Predictive Factors of Purchase Behaviors on Facebook
Corresponding email: ycancino@ecci.edu.co
Published at : 28 May 2025
Volume : IJtech
Vol 16, No 3 (2025)
DOI : https://doi.org/10.14716/ijtech.v16i3.6432
Gómez, YAC, Duque, PMT , Orjuela , LCA, Guerrero, LMB & Rozo, JJP 2025, ‘Predictive factors of purchase behaviors on facebook’, International Journal of Technology, vol. 16, no. 3, pp. 835-845
| Yezid Alfonso Cancino Gómez | Department of Marketing and advertisement, Faculty of Economics and Business Science, Universidad ECCI, Cra. 19 No. 49-20, Bogotá, Colombia 111311 |
| Pedro Mauricio Torres Duque | Postgraduate School, Universidad ECCI, Cra. 19 No. 49-20, Bogotá, Colombia 111311 |
| Laima Catherine Alfonso Orjuela | School of Economics and Administrative Sciences. Specialization in Strategic Marketing, Corporación Universitaria UNITEC, Cl. 73 No. 20A-52, Bogotá, Colombia 111211 |
| Lugo Manuel Barbosa Guerrero | Department of Business Administration, Universidad Colegio Mayor de Cundinamarca Cra. 5 #28-85, Bogotá, Colombia 110311 |
| Jairo Jamith Palacios Rozo | Department of Social Work, Universidad Colegio Mayor de Cundinamarca Cra. 5 #28-85, Bogotá, Colombia 110311 |
Facebook, as a social networking platform, has become a significant marketing tool, serving both as an advertising medium and a sales channel. Considering the diversity in the purchasing behaviors of users, this research aimed to identify patterns that can be used to predict buyer types on Facebook. In order to effectively determine the relevant variables and predictors influencing buyer behavior on this platform, data were collected through a survey administered to 663 participants. Accordingly, an exploratory factor analysis was first conducted, followed by a multimodal logistic regression. The obtained results led to the acceptance of two out of the four proposed hypotheses. Based on the observations made, the majority of the demographic variables were found to be poor predictors of buyer type except for perceived security and responsiveness to advertising. These variables were observed to be strong predictors, significantly influencing the categorization of buyers within the Facebook environment.
Buyer security perception; Digital advertising; Predictive purchase behavior; Social network
The Facebook social network has been observed to attract individuals between the ages of 18 and 44, as reported by Mejia (2022). This demographic characteristic makes Facebook an effective platform for targeted communication with potential consumers. As a result, the platform has become a valuable marketing tool, as stated by González et al. (2015). Based on observation, Facebook has shown extensive market penetration in Colombia, reaching approximately 35,150,000 users.
Several factors have been identified across various previous research as influencers of purchase intention, including perception (Cavazos-Arroyo, 2022; Chica and Ruiz, 2017; Cea-Valencia et al., 2016; Hernández and Leal, 2013), utility (Cruz et al., 2019; Boluda and Fernández, 2013), and e-service quality (Albugami and Zaheer, 2023). Perception, in particular, arises from an individual’s experience, access to, or knowledge of an object. This invariably shows that the purchasing decisions of customers can be significantly influenced by marketing tools such as advertising on social networks since these platforms prompt user interpretations, which are closely tied to purchase intentions. Due to the interactive nature of digital advertising (Gómez-Nieto, 2016), users are exposed to experiences that influence respective purchase decisions. As stated in a previous investigation, advertising content on Facebook can be shared directly by brands, redistributed by followers, or marketed through paid promotions (Lipsman et al., 2012).
Regardless of the fact that active buyers on Facebook have been observed to predominantly engage with both organic content and sponsored advertisements, the platform’s user base and marketing environment have evolved. Various significant shifts have been made within the aspects of software development, advertising strategies, content creation, as well as media publishing, and these shifts have substantially altered user interaction patterns (Helmond et al., 2019) or customers’ behavior changes after of COVID-19 outbreak (Agus et al., 2021). As a result, there is a need to re-examine the factors influencing buyer behavior. In response, the aim of this research is to identify patterns that can be used to predict buyer types on Facebook.
Previous investigations have explored the prediction of shopper behavior on Facebook through multiple dimensions, including usage intensity and browsing habits (Leong et al., 2018), the role of trust (Shamim and Islam, 2022; Bugshan and Attar, 2020; Leung et al., 2020), the impact of influencers (Zafar et al., 2021; Hsu, 2020), the social media effect on customer experience (Benchekroun et al., 2024) and the influence of word-of-mouth (Joshi and Singh, 2017). These explorations have been observed to consistently emphasize the relevance of factors such as utility, interactivity, informativeness, relevance, and the perceived importance of advertising (Alalwan, 2018), as well as consumer attitudes toward advertisements, in shaping purchase intentions (Bandil et al., 2023; Ho et al., 2022).
Various other research have aimed to identify the specific factors influencing purchase behavior on Facebook. For instance, Mendoza et al. (2020) identified factors namely content quality and promotional strategies, as key behavioral determinants among consumers engaging with tour operators. Similarly, Hoque et al. (2020) reported that satisfaction with Facebook-based purchases is significantly shaped by variables such as after-sales service, home delivery, product quality, pricing, and a sense of security. Bhattacharyya and Bose (2020) further stated that simply clicking “like” on a product or advertisement can be associated with actual purchasing actions. Powers et al. (2012) also supported this perspective by reporting that approximately 20% of consumers believed social media played a very important role in influencing respective final purchase decisions. Based on these insights, the following hypothesis was proposed during the course of the research:
H1: The type of Facebook shopper is predicted by the purchases they make on the social network.
In the context of advertising, it has been established that online communications significantly influenced a company's competitiveness (Chan et al., 2022), particularly because social media advertising was observed to significantly support consumers in making purchasing decisions (Chandra et al., 2012). This form of advertising has been shown to positively affect both purchase intention and actual purchases made through social networks (Duffett, 2015). An example of this influence is found in consumer behavior within the cosmetics sector, where Facebook advertisements have been shown to shape the attitudes, behaviors, and perceptions of women toward cosmetics, thereby driving sales and purchase decisions (Agneta, 2018). Attitudes toward advertising have been widely recognized as a key factor associated with purchase intention (Indrawan et al., 2022; Banik and Dhar, 2021), as has consumers’ liking for advertising content (Pelet and Ettis, 2022), along with the impressions generated by advertisements (Ertimur and Gilly, 2012). Moreover, interactivity on Facebook has been positively correlated with both purchase intention and favorable brand attitudes (Persuad, 2013), Based on this information, the following hypothesis was formulated:
H2: Facebook buyer type can be effectively predicted through respective attitudes towards advertising on the social network.
According to Bauman and Bachmann (2017), online trust is a very important element in digital transactions, specifically considering the fact that the perception of safety typically precedes purchase behavior. Perceived risk has been found to have a moderating effect on purchase intention (Qalati et al., 2021), while trust was reported to positively influence both purchase intention (Al-Adwan and Kokash, 2019) and actual purchase behavior (Oghazi et al., 2018). Considering these findings, hypothesis 3 was formulated as follows:
H3: The type of Facebook shopper is predicted by the perception of safety in Facebook shopping.
The attentional process toward advertising has been observed to operate at various levels. Research have shown that men tend to possess a higher degree of attentiveness to advertising compared to women (Fondevila-Gascón et al., 2020). However, it is important to acknowledge that promoting increased attention to invasive advertising on Facebook may have adverse effects, particularly by diminishing brand acceptance (Voorveld et al., 2018). This insight led to the formulation of hypothesis 4.
H4: The type of Facebook buyer is predicted by the attentiveness to advertising on the social network.
The present research aims to establish the rules of a predictive model that examines the effects of factors. The rules in this regard include liking for advertising, perceived safety in shopping, shopping behavior on social networks, and attention to advertising (Figure 1).
Figure 1 Research model
A descriptive, cross-sectional, non-experimental study with a quantitative approach was adopted using a statistical test of logistic regression with the aim of developing a model to predict purchase intention based on factors of perception and interaction on Facebook. The sample had as parameters a population of 32,912,292 people between 15 and 65 years of age (DANE, 2018) residing in Colombia, a confidence level of 95% and an error of 3.8%. The final sample consisted of 663 participants, all of whom were previously validated as active Facebook users and selected using the snowball sampling approach.
Data collection was carried out through the administration of an electronic survey via email and instant messaging. In addition to collecting socioeconomic and demographic data, the main variables were measured using a 5-point Likert scale. The dimensions, alongside respective corresponding variables, were determined through exploratory factor analysis, and the final statistical test applied was logistic regression, which was used to establish the predictive equation of purchasing behavior.
2.1. Sample Characterization
The final sample comprised 53.2% women and 46.8% men, predominantly from the middle class (61.2%), followed by the working class (29.0%) and the upper class (3.4%). In terms of occupation, 21.6% identified as students, 36.5% as working individuals, and 37.7% reported engaging in both activities. Educational attainment included high school graduates (18.4%), technicians (17.9%), technologists (27.0%), and professionals (30.8%). Geographically, 75% of the observed participants resided in the nation's capital, 11.8% in departmental capitals, and the remaining 12.9% in intermediate or small cities. Lastly, regarding personal monthly income, 35.1% earned less than US$208, 52.8% between US$208 and US$622, as well as 10.1% between US$622 and US$1,452.
3.1. Exploratory Factor Analysis
An exploratory factor analysis was conducted to identify the variables within each dimension. For Dimension 1, as shown in Table 1, nine variables were grouped with factor loadings exceeding 0.7. Bartlett’s test of sphericity for this dimension was significant (p < 0.0001), and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was considered excellent (0.933). Furthermore, the internal consistency of the variables was high, as evidenced by the obtained Cronbach’s alpha value of 0.931.
Table 1 Variables in dimension 1 Facebook advertising liking
Note: 1 Factor loading, extraction method: principal axis factorization, one factor extracted, 4 iterations required, 2 Cronbach's alpha index, 3 Cumulative variance, 4 Kaiser-Meyer-Olkin measure of sampling adequacy 5 Bartlett's test of sphericity.
For Dimension 2, presented in Table 2, the analysis included a group of four variables with factor loadings above 0.7. The test of sphericity was also significant (p < 0.0001), and the KMO value of 0.812 showed good sampling adequacy. The reliability of the variables was similarly high, with a Cronbach’s alpha value of 0.895.
Table 2 Variables in dimension 2 Safe shopping on Facebook
Note: 1 Factor loading: extraction method: principal axis factorization, one factor extracted, 5 iterations required, 2 Cronbach's alpha index, 3 Cumulative variances, 4 Kaiser-Meyer-Olkin measure of sampling adequacy 5 Bartlett's test of sphericity.
The exploratory factor analysis for Dimension 3, as presented in Table 3, grouped five variables with factor loadings greater than 0.7. Bartlett’s test of sphericity was significant (p < 0.0001), and the KMO measure of sampling adequacy was good (0.860). The variables showed a high level of internal consistency, with a Cronbach’s alpha value of 0.860.
Table 3 Variables that make up dimension 3 Shopping on Facebook
Note: 1 Factor loading: extraction method: principal axis factorization, one factor extracted, 2 Cronbach's alpha index, 4 iterations required, 3 Cumulative variance, 4 Kaiser-Meyer-Olkin measure of sampling adequacy 5 Bartlett's test of sphericity.
The exploratory factor analysis for Dimension 4, as presented in Table 4, led to the grouping of five variables with factor loadings above 0.5. Bartlett’s test of sphericity was significant (p < 0.0001), and the KMO measure of sampling adequacy was good (0.850). The variables also showed a high level of reliability, with a Cronbach’s alpha of 0.874.
Table 4 Variables in dimension 4 Attention to Facebook advertising
Note: 1 Factor loading: extraction method: principal axis factorization, one factor extracted, 6 iterations required, 2 Cronbach's alpha index, 3 Cumulative variances, 4 Kaiser-Meyer-Olkin measure of sampling adequacy 5 Bartlett's test of sphericity.
3.2. Multinomial Logistic Regression
The logistic regression model used purchase behavior on the social network as the observed dependent variable. Participants were categorized as non-buyers (23.1%), occasional buyers (53.1%), and frequent buyers (23.8%). Accordingly, several covariates, namely age, economic dependence, city size, liking of advertising, purchases on Facebook, generation, and income, were excluded from the model due to a lack of explanatory power and poor goodness-of-fit performance. The entire fit of the model, as shown in Table 5, is supported by a Chi-square value of 546.283 with a significance level below 0.05. The pseudo-R² values, Cox and Snell (0.561) and Nagelkerke (0.646) showed that at least one covariate significantly contributed to predicting the buyer type.
The model (Table 5) showed that the dimensions of shopping security and attentiveness to advertising are significant predictors of occasional buyer behavior. Within this context, a one-unit increase in perceived security was observed to increase the probability of transitioning from non-buyer to occasional buyer by a factor of 2.024 (95% CI Odds = 1.772–2.312). Similarly, a one-unit increase in attentiveness to advertising was found to raise this likelihood by a factor of 1.11 (95% CI Odds = 1.037–1.188). It is important to state that in this model, gender did not significantly influence the dependent variable.
Table 5 Model fit information
Note: Goodness of fit: Pearson (Chi-Square 730.068. Sig 0.009). Cox and Snell 0.561 and Nagelkerke 0.646
The model showed that shopping security, attentiveness to advertising, and gender significantly predicted frequent buyer behavior (Table 6). An increase of one unit in the level of perceived security increased the probability of moving from non-buyer to frequent buyer by a factor of 2.419 (95% CI Odds = 2.086–2.806), while a one-unit rise in advertising attentiveness increased this probability by a factor of 1.21 (95% CI Odds = 1.111–1.318). For this model, female gender has an effect on the dependent variable of 1: 1.095 (95% CI Ods=1.003-1.196), it is a condition that affects the dependent variable very slightly.
A model designed to predict the behaviour of Facebook shoppers necessitates the incorporation of advertising attention as a pertinent latent variable for forecasting. This is partially due to the critical role that informativeness in advertising plays in generating attention and fostering a positive perception towards the advertisement, as highlighted by Hayes and King (2014). In the context of our present research, we introduce novel measurement variables that pertain to the type of content that resonates with the Facebook audience. The connection between advertising relevance and attention is frequently established through scales that gauge the degree of attention (Caviedes-Caviedes et al., 2024; Jung, 2017). Regarding relevance, Bang and Wojdynski (2016) contended that personalized advertising garners increased attention, a phenomenon similarly observed when advertising holds intrinsic value for the viewer (Cespedes, 2021).
Table 6 Parameter estimation
Note: a The reference category is: Non-buyer. b This parameter is set to zero because it is redundant. 1* Occasional buyer. 2** Frequently buyer
Companies with a longer history in the market commonly incorporate social networks into their ICT strategies (Alfonso and Orjuela et al., 2022). This trajectory fosters a sense of security that significantly influences purchase intentions (Naradin et al., 2020; Rodrigues et al., 2019). As stated by Kim et al. (2012), trust is an essential component in online shopping and serves as a mediator in repurchase decisions (Liang et al., 2018). The seller plays a pivotal role as a source of trust, which is closely linked to purchase intentions (Leerapong, 2013) and has a notable impact on purchase perceptions (Pavlou and Gefen, 2004). The verification of suppliers enhances consumers' perceptions of safety (Xu and He, 2021), and high ratings provided by users positively affect purchase intentions (Xu and He, 2021). When consumers exhibit a higher degree of trust in e-commerce, it is likely to result in increased purchase behaviour (Leong et al., 2018).
The security dimension identified in purchase behavior includes variables such as account verification and positive feedback from other users. In addition, this exploration introduces novel contributors to perceived security, specifically, the option for cash-on-delivery payments and the availability of physical sales locations provided by sellers.
Table 7 presents the model’s predictive accuracy, showing an entire prediction efficiency of 72.7%. Based on observation, the model was able to predict non-buyers with 83% accuracy, occasional buyers with 84.1% accuracy, and frequent buyers with 36.7% accuracy. This simply showed that the model is most effective at predicting occasional buyer behavior and least effective at predicting frequent buyers.
Table 7 Observed and predicted forecasts
Note: Observed and predicted cases from the model
A predictive model for profiling buyers and non-buyers within the Facebook social network was developed in this research based on two key constructs namely, the perception of security during the purchase process and the appeal of the advertising content preferred by users. The observations made showed that demographic variables, including social class, educational level, city of residence size, and income level, had no significant influence on buyer profiling within this platform. Perceptions of purchasing security on social networks such as Facebook were found to be largely shaped by verified sellers and commercial accounts that provide clear information regarding physical points of sale or cash-on-delivery options. This sense of security was observed to be further reinforced when purchase-related information was validated through user-generated comments and reviews. Advertising on social networks that emphasized available payment methods was found to effectively and predominantly capture the direct attention of users. Additional elements such as special promotions and delivery services also played an important role in this regard. Depending on the degree of attention paid to advertisements featuring these attributes, comments from other users had a strong influence on purchase intentions. Furthermore, both advertising appeal and purchase security appeared to converge on a common influencing factor, which is the feedback provided by previous buyers regarding past shopping experiences. This suggested that efforts to craft attention-grabbing advertisements and strengthen the perception of security could increase the probability of a user transitioning from a non-buyer to an occasional or even frequent buyer on Facebook. In order to effectively enhance user purchase responses, it is important to incorporate elements that strengthen the perception of security during the purchasing process. This particular construct wields a more substantial impact on purchasing behavior compared to the allure of attention-grabbing advertising. The current research was subject to three limitations. In order to develop and validate a more comprehensive predictive model, future research should consider additional variables associated with purchase behavior on Facebook, including the influence of social media influencers, word-of-mouth recommendations, browsing intensity, and frequency of platform use.
Author Contributions
Yezid Alfonso Cancino-Gómez: Introduction, methodology, data collect, data analysis Pedro Mauricio Torres-Duque: Methodology, data collect Laima Chaterine Alfonso-Orjuela: Introduction, data anlysis, conclusions Lugo Manuel Barbosa-Guerrero: Data analysis, conclusions Jairo Jamith Palacios-Rozo: Data analysis, conclusions
Conflict of Interest
The authors declare that there is no conflict of interest.
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