Revista Industrial Data 26(1): 7-31 (2023)
ISSN: 1560-9146 (Impreso) / ISSN: 1810-9993 (Electrónico)
Physical Attributes of Fashion Apparel that Influence the Purchase Intention of 20 to 24-Year-Old University Women in Lima
Jorge Hugo Omar Arroyo Salazar[1]
Carlos Antonio Quispe Atúncar[2]
Nancy Elizabeth Barreda de Miranda[3]
Design and Technology
Submitted: 26/01/2022 Accepted: 30/01/2023 Published: 17/07/2023
DOI: https://doi.org/10.15381/idata.v26i1.22129
The objective of this research is to determine whether the physical attributes of fashion apparel significantly affect the purchase intention of 20 to 24-year-old university women in Lima, Peru. Based on the answer to this question, product development activities oriented at satisfying this consumer are more assured, because a producer can direct resources to key activities in order to obtain competitive products. For this reason, an applied study following a correlational approach was conducted, in which the statistical method was used according to the multiple regression test. Data collection was carried out using the survey technique in the study unit. The analysis of the responses leads to measuring the influence that certain factors have on purchase intention, ranking physical attributes on a scale of importance, and concluding that physical attributes significantly influence purchase intention.
Keywords: physical attributes, purchase intention, product development, fashion apparel, theory of planned behavior.
The global fashion industry’s sales are immense. The apparel market is diverse and competitive. In 2019, Peru exported US$274,976,022 (Asociación de Exportadores [ADEX], 2020) in fashion apparel. According to the Sociedad Nacional de Industrias (SNI, 2021), Peru’s textile and apparel sector generates more than 398,000 formal jobs, meeting domestic and foreign market requirements. This sector represents 6.4% of the country's manufacturing GDP. There has been a decline in this sector in recent years due to the COVID-19 pandemic. To increase sales in domestic and international markets, it is necessary to determine consumers’ aesthetic, physical, and emotional needs and interpret them correctly. The low competitiveness of the Peruvian fashion sector stems from several factors: weak development of productive forces, technological backwardness, and inadequate interpretation of customer needs, among others. Product development cycles are variable and difficult to control.
If product development takes a long time, the competition anticipates new products, and thus losses are incurred. This study contributes to the advancement of knowledge because it allows for an understanding of the perception of Lima consumers of a fashion product. The results provide guidance to the producer to focus on the most important qualities of the products to stimulate purchase intention. It is of interest to companies that manufacture garments. The objective of the research study is to determine the importance of the physical attributes of fashion apparel that influence the purchase intention of 20 to 24-year-old women in Lima. The theory of planned behavior (TPB) is used to determine the influences of the three physical attributes on purchase intention. No similar studies have been conducted using this method. Knowing the importance of the attributes allows an understanding of consumer preferences. The results are valuable because they allow the allocation of resources to strengthen the most important qualities of the product, thus favoring producers, who are more likely to invest in a new product line.
The first research on physical attributes sought to determine the quality attributes perceived by the customer. Abraham-Murali and Littrell (1995) compiled a list of attributes of fashion apparel (see Tables 1 and 2). Hines and O’Neal (1995) and Forsythe et al. (1996) conducted research that sought to understand the intrinsic characteristics of fashion products; they conducted studies to understand product quality characteristics. In contrast, De Klerk and Lubbe (2004, 2008) studied the importance of aesthetics in determining the quality of fashion garments of female consumers in the purchase decision. They found that the sensory, emotional and cognitive dimensions of aesthetic experience play a major role when female consumers determine the quality of products during the purchase decision. The product’s design and materials should relate to the aesthetic dimension. Color and texture are especially of great importance in the aesthetic experience. O’Cass (2004) studied the effects of materialism, self-image, and product-image congruency on consumers’ involvement in fashion. He examined purchase decision involvement, subjective fashion knowledge, and consumer confidence. The results indicated that involvement in fashion apparel is significantly affected by the degree of materialism, gender, and age of the consumer.
As for purchase intention, De Cannière et al. (2009) compared the Relationship Quality model (RQ) and the Theory of Planned Behavior, concluding that the TPB is a better predictor of consumer behavior. The explanation is that the attitudes measured by it are closer in time than the quality evaluations made by the customer, as defined in the theory of the quality relationship. Likewise, Vieira (2010) made a study on the causes that influence the purchase decision-making of fashion products and reached the following conclusions: materialism does not determine the purchase, both men and women have the same predisposition to buy, younger people have more predisposition to buy than older people, knowledge of fashion products also increases the predisposition to buy, and the perception of commitment to fashion also increases willingness to buy.
Torres and Padilla (2013) measured the intention to purchase consumable goods from the market via the logistic regression model. The products studied were soft drinks, chewing gum, and beer. They concluded that this model is adequate to measure and, at the same time, to pose a predictive equation on purchase intention. For Kotler and Armstrong (2017), the sequence of the customer decision comprises the following stages: understanding the customer’s need, locating the data, comparing options, making a purchase choice, and taking action after the purchase. It should be noted that the acquisition process starts before the actual purchase and continues for quite some time afterward; therefore, sales experts need to focus on the entire buying process, not just on the final stage of the purchase. During the testing phase, the customer rates the brands and forms purchase intentions. Generally, they would choose to purchase the brand of their choice; however, two factors may come between the purchase intention and the purchase decision. The attitude of others is the first factor. People’s decisions are greatly influenced by what the people around them may think. For instance, if two cars are to be compared, such as a luxury car and an economy car, if someone who has influence over another person thinks that the price of the luxury car is too high, then the buyer will most likely take this opinion into account and decide not to buy the luxury car.
In addition, López and Terán (2018) proposed and evaluated an instrument to measure the variables involved in the TPB model, including attitude, subjective norm, perceived behavioral control, and purchase intention, related to organic products for agriculture. Instruments were compiled and a questionnaire was administered via e-mail, using the Google Drive format. The final results demonstrated the validity of the instrument within acceptable values. The authors state that this instrument can be used by companies to obtain empirical information that may serve to make decisions, as well as to back up and support some proposed ideas and theories about purchase intention, which have not been evaluated in the field of organic products for agriculture.
The theory of planned behavior developed by Ajzen (2020) has been widely applied to the prediction and change of behavior, including behavior related to the use of technology. He briefly describes the theory, answering a number of issues and questions that have been raised regarding the TPB.
Among the issues discussed are the difference between TPB and the theory of reasoned action, perceived behavioral control versus self-efficacy, the difference between perceived behavioral control and locus of control, the possibility of including additional predictors in TPB, non-availability of standard TPB questionnaires, predicting behavior in a choice situation, the intention-behavior gap, and a comparison of the technology acceptance model to the TPB. (Ajzen, 2020, p. 134)
Physical attributes. For Abraham-Murali and Littrell (1995), intrinsic attributes are physical or chemical characteristics of the product related to its function; if modified, they alter the intrinsic characteristics of the product. These attributes are related to the material and technical characteristics of the product and are important because their qualities represent the quality of the item. Attributes are a property or set of properties that determine the ability of the product to satisfy the customer’s demands. They are the product itself. They are the qualities of products perceived by consumers during purchase or use. Abraham-Murali and Littrell (1995) compiled a composite list of attributes of fashion apparel by sorting them into conceptual categories and dimensional levels and examined them in ways most useful to retail marketers. The attributes determined are shown in Tables 1 and 2 below.
Table 1. Model of Unidimensional and Multidimensional Clothing Attributes.
Clothing Unidimensional Attributes |
||||
Physical Appearance |
Physical Performance |
|||
Fiber content |
Fabric shrinkage |
|||
Fiber weight |
Fabric hang |
|||
Solid color |
Fabric stretch |
|||
Pattern |
Fabric wrinkle |
|||
Plaids match |
Fabric soil |
|||
Wide hem |
Fabric itchy |
|||
Seam stitch |
Fabric pill |
|||
Casing |
Ironing marks |
|||
Collar size |
Color fade |
|||
1 or 2-piece |
Washable |
|||
Neckline style |
Dry clean |
|||
Waist finish |
Cost/time care |
|||
Garment length |
Stain removal |
|||
Sleeve length |
Ironing |
|||
Extrinsic |
Seams strong |
|||
Price |
Collar fraying |
|||
Store type |
Trims breakage |
|||
Care label |
Buttonhole size |
|||
Quick service |
Garment shape |
|||
Wide selection |
------------- |
|||
Sales person |
------------- |
|||
Clothing Multidimensional Attributes |
||||
Physical Appearance |
Physical Performance |
Expressive |
Extrinsic |
|
Fabric structure |
Fabric soft |
Versatility |
Global price |
|
Trim coordinates |
Fabric warmth |
Compliments |
Prestige |
|
Tactile effect |
Fabric look |
Style & body type |
Familiarity |
|
Garment grain |
Global fabric |
Color & personal features |
Store presentation |
|
Garment finish |
Easy care |
Appropriate for age/person |
Convenience |
|
Global construction |
Global workmanship |
Makes me look… |
Global services |
|
Distinctive features |
Garment fit |
Global look |
Value for money |
|
Uncluttered style |
Garment is easy to put on/take off |
Unusual/new |
Satisfaction |
|
Top & bottom appropriate |
Garment features |
Fun/adventurous |
Brand name |
|
Accessories coordinate |
Durability |
Global individuality |
Investment |
|
Interaction of fabric & style |
Garment comfort |
Appropriate for occasion |
Promotional campaign |
|
Classic vs. fashionable style |
------------- |
Global lifestyle |
------------- |
|
Global style |
------------- |
Coordinates |
------------- |
|
------------- |
------------- |
Confidence |
------------- |
|
Source: Abraham-Murali & Littrell (1995).
Brown and Rice (2001) state that apparel quality has two dimensions: a physical dimension, and a performance dimension. Given that physical features determine product performance consumers typically select products according to the physical features that they believe will cause a specific performance. It is unclear, however, whether consumers have adequate knowledge as to which physical features are likely to bring about specific behaviors. In many cases, the knowledge is practically non-existent.
In order to have a more synthesized understanding of the intrinsic attributes, we propose to group some of them into three basic dimensions: shape, mechanical properties of fabric, and color, based on the classification by Abraham-Murali and Littrell, as follows (Table 2):
Table 2. Model of Unidimensional and Multidimensional Clothing Attributes.
Shape |
Mechanical Properties of Fabric |
Pattern |
Fiber content |
Wide hem |
Fiber weight |
Seam stitch |
Fabric structure |
Casing |
Tactile effect |
Collar size |
Fabric grain |
1 or 2-piece |
Fabric shrinkage |
Neckline style |
Fabric hang |
Waist finish |
Fabric stretch |
Garment length |
Fabric wrinkle |
Sleeve length |
Fabric soil |
Trim coordinates |
Fabric itchy |
Garment finish |
Fabric pill |
Global construction |
Ironing marks |
Distinctive features |
Color fade |
Uncluttered style |
Washable |
Top & bottom appropriate |
Dry clean |
Accessories coordinate |
Stain removal |
Interaction of fabric & style |
Ironing |
------------- |
Collar fraying |
COLOR |
Fabric soft |
Solid color |
Fabric warmth |
Plaids match |
Fabric look |
------------- |
Global fabric |
------------- |
Easy care |
------------- |
Durability |
Source: Prepared by the authors based on Abraham-Murali & Littrell, 1995.
This classification was used as the basis for this research study, allowing us to analyze variables and their relationships in detail.
Garment Shape. A garment’s shape enables the fit of the product, achieving a particular silhouette, style, or look. The proper use of fabric grain, carefully chosen according to the requirements of the patterns, and support accessories that help a garment maintain its shape distinguish a high-quality garment from a low-quality one. Attention to shape and support is important in all garments, especially in tailored clothing (Brown & Rice, 2001, p. 114).
Mechanical Properties of Fabric. The mechanical properties of a fabric determine its performance. A single physical characteristic is not responsible for its performance; rather, the interaction of all physical dimensions of a fabric determines it. Understanding how they influence aesthetics, utility, durability, and cost is important to evaluate the components and quality of the fabric. These features include its fibers and yarns, the structure of the fabric, its weight, its strength, and how it is dyed, printed, and finished. Although the fabric is not the sole determinant of a garment’s quality, it is a critical ingredient (Brown & Rice, 2001, p. 174).
Color. “Color is critical to consumers when considering which garment to purchase” (Brown & Rice, 2001, p. 182). Likewise, Kodžoman et al. (2022) state that people use colors to express their social identity, hierarchy, emotions, political attitude, personal identity, self-image, and aesthetic taste. Clothing choices are a statement, and fashion is related to the alter-ego: who the wearer wants to be at the time. Part of the meaning and communication of fashion can be explained in terms of color in the psychology of clothing. Color is an important factor in the visual perception of products, as well as in brand recognition. It is critical for designers to understand consumer color preferences as part of an effective design plan.
Purchase Intention (PI). Consumer purchase intention is constructed by attitude, customer rating, and extrinsic factors (Ajzen, 2020). To Chu and Lu (2007), purchase intention is the degree to which consumers would like to purchase a product in the future; it is usually related to attitudes, behavior, and perceptions (Mirabi et al., 2015). Purchase intention comes from the prospective buyers’ knowledge about the level of service, product features, and other subjective variables that come from their perception, which define the customer's interest in purchasing.
According to Ajzen (2020), intentions are indicators of the extent to which consumers are willing to engage in a particular behavior; they also indicate the efforts consumers make to perform a given behavior. Ajzen (1991) proposed the TPB shown in Figure 1 below.
Figure 1. Theory of Planned Behavior.
Source: Ajzen (1991).
According to this theory, the following variables influence purchase intention:
Purchase Attitude (PA). Typically, beliefs about objects are formed by associating them with certain attributes, other objects, characteristics, or events. In the case of attitudes toward the way of being, each belief is linked to behavior toward a certain future, or to some other attribute such as the cost caused incurred by the behavior. Since such attributes are perceived negatively or positively, people automatically and simultaneously acquire an attitude toward behavior (Ajzen, 2020).
Subjective Norm (SN). It is determined by the pressure exerted on the prospective buyer by the people around him. Human beings often factor in what the people they care about will think; it is a kind of social pressure that influences the purchase intention, which can be decisive in some cases (Ajzen, 2020).
Perceived Behavioral Control (PBC). In Ajzen’s view (2020), PBC refers to the emphasis on factors related to a particular behavior; it is the individual’s perception of the difficulty or ease of performing a behavior of their interest. Behavioral control varies depending on the situation or actions being performed. It involves the judgments or conclusions of how well a person can execute a required action in a future event, therefore, it can be used to predict future behavior.
General Hypothesis
Physical attributes of fashion apparel have a significant effect (SE) on the purchase intention (PI) of 20 to 24-year-old university women in Lima.
Specific Hypotheses
H1 Shape of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
H2 The mechanical properties of fabric of fashion apparel have a SE on the PI of 20 to 24-year-old university women in Lima.
H3 The color of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
Identification of Variables
Independent variable (IV): Physical attributes (X)
Dependent variable (DV): Purchase intention (Y)
The variables were determined based on the classification by Abraham-Murali and Littrell (1995). Characteristics were grouped until the following were obtained: shape (X1), mechanical properties of fabric (X2), and color (X3).
This is an applied study with a non-experimental design, following a quantitative, cross-sectional, and correlational-causal approach. A representative sample of women in Peruvian society was deemed necessary to understand the consumer. We decided to conduct this study at Universidad Nacional Mayor de San Marcos, administering a questionnaire to female students. The questions were related to the three physical attributes identified in the theoretical framework, which were related to purchase intention. The influence of the attributes was measured based on the three dimensions defined in the TPB, thus determining their relationship with the dependent variable. The relationship degree was measured using the multiple regression statistical method.
The sample size was determined considering an infinite or unknown population of 20 to 24-year-old female university students in Lima. Based on this data, we can infer a sample of 196 respondents using the formula for infinite population sample determination (Z = 1.96 at 95%, margin of error = 0.07, p = 0.5, q = 0.5). A margin of error of 7% was chosen, as the complexity involved in measuring perceptions can lead to slightly larger errors. Respondents were 20 to 24-year-old female undergraduate students from Universidad Nacional Mayor de San Marcos (UNMSM), who were contacted via e-mail and WhatsApp, and voluntarily participated in the study.
A questionnaire was created and distributed via the web to analyze the variables. Before conducting the main study, the questionnaire was tested on 11 participants to ensure completeness, correct wording, clarity, and appropriate structure. After a few modifications, the final questionnaire had five core sections: Block 1 that collected important general information from the respondents; Blocks 2, 3, and 4 that measured attitude, subjective norm, and perceived behavioral control towards shape, mechanical properties of fabric, and color; and Block 5 that measured purchase intention for a product. Data were collected using two techniques: documentary analysis and surveys, which made it possible to understand the variables and draw the conclusions described.
The instrument was designed using three variables and 25 specific questions (Table 3). Cronbach’s alpha was used to measure reliability, obtaining a value of 0.892 (Table 4), which supports the design of this questionnaire. It was developed based on other relevant research with high reliability and validity in its variables. The references of the attitude were taken from Zhang et al. (2017) and Singh and Verma (2017); the instrument to measure subjective norm was taken from Singh and Verma (2017) and Al-Swidi et al. (2014); the index of perceived behavioral control were taken from Yadav and Pathak (2016) and Al-Swidi et al. (2014); and, finally, the tools used to determine acquisition intention were taken from Zhang et al. (2017). The questions were adapted to the fashion industry; a Likert-type scale was used.
Table 3. Questionnaire per Dimension.
Shape |
Attitude |
SPA1. Wearing a fashion apparel in the shape of my preference makes me to feel more confident and attractive. |
SPA2. I believe that fashion apparel with the shape of the consumer’s preference satisfy the consumer. |
SPA3. Fashion apparel with the shape of the consumer’s preference are higher quality. |
Subjective Norm |
SSN4. If my close friends and family consumed fashion apparel with the shape of their preference, would I? |
SSN5. The trend of buying fashion apparel with the shape of their preference among people around me is increasing. |
SSN6. People around me generally believe that it is better to wear fashion apparel with the shape of their preference to feel comfortable. |
FNS7. I would have the support of my close friends and family if I bought fashion apparel with the shape of my preference. |
Perceived Behavioral Control |
SPBC09. I consider myself financially capable of buying fashion apparel with the shape of my preference. |
SPBC10. I think that fashion apparel with the shape of my preference can be found in stores in Lima. |
SPBC11. Buying or not buying fashion apparel in the shape of my preference is entirely up to me. |
Mechanical Properties of Fabric |
Attitude |
PPA8. Wearing fashion apparel with my preferred mechanical properties will give me greater comfort and safety. |
PPA9. I think that fashion apparel with my preferred mechanical properties satisfy me. |
PPA10. Fashion apparel with my preferred mechanical properties are higher quality. |
Subjective Norm |
PSN11. If my close friends and family consumed fashion apparel with my preferred mechanical properties, would I? |
PSN12. The trend of buying fashion apparel with mechanical properties of their preference among people around me is increasing. |
PSN13. People around me generally believe that it is better to wear fashion apparel with mechanical properties of their preference to live better. |
PSN14. I would have the support of my close friends and family if I bought fashion apparel with my preferred mechanical properties. |
Perceived Behavioral Control |
PPBC20. I consider myself financially capable of buying fashion apparel with my preferred mechanical properties. |
PPBC21. I think that fashion apparel with my preferred mechanical properties can be found in stores. |
PPBC22. Buying or not buying fashion apparel with my preferred mechanical properties is entirely up to me. |
Color |
Attitude |
CPA15. Wearing fashion apparel in the color of my choice makes me feel more confident and attractive. |
CPA16. I believe that fashion apparel satisfy the wearer when they have a color of their preference. |
CPA17. Fashion apparel that have a color of my preference have higher quality. |
Subjective Norm |
CSN18. If my close friends and family consumed fashion apparel in a color of their preference, would I? |
CSN19. The trend of buying fashion apparel in the color of their preference among people around me is increasing. |
CSN20. People around me generally believe that it is better to wear fashion apparel in the color of their preference to live better. |
CSN21. I would have the support of my close friends and family if I bought fashion apparel in the color of my preference. |
Perceived Behavioral Control |
CPBC22. I consider myself financially capable of buying fashion apparel in my preferred color. |
CPBC23. I think that fashion apparel in the color of my preference can be found in stores in Lima. |
CPBC23. Buying or not buying fashion apparel in the color of my preference is entirely up to me. |
Purchase Intention |
PI25. How do the physical attributes of fashion apparel influence your purchase intention? |
Source: Prepared by the authors.
Table 4. Reliability Results.
Cronbach’s Alpha |
Cronbach’s Alpha Based on Standardized Items |
No. of Items |
.889 |
.892 |
4 |
Source: Prepared by the authors.
Multiple regression was used to analyze the data; therefore, hypothesis testing was performed to demonstrate that the proposed variables influence the purchase intention of fashion apparel.
General Hypothesis
H0: Physical attributes of fashion apparel do not have a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
H1: Physical attributes of fashion apparel have a SE on the PI of 20 to 24-year-old university women in Lima.
One or more ≠ 0
Specific Hypothesis 1
H0: Shape of fashion apparel does not have a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
H1: Shape of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
≠ 0
Specific Hypothesis 2
H0: Fabric of fashion apparel does not have a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
H1: Fabric of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
Specific Hypothesis 3
H0: Color of fashion apparel does not have a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
H1: Color of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
= 0
There were 196 valid surveys, that is, the number statistically determined for the generation of the model with the information obtained from the surveys. Data were organized according to five categories. The SPSS software was used to run the normality test (Table 5), and a summary of the model was obtained (Table 6), showing that 52.4% of the variable is explained by this model, followed by the ANOVA test (Table 7) and finally the multiple regression test (Table 8). The results were as follows:
Normality Test
H0: The variable follows a normal distribution.
H1: he variable does not follow a normal distribution.
Due to the volume of data, the Kolmogorov-Smirnov test is used.
Table 5. Tests of Normality.
|
Kolmogorov-Smirnova |
Shapiro-Wilk |
||||
Statistic |
df |
Sig. |
Statistic |
df |
Sig. |
|
Shape |
.234 |
196 |
.000 |
.878 |
196 |
.000 |
M. P. of Fabric |
.253 |
196 |
.000 |
.880 |
196 |
.000 |
Color |
.251 |
196 |
.000 |
.885 |
196 |
.000 |
Purchase Int. |
.210 |
196 |
.000 |
.860 |
196 |
.000 |
a. Lilliefors Significance Correction.
Source: Prepared by the authors.
Table 6. Model Summary.
Model |
R |
R² |
Adjusted R² |
Std. Error of the Estimate |
1 |
.724a |
.524 |
.517 |
.720 |
a. Predictors: (Constant), color, shape, mechanical properties of fabric.
Source: Prepared by the authors.
The p-value obtained for each variable (> 0.05) means that H0 cannot be rejected; therefore, the data normality assumption is valid. Verification of data normality is necessary to select the statistic to be used.
Hypothesis Testing
General Hypothesis
p-value = 0%, the null hypothesis (H0) is rejected (see Table 7).
If p-value < 5%, then H0 is rejected.
As the p-value < 5%, the H0 is rejected; therefore, physical attributes of fashion apparel have a SE on the PI of 20 to 24-year-old university women in Lima.
Specific Hypothesis 1
p-value = 60.1% (see Table 8)
If p-value < 5%, then H0 is rejected.
In this case, H0 is accepted (p-value > 5%); therefore, the shape of fashion apparel does not have a SE on the PI of 20 to 24-year-old university women in Lima.
Specific Hypothesis 2
p-value = 0.1 % (see Table 8)
If p-value < 5%, then H0 is rejected.
In this case, H0 is rejected (p-value < 5%); therefore, the mechanical properties of fabric of fashion apparel have a SE on the PI of 20 to 24-year-old university women in Lima.
Specific Hypothesis 3
p-value = 0% (see Table 8)
If p-value < 5%, then H0 is rejected.
In this case, H0 is rejected (p-value < 5%); therefore, the color of fashion apparel has a SE on the PI of 20 to 24-year-old university women in Lima.
Table 7. Analysis of Variance (ANOVA).
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
1 |
Regression |
109.556 |
3 |
36.519 |
70.464 |
.000a |
Residuals |
99.505 |
192 |
.518 |
|
|
|
Total |
209.061 |
195 |
|
|
|
|
Table 8. Regression Analysis.
Coefficientsa |
||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
.737 |
.231 |
|
3.188 |
.002 |
Shape |
.048 |
.092 |
.040 |
.524 |
.601 |
|
M. P. of Fabric |
.302 |
.091 |
.261 |
3.336 |
.001 |
|
Color |
.529 |
.088 |
.480 |
6.010 |
.000 |
a. Dependent variable: Purchase intention
Source: Prepared by the authors.
Regression Analysis No. 2
In the second analysis, the variable shape is removed to develop a model that can predict the purchase intention based on the two remaining variables: color and physical characteristics of the fabric (see Table 9).
All coefficients are statistically significant in this case. Color remains the most influential. This equation can explain the consumer’s purchase intention. Other extrinsic factors are known to influence this dependent variable; however, this study intends to focus only on the importance of the physical variables of which the fashion apparel is composed. A p-value of over 5% means there is no relationship of dependence between the variables.
Table 9. Regression Analysis No. 2.
Coefficientsa |
||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
||
B |
Std. Error |
Beta |
||||
1 |
(Constant) |
.765 |
.224 |
|
3.414 |
.001 |
M. P. of Fabric |
.320 |
.084 |
.276 |
3.823 |
.000 |
|
Color |
.548 |
.080 |
.497 |
6.881 |
.000 |
a. Dependent variable: Purchase intent.
Source: Prepared by the authors.
Analysis of variance and multiple regression were used as analytical tools to test the hypotheses. Upon performing the ANOVA test for the general hypothesis, the results yielded a p-value = 0% (see Table 7); therefore, the null hypothesis was rejected and the alternative was accepted. Overall, physical attributes influence purchase intention.
Regarding the first specific hypothesis, the result of the multiple regression (Table 8) validates the null hypothesis stating that shape does not influence purchase intention with a p-value = 60.1%. Therefore, the alternative hypothesis stating that shape influences purchase intention is rejected. We can reflect on the circumstances in which young women of these ages make their purchases. They are very young women who are learning to shop, are entering the labor market, and have a different appreciation of what they buy, as opposed to more experienced shoppers. These are their first purchases, so they value new features in the products. This appreciation may change and consolidate for another age range, placing a different value on the shape of the product. This leads to a field of new research that can be proposed for other age groups. However, this result is at odds with results obtained in other studies, such as those of De Klerk and Lubbe (2004, 2008), who concluded that aesthetics was significant in determining product quality. Furthermore, Davis (1985) found a high correlation between physical attributes and the perception of product quality.
The multiple regression analysis (Table 8) validates the second specific hypothesis stating that fabric has a significant effect on purchase intention; the null hypothesis is rejected because the p-value = 0.1%, which is less than 5%. The results also show a coefficient of 0.302, which is the slope of the straight line of this variable in the model to be plotted; therefore, the specific hypothesis stating that the physical characteristics of the fabric influence the purchase intention of the product is accepted. The third specific hypothesis is also accepted because the null hypothesis is rejected as the p-value = 0%. This variable has a steeper slope of 0.529; therefore, the hypothesis stating that color influences purchase intention is accepted. The relationship between slopes leads to the conclusion that color has the greatest influence on this type of customer.
According to the results, garment shape does not have a decisive influence on the customer’s purchase intention; for this reason, a new model in which this variable is removed was proposed. This new model uses multiple regression (Table 9), but only with the variables mechanical properties of the fabric and color. The two specific hypotheses are validated in this analysis, where it is observed that color has a greater influence on purchase intention. This new model is not a general equation, but it provides some insight into how these variables work with each other. Many other variables could influence the dependent variable, such as extrinsic variables: price, packaging, image, etc. However, this paper has intended to address the analysis of the physical (intrinsic) variables of fashion apparel.
The following conclusions can be drawn from the analysis:
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[1] Master’s degree in Industrial Engineering from Universidad Nacional Mayor de San Marcos [UNMSM]. Currently working as head of CERSEU of the School of Industrial Engineering of UNMSM (Lima, Peru).
Orcid: https://orcid.org/0000-0003-2803-095X
Corresponding author: jorge.arroyo@unmsm.edu.pe
[2] Currently working as professor at the School of Industrial Engineering of UNMSM (Lima, Peru).
Orcid: https://orcid.org/0000-0001-7527-2666
E-mail: cquispea@unmsm.edu.pe
[3] Currently working as professor at the School of Industrial Engineering of UNMSM (Lima, Peru).
Orcid: https://orcid.org/0000-0001-7545-2851
E-mail: nancy.barreda@unmsm.edu.pe