Analysis of the effects of store image,trust and perceived quality on consumption habits of textile products in the COVID-19 pandemic

Abstract

Consumer clothing presents behaviors defined by pre-established trends and patterns in contemporary societies, and in general the consumption of textile products follows this trend. However, as a result of the COVID-19 pandemic and the restrictions perpetuated as a consequence of it, the consumption of textile products has been affected throughout the world. Under this premise, the objective of this research is to analyze the effect of store images, trust and perceived quality on the habits of the textile consumer in the context of the COVID-19 pandemic, for which, firstly, a review of the literature was carried out regarding the variables of the habits of the textile consumer and their relationship with the store image, trust and perceived quality, for which documents from academic search engines were taken into account, such as Scopus, Web of Science, ResearchGate and Google Scholar. On the other hand, a survey was conducted among textile consumers in Ecuador. The measurement tool was completed by 500 participants. In this way, the survey was conducted virtually through Google Forms and through the use of IBM SPSS software. The sampling technique consisted of convenience sampling. For the specific case of this investigation, it was decided to opt for the use of 500 valid questionnaires. This allowed one to propose a model of structural equations based on constructs associated with reference investigations. The main results of this research confirmed that there is a positive impact of the image of the trusted establishment on the product, as well as a positive impact on the general perceived quality of consumption habits (comparison) and on the effect of the quality of perceived service in consumption habits (planning).

Keywords

Textile industry consumer behavior quality trust

The textile industry represents one of the most important markets worldwide, with a growth that has accelerated steadily over the last 150 years in the wake of the industrial revolution.¹ This industry is among the most profitable and essential within the consumer goods sector, accounting for more than 37% of industrial activity in Europe, and has an employment capacity of around 1.7 million people at the European level.²

The top five textile producing countries are China, India, Italy, Germany and Turkey. Globally, China is the largest exporter of textiles, making the Chinese textile industry the largest in the world in terms of total production and exports, followed by India, the second largest producer of textiles. According to Kumari,³ the Indian textile industry contributed 2% of gross domestic product (GDP), 12% of export earnings and 5% of world trade in textiles and garments in 2018–2019. The textile industry contributes 14% of the added value of the manufacturing sector. It is followed by the United Arab Emirates, which has a large share of exports in the textile and clothing sector.⁴

However, focusing on Ecuador, at the regional level, according to the Association of Textile Industries of Ecuador (AITE), the period from 2019 to 2020 showed encouraging data for the country, and the first half of the year closed with an increase of up to 5.8% in international sales of textile products, representing an economic value of 48.4 million USD. Of the total exports made that year, 64% corresponded to finished products, a category that until the previous year was less in demand. In this way, improvements were made in aspects of added value, quality and expansion of the commercial offer.^5,6 On the other hand, the Ecuadorian textile industry has substantial advantages over its potential competitors in the region, since the annual operating cost of these industries is 18% lower than the average of other countries in the region, such as Argentina, Chile, Costa Rica and Colombia.⁷ However, the COVID-19 pandemic has led to a break in the growth of the global textile industry.⁸ Factories were forced to halt production and the economic crisis resulting from the toughest restrictions forced employers in the sector to take extreme measures to survive, including layoffs or wage reductions.⁹ As Zhao and Kim¹⁰ point out, the pandemic has created uncertainties that forced clothing and textile companies to question their practices and to make urgent changes to help secure their future. In Ecuador, the textile industry currently accounts for approximately 6% of the industrial sector and contributes almost one percentage point of the national GDP. Moreover, according to the president of the AITE, its real importance lies in the strong interdependencies with 33 other sectors,⁹ with those activities related to final sales being precisely the ones most affected, with a 36% decrease in their operations during 2020. In this sense, specialists and researchers state that the effects of the crisis could be prolonged¹¹; for example, those linked to consumer habits that have an impact on retailing,¹² due among other reasons to the forced overexposure to online shopping.

In the current global context of economic recession and health concerns, it is particularly important to explore the determinants of textile consumer habits, more specifically those related to the revival of end-consumer sales. This is especially so when retail purchases of textiles in physical shops have declined dramatically.¹³ This issue is of particular relevance in contributing to the future revival of activity and to the design of appropriate measures by the stakeholders involved. Therefore, the work presented here is justified by the exceptional circumstances experienced by companies related to the textile sector described above and responds to the call for research by Ponte et al.,¹⁴ among others, to obtain additional empirical evidence for a comprehensive understanding of the problem. Previous works have shown how the quality of complementary services positively impacts on trust and has positive effects on satisfaction.^15,16 However, none of them has been conducted in a pandemic context. This research fills this gap, and aims to analyze the relationships between store image, trust and perceived quality with consumer habits for textile products in Ecuador.

Literature review

Textile purchasing decisions

The consumer purchase decision concerning fashion apparel has been studied. Although fashion always looks for novelty and uniqueness, fast and slow fashion consumers look for products that fit their own image.¹⁷

Mass customization is an alternative marketing technique that has been used by manufacturers and buyers to quickly meet the needs and wants of customers, thereby influencing their purchasing decisions. To this end, manufacturers and marketers must consider the level of consumer involvement as it influences purchase decision making.¹⁸

Likewise, quality has long been considered the most important element for customers in apparel appearance. However, special attention is required for 13-year-olds, as they are people who are in early adolescence and are an age group that overlaps with the consumer market of the 9–13 age group.¹⁹ suggested that early adolescent consumers do not have the experience, knowledge and cognitive skills that allow them to realistically assess the quality of clothing during the purchase decision (De Klerk y Tselepis, 2007). These authors suggest that early adolescent female consumers' expectations, evaluation and satisfaction are affected not only by functionality (i.e., contributing to comfort or sensory beauty) but also by emotional pleasure and cognitive meanings. Emotional needs are the most important quality dimension affecting consumer satisfaction.

Continuing with different profiles, it is stated that fast fashion consumers avoid regret by buying cheap clothes, but slow fashion consumers focus on the concept of quality over quantity. Achieving hedonism is possible through the shopping experience, store atmosphere, social experiences, bulk buying and product search for fast fashion shoppers. For both fast and slow fashion consumers, clothing style aligned with their self-image results in an essential factor influencing purchase or consumption.²⁰

However, fast and slow fashion consumers tend to differ in terms of utilitarianism, the former maximizing their utility by purchasing a larger quantity of clothing at a lower price, while the latter do so by purchasing high-quality clothing.

In this context, the stages of the consumer decision process for textile products tend to focus on utilitarianism, hedonism and style congruence or self-image.²¹

In representative numerical terms, a consumer who is satisfied with the perceived quality of the product will be 72 times more likely to purchase the garment than if they were dissatisfied with the garment. A consumer who is satisfied with the design of the product will be 22 times more likely to buy the garment than if they were not satisfied with it. If they are regular customers, they will be three times more likely to buy than if they are not. For every additional year of age that the consumer is older, the chances of purchase are multiplied by a factor of 1.02. According to the attribute classifications discussed at the beginning, intrinsic characteristics, such as design and quality, are positively valued by the consumer when finalizing the purchase process.^21,22 The company's efforts to position the brand and build customer loyalty also have an impact on the consumer's purchase decision.

The following is an analysis of the relationship between the variables discussed above.

Relationship between image and perceived quality

Brands are constantly evaluating consumer requirements since they constitute an opportunity to meet the needs and expectations of the customer. In the case of the textile industry, fashion is permanently updated and, as such, the consumer requires avant-garde garments, but that at the same time those that satisfy other needs associated with comfort and cost.^23,24

From the perspective of both users and creators, significant contributions are generated for decision making and, consequently, to achieve customer satisfaction. However, when comparing the requirements of the designer–users, the needs of the consumer identified by the designer, the purposes of design and the relationships between necessities and design, it was determined that users are more interested in accessible, fashionable and pleasant clothing. It was also established that designers consider that the most important consumer needs are functionality, absence of technical problems and accessibility.²⁵

In terms of design purposes, these focused on function and technical problem solving. Furthermore, it was determined that, while designers understood some of the users' needs, their design purposes did not always meet these consumer requirements.^25,26 Consequently, gaps are reported among user needs, consumer necessities identified by the designer and design purposes, which evidently suggests that the textile industry must rethink its interrelation processes between designers and consumers.

Currently, textile industries have focused on imperatively understanding consumer behavior, covering in this field the relationship between image and perceived quality.²⁷

Wydyanto and Hamdan²⁸ report that, in order to achieve the sustainability of brands in the textile industry, multiple aspects of service quality must be covered with excellence. However, it is evident that factors such as product image can influence the quality perception of consumers and potential customers.

In addition, Dagogo-Jack et al.²⁹ point out that if the image projected by a product does not respect the criteria of sustainability, fair trade and social and environmental responsibility, this could have a negative impact on the quality perceived by consumers and a consequent search for emerging markets that meet these needs.

Multiple studies agree that color has a great impact on product marketing. It was found that color influences consumer behavior in different fields of design products. Indeed, Motoki et al.³⁰ and Roggeveen et al.³¹ studied this behavior in terms of psychophysical reactions between color and consumer decisions, so that it was possible to establish that colors can be used as an effective marketing tool to impact customers' appetite, mood and perception of waiting time, among others.³²

The corporate image is perceived as the very quality of the products or services offered, so its constitution affects the perceived quality in categories related to the influence of trends that raise consumer expectations regarding a certain product or service.^33–35 In this context, image has a positive and significant effect on perceived quality.

The perception of quality in an organization may vary depending on the approach, so that it may refer to global aspects involving the entire business organization or specifically to aspects inherent to the service received by the consumer. Consequently, the following hypotheses are put forward.
Hypothesis 1a: image has a direct and positive influence on overall perceived quality.

Hypothesis 1b: image has a direct and positive influence on the perceived quality of customer service.

Relationship between image and consumer confidence

The image of companies in the textile sector is determined by the actions of the organization, such as the way it treats its employees, customers and society, while its effects are associated with consumer trust.³⁶ Likewise, Lin and Lu³⁷ determined that corporate image has a significantly positive influence on trust, revealing the need for and importance of a company to continuously improve its corporate image, in order that the image of a company or business sector is based on the impressions and perceptions that stakeholders have about the organization.³⁸

The results obtained by Flavián et al.³⁹ show that in distribution through traditional channels there are no significant differences in the intensity of the effect of image on trust. It should be noted that, based on the theory of consumer behavior, it is proposed that the corporate image has a significant but indirect impact on customer loyalty and trust in textile products.

In addition, Chen and Tseng⁴⁰ conducted a study in which they were able to find out users' perception of the brand value of an airline in Taiwan, demonstrating that image has an indirect effect on brand value and, consequently, on the trust that the consumer reflects concerning a certain product or brand.

It has also been determined that the image of textile organizations plays an important role in purchasing behavior habits, and brand trust also turned out to have a mediating role, although not entirely significant, in the relationship between image and purchasing behavior.⁴¹

In addition, customer confidence can be associated with various parameters, such as the product, including its qualities and advantages for the consumer, as well as the service provided by employees.

In view of the above, the following hypotheses are proposed.
Hypothesis 2a: image has a direct and positive influence on trust toward the product.

Hypothesis 2b: image has a direct and positive influence on trust toward employees.

Relationship between image and consumption habits

The image of a brand encompasses variables such as product quality, price perception and promotion that considerably affect consumption habits. Under this approach, Rodriguez-Colomo⁴² denotes that this relationship can be negative if the company's image does not correspond to contemporary social standards, an idea verified by authors such as Ziyad et al.,⁴³ stating that image does not directly influence purchase decisions or consumption habits, showing that it is totally mediated by brand trust. On the other hand, product information has a positive and significant effect on purchase decisions for textile products.

Flórez et al.⁴⁴ mention that industries and the market unfold in all possible contexts, which is why various manufacturing companies producing garments have positioned themselves in all sociocultural contexts. In particular, consumers' perceptions, reactions and habits change rapidly, since about 73% of brands lose relevance for consumers over time. For this reason, it is reported that brands whose corporate image reflects the company's commitment to a social mission have popular products, whose image is supported by messages of a social nature disseminated in traditional and emerging media, and advertising, with a significant effect on consumer trends and habits.⁴⁵ Similarly, it is reported that consumption habits can be addressed through the comparative and planning approach of consumers who decide to purchase a product and build brand loyalty.⁴⁶

Accordingly, the following hypotheses are proposed.
Hypothesis 3a: image has a direct and positive influence on comparative consumer habits.

Hypothesis 3b: image has a direct and positive influence on the planning of consumption habits.

Relationship between perceived quality and consumption habits

It is clear that, nowadays, producers and distributors are increasingly interested in understanding the purchasing behavior of consumers, and that these habits or trends in consumption are associated with various factors, including the consumer's perceived quality of clothing or apparel.^27,46,47

It is important to reference that the quality perceived by consumers in the textile sector with respect to a certain product or service can significantly influence consumption habits. This is corroborated by Nurkhasanah and Santoso,⁴⁸ who state that product quality, as well as promotion, influences purchase decisions. The product quality variable in purchase decisions has a positive and significant effect.

However, if the quality perceived by consumers is not as expected, this will generate highly negative effects on consumers' purchasing habits and, therefore, substantially alter their preferences based on one product over another. In addition, it is evident that manufacturers and distributors in the different commercial sectors have the responsibility to better understand consumer needs and expectations in order to develop appropriate marketing strategies, to make internal improvements in quality management and to ensure the creation of value, brand loyalty and consumer habits.

As mentioned above, customer-perceived quality and consumer habits can be approached from different angles using criteria associated with the consumer's approach. In any case, both variables can present a significant correlation to determine the consumer behavior for textile products.

Therefore, the following hypotheses are proposed.
Hypothesis 4a: overall perceived quality has a direct and positive influence on the comparison of consumption habits.

Hypothesis 4b: the perceived quality of customer service has a direct and positive influence on the comparison of consumption habits.

Hypothesis 4c: overall perceived quality has a direct and positive influence on the planning of consumption habits.

Hypothesis 4d: the perceived quality of customer service has a direct and positive influence on the planning of consumption habits.

Relationship between trust and consumption habits

The trust that consumers have in the quality and reputation of a given brand or textile product has a marked influence on consumption habits. In this context, Sánchez-Alzate and Montoya-Restrepo⁴⁹ highlight the main factors that affect consumer confidence, in such a way that consumers feel less fear and more predisposition to purchase when confidence is higher.

Consumer trust is defined as customer satisfaction with a product and business performance as an ability to generate profits. Given this, authors such as Castro-Fonseca,⁵⁰ Castelló-Martínez and Pino⁵¹ and Muñoz-Cubides⁵² highlight the importance of performing a quality analysis with respect to the trust of a brand rooted in factors such as customer satisfaction and business performance in the industry, demonstrating the high influence of customer trust on business performance, competitiveness and consumption habits. Although there is a correlation between these factors, it has been shown that the results were due to other factors, which were mostly statistically non-significant.⁵³

By defining trust and consumption habits as parameters of interest for the evaluation of behavior and consumption trends of textile products,⁵¹ it is important to analyze these variables from the different approaches that can be perceived by the consumer, since the focus based on the trust generated by the product, the trust produced by employees and the stages of analysis of consumption habits – comparison and planning – will promote the specific determination of the relationship between the variables commented on.

Indeed, the following hypothetical approaches are proposed.
Hypothesis 5a: trust toward the product has a direct and positive influence on the comparison of consumption habits.

Hypothesis 5b: trust toward employees has a direct and positive influence on the comparison of consumption habits.

Hypothesis 5c: trust toward the product has a direct and positive influence on the planning of consumption habits.

Hypothesis 5d: trust toward employees has a direct and positive influence on the planning of consumption habits.

In accordance with the aforementioned hypotheses, a model is proposed that addresses the relationships between the proposed constructs (Figure 1).

Methodology and data

Sample and measurements

The study was conducted among consumers of textile products in facilities in Ecuador. The measurement scales used were based on previous research. Thus, the scale on consumer habits is taken from the work of Rodríguez-Vargas.⁵⁴ To measure the image of consumers with respect to commercial premises, the proposals of Palacios-Florencio et al.,⁵⁵ Rust et al.⁵⁶ and Ryglova and Vajenerova⁵⁷ were used. The items that make up the consumer trust construct were based on the proposals of Ball et al.,⁵⁸ Palacios-Florencio et al.⁵⁵ and Swaen and Chumpitaz.⁵⁹ Finally, the perceived quality subscale has been adapted from the tools designed by Konuk⁶⁰ and Cronin et al.⁶¹

Fieldwork was conducted with a survey in March/April 2021 in De Prati, RM, and Pinto, Ecuador. The measurement tool was completed by a total of 500 subjects. In this way, the survey was carried out virtually through Google Forms and through the use of IBM SPSS software.

The sampling technique consisted of convenience sampling. Convenience sampling is a non-probability sampling technique where subjects are selected given the convenient accessibility and proximity of the subjects. For the specific case of this investigation, it was decided to opt for the use of 500 valid questionnaires.

According to Blazquez-Sánchez,⁶² the validation process is dynamic and continuous, and a tool with stronger psychometric properties has internal consistency, which provides the basis for its use in different cultures, populations and subjects.

Validation demonstrates that a tool measures what it claims to measure, demonstrating that it is fit for purpose as intended by the developers. All the questionnaires received were useful, so they are considered valid as they did not offer missing values in the items of the constructs considered in the study.

The questions were elaborated from the consumer behavior models proposed by Naumov⁶³ based on research in the area of economic, social and psychological theories and empirical evidence regarding consumer decision making when purchasing goods.

The data in Table 1 indicate that the number of women is twice that of men. The majority of the subjects are employed (40.8%), which is consistent with the fact that in terms of age more than 50% of the subjects are between 35 and 54 years old. Almost one third of the respondents are either married or living with a partner. More than 60% of the consumers surveyed live in homes with three or more people. Almost all of the respondents are Ecuadorian.

Table 1.
Sample profile

CRITERIA LEVELS %

Gender Men 36.6

Women 63.4

Age 18–25 5.4

26–34 9.0

35–44 31.2

45–54 24.2

55–64 27.8

65 or more 2.4

Laboral activity Student 6.4

Self-employed 23.2

Employed 40.8

Unemployed 12.2

Retired 6.8

Housekeeper 10.6

Marital status Single 9.6

Married 35.2

Couple 28.2

Separated 24.8

Widowed 2.2

Household Individual 0.8

2 members 23.8

3 members 27.8

4 members 30.8

5 or more members 17.2

Country Ecuador 90.4

Others 9.6

Data analysis

For the analysis of the proposed model and the proposed hypotheses, a variance-based technique,⁶⁴ specifically the partial least squares (PLS) technique, was used. The use of this technique for this study has been executed in line with the suggestions of Carrión et al.,⁶⁵ Roldán and Sánchez-Franco⁶⁶ and Sarstedt et al.⁶⁷ We used the PLS SmartPLS 3.3.6 software.⁶⁸

Given that the measurement instrument used is self-reported, it is necessary to test for the existence or not of common variance among the latent variables of the model. In this sense, all the steps identified by the literature have been followed, both in the design of the questionnaire^69,70 and Harman's test (1976) for the existence of common influence. The latent variables incorporated in the model, made up of 41 items, are grouped into a total of 19 components or factors, of which the largest explains 4.08% of the variance.⁷¹ Therefore, following Podsakoff and Organ,⁷² it should be noted that there is an absence of a common influence factor among these items.

As is usual in the analysis of variance-based structural equation model (SEM), both the measurement model and the structural model were validated.

Analysis of results

Our proposal includes four variables, modeled as composite factors with a formative format (mode B). This assumes that the variables are formed by a combination of different indicators, although since they are measured in mode B, their components are not allowed to correlate with each other.

The evaluation of the measurement model, given that all the constructs are modeled in mode B, involves considering the following aspects: the collinearity of the indicators and analysis of the external weights of each indicator in its corresponding construct. The final results of this analysis are given in Table 2. This leads to the recognition of six latent variables in the model. The image variable is made up of four elements. The construct trust has been further divided into two variables, each with two indicators: trust toward product and trust toward shopping staff. Another variable is global perceived quality, made up of four items. Two indicators make up the customer service perceived quality construct. The next variable in the model is consumption habits: planning is made up of five items of the questionnaire. Finally, the construct consumption habits: comparison is formed by five components or items.

Table 2.
Reliability measures

Weight VIF

IMAGE

I1. The interior of the commercial premises selling textile or clothing products is appropriate for its category. 0.278 1.005

I3. I can clearly distinguish the establishments of this chain of textile or clothing stores 0.872 1.003

I5. I pay attention to the information sent to me from this textile or clothing store 0.365 1.003

I6. This textile and apparel retailer is known for its good social behavior 0.208 1.002

TRUST TOWARD PRODUCT

M2. The services of this textile and clothing store are a guarantee of quality 0.939 1.015

M4. When this textile or apparel retailer offers an offer, this offers benefits me 0.247 1.015

TRUST TOWARD SHOPPING STAFF

M1. The service I received in this textile or clothing store makes me feel safe and secure 0.418 1.000

M5. This textile or apparel retailer treats me honestly when I make a purchase 0.901 1.000

GLOBAL PERCEIVED QUALITY

L3. The textile products or garments of this store are of very good quality 0.306 1.008

L8. The employees of this textile and clothing store are polite and respectful 0.292 1.008

L11. The facilities of this textile and clothing store are clean and tidy 0.706 1.007

L12. The facilities of this textile or apparel retail store provide an environment free of hazards and risks 0.518 1.010

CUSTOMER SERVICE PERCEIVED QUALITY

L5. The employees of this textile and clothing store are willing to provide a timely service 0.525 1.000

L10. The employees of this textile and clothing retail store make every effort to understand my needs 0.843 1.000

CONSUMPTION HABITS: PLANNING

G4. I select textile products or garments according to their price 0.598 1.009

G8. I inquire about the warranty period for textile or apparel products 0.500 1.003

G10. I buy textile products or garments in the street trade 0.447 1.011

G11. I plan textile or apparel purchases in advance 0.160 1.008

G16. If I apply for credit, I ask about interest rates and the increase in the final price of the textile or apparel product 0.332 1.006

CONSUMPTION HABITS: COMPARISON

G2. I select textile products or garments according to their quality 0.346 1.003

G3. To allocate my money I order textile products or garments before purchasing 0.279 1.005

G12. I read the labels of textile products or garments to know the characteristics and care required 0.456 1.020

G14. I examine in detail the textile products or garments that I buy 0.331 1.022

G17. If I buy on credit, I compare interest rates at different textile or clothing stores 0.626 1.008

Regarding the validation of the structural model, the steps suggested in the literature⁶⁴ have been carried out. Firstly, the absence of multicollinearity between the antecedent variables of each endogenous construct was checked. According to Hair et al.,⁶⁴ the absence of multiple collinearities occurs when the values of the variance inflation factor (VIF) indicator are less than 5. This condition is met for the model analyzed. In our case this condition is met, since all the values are lower than the cut-off point. In fact, in no case is the value 1.1 exceeded.

To test the hypotheses suggested in the model and the corresponding assessment of their significance and relevance, the bootstrapping technique was used (with 5000 subsamples). The results of this analysis are shown in Table 3. Although six of the fourteen hypotheses are significant, only three are confirmed: the positive effect of the image of the entity on trust toward the product; the positive incidence of global perceived quality on consumption habits: comparison; and, finally, the effect of customer service perceived quality on consumption habits: planning.

Table 3.
Hypotheses test

Hypotheses Suggested effect Path coefficient (β) T-value Confidence interval

5.0% 95.0%

H1A: image → global perceived quality (+) 0.075 ns 1.066 –0.050 0.183

H1B: image → customer service perceived quality (+) –0.161*** 3.165 –0.243 –0.083 Sig

H2A: image → trust toward product (+) 0.122* 1.971 0.017 0.217 Sig

H2B: image → trust toward shopping staff (+) –0.036 ns 0.522 –0.146 0.083

H3A: image → consumer habits: comparison (+) 0.078 ns 0.988 –0.073 0.189

H3B: image → consumer habits: planning (+) –0.194 2.833 –0.293 –0.090 Sig

H4A: global perceived quality → consumer habits: comparison (+) 0.189* 2.427 0.052 0.290 Sig

H4B: customer service perceived quality → consumer habits: comparison (+) –0.060 ns 0.703 –0.175 0.119

H4C: global perceived quality → consumer habits: planning (+) –0.011 ns 0.142 –0.132 0.127

H4D: customer service perceived quality → consumer habits: planning (+) 0.122* 2.197 0.029 0.206 Sig

H5A: trust toward product → consumer habits: comparison (+) 0.090 ns 1.223 –0.046 0.193

H5B: trust toward shopping staff → consumer habits: comparison (+) 0.065 ns 0.646 –0.123 0.213

H5C: trust toward product → consumer habits: planning (+) –0.102* 1.738 –0.193 –0.001 Sig

H5D: trust toward shopping staff → consumer habits: planning (+) 0.071 ns 1.179 –0.037 0.162

p < 0.001; p < 0.01; p < 0.05; t (0.05; 4999) = 1.64791345; t (0.01; 4999) = 2.333843952; t (0.001; 4999) = 3.106644601. Sig. denotes a significant direct effect at 0.05.

The model estimation is presented in Figure 2.

Figure 1.
Proposed model.

Figure 2.
Model estimation.

The predictive power of any model can be evaluated by the coefficient of determination (R²). This coefficient indicates the construct’s quantity of variance explained by this endogenous construct’s predictor variables (Table 4).

Table 4.
Effect on the endogenous variables

R ² Q ² Direct effect Correlation Variance explained (%) Effect size (f ²)

Global perceived quality

H1A: image 0.006 –0.002 0.075 0.075 0.56 0.006

Customer service perceived quality

H1B: image 0.026 0.010 –0.161 –0.161 2.59 0.027

Trust toward product

H2A: image 0.015 0.005 0.122 0.122 1.49 0.015

Trust toward shopping staff

H2B: image 0.001 –0.003 –0.036 –0.036 0.13 0.001

Consumer habits: comparison

H3A: image 0.062 –0.002 0.078 0.110 0.86 0.006

H4A: global perceived quality 0.189 0.193 3.65 0.038

H4B: customer service perceived quality –0.060 –0.071 0.43 0.004

H5A: trust toward product 0.090 0.100 0.90 0.008

H5B: trust toward shopping staff 0.065 0.052 0.34 0.005

Consumer habits: planning

H3B: image 0.083 0.009 –0.194 –0.229 4.44 0.039

H4C: global perceived quality –0.011 –0.028 0.03 0

H4D: customer service perceived quality 0.122 0.159 1.94 0.016

H5C: trust toward product –0.102 –0.128 1.30 0.011

H5D: trust toward shopping staff 0.071 0.087 0.62 0.005

The proposed model, based on the indications of Hair et al.,⁶⁴ is only capable of explaining the various latent variables considered at low levels, where the R² values do not reach 0.15. So, the proposed model, based on the indications of Hair et al. (2014), only explains the various variables considered at low levels, the R2 values do not reach 0.15. However, it could be acceptable due to (Moksony & Heged, 1990).⁷³ In this sense, the explanatory power of the model is probably attenuated for the same reason, since the greatest variance explained is 4.44%.

The explanatory capacity of the variables in the model is conspicuous, since the highest level of variance explained is 4.44% (consumption habits: planning). Furthermore, the effect sizes are, according to Cohen's (1988) f ² statistic, minimal.⁷⁴ The model does not show significant significance values, as three of the constructs reach negative Q² values and another three (customer service perceived quality, trust toward product and consumption habits: planning, raised to non-significance).

Discussion and conclusions

Understanding the factors that influence consumer behavior is one of the most widely used techniques in the contemporary marketing of textile products.^75,76 However, it is evident that there is currently a deficit of research on consumer behaviors for textile products in both the economic and marketing aspects. Therefore, it is essential to address the trends, characteristics and consumer behavior for textile products and analyze how the general theories of consumer behavior are applied and what are the underlying causes that cause a particular trend. This is even more so in a context affected by an unprecedented crisis.

The results presented above highlight, firstly, that the consumption habits of textile product users, both in terms of comparison and planning, are poorly explained by the variables proposed in our model. In this regard, the research of Lee and Chen-Yu⁷⁷ reports that the comparative aspect is an integral part of the apparel consumer habit, considering as significant responses to the savings perceived by the customer. For their part, Aus et al.⁷⁸ refer to circular fashion strategies involving upcycling processes that positively influence the textile consumer's planning ability. Therefore, although this research did not determine a causal relationship between these aspects and the variables studied, the new trends of environmentally and economically sustainable textile consumption are highlighted as response mechanisms to value and promote the planning and comparison capacity of the user.

In addition, it was established that our results indicate the existence of certain effects on consumer habits. Regarding their planning, negative effects of the image of the establishment and the confidence of users toward the product together with a positive effect of the quality of service toward the consumer are found. However, De and Singh⁷⁹ conclude that the image of the establishment and consumers' trust of textile products causes a positive effect on consumption habits from the planning perspective. On the other hand, with respect to consumption habits in their comparison aspect there is a positive influence of overall service quality. The quality of service is a key element in the apparel marketing industry, due to the fact that in general terms the user compares the service offered in a certain place with that received in another commercial establishment, which triggers a positive consequence of the quality for the consumption habit under this approach.⁷⁷

Thirdly, the findings also show that store image has significant effects on both perceived customer service quality (negative) and trust in the product (positive). Store image is a perceptual factor in consumers that evidently affects their purchasing decisions; the research by Bhakuni et al.⁸⁰ mentions significant relationships between this variable and perceived trust and quality. However, it is mentioned that there may be differences in the type of correlation, since aspects such as perceived quality and trust in the product also depend on the consumer profile, the cultural and commercial context and the importance of the textile sector in the economy.

The consumer of textile products has been extensively characterized, demonstrating that there are several factors that affect both positively and negatively the purchase decisions reflected in this commercial segment. It is evident that sociodemographic characteristics can have an important influence on consumer behavior. In particular, perceived quality can be a mediating element between consumption habits and factors such as the image of the brands offering textile products. Likewise, aspects such as image, trust and perceived quality show a tendency toward a direct and positive effect on consumer habits of textile products, which can define behavioral patterns that are useful in the positioning strategies of products, brands and business models of the Ecuadorian textile industry. In this way, the development of long-term policies is essential for Latin America to be able to compete in the international market and promote the structural transformation of textile production at the national level.

In general terms, the proposed model will facilitate the understanding of consumer behavior in the textile sector, significantly streamlining the processes of attracting attention and sales.

Limitations and future lines of research

Although this study adds to the knowledge base on textile consumer behavior in the Ecuadorian pandemic context, it is not without limitations. The first has to do with the exploratory nature of the work. At the time of the questionnaire, some consumers could still perceive a risk of spreading the virus, even avoiding physical stores. This could have affected the results, given that the successive waves of the virus have involved drastic changes in such sensitive aspects as shopping behavior and the perception of the store. Thus, cross-sectional quantitative research provides useful guidance in the absence of studies addressing the effect of COVID-19. As another limitation, this study does not consider the perceived quality of customer service, which would normally be part of the “overall perceived quality.”

In the future, it would be advisable to open lines of research that involve replicating the study to capture contextual effects, and changes resulting from the pandemic, in other countries and cultures. Secondly, from the methodological point of view, previously applied scales were used. However, some of the constructs used require future validation in order to design specific measurement instruments for the store image, perceived quality and trust. Thirdly, the research was conducted on the basis of a relatively large convenience sample, which could affect the generalizability of the results. Finally, it should be noted that the proposed theoretical model represents a novelty and is susceptible to extension and refinement. Therefore, future research should incorporate elements of singular relevance, such as the buyer's personality traits, as well as their degree of risk aversion. In general terms, the textile market can be subdivided into several sectors; however, in all cases an extensive and complex supply chain is generated, which can have an impact on consumption trends. Another factor that can affect consumer behavior is social and environmental sustainability, as this industry faces multiple challenges in terms of environmental pollution and social responsibility.^81,82

CRITERIA	LEVELS	%
Gender	Men	36.6
Women	63.4
Age	18–25	5.4
26–34	9.0
35–44	31.2
45–54	24.2
55–64	27.8
65 or more	2.4
Laboral activity	Student	6.4
Self-employed	23.2
Employed	40.8
Unemployed	12.2
Retired	6.8
Housekeeper	10.6
Marital status	Single	9.6
Married	35.2
Couple	28.2
Separated	24.8
Widowed	2.2
Household	Individual	0.8
2 members	23.8
3 members	27.8
4 members	30.8
5 or more members	17.2
Country	Ecuador	90.4
Others	9.6

	Weight	VIF
IMAGE
I1. The interior of the commercial premises selling textile or clothing products is appropriate for its category.	0.278	1.005
I3. I can clearly distinguish the establishments of this chain of textile or clothing stores	0.872	1.003
I5. I pay attention to the information sent to me from this textile or clothing store	0.365	1.003
I6. This textile and apparel retailer is known for its good social behavior	0.208	1.002
TRUST TOWARD PRODUCT
M2. The services of this textile and clothing store are a guarantee of quality	0.939	1.015
M4. When this textile or apparel retailer offers an offer, this offers benefits me	0.247	1.015
TRUST TOWARD SHOPPING STAFF
M1. The service I received in this textile or clothing store makes me feel safe and secure	0.418	1.000
M5. This textile or apparel retailer treats me honestly when I make a purchase	0.901	1.000
GLOBAL PERCEIVED QUALITY
L3. The textile products or garments of this store are of very good quality	0.306	1.008
L8. The employees of this textile and clothing store are polite and respectful	0.292	1.008
L11. The facilities of this textile and clothing store are clean and tidy	0.706	1.007
L12. The facilities of this textile or apparel retail store provide an environment free of hazards and risks	0.518	1.010
CUSTOMER SERVICE PERCEIVED QUALITY
L5. The employees of this textile and clothing store are willing to provide a timely service	0.525	1.000
L10. The employees of this textile and clothing retail store make every effort to understand my needs	0.843	1.000
CONSUMPTION HABITS: PLANNING
G4. I select textile products or garments according to their price	0.598	1.009
G8. I inquire about the warranty period for textile or apparel products	0.500	1.003
G10. I buy textile products or garments in the street trade	0.447	1.011
G11. I plan textile or apparel purchases in advance	0.160	1.008
G16. If I apply for credit, I ask about interest rates and the increase in the final price of the textile or apparel product	0.332	1.006
CONSUMPTION HABITS: COMPARISON
G2. I select textile products or garments according to their quality	0.346	1.003
G3. To allocate my money I order textile products or garments before purchasing	0.279	1.005
G12. I read the labels of textile products or garments to know the characteristics and care required	0.456	1.020
G14. I examine in detail the textile products or garments that I buy	0.331	1.022
G17. If I buy on credit, I compare interest rates at different textile or clothing stores	0.626	1.008

Hypotheses	Suggested effect	Path coefficient (β)	T-value	Confidence interval
H1A: image → global perceived quality	(+)	0.075 ns	1.066	–0.050	0.183
H1B: image → customer service perceived quality	(+)	–0.161***	3.165	–0.243	–0.083 Sig
H2A: image → trust toward product	(+)	0.122*	1.971	0.017	0.217 Sig
H2B: image → trust toward shopping staff	(+)	–0.036 ns	0.522	–0.146	0.083
H3A: image → consumer habits: comparison	(+)	0.078 ns	0.988	–0.073	0.189
H3B: image → consumer habits: planning	(+)	–0.194**	2.833	–0.293	–0.090 Sig
H4A: global perceived quality → consumer habits: comparison	(+)	0.189***	2.427	0.052	0.290 Sig
H4B: customer service perceived quality → consumer habits: comparison	(+)	–0.060 ns	0.703	–0.175	0.119
H4C: global perceived quality → consumer habits: planning	(+)	–0.011 ns	0.142	–0.132	0.127
H4D: customer service perceived quality → consumer habits: planning	(+)	0.122*	2.197	0.029	0.206 Sig
H5A: trust toward product → consumer habits: comparison	(+)	0.090 ns	1.223	–0.046	0.193
H5B: trust toward shopping staff → consumer habits: comparison	(+)	0.065 ns	0.646	–0.123	0.213
H5C: trust toward product → consumer habits: planning	(+)	–0.102*	1.738	–0.193	–0.001 Sig
H5D: trust toward shopping staff → consumer habits: planning	(+)	0.071 ns	1.179	–0.037	0.162

	R ²	Q ²	Direct effect	Correlation	Variance explained (%)	Effect size (f ²)
Global perceived quality
H1A: image	0.006	–0.002	0.075	0.075	0.56	0.006
Customer service perceived quality
H1B: image	0.026	0.010	–0.161	–0.161	2.59	0.027
Trust toward product
H2A: image	0.015	0.005	0.122	0.122	1.49	0.015
Trust toward shopping staff
H2B: image	0.001	–0.003	–0.036	–0.036	0.13	0.001
Consumer habits: comparison
H3A: image	0.062	–0.002	0.078	0.110	0.86	0.006
H4A: global perceived quality			0.189	0.193	3.65	0.038
H4B: customer service perceived quality			–0.060	–0.071	0.43	0.004
H5A: trust toward product			0.090	0.100	0.90	0.008
H5B: trust toward shopping staff			0.065	0.052	0.34	0.005
Consumer habits: planning
H3B: image	0.083	0.009	–0.194	–0.229	4.44	0.039
H4C: global perceived quality			–0.011	–0.028	0.03	0
H4D: customer service perceived quality			0.122	0.159	1.94	0.016
H5C: trust toward product			–0.102	–0.128	1.30	0.011
H5D: trust toward shopping staff			0.071	0.087	0.62	0.005

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Karen Estefanía Zambrano Roldán

Javier Cabeza Ramírez

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