Personality traits, digital environment, and consumer decision-making: evidence from online and offline retail in Azerbaijan

1. INTRODUCTION

    

The rapid development of digital technology has fundamentally transformed the consumer market, shifting shopping methods from traditional brick-and-mortar stores to online channels. Today, e-commerce websites, social media, mobile applications, and algorithm-based recommendation systems constitute a constantly evolving digital environment that influences how consumers search for, compare, and purchase products. These changes raise important questions about the true factors influencing consumer decision-making within these environments. This also underscores the importance of understanding how stable psychological traits and more volatile external factors interact and influence purchasing behavior.

Personality traits have long been considered a key psychological factor in determining consumer behavior. The Big Five model — openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism — offers a well-established framework for understanding how individual differences in how people consume and make purchasing decisions (John & Srivastava, 1999; McCrae & Costa, 2008). Previous studies have consistently indicated that openness is linked to greater technology adoption and stronger engagement with digital environments, while neuroticism is often associated with higher levels of perceived risk in online transactions (Bhatnagar, 2000). However, it remains unclear whether these personality-based influences are equally pronounced in online and offline shopping environments, especially in transitional markets where both retail formats coexist.

Azerbaijan provides an ideal setting for this research. Its retail environment is characterized by the coexistence of rapidly evolving digital commerce and long-established brick-and-mortar shopping channels. However, few studies simultaneously examine personality traits and digital environmental factors in online and offline retail, especially in emerging markets like Azerbaijan. Therefore, our understanding of how intrinsic psychological traits and extrinsic digital factors interact to influence consumer decisions remains limited. The following research questions are addressed: (1) Do personality traits significantly predict online and offline shopping frequency when controlling for demographics and digital environment factors? (2) Do respondents differ significantly in their online versus offline shopping frequency? (3) How do personality traits correlate with shopping behavior across retail channels? (4) Do gender and age significantly differentiate shopping frequency?

2. LITERATURE REVIEW

2.1. Consumer Decision-Making and Personality Traits

The consumer decision-making process includes the selection, purchase, use, and final disposal of goods and services. Traditional models, such as the Engel-Korat-Blackwell (EKB) model, describe this process as a series of cognitive steps: needs identification, information search, opportunity assessment, purchase, and post-purchase analysis. However, more recent research questions the strict linearity of this model, as digital environments now allow consumers to skip, combine, or reorder different stages of the decision process (Kotler & Keller, 2022). Ajzen's (1991) Theory of Planned Behavior adds another important perspective by introducing a psychological dimension, proposing that behavioral intentions (influenced by attitudes, social norms, and perceived behavioral control) serve as a link between individual tendencies and actual behavior.

Among psychological factors, the Big Five personality traits are widely recognized for their strong and consistent predictive power (John & Srivastava, 1999; McCrae & Costa, 2008). Openness to experience, which reflects curiosity and a willingness to try new things, is often associated with early adoption of technology and greater engagement with digital shopping platforms. Conscientiousness is associated with organization and purpose, and often fosters more structured decision-making and more thorough information gathering—something online platforms achieve through tools such as filters, reviews, and product comparisons. According to Rentfrow & Gosling (2003), extraversion is associated with social skills and a preference for stimuli, and is more likely to be associated with greater participation in offline shopping, as interpersonal interaction plays an important role in offline shopping. Agreeableness may increase sensitivity to social influence and peer recommendations, while neuroticism is often connected to higher perceived risk in online transactions, which can reduce willingness to shop digitally (Bhatnagar, 2000). In summary, these relationships suggest that the influence of personality traits on purchasing behavior is not static, but rather depends on the consumption environment and differs between online and offline environments.

2.2. Digital Environments and Consumer Behavior

The digital retail environment also includes mechanisms that influence not only individual characteristics but also consumer decision-making. Personalization systems, powered by machine learning and behavioral data, provide targeted recommendations that reduce cognitive effort and narrow the range of options consumers consider (Arora et al., 2020). Electronic word-of-mouth (eWOM) also plays an important role, as online reviews and peer ratings can increase purchase intention by reducing uncertainty and improving perceived credibility. These environmental factors suggest that external digital cues can enhance or replace the influence of personality traits, supporting a comprehensive model in which both internal traits and external factors influence consumer behavior.

The Technology Acceptance Model (Davis, 1989) provides a key conceptual framework for understanding the process of technology adaptation, emphasizing that perceived usefulness and ease of use are key factors in adaptation. Later extensions that include trust, social influence, and perceived risk are especially relevant in digital retail settings (Venkatesh & Morris, 2000). From a broader perspective, personality traits can be viewed as long-term factors that shape general behavioral tendencies, while digital influences act as immediate situational triggers that can strengthen or weaken these tendencies at the moment of decision-making. Dwivedi et al. (2021) and Lemon and Verhoef (2016) also show that the customer journey today is shaped by multiple connected digital touchpoints, which makes consumer behavior much more context-dependent than earlier models suggested.

2.3. Online versus Offline Retail and the Azerbaijan Context

Online and offline retail channels create unique environments that influence consumer behavior differently. Online platforms offer convenient shopping experiences, a wider selection of products, transparent pricing, and continuous, personalized recommendations, thereby encouraging consumers to make more analytical and data-driven decisions. This environment may enhance the influence of consumer traits such as openness and responsibility. In contrast, offline shopping provides sensory experiences and direct social interaction, which can appeal more to extraverted and experience-oriented consumers (Pantano & Priporas, 2016). In transitional markets where both online and offline channels dominate, this presents a valuable opportunity to study how personality traits influence consumer behavior differently across various shopping contexts.

Azerbaijan is a prime example of this hybrid retail model. In Azerbaijan, internet use and social media penetration are growing rapidly, while offline shopping remains crucial. Online consumers in Azerbaijan generally place great emphasis on trust signals, platform reputation, and peer recommendations (Azerbaijan Digital Economy Research, 2023), while brick-and-mortar retail continues to play a significant role in daily shopping habits. This combination makes Azerbaijan an ideal location to study whether personality traits influence consumer behavior across different channels, and how factors such as age and gender affect digital consumption in transitional markets (UNCTAD, 2022).

2.4. Conceptual Framework and Hypotheses

The literature review reveals a long-standing gap: personality traits and digital environment factors are rarely examined simultaneously in the same empirical model, and this is also significantly lacking in the existing evidence for emerging markets like Azerbaijan. This study examines five core personality traits as independent variables to predict online and offline shopping frequency. Furthermore, gender and age are included as control variables, and their individual effects on shopping frequency are analyzed.

Based on the theoretical arguments developed above, the following hypotheses are proposed:

• H1: Big Five personality traits significantly predict online shopping frequency.
• H2: Big Five personality traits significantly predict offline shopping frequency.
• H3: Respondents differ significantly in their online versus offline shopping frequency.
• H4: Shopping frequency differs significantly between gender groups.
• H5: Shopping frequency differs significantly across age groups.

3. RESEARCH METHODOLOGY

3.1. Research Design and Data Collection

This study employed a cross-sectional quantitative research method. Data were collected through a structured online questionnaire, targeting residents of Azerbaijan. Convenience sampling was used, and 111 responses were received, including 65 from males (58.6%) and 46 from females (41.4%). The sample was predominantly composed of young adults, with the oldest group aged 18-24 (57.7%), followed by 25-34 (17.1%), under 18 (12.6%), 35-44 (9.9%), and over 45 (2.7%). Therefore, the sample is predominantly young adults, which may influence their online shopping behavior and limit the applicability of the findings to other age groups.

Table 1. Sample Demographic Distribution

3.2. Measurement Instruments

Personality traits were assessed using five independent items adapted from the Big Five Personality Inventory (BFI-10), each item rated on a five-point Likert scale. 

These items included openness ("open to trying new experiences"), conscientiousness ("planning and following schedules"), extraversion ("enjoying social interaction"), agreeableness ("being trusting and supportive"), and neuroticism ("feeling anxious or worried"). The scale demonstrated acceptable reliability, with a Cronbach's alpha coefficient of 0.749, exceeding the recommended threshold of 0.70 (Nunnally, 1978).

Digital environmental variables were introduced to describe shopping behavior in different contexts. For online shopping, these variables included product search, price comparison, reading reviews, and safety concerns. Variables for offline shopping focused on planned purchases and the in-store shopping experience. Shopping frequency for both online and offline channels was assessed using a five-point scale, from 1 (Never) to 5 (Very frequently). 

3.3. Analytical Strategy

Data analysis was performed using IBM SPSS Statistics software. Methods included descriptive statistics, Pearson correlation analysis, paired t-tests, independent samples t-tests, and Welch one-way ANOVA. Additionally, two multiple regression models were used to examine how personality traits, digital environment factors, and demographic characteristics (gender and age) influenced online and offline shopping frequencies.

4. FINDINGS AND DISCUSSION

4.1. Descriptive Statistics

Table 2 lists the means and standard deviations for all variables. Respondents reported a higher frequency of in-store shopping (M = 3.93, SD = 0.99) than online shopping (M = 2.72, SD = 1.05), indicating that in-store shopping remained more frequent in this sample. Regarding personality traits, conscientiousness (M = 3.92) and extraversion (M = 3.84) scored the highest, while neuroticism (M = 3.22) scored the lowest. Regarding digital factors, reliance on online reviews scored the highest (M = 4.37), highlighting the significant influence of digital recommendations.

Table 2. Descriptive Statistics for Study Variables

Note. N = 111. All items measured on a 5-point Likert scale (1 = Never/Strongly disagree; 5 = Always/Strongly agree).

4.2. Correlation Analysis

Table 3 presents the Pearson correlation matrix for key variables. Online shopping frequency was significantly positively correlated with openness (r = 0.447, p < 0.001), extraversion (r = 0.345, p < 0.001), neuroticism (r = 0.332, p < 0.001), and agreeableness (r = 0.216, p = 0.023). Offline shopping frequency was only significantly correlated with conscientiousness (r = 0.213, p = 0.025). The relationship between online and offline shopping was negative, but not statistically significant (r = −0.168, p = 0.078), indicating that they were relatively independent. Overall, the results suggest that at the bivariate level, personality traits are more strongly correlated with online shopping behavior.

Table 3. Pearson Correlation Matrix

Note. * p < .05, ** p < .01, *** p < .001. N = 111.

4.3. Online versus Offline Shopping Frequency

A paired t-test compared the online and offline shopping habits of the same group of participants. The analysis showed a significant difference (t (110) = -8.14, p < 0.001), with respondents reporting a significantly higher frequency of offline shopping (M = 3.93) than online shopping (M = 2.72). This indicates that despite the rapid development of digital platforms, physical stores remain the primary shopping method for Azerbaijani consumers surveyed.

Table 4. Paired Samples T-Test: Online versus Offline Shopping Frequency

4.4. Gender and Age Group Differences

Independent samples t-tests showed a significant gender difference in online shopping frequency (t (109) = 2.457, p = 0.016), supporting hypothesis H4 regarding online shopping behavior. In contrast, there was no significant gender difference in offline shopping frequency (t (109) = 0.133, p = 0.895). Furthermore, Welch one-way ANOVA showed significant age differences in both online shopping (F (4, 12.4) = 3.99, p = 0.026) and offline shopping (F (4, 12.3) = 6.17, p = 0.006), supporting hypothesis H5. These results are detailed in Tables 5 and 6.

Table 5. Independent Samples T-Test: Gender and Shopping Frequency

Table 6. One-Way Welch ANOVA: Age Group and Shopping Frequency

4.5. Regression Analysis with Full Controls

Two multiple linear regression models were constructed, incorporating personality traits, digital environment factors, and demographic control variables (gender and age), respectively. The model including online shopping had better explanatory power than the model including only personality traits (R² = 0.294 vs. 0.237), explaining 29.4% of the variance. As shown in Table 7, only openness remained a significant predictor (β = 0.257, p = 0.017), making it the strongest and most reliable personality factor for online shopping. Gender was close to significant (β = -0.319, p = 0.090), indicating slightly lower online shopping activity among women. All digital environment factors were insignificant, suggesting they may function more as background factors than direct predictors. The model including offline shopping explained less variance (R² = 0.125 vs. 0.075 of the baseline model). As shown in Table 8, no personality trait, demographic factor, or digital environment variable significantly predicted offline purchasing behavior, confirming that the influence of personality traits is mainly related to the online environment.

Table 7. Regression Results – Online Shopping Frequency (with Controls)

Table 8. Regression Results – Offline Shopping Frequency (with Controls)

This study analyzes in detail how personality traits, digital environment, and demographic factors influence consumer behavior in Azerbaijan, highlighting five key findings. 

First, paired t-tests showed that offline shopping was significantly more prevalent than online shopping (M = 3.93 vs. M = 2.72, p < 0.001). This indicates that despite the increasing adoption of digital technologies, brick-and-mortar retail remains dominant in Azerbaijan. These findings are consistent with data from UNCTAD (2022) and the Azerbaijan Digital Economy Study (2023), both of which point to trust issues and deeply ingrained shopping traditions in transitional markets.

Second, the study found that openness was the only personality trait that consistently predicted online shopping behavior (β = 0.257, p = 0.017; R² = 0.294). This aligns with theoretical expectations, as open-minded individuals tend to seek novelty and feel more comfortable in digital spaces that offer diversity and personalization (Guo et al., 2021; Arora et al., 2020). The continued presence of this factor in the model underscores its importance as a key factor influencing online shopping behavior. 

Third, correlation analysis revealed significant associations between extraversion, neuroticism, and online shopping engagement, although these associations were no longer significant in regression analysis. This suggests an overlap effect of personality traits. The positive correlation between neuroticism and online shopping is particularly noteworthy, as it may indicate a greater inclination towards convenience and control than risk aversion (Bhatnagar, 2000). 

Fourth, personality traits were not significant predictors of offline shopping (R² = 0.125), suggesting that brick-and-mortar retail behavior is more influenced by situational and habitual factors than by psychological traits. This is consistent with the findings of Pantano and Priporas (2016), who emphasized the sensory and experiential aspects of offline shopping. Furthermore, gender and age differences highlight the importance of demographic segmentation. Young consumers who grew up in the digital age are particularly active online, suggesting that targeted digital marketing strategies emphasizing openness and novelty may be especially effective.

Overall, the findings indicate that personality traits have a greater influence on online shopping than offline shopping, and openness is a key determinant of Azerbaijani consumers' digital shopping behavior.

6. CONCLUSION AND RECOMMENDATIONS 

This study, based on data from 111 respondents, comprehensively analyzed personality traits, digital environment factors, and demographic characteristics influencing online and offline shopping behavior in Azerbaijan. The study employed correlation analysis, t-tests, analysis of variance, and multiple regression. Five main conclusions were drawn:

1. Offline shopping remains significantly higher than online shopping (t (110) = -8.14, p < .001), confirming the continued dominance of brick-and-mortar retail in Azerbaijan and supporting hypothesis H3.

2. Openness was the only personality trait that consistently predicted online shopping behavior (β = 0.257, p = .017), and remained significant after controlling for other variables. Hypothesis H1 was partially confirmed.

3. None of the personality traits significantly predicted offline shopping behavior (R² = 0.125), indicating that the influence of personality traits is primarily reflected in the digital environment. Hypothesis H2 was not confirmed. 

4. Gender differences significantly impacted online purchases (p = 0.016) but not offline purchases, supporting only hypothesis H4 regarding online channels.

5. Age significantly impacted both online (p = 0.026) and offline (p = 0.006) purchases, fully supporting hypothesis H5.

Practical application suggests that these findings indicate digital marketers in Azerbaijan should focus on discovery-based segmentation, leveraging novelty-driven content, personalized recommendations, and exploratory product experiences, particularly targeting younger consumers. Age and gender differences highlight the importance of personalized communication strategies rather than a one-size-fits-all approach. Limitations of this study include convenience sampling, a predominantly young population, and the use of single-item personality tests, which may affect the accuracy and broader applicability of the research. Future research should use larger, more representative samples, employ validated multi-item measures of the Big Five personality traits, and utilize more sophisticated methods such as structural equation modeling. Including longitudinal and cross-national studies would also help improve the external validity of the research. Overall, this study indicates that personality traits have a greater impact on online shopping than on offline shopping, and openness is the most reliable predictor of online shopping behavior in Azerbaijan.