1. Introduction
According to the Organization for Economic Cooperation and Development (OECD), the rise of the Internet in the 1990s led to the growth of electronic commerce (e-commerce), consequently placing it on the international policy agenda (OECD and AIMX 2020). Many predictions have been made about its benefits for businesses, organizations, and consumers. Researchers have expressed that e-commerce would reform communication and simultaneously grant businesses greater involvement in functions. Moreover, the quality of the provided information would become a value proposition for users (OECD and AIMX 2020). While most of these predictions have been realized, new opportunities have emerged to further develop the potential of e-commerce. Thus, a deeper understanding of this type of commerce becomes necessary.
Internationally, there are numerous studies that delve into the importance of e-commerce, consumers, and the existence of a set of factors influencing online purchases. Nevertheless, there are few studies regarding the Mexican market, making it imperative to conduct a rigorous analysis of e-commerce trends from the perspective of internet users, its continuous growth due to the advantages it offers, and the various elements influencing decision-making.
In the context of Mexico, in 2021, the e-commerce market reached approximately 64 million users (Statista, 2022). The following year, the Mexican Online Sales Association determined that 65 million people made purchases online (Mexican Online Sales Association [AMVO], 2023). According to Statista figures (2021), the growth of e-commerce in Mexico has outpaced that of Brazil. This suggests that Mexico has the potential to become a leader in e-commerce in Latin America.
However, according to Zubirán and López (2009), the level of e-commerce adoption in Mexico is significantly lower compared to developed countries like the United States and Canada. Consequently, given the substantial growth of e-commerce in Mexico, there is a need to understand what drives Mexican consumers to make online purchases and increase the likelihood of forming long-term relationships with them (Kotler and Armstrong, 2013).
This underscores that e-commerce has become a valuable tool for internet users and their new lifestyles. Consequently, its study is not only relevant for understanding its level of acceptance among internet users but also for assisting companies in meeting the needs of their current and future users.
Despite the expressed need, there is no common approach in addressing the topic. Zubirán and López (2009) state that there are few research studies associated with the study of Mexican e-commerce that can explain its level of acceptance. Although various studies reveal that there are factors that can negatively influence e-commerce use, others, on the contrary, can motivate its use and have a favorable connection with purchase intention. To the extent that users perceive that buying through the Internet is easy, useful, reliable, convenient, and that they also obtain favorable prices, a variety of options, access to information, among other benefits, their intention to make a purchase in the future will be greater.
In the quest to expand the frontier of knowledge, the question arises: What are the factors that determine online purchase intention, allowing for the development of a theoretical model in the context of the Mexican internet user?
To this end, a measurable theoretical model of online purchase intention with observed variables was conceived. In accordance with this, various theoretical constructs were integrated based on the factors determining online purchase intention, generating general propositions about these relationships.
This research aims to explore and describe the concepts related to purchase intention, based on conceptions of the factors that drive internet users to make a purchase or acquire a service via the Internet. The proposed model will serve as a basis to confirm the presence of causal relationships between the indicators and the proposed constructs through validation. Furthermore, it will contribute to understanding of how specific factors can serve as catalysts for e-commerce and help identify areas for improvement that necessitate solutions to mitigate future complications.
The research employs an exploratory and descriptive approach with an informative documentary character. It was developed through the analysis and synthesis of theories regarding factors influencing online purchase intention, as well as focus group discussions to determine the constructs and indicators of the theoretical model. The research also utilizes the initial step of the technique proposed by Hair et al. (1999) known as the Structural Equation Models (SEM) for data analysis.
2. Theoretical framework
2.1 Analysis of e-commerce
In the literature, various authors and organizations attempt to define e-commerce. The World Trade Organization describes it as a series of processes that encompass everything from the manufacturing of goods, logistics for transporting products from their origin to delivery to the end consumer, to the use of electronic tools for their acquisition (World Trade Organization [WTO], 1998). The way the WTO defines e-commerce makes it a very comprehensive definition, as it considers all phases from production to the sale of goods and services.
However, there are diverse definitions of e-commerce with heterogeneity and richness, providing relevant aspects that characterize this economic activity. As the adoption of e-commerce increases, these definitions have allowed for its classification into various categories of business relationships.
In contrast to the WTO, the Organization for Economic Cooperation and Development (OECD) suggests that e-commerce involves the purchase of goods or services using methods specifically designed to place orders. Furthermore, it clarifies that neither the final delivery nor payment necessarily needs to occur through this medium (OECD and IDB, 2016).
Additionally, scholars such as Gariboldi (1999) and Fernandez and Medina (2002) thoroughly analyze the components, characteristics, and agents involved in e-commerce. Based on their research, they indicate that e-commerce operations can be conducted by individuals, organizations, or electronic agents using digital networks, in a virtual, timeless, and geographically limitless environment. They emphasize that transactions occur virtually, in contrast to traditional purchases.
Some authors further expand the analysis by emphasizing the commercial relationship established in this type of trade and the payment method. Reginfo (1999) and Guerrero and Rivas (2005) consider e-commerce to be any commercial activity that leverages market opportunities with the help of the Internet. Similarly, Laudon (2009) adds that it encompasses all digital-capable commercial transactions between organizations and individuals, and Fernandez et al. (2015) specify that payments are made electronically.
Conversely, Cisneros (2016) provides a concise definition by asserting that e-commerce is an economic activity based on offering goods for sale through the Internet. However, it is now recognized that sales can occur through any device with Internet access and through different systems, such as online stores, website forms, or applications (OECD and AIMX, 2020).
In summary, the presented conceptualizations are valid and complement each other. They also adapt to technological advancements, their usage, the digital economy, and the business models of incorporating companies.
Despite its conceptualization, it is important to note that in addition to its countless advantages, e-commerce has transformed the buying habits of users, significantly promoting the development of more stable and enduring market relationships.
However, e-commerce also presents some disadvantages related to security, availability, as well as knowledge and use. These aspects include consumer uncertainty associated with distrust in making purchases, the lack of timely information, and skills in technology use, among others.
2.2. E-commerce and consumer perspective
Consumer behavior has been the subject of extensive analysis, leading to numerous judgments focusing on consumer actions (Kotler and Armstrong, 2013; Sánchez, 2015).
According to Sánchez (2015), consumer behavior encompasses the myriad tasks in which consumers engage that influence the actions preceding, leading to, and resulting in purchasing decisions, wherein consumers make their choices. Molla et al. (2006) further describe this process as comprising three phases: pre-purchase, purchase, and post-purchase.
The pre-purchase phase is when users express their needs, research, identify offers, evaluate options, and make selections from available choices. The purchase phase occurs when consumers select the seller and define the transaction terms from the options offered by the store. Finally, the post-purchase phase ensues when consumers use and assess the acquired goods, expressing their satisfaction or dissatisfaction based on their evaluation.
Clearly, the behavior of e-commerce consumers differs from the traditional forms mentioned earlier. A study conducted by Salas et al. (2021) addresses the factors that foster internet users’ intention to use the internet as a transaction method.
To provide a robust theoretical foundation, the authors employ the Technology Acceptance Model (TAM). Additionally, they assert that the construct of attitude remains relevant in the scientific domain due to its complexity, as it indicates buying behavior, and propose a model of internet use as a means of purchase.
In this way, the purchase intention is recognized as a variable of e-commerce consumer behavior, and this, in turn, leads to the decision to make or not make a purchase, as it signifies the willingness to take a specific action (Salas et al., 2021).
In summary, the behavior of e-commerce customers requires studies that differentiate purchase intention or internet use from actual buying behavior, from both a conceptual perspective and an analysis of the factors influencing one or the other.
Regarding buying behavior, the OECD and AIMX (2020) present the main trends of e-commerce, emphasizing that businesses have begun to make purchases through online platforms aimed at internet users. As a result, entrepreneurs can establish direct relationships with their customers.
The fact that e-commerce allows more and more companies to engage in cross-border sales, even small ones, increases the available alternatives for consumers. Furthermore, it has been observed that products with high demand across borders are often physical items (OECD and AIMX, 2020).
According to Torres and Padilla (2013), purchase intention involves understanding how a user will proceed with a purchase in the future. However, Chu and Lu (2007) suggest it is the level to which a buyer wants to acquire a product in the future. Similarly, Shaouf et al. (2016) consider online purchase intention as the desire to obtain a product or service on a website.
Other related concepts that address potential factors influencing online purchase intention are discussed in the study by Beneke et al. (2016), who state that it is the likelihood of an individual buying a specific item based on their needs, attitude, and perception of the product or brand. Others, such as Kamalul et al. (2018), propose that purchase intention can be the user’s interest in acquiring a specific item. Finally, Peña et al. (2020) conclude that it is the user’s inclination level to buy a product through an online store.
For the purposes of this study, online purchase intention is assumed to be the user’s willingness to acquire a product or service through an online store or mobile application, depending on certain factors. This phenomenon, according to the works of Calvo-Porral et al. (2013), Dachyar and Banjarnahor (2017), Ha et al. (2019), Herrero et al. (2006), Lim et al. (2016), Nuseir (2019), Othman et al. (2019), Peña (2014), Rehman et al. (2019), and Wen et al. (2019), is related to the consummation of the purchase action.
In addition to the above, the investigations of Chan et al. (2003) and Chiu et al. (2012) should be considered, as they assert that the motivations that drive users to make their first online purchases differ from those that stimulate repeat purchases.
Recognizing that many of the determining factors of purchase intention in more traditional market forms will remain intact for online searches, the essential factors and the way they influence purchase intention must be adequately analyzed and contextualized. This will result in the establishment of a typology of factors in line with the theoretical dimensions or constructs recognized in the literature as the foundation for the development of the theoretical model.
2.3. Theories on factors influencing online purchase intention
To understand this, it is essential to analyze and interpret the theories that underpin the factors influencing online purchase intent.
2.3.1. Theory of Reasoned Action (TRA).
Developed by Ajzen and Fishbein, it posits that the intention to perform a behavior is comprised of two elements: attitude and subjective norm (Haro, 2018; Zuluaga, Vargas, and Valencia, 2020). According to Encina (2018), attitude is the individual evaluation of an object and a specific attribute. In contrast, the subjective norm is made up of normative or social beliefs (Avila, 2019).
According to González (2016), TRA was one of the first proposals for estimating technology acceptance by users and has been used to predict intentions and behaviors.
2.3.2. Technology Acceptance Model (TAM).
Developed in 1989 by Fred Davis, its purpose is to predict system acceptance and the consequences of user behavior when using it (Dachyar and Banjarnahor, 2017). TAM is an extension of TRA. This model explains user motivations influenced by external stimuli (Baby and Kannammal, 2020). Mendoza (2018) asserts that this theory primarily aims to investigate factors influencing people’s behavior and intentions.
According to TAM, consumer behavior is affected by both perceived ease of use and perceived usefulness. Perceived usefulness indicates how much users estimate that technology will improve their performance. On the other hand, perceived ease of use represents the level of technology complexity (Chen and Tsai, 2019).
2.3.3. Theory of Planned Behavior (TPB).
Proposed by Fishbein and Ajzen in 1975, it posits that the intention to engage in a specific behavior is the most relevant indicator, as an individual does something they had the will to do (Zuluaga et al., 2020). Mastrangelo (2018) states that the formation of intentions is influenced by three psychosocial factors called attitude, subjective norm, and behavioral control. According to Avila (2019), behavioral control directly impacts behavioral intention.
2.3.4. Theory of Diffusion of Innovations (IDT).
Developed by Everett Rogers in 1962, it was considered the driving force behind the modernization of society (Beltrán, 2006). According to Orts (2015), it promotes the dissemination of new knowledge, procedures, behaviors, goods, and services within a community or the adoption of something new and different for an individual or company.
In 1969, Bass claimed that potential customers of an item they had not purchased before would be motivated to buy it for two reasons. First, due to the social effect of innovation, and second, due to the influence of prior users (Munuera and Rodríguez, 2007). Jiménez and Martín de Hoyos (2007) express that the influence of people accompanying the adopter, whether an individual or an organization, is a fundamental factor in innovation adoption.
Related to this, Orts (2015) asserts that the process of adopting an innovation does not occur suddenly; rather, it takes place over time through specific actions. Urbizagástegui (2019) states that this decision is an individual event that occurs over a lengthy period and involves stages such as awareness, interest, decision, implementation, and, finally, innovation.
2.3.5. Unified Theory of Acceptance and Use of Technology (UTAUT).
UTAUT integrates contributions from various theories and theses regarding the adoption of information technologies to interpret an individual’s willingness to use a specific technology and their subsequent evaluations. It comprises four essential constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions (Avila, 2019). These constructs are, in turn, influenced by variables such as age, gender, experience, and willingness to use (Cartagena and Chumpitaz, 2020).
This model is the result of the unification of various theories, including TRA, TAM, the Motivational Model (MM), TPB, the Mobile Computer Usage Model (MPCU), IDT, and the Social Cognitive Theory (SCT) (Palma et al., 2019).
Performance expectancy is the extent to which an individual believes that the method employed will enhance their performance. Effort expectancy, on the other hand, refers to the level of complexity of the technology. Social influence is the process by which an individual is motivated to use a new system encouraged by those whose opinions they value. Finally, facilitating conditions are the elements, procedures, and infrastructure that support system use (Alonso, 2016; Ortega, 2017).
In conclusion, based on the literature review, it can be inferred that TAM surpasses TPB in terms of its predictive capability for the adoption of online purchases. However, TPB provides a broader perspective on the factors affecting this behavior. Therefore, TPB is more suitable when the purpose is to examine the causes of acceptance rather than predict whether it will occur or not.
In general, the theories supporting models of factors that positively influence purchase intention can be considered the most important theoretical-methodological foundations and are valid for the formation of the necessary theoretical constructs for understanding the topic: perceived usefulness, perceived ease of use, perceived trust, subjective norm.
3. Methodology
3.1. Theoretical development
The models used in research on this topic are diverse. Some are associated with the study of consumer profiles, others investigate the patterns that drive consumption through e-commerce, and finally, there are studies on the factors that determine online purchase intent. These latter studies have emerged due to the need for various sources and statistical records that position e-commerce as one of the best alternatives for companies to grow, become more competitive, and generate better revenues.
From a methodological perspective, the Structural Equation Modeling (SEM) technique proposed by Hair et al. (1999, pp. 612, 617-620) was followed, considering the following:
This technique requires prior decision-making regarding theoretical considerations about the variables that need to be combined in the theoretical model proposal.
It uses a measurement model in which the indicators of latent variables are competitive with each other and represent manifestations of these variables. There can be relationships between latent variables (Bollen, 1989).
It allows the researcher to establish causal relationships between the analyzed variables.
It permits the study of qualitative variables that are not quantifiable, even if they are neither directly measurable nor directly observable. This allows the measurement of an abstract concept through latent variables.
It can estimate quantitative, direct, indirect, and total interdependencies between the variables under study.
According to Hair et al. (1999), applying this technique involves adhering to seven essential steps. However, the progression for implementing this method is generally summarized into six steps according to Kaplan (2000) and Kline (2005), which are: a) Specification, b) Identification, c) Parameter Estimation, d) Fit Evaluation, e) Model Respecification, and f) Results Interpretation.
The progress in the current work covers the aspects of the first step proposed by Kaplan (2000) and Kline (2005) Additionally, the focus group (Hernández-Sampieri and Mendoza, 2018) was used as a content validity technique to demonstrate that the proposed items for latent variables represent the causal relationships derived from the analysis of constructs (Cristobal, 2001).
Specification. Developing a theoretically supported model presupposes theoretical grounding for specifying the relationships of dependence and interdependence among the interconnected set of variables (constructs) that can provide a comprehensive and consistent clarification of the analyzed phenomenon and its relationships based on the defined objectives. In the case of this work, the guideline proposed in Table 1 was followed.
Table 1 Theoretical Model of Online Purchase Intention
Theories and factors | Output | ||
---|---|---|---|
Theories TAM, TPB, TRA, IDT, UTAUT | Dimensions | Determining factors of online purchase intention of Mexican cybernauts | Theoretical model on the online purchase intention of the Mexican internet user |
Source: Authors’ own elaboration.
To provide further content validity, the focus group technique was employed, with the participation of six specialists, to discuss and propose both constructs and indicators based on the theoretical systematization conducted. In their selection, specialists from universities, companies, and institutions were considered, with the criterion that they were experts in business strategies, networking, sales, digital marketing consulting, marketing plans, marketing, customer relationship management, customer service, and quality services, website development, among others.
The constructs of the model were formed based on hypotheses derived from the analysis of different theories and models recognized as antecedents of this research. Studies conducted by Rodriguez and Herrero (2008), Calvo et al. (2013), Padrón et al. (2014), Peña (2014), Escobar and Bonsón (2014), Dachyar and Banjarnahor (2017), particularly those of Cristobal (2001), Salas et al. (2021), and Peña (2016), were assumed to be the most relevant to the analysis of factors determining online purchase intention.
Cristobal (2001), in a study conducted for a virtual establishment, contrasted, through the service performance (SERVPERF) performance assessment model, the existence of a significant relationship between the constructs of perceived quality, customer satisfaction, and online purchase intention.
Meanwhile, in the model proposed by Salas et al. (2021), using confirmatory factor analysis, a strong predictive power of intention and actual use of e-commerce in the context of Mexico was demonstrated. The intention to use is present in the model and is solely influenced by the attitude variable, which, in turn, encompasses the perceived benefits (advantages) and the degree of difficulty (complexity) in using e-commerce.
The most comprehensive study was conducted by Peña (2016), given the breadth of factors analyzed and the confirmation of the relationships established in the theoretical model. The SEM analysis for the cases of Colombia and Spain revealed that online purchase intention is preceded by attitudes, subjective norms, perceived control, self-efficacy, purchase impulse, and compatibility. Support was also found in the analysis to affirm the explanatory variables. However, for the consumer loyalty construct, they are entertainment, perceived value, trust, perceived risk, and satisfaction. Perceived value is influenced by price.
In summary, the mentioned models extensively develop the theories of TAM, TPB, TRA for the grounding of the constructs of the theoretical model. They address, although not all, the multitude of factors influencing purchase intention and use confirmatory factor analysis to test hypotheses about relationships between variables. Only one study was identified for the Mexican context.
3.2. Theoretical model of online purchase intention
Based on the theories and theoretical models previously analyzed, a theoretical model of purchase intention was proposed, comprising seven constructs as latent variables with their respective indicators.
Perceived usefulness is considered as internet users’ perception of the advantages and benefits resulting from using e-commerce (Chiu et al., 2012). If a customer believes that online shopping offers benefits in terms of product search, such as a variety of offers, time savings, and fast service, this influences purchase intention.
Perceived ease of use is conceived as the ease of operation and navigation on shopping websites, as well as the simplicity of the online shopping process via websites or mobile applications (Chen and Tsai, 2019). The perception of ease of technology use is positively related to online purchase intention, indicating that users who perceive online shopping as easy are more likely to have the intention to make a purchase in the future.
On the other hand, perceived trust is the expectation of security in making purchases through e-commerce, influenced by the seller’s reputation, website quality, and security (Chiu et al., 2012). If a customer considers that digital applications, online platforms, and web pages are reliable and that the information they contain and provide is accurate, relevant, understandable, and delivered on time, this will influence credibility and, consequently, purchase intention.
Perceived norm is an individual’s impression of e-commerce generated by the influence of their family, colleagues, partners, friends, reviews, recommendations from previous buyers, as well as the influence of eWOM (Haro, 2018; Zuluaga et al., 2020). Online purchases are influenced not only by customers’ own experiences but also by the opinions, reviews, and successful buying experiences shared among other consumers.
Regarding the construct of perceived value, it is defined as the extent of benefits or advantages obtained by the user through e-commerce: adherence to the lifestyle, buying habits, perception of customer service, responsiveness, company compliance, contact, warranties, and returns (Peña, 2016). When a customer considers that the advantages obtained through online shopping, such as benefits, enjoyment, accessibility, and instant connectivity, outweigh those of physical store purchases, there is a higher intention to buy.
The factor analyzed in the literature is price sensitivity in its relationship with quality (Peña, 2016). In this study, the construct is assumed to be price sensitivity and is the internet user’s response to variations in price levels available in e-commerce. The ability to compare prices, take advantage of better deals, and obtain better prices influences purchase intention.
Finally, perceived risk is conceived as the uncertainty or potential negative outcomes that internet users may face when making online purchases (Chan et al., 2003). Customers’ insecurity about potential outcomes of a purchase negatively influences online purchase intention.
Relationships were established between the constructs. Perceived usefulness is positively influenced by perceived ease of use, perceived norm, and price sensitivity. In turn, perceived trust is positively influenced by perceived norm and negatively influenced by perceived risk. Lastly, price sensitivity directly and positively influences perceived value.
Once the theoretical model was developed, the relationships were presented using a causal diagram linking dependent and explanatory variables. The sequence diagrams follow specific forms that must be considered to formulate the equations for each relationship (Pérez et al., 2013, p. 53). The notation used was from the Analysis of Moment Structures (AMOS) program.
In the case of this work, assuming a theoretically proposed model amenable to measurement through surveys, a measurable and structural model of online purchase intention was constructed (Hair et al., 1999), which includes dependent (latent) variables or constructs and indicators (items) as explanatory variables (Annex 1).
The proposed indicators were the result of the focus group’s work, which was used as a content validity technique. The proposed items for the latent variables represent the causal relationships derived from the analysis of the constructs.
Both exogenous and endogenous constructs were conceptualized (Hair et al., 1999, p. 623). The use of variables measured on a Likert scale from 1 to 5 (with 1 being “Completely Disagree” and 5 being “Completely Agree”) was proposed, as argued in Lloret et al. (2014). The causal relationship diagram resulting from the focus group’s work is shown in Figure 1.

Source: Authors’ own elaboration.
Figure 1 Causal diagram of the theoretical model of online purchase intention of Mexican internet users
With these results, we can proceed with the following steps of SEM as an appropriate technique to confirm the assumed relationships in the proposed theoretical model. Various theories and models need to be validated in the context of Mexico to contribute to the advancement of research on online consumer behavior.
4. Conclusions
Examining the frontier of knowledge supports the proposal of a theoretical model that integrates research on e-commerce, as well as various useful models and theories with a high predictive capacity for explaining the adoption of internet use. These can be applied to the purchase intention of Mexican internet users.
This article attempts to fill the gap in the literature on e-commerce in Mexico by first identifying different perspectives on purchase intention and then creating a theoretical model that underpins the various factors influencing the online purchase intention of Mexican internet users.
The validation of the proposed model in the Mexican context through SEM will provide information that enhances the understanding of user buying patterns, their sociodemographic characteristics, and the factors that may limit their purchase intention.