ISSN 0798 1015

Vol. 40 (Number 7) Year 2019. Page 12

Determinants of purchase intention in online Latin American consumers

Determinantes que convierten la intención en compra del consumidor digital en Latinoamérica

Christian Wladimir BERMÚDEZ 1; Verónica Carolina CORONEL 2; Erick Paul MURILLO 3; Stalyn Freddy CELI 4

Received: 28/10/2018 • Approved: 24/01/2019 • Published 04/03/2019

Contents

1. Introduction

2. Methodology

3. Results

4. Conclusions

References

1. Introduction

Globally, the use of the Internet has facilitated and accelerated online shopping transactions in the last two decades. 53% of Internet users express that it is an ideal mean to purchase products or services (Forsythe & Shi, 2003) because of their flexibility and agility. It has become a platform that facilitates business transactions and information exchange in companies, giving rise to what is known as e-commerce or electronic commerce (McLaren & McLaren, 2000).

Nowadays, the Internet is an instrument of great potential for buying and selling goods and services, both for companies and for people. Modern companies consider it a way to reach their customers and to generate significant benefits such as convenience and ease of making purchases regardless of distance and time. These are the main aspects perceived by digital consumers, in addition to home delivery, product comparison, offers and discounts. Companies recognize higher revenues thanks to this type of transactions, which allow markets to expand, generate lower prices, and reduce costs (Rovira & Stumpo, 2013).

Electronic commerce has been affected by the application of fees or tariffs on imports in Latin America. E-commerce grew by 80% between 2012 and 2014 in Ecuador, according to the latest technology survey of the Ecuadorian Institute of Statistics and Census (INEC, 2016). However, they received regulations through tax rates and tariffs according to consumption categories (SENAE, 2014).

Such regulations cause negative effects on couriers and consumers. The couriers reduce their operations in the postal traffic service and therefore generate lower profits according to the Ecuadorian Association of Private Post. 28% of consumers stated they did not make any more online purchases, and 97% of them disagreed with such a standard (Montalvan, 2015). This affects the growth of business to costumer (B2C) business model within Latin America.

Academics and administration have shown interest in studies that propose explanatory models of online shopping behavior (Lim, Osman, Salahuddin, Romle & Abdullah, 2016), as well as factors that influence digital purchase intention (Bigne, Ruiz & Sanz, 2005; Forsythe & Shi, 2003; Li, Kuo & Russell, 1999), and digital consumer typologies (Keng, Tang & Ghose, 2003). These investigations have been developed in countries where consumers can have local delivery; therefore, import tax rates do not apply. This tax is common in Latin American countries, directly affecting the total cost of the product purchased.

This study attempts to validate the theoretical determinants that convert intention into effective purchases, considering the tax variable or import tariffs, common in Latin American countries such as Ecuador. The hypothesis on the degree of association between variables constitutes the theoretical contribution of this research, as it obtains a model that explains electronic purchase for a better approach to B2C business marketing strategies. Finally, the profile of the digital consumer in Ecuador is described, which will be a reference for further studies on the matter.

1.1. Literature review

Previous studies that have tried to model the digital consumer’s behavior from a theoretical and empirical point of view are discussed. Authors in the field of electronic commerce and marketing agree that purchase intention precedes purchase behavior, therefore, they suggest it must be analyzed in its factorial structure. The literature reviewed is circumscribed to Latin America, where there is a different electronic commerce scenario as compared to Europe, Asia, or the United States, since there is no need to import products.

Digital consumer behavior

From the theoretical perspective, one of the models used to explain online shopping behavior is Ajzen's planned behavior model, through attitude, perceived control, and subjective norms (George, 2002; Sanz, Ruiz & Pérez, 2013). Results suggest that in order to influence the consumers' purchase intention, every effort should be directed towards attitude formation rather than personality traits, or personal innovation.

Lim, Osman, Salahuddin, Romle & Abdullah (2016) determined a factorial model of online shopping behavior based on the subjective norms of the planned behavior model, perceived utility, and purchase intention. The results determined the latter variable as a reliable indicator of online purchasing behavior, and subjective norms such as perceived utility are positive and significant influencing factors. Li, Kuo & Russell (1999) determined that education, convenience and experience orientation, familiarity with the mean, perceived distribution utility, and perceived accessibility, are robust predictors of online shopping behavior.

On the other hand, from the empirical perspective, authors recommend that, before establishing parameters of digital consumer behavior, the consumer profile should be defined, which is basically related to sociodemographic variables and ICTs.

The virtual consumer is young, with a positive attitude towards Internet use. They have had a good education and a considerable income (Brennan, 2000; Sim & Koi, 2002). This sociodemographic profile is confirmed in international contexts such as the USA (Hoffman & Novak, 1996), Australia (Jarvenpaa & Todd, 1997), and Greece (Vrechopoulos, Siomkos & Doukidis, 2001).

Regarding the relationship with ICTs, Perea, Monsuwé, Dellaert & De Ruyter (2004) highlighted that the consumer expects a return by investing in a computer and learning the purchase process on the Internet. If this return meets expectations, the experience with the environment will be positive, since it provides greater benefits and functions than promised. Liao & Cheung (2001) highlighted that the experience with alternative shopping channels from home positively influences the adoption of the Internet as a means to acquire products and develops the ability to buy without previously performing a physical inspection.

The interest of obtaining a standardized theoretical model that explains digital consumer behavior is evident, as well as a growing need for new knowledge, theories and models of online consumer behavior (Souza & Baldanza, 2017). The literature denotes the purchase intention as its antecedent variable that captures a large part of the information; therefore, special attention must be paid to it.

Purchase intent of digital consumers

The theoretical support of this variable comes from the models of social psychology that link attitudes and behaviors. Marketing literature shows the relationship between purchase intention and subsequent behavior, which has been measured in different types of products and is widely used in for marketing. The segment that a priori has a high propensity to buy is more likely to end with positive purchase intent (Morwitz & Schmittlein, 1992). Authors such as Cecere, Corrocher & Guerzoni (2018) agree to use this variable to predict sales of existing products as a good indicator of consumer behavior.

This variable’s analysis is directly related to the product that the consumer tries to buy, such as organic products (Ramayah, Lee & Mohamad, 2010), vehicles (Cecere, Corrocher & Guerzoni, 2018; Chéron & Zins, 1997), online services (Izquierdo, Martínez & Jiménez, 2010), or online products (Ranganathan & Jha, 2007; Sheppard, Hartwick & Warshaw, 1988).

Online products were found to be statistically significant with R2 = 61% in the modeling of structural equations in a quantitative study conducted in Mexico, considering the perception of utility, safety, and compatibility for its measurement (Zubirán & López, 2009).

A prime factor, as confirmed by Yusta, Pilar, Ruiz, Isabel & Zarco (2010), is the confidence in buying from a certain electronic provider, which is directly related to familiarity with Internet use. That is to say, digital purchase intent depends on the use of online stores up to the transactional stage of purchase and logistics, affirmed by Perea et al. (2004), who expressed that previous experiences of online purchase and trust are aspects that favor the process. Furthermore, the perceptions of ease of use, utility, and enjoyment, exogenous factors such as consumption characteristics, and situational factors such as attitude and perceived risk must be taken into account.

It can be understood that people with higher levels of experience or frequent consumers in e-commerce acquire greater knowledge in ICTs use and, therefore, have a greater possibility of making online purchases, or be updated with information regarding new products (Dickerson & Gentry, 1983). Automated search engines provide greater knowledge to users about their interests and needs, which will determine their future purchases. If experience with new products is a determining factor, it is also necessary to have certain knowledge to access virtual navigation (Citrin, Stem, Spangenberg & Clark, 2003; Dickerson & Gentry, 1983; Kwak, Fox & Zinkhan, 2002).

Jarvenpaa, Tractinsky, Saarinen & Vitale (1999) analyzed attitudinal variables and stressed that trust in the seller and perceived risk can represent barriers that affect the intention to buy online. This aspect is not significant in users who appreciate the experience of buying online by itself, enjoy the activity, and seek entertainment resulting from the fun that is born from the online shopping experience (Holbrook, 1994).

One of the seven key questions that researchers must solve is about the specific factors or determinants that turn intent into buying behavior (Morwitz & Schmittlein, 1992). Previous studies that attempt to answer this question are presented below.

Determinants that turn digital consumer intent into purchases

Although there are studies that explain both purchase intention and subsequent behavior, a simplifying model has not yet been globally standardized. Sociodemographic factors and others related to ICT use that convert intent into purchase are grossly specified below. Section 2.3 lists the respective working hypotheses to be tested.

Li, Kuo & Russell (1999) establish there are significant sociodemographic effects regarding gender, education and income level in three types of consumers: occasional, frequent, and non-purchasers. Buyers have more training than non-purchasers, and have higher income levels. Men buy more often than women, but there is no significant difference between buyers and non-buyers of both genders. As suggested in H1 and H2  hypothesis, gender and education relation to online shopping is researched (section 2.3).

According to Garzón (2014), income is grouped in a factor with age and marital status. The higher the age, the higher the income, and both positively influence the consumer. In Ecuador, 57% of digital buyers correspond to the average social stratum, and 30% to medium and high stratum, according to the Ecuadorian Chamber of Electronic Commerce (CECE, 2017). Therefore, the relationship with income is tested through hypothesis H3.

As mentioned above, age is another determinant factor, as young people develop more browsing and information search skills in the network, thus demonstrating a greater willingness to buy (Bigne et al., 2005; 2014). They represent the most satisfied market segment with online services, and those with the best experience on the web. They are used to reading the press and consulting any information online (Cristobal, Hernández & Daries, 2017). 40% of Ecuador’s digital buyers are between 26 and 33 years old (CECE, 2017). H4 hypothesis seeks to measure the relationship between age and electronic purchases.

Regarding ICTs, digital consumers value knowledge and exposure to the Internet. Knowledge of the medium measures seniority or experience as an electronic buyer. As indicated by Citrin et al. (2003), products’ tactile experiences determine the probability of purchase through the web, a relationship that is more evident in females. Similarly, Li et al. (1999) noted that knowing the Internet positively influences the attitude towards buying, and is perceived as a benefit derived from its use. Forsythe & Shi (2003) highlighted the importance of the time and effort that consumers need for learning to use the Internet. Knowledge is different for each group of individuals studied, which leads to hypothesis H5.

Ruíz & Sanz (2006) regarded medium exposure in a study conducted in Spain with more than 3,000 Internet users. Results showed that the longer people are exposed to the Internet, the greater the benefits they obtain, e.g. better promotions, additional product features, or reduced costs. Those with lower exposure were less motivated to purchase. H6 hypothesis tests Internet exposure with online purchase.

Taxes’ impact on e-commerce was analyzed by Goolsbee (2000) surveying on the purchase decisions of approximately 25,000 users with Internet access in 21 states of North America. Internet sales were very sensitive to local taxes, revealing a 24% reduction on the amount of buyers. This effect will be assessed in a Latin American context where taxes are applied to imported products when virtual stores are abroad. Finally, this study suggests estimating the relation between tax and digital purchases through H7 hypothesis.

The conceptual framework that summarizes the study hypotheses is shown in Figure 1. The online purchasing variable is reflected in the expense and frequency of purchase.

Figure  1
Conceptual model of determinants that turn digital consumer intent into purchase

The study was developed under the positivist paradigm and quantitative approach that sustains research objectively starting from literature review, data collection and analysis using statistical techniques to test study hypotheses, in order to establish behavioral patterns of the study variables (Hernández, Fernández & Baptista, 2006; Ricoy, 2006).

With deductive logic, the theories were delineated and the hypotheses were derived to confirm the relationships between the study variables by means of a non-experimental type strategy with correlational descriptive scope, since the intention is to detail the relevant characteristics of the people who buy online and measure the degree of relationship between them.

A transectional design was used, applying the data collection method in a single instance of a specific time. Correlational statistical techniques were used to analyze data and help validate the proposed relationships with a 5% level of significance. The statistical software SPSS version 22 was used to process data.

2.1. Population and sample

The study was carried out in the city of Guayaquil. Its population contains the largest number of inhabitants in the country, representing 17% of its total, that is, more than 2.5 million people. According to INEC (2016), 63.7% of this population uses the Internet from their homes, place of work, or a public access center. In total, the target population is 1.6 million people who can potentially make an electronic purchase.

Questionnaires used in previous investigations were consolidated (Bigné & Ruiz, 2006; Garzón, 2014), and adapted to the context for data collection. Three experts helped: an e-commerce specialist, a commercial analyst from the Chamber of Small Industries of Guayaquil, and a researcher in the area of administration. The questionnaire was validated through a pilot survey, and the suggested adjustments were made, such as changing the order of the questions and adding the other option in the question referring to the products purchased through the Internet. Finally, the survey was applied to a simple random sample in the three geographic sectors, north, center and south of the city of Guayaquil.

A sample of size 384 was obtained with these values in formula parameters. However, given the lack of information on the real amount of people that have actually purchased online, a sample of 540 people was taken. After data purification, 508 valid surveys were obtained and 6% of atypical cases were eliminated.

2.2. Hypothesis

Based on the theoretical framework and the research objective, the approach of the seven research hypotheses is suggested. From each one of them a null hypothesis is derived, and these were tested with a 5% level of significance.

H1: There is a relationship between the gender of the digital consumer and online shopping.

H2: There is a relationship between digital consumer education and online shopping.

H3: There is a relationship between the income of the digital consumer and online shopping.

H4: There is a relationship between the age of the digital consumer and online shopping.

H5: There is a relationship between knowledge of the digital medium and online shopping.

H6: There is a relationship between exposure to the digital medium and online shopping.

H7: There is a relationship between the taxes and the digital consumer shopping behavior.

As presented in the conceptual model, the online purchase will be measured with the variable expenditure and purchase frequency.

Results will be shown in two stages: first, the descriptive analysis will be developed, revealing the profile of the digital consumer through contingency tables; and second, both the correlation coefficients and the levels of statistical significance for each proposed hypothesis will be calculated.

3.1. Descriptive analysis

Of the total of 508 valid surveys, 59% of people have made online purchases in the last 12 months, that is, 300 users of the total sample; and 41% have not made purchases of this type. Of those who have purchased as for those who have not, most are male (34%, and 28% respectively). The reasons for not having purchased are Internet distrust (38%), not having a credit card (35%), and not knowing how to buy online (12%).

Table 1
Percentage structure of respondents by gender

For the subsequent analysis, the 300 cases that have purchased online will be considered. Table 2 describes the profile of the digital consumer through the frequency table of the demographic variables such as gender, age, level of education and income level.

Table 2
Description of the digital consumer profile

It may be said that the digital buyer is male (57%), an adult between 35 and 40 years of age (41%), has a college education (78%), and their income ranges between $401 and $802 (38%). A fifth of the respondents who have already had some e-commerce experience are young people between 26 and 31. Regarding purchase frequency (see Table 3), half of the respondents (49%) make at least one purchase annually, others a semi-annual purchase (29%). As for accumulated expenses, 42% claims to exceed $300; and others (52%) spend between $61 to $300.

Table 3
Indicators of digital purchasing activity

Analysing the expense incurred in online purchases, both men and women spend similar amounts. Table 4 shows that 44% of men spend a total of more than $301, although 40% make minimum purchases between $61 and $150. This group is considered to be young people who do not yet have a fixed income. About 48% of women spend between $151 and $300, while the remaining 39% spend more than $301.

Table 4
Expenditure incurred in digital purchases
according to the consumer's gender

The preferences for both purchased products and payment methods were analyzed (Table 5). Participants were asked to choose three options and prioritize them in high, medium, or low. Electronic equipment is a high priority, while clothing and air tickets are a low priority for men. Meanwhile, clothing items are high priorities, and medicine and electronic equipments are low priorities for women. Men indicated they seldom buy footwear, and women, perfume through e-commerce.

Regarding payment method, both men and women prefer credit cards because they are familiar with their use in traditional purchases; meanwhile electronic transfers are less prefered, and PayPal is still not a common option, since it is new and there is distrust towards it (Table 5).

Table 5
Purchase and payment method priorities for digital consumers according to their gender

3.2. Hypothesis testing

The seven hypotheses that validate the relationships of the conceptual model were tested. Table 6 shows H1 to H7 that raises the relationship between each variable with the expense and frequency of purchase, and the respective null hypothesis H01 to H07 proposes that there is no relationship between the variables. The proper procedure of analysis to measure the association between variables with ordinal scales is Kendall’s Tau , which quantifies the degree of pair concordance by observations (Boone & Boone, 2012).

A tau-C type was applied because not all the contingency tables are square. Its value is between -1 and 1, the sign being the direction of the relationship and its absolute value the strength of association, so being close to 0 will be weak and close to ± 1 is strong.

Table 6
Correlations and hypothesis contrast of the conceptual model

 Note. **. The correlation is significant at the 0.01 level (bilateral).
*. The correlation is significant at the 0.05 level (bilateral).

All variables have a direct relationship to purchase expense except for the tax, which is reverse. This confirms that the higher the tax rates the electronic purchase will be lower. Analyzing the magnitudes of the associations in Table 6, gender, income, education, and age are greater than 0.12 and are significant at a 0.01 level. On the other hand, there is not enough evidence to reject hypotheses H5 and H6 because p-value is greater than 0.05, therefore there is no relationship between medium knowledge and exposure with digital purchase.

All correlations with purchase frequency are reversed, except for age that is direct. This indicates that at higher levels of the antecedent variables, users consume less frequently, but with larger amounts, since the correlation between frequency and purchase expense is -0.375, being significant at a 0.01 level. All except for H4 and H7 hypotheses were rejected for presenting sufficient evidence in the data; that is, gender, income, education, medium knowledge and exposure are determining factors.

Age had a direct and moderate correlation with training (0.522) and education (0.514), both significant at 0.01, this indicates an indirect relation to online shopping. The same result is repeated between income and education, where the correlation magnitude is 0.655 and a p-value <0.01. This indicates that higher education consumers perceive higher incomes.

Taking into account both perspectives, frequency and amount of digital purchase spending, the variables that remain as significant determinants are gender, income and education. Age is a determinant in spending, but not in purchase frequency, while knowledge and exposure to the Internet are influential in the frequency of purchase and not in the amount spent.

It is confirmed that tax impacts B2C business model in Latin American countries. As new tariffs or taxes are applied, both the frequency and the spending on purchases decrease. Regardless, this is not significant in this sample. It is suggested to specify this variable in its structure for a better interpretation of its relationship with digital purchase.

The Guayaquil digital consumer is male between 35 to 40 years old, with college education, and an average income between $401 and $802. They prefer credit cards as payment method, and their purchase choice is oriented to electronic equipment in males, and clothing in females. They usually make at least one purchase annually spending more than $ 301.

Finally, it is recommended to use factor analysis to confirm model fit to the data and obtain a robust factor structure. This study may be replicated in other contexts to verify the results consistency, and to generalize the determinants of purchase intent online.

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1. Faculty of Administrative Sciences. University of Guayaquil. Computer Engineer, Master in Management Computing and New Technologies. christian.bermudezg@ug.edu.ec

2. Faculty of Administrative Sciences. University of Guayaquil. Commercial Engineer, Master in Business Administration. veronica.coronelpe@ug.edu.ec

3. Faculty of Administrative Sciences. University of Guayaquil. Aquaculture Engineer Master in Business Administration. erick.murillod@ug.edu.ec

4. Faculty of Administrative Sciences. University of Guayaquil. Commercial Engineer. stalyn.celib@ug.edu.ec

5. From top to bottom on the left column: gender, education, income, age, medium knowledge, exposure to the medium, taxes. Middle column: online buying. Far right column from top to bottom: purchase expense, purchase frequency.