, labor exploitation, and unequal profit distribution, occur behind the profit pursuit of large global fashion companies, consumer awareness has increased, and ethical fashion such as FT and eco-friendly fashion has also emerged [89]. Based on the evidence from many studies that consumers’ knowledge and attitudes are improved through education [90], consumers’ ethical consumption behavior and active FT education encourage the expansion of the overall size of the FT market.

6.3. Limitation and Future Research

First, the sample for the experiment was limited to a specific country, gender, and age group. We concentrated on U.S. residents due to the experimental stimuli and questionnaires being in English. Additionally, we included only female consumers, who are expected to have a high interest in fashion and fair trade (FT). Given the limited space, future studies could expand discussions to provide more detailed explanations for all hypotheses. Second, although the stimuli were created virtually, we referenced existing traditional handicrafts from India to depict the tradition, culture, and history of the mock brand. Individual preferences or prejudices towards certain countries may influence perceptions. Third, the study utilized a post-test-only control group design. Data was collected immediately after survey participants were exposed to the stimuli. Further analysis might be performed to compare the characteristics of the two groups (treatment and control) and report results separately. Finally, the sample for this study was collected through Qualtrics. Gathering samples from a large population of American female consumers would have been challenging without using specialized agencies like Qualtrics. Additionally, the COVID-19 pandemic negatively impacted sample gathering, limiting the research method to an online platform.

Author Contributions

Conceptualization, E.L. and L.Z.; methodology, E.L. and L.Z.; formal analysis, E.L.; writing—original draft preparation, E.L.; writing—review and editing, L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Institutional Review Board of University of Missouri (protocol code 2020361 and date of approval: 3 June 2020).

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
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Figure 2. The entire conceptual model for hypotheses H1–H14.
Figure 2. The entire conceptual model for hypotheses H1–H14.
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Figure 3. Experiment process.
Figure 3. Experiment process.
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Figure 4. Comparison of moderating effect by FTK for PQ and BA. Note: Axis X indicates the fair trade knowledge (FTK) level and axis Y indicates an estimation.
Figure 4. Comparison of moderating effect by FTK for PQ and BA. Note: Axis X indicates the fair trade knowledge (FTK) level and axis Y indicates an estimation.
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Figure 5. The results of hypothesis testing. Note: Bold line indicates a hypothesis accepted.
Figure 5. The results of hypothesis testing. Note: Bold line indicates a hypothesis accepted.
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Table 1. Factor loadings and reliability after the item excluding.
Table 1. Factor loadings and reliability after the item excluding.
FactorFactor LoadingCommunalitiesVariance %Cronbach’s α
Factor 1 49.465
(49.465)		0.848
PP10.3650.386
PP20.4190.735
PP30.9500.999
Factor 2 15.626
(65.091)		0.939
PQ10.8280.726
PQ20.9370.848
PQ30.8540.876
PQ40.8190.783
Factor 3 7.343
(72.434)		0.890
BA10.8010.620
BA20.8690.835
BA30.8370.781
Factor 4 4.600
(77.035)		0.939
UQ1−0.8590.820
UQ2−0.9900.852
UQ3−0.8780.855
Factor 5 4.308
(81.343)		0.896
PI10.5630.726
PI20.8160.848
PI30.7550.876
Factor 6 2.973
(84.316)		0.877
0.881
BS2−0.6860.641
BS3−0.6840.703
BS4−0.8080.810
KMO and Bartlett Sphericity test. KMO = 0.914, Bartlett’s χ2 = 3505.646, df = 171, p < 0.001; Chi-square Test; χ2 = 129.213, df = 72, p < 0.001, RMSEA = 0.062. Note: The number of factors to extract is six. The extraction method is maximum likelihood.
Table 2. Results of exploratory factor analysis and confirmatory data analysis.
Table 2. Results of exploratory factor analysis and confirmatory data analysis.
ItemFactor LoadingAVEα
Factor 1: Willing to pay a price premium (PP) 0.6640.848
PP1. The price of X would have to go up quite a bit before I would switch to another textile and clothing handicraft brand.0.365
PP2. I am willing to pay a higher price for X brand of textile and clothing handicrafts than for other brands of textile and clothing handicrafts.0.419
PP3. I am willing to pay a lot more for X brand of textile and clothing handicrafts than for other brands of textile and clothing handicrafts.0.950
Factor 2: Perceived quality (PQ) 0.7980.939
PQ1. The likelihood that X would be reliable is:0.828
PQ2. The workmanship of X is probably:0.937
PQ3. The quality of X’s product is likely to be:0.854
PQ4. The likelihood that X’s product is dependable is:0.819
Factor 3: Brand awareness (BA) 0.7320.890
BA1. I know what X looks like.0.801
BA2. I can recognize X among other competing brands.0.869
BA3. Some characteristics of X come to my mind quickly.0.837
Factor 4: Uniqueness (UQ) 0.8370.939
UQ1. X is distinct from other textile and clothing handicraft brands.−0.859
UQ2. X really stands out from other textile and clothing handicraft brands.−0.990
UQ3. X is unique from other textile and clothing handicraft brands.−0.878
Factor 5: Purchase intention (PI) 0.7560.896
PI1. I would buy X’s textile and clothing handicrafts rather than any other brands’ textile and clothing handicrafts available.0.563
PI2. I am willing to recommend others to buy X’s textile and clothing handicrafts.0.816
PI3. I am willing to purchase X’s textile and clothing handicrafts in the future.0.755
Factor 6: Brand association (BS) 0.7100.877
BS1. X has a very unique brand image, compared to competing handicraft brands (Excluded).−0.194
BS2. I respect and admire people who wear X.−0.686
BS3. I like the brand image of X.−0.684
BS4. I like and trust X, which makes textile and clothing handicrafts.−0.808
Note: AVE = Average Variable Extracted; α = Cronbach’s α; The number of factors to extract is six. The extraction method is maximum likelihood. The rotation method is direct oblimin. Factor loadings were from the pattern matrix.
Table 3. Squared correlations with average variance extracted.
Table 3. Squared correlations with average variance extracted.
Latent Variable
PQBABSUQPPPI
PQ (perceived quality)0.798
BA (brand awareness)0.047 **0.732
(0.095)
BS (brand association)0.464 ***0.348 **0.71
(0.111)(0.114)
UQ (uniqueness)0.479 **0.159 **0.555 ***0.837
(0.104)(0.096)(0.106)
PP (willing to pay a price premium)0.086 **0.367 **0.359 **0.316 **0.664
(0.084)(0.113)(0.102)(0.094)
PI (purchase intention)0.172 **0.346 **0.572 **0.389 **0.719 **0.756
(0.098)(0.118)(0.119)(0.103)(0.128)
Note: Significance levels are ** p < 0.01 and *** p < 0.001 (2-tailed). ( ) indicates standard error. Bold indicates AVE; PQ = perceived quality; BA = brand awareness; BS = brand association; UQ = uniqueness; PP = willing to pay a price premium; PI = purchase intention.
Table 4. The results of the latent mean analysis.
Table 4. The results of the latent mean analysis.
HypothesisLatent VariableLatent MeanPolled VarianceCohen’s d
ControlTreatment
H1PQ0−0.0901.173−0.077
H2BA00.1591.2520.127
H3BS00.1501.0820.139
H4UQ0−0.0521.034−0.050
H5PP00.289 *0.8870.326
PI00.2411.1340.213
Goodness of Fit Indices; χ2 (df = 300, N = 206) = 459.051, p < 0.001, CFI = 0.955, TLI = 0.948, RMSEA = 0.051, SRMR = 0.048. Note: The estimation method is maximum likelihood. Significance level is * p < 0.05 (2-tailed).
Table 5. The results of assessing the structural model for interactions between GI and FTK.
Table 5. The results of assessing the structural model for interactions between GI and FTK.
HypothesisPathBβSEC.R.
H6GI*MC_FTK→PQ0.1270.2260.0472.700 **
H7GI*MC_FTK→BA0.0810.1380.0531.526
H8GI*MC_FTK→BS0.0980.1840.0472.094 *
H9GI*MC_FTK→PP0.0510.1030.0451.126
GI→PQ−0.122−0.0570.141−0.867
GI→BA0.1280.0570.1590.805
GI→BS0.1160.0570.1400.831
GI→PP0.2770.1460.1382.010 *
FTK→PQ0.0900.2580.0293.079 **
FTK→BA0.0700.1930.0332.124 *
FTK→BS0.0870.2640.0292.983 **
FTK→PP0.0370.1220.0281.325
Goodness of Fit Indices; χ2 (df = 86, N = 206) = 134.714, p < 0.001, CFI = 0.978, TLI = 0.969, RMSEA = 0.053, SRMR = 0.041; Squared Multiple Correlation; R2 on PQ = 0.191, R2 on BA = 0.093, R2 on BS = 0.169, R2 on PP = 0.065. Note: The estimation method is maximum likelihood. Significance levels are * p < 0.05 and ** p < 0.01 (2-tailed). B = unstandardized estimate; β = standardized estimate; SE = standard error; C.R. = critical ratio.
Table 6. The results of assessing structural model for fair trade handicraft brand equity and purchase intention.
Table 6. The results of assessing structural model for fair trade handicraft brand equity and purchase intention.
HypothesisPathBβSEC.R.
H10PQ→PI−0.095−0.1000.068−1.396
H11BA→PI−0.009−0.0100.059−0.148
H12BS→PI0.4940.4960.1014.906 ***
H13PP→PI0.6990.6330.0917.707 ***
H14UQ→PI0.5260.5720.0766.908 ***
Goodness of Fit Indices; χ2 (df = 141, N = 206) = 303.612, p < 0.001, CFI = 0.953, TLI = 0.943; RMSEA = 0.075, SRMR = 0.082; Squared Multiple Correlation; R2 on PI= 0.801, R2 on PP = 0.327. Note: The estimation method is maximum likelihood. Significance level is *** p < 0.001 (2-tailed). B = unstandardized estimate; β = standardized estimate; SE = standard error; C.R. = critical ratio.
Table 7. Confidence intervals of direct and indirect effects for purchase intention.
Table 7. Confidence intervals of direct and indirect effects for purchase intention.
PathDirect EffectIndirect Effect
B95% CIβ95% CI
PQ→PI−0.095 (−0.100)[−0.260, 0.026] a
[−0.254, 0.030] b
BA→PI−0.009 (−0.010)[−0.181, 0.167] a
[−0.192, 0.179] b
BS→PI0.494 (0.496) ***[0.313, 0.720] a
[0.314, 0.701] b
UQ→PI 0.368 (0.362) **[0.252, 0.521] a
[0.271, 0.468] b
PP→PI0.699 (0.633) **[0.468, 1.074] a
[0.496, 0.756] b
Note: The estimation method is maximum likelihood. Significance levels are ** p < 0.01 and *** p < 0.001 (2-tailed). B = unstandardized estimate; ( ) = standardized estimate; CI = confidence interval; a Unstandardized CI; b Standardized CI.
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Lee, E.; Zhao, L. Understanding Purchase Intention of Fair Trade Handicrafts through the Lens of Geographical Indication and Fair Trade Knowledge in a Brand Equity Model. Sustainability 2024, 16, 49. https://doi.org/10.3390/su16010049

AMA Style

Lee E, Zhao L. Understanding Purchase Intention of Fair Trade Handicrafts through the Lens of Geographical Indication and Fair Trade Knowledge in a Brand Equity Model. Sustainability. 2024; 16(1):49. https://doi.org/10.3390/su16010049

Chicago/Turabian Style

Lee, Eunmi, and Li Zhao. 2024. "Understanding Purchase Intention of Fair Trade Handicrafts through the Lens of Geographical Indication and Fair Trade Knowledge in a Brand Equity Model" Sustainability 16, no. 1: 49. https://doi.org/10.3390/su16010049

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