Editorial Board

A Refereed Monthly International Journal of Management

Analyzing Consumer Behavior in Electronics Sales: A Study on Product Categories, Price Distribution, and Purchase Intent

Evgenia Breusova

Associate Professor,

Department Economics and Management,

Institute of the Service Sector and

Entrepreneurship (branch) DSTU in Shakhty

https://orcid.org/0000-0002-0852-3610

eva_breusova@mail.ru

Natalia Kolomoets

Associate Professor

Department Economics and Management,

Institute of the Service Sector and

Entrepreneurship (branch) DSTU in Shakhty

https://orcid.org/0009-0006-2162-4463

kolomoec2003@mail.ru

Andrey Novikov

Associate Professor

Department Economics and Management,

Institute of the Service Sector and

Entrepreneurship (branch) DSTU in Shakhty

https://orcid.org/0000-0003-4359-979X

novikov-1962@bk.ru

Maria Vilisova

Associate Professor

Department Economics and Management,

Institute of the Service Sector and

Entrepreneurship (branch) DSTU in Shakhty

https://orcid.org/0000-0001-8873-7055

villisbrus@mail.ru

Evgeny Moskvitin

Associate Professor

Department Economics and Management,

Institute of the Service Sector and

Entrepreneurship (branch) DSTU in Shakhty

https://orcid.org/0002-0002-7756-5048

djulm@rambler.ru

Abstract

This study explores consumer behavior in the electronics market, focusing on factors that influence purchase intent, including customer satisfaction, demographic characteristics, and product-related variables. Using a dataset from Kaggle with detailed sales information on electronics, the study analyzed the relationships among key variables such as age, gender, product category, and customer satisfaction. Descriptive statistics and Pearson’s correlation were employed to examine associations between these factors. A binary logistic regression model was also applied to predict purchase intent. The model achieved a predictive accuracy of 79%, highlighting that higher satisfaction and male gender were associated with increased purchase intent. Visual analysis through histograms and box plots provided insights into customer age distribution, product category trends, and price ranges, identifying stable pricing across categories and a balanced distribution of customer ages. The findings suggest that customer satisfaction and specific demographic factors strongly influence purchasing behavior in the electronics sector, providing valuable insights for marketers and retailers aiming to tailor strategies and enhance customer engagement in this competitive market.

Keywords: Electronic goods, Purchase intention, Customer satisfaction, Gender, Age

Introduction

The consumer electronics market has seen rapid growth in recent years, driven by advancements in technology and increasing customer demand. The industry is characterized by dynamic and competitive markets, where understanding the factors influencing purchase decisions is crucial for maintaining market share. With the rise of e-commerce platforms and access to a wide range of products, consumer purchasing patterns have become more complex. Gaining insights into these patterns can help businesses enhance their marketing efforts and tailor their offerings to meet the needs of different customer segments.

In particular, purchase intent—the likelihood that a customer will buy a product—and customer satisfaction—how satisfied a customer is with their overall shopping experience—are two critical metrics that businesses closely monitor. These two factors are not only indicative of current sales performance but are also predictors of long-term customer loyalty and brand advocacy (Oliver, 1999).

Purchase intent reflects a customer’s inclination to buy a product, influenced by various factors such as product quality, brand reputation, price, and customer experience (Sivaram et al. 2019). Understanding what drives purchase intent is essential for developing effective marketing strategies and product offerings. According to research by Curtis et al. (2017), purchase intent is often shaped by a combination of rational evaluations (e.g., price and product features) and emotional connections with the brand. In the context of consumer electronics, where customers have access to a wide array of products and brands, purchase intent was found to be influenced by the factors such as attitude toward the brand, influence of family, friends, and online reviews and consumers' perception of their ability to make informed decisions influences their buying intentions (Almeida et al., 2017).

Customer satisfaction is another key variable that plays a significant role in shaping purchase intent. Satisfied customers are more likely to make repeat purchases and recommend products to others (Almursyid et al. 2024). Several studies have demonstrated a strong correlation between customer satisfaction and purchase intent across industries. For instance, Hult et al. (2023) found that customer satisfaction directly influences the likelihood of future purchases in retail settings suggesting that repeat purchases are influenced by their satisfaction level. Similarly, Ngubelanga (2020) noted that in the consumer electronics market, a positive post-purchase experience significantly boosts the probability of repeat purchases, even in cases where initial purchase intent was low.

Moreover, the relationship between satisfaction and purchase frequency is also important. Repeat customers are often more satisfied with the brand and service, creating a virtuous cycle where higher satisfaction leads to more frequent purchases, which in turn enhances satisfaction further. In the consumer electronics sector, Seminari et al. (2023) highlighted that customers who experience satisfaction with one purchase are more likely to show higher purchase intent for related products in the future, such as upgrading their devices or buying complementary gadgetsn in case of electronic goods.

Recent research also emphasizes the importance of personal factors such as gender in shaping purchase intent. Studies suggest that male and female customers may have different priorities when purchasing electronics, with men generally placing more emphasis on technical features and price, while women might prioritize ease of use and customer service (Okazaki & Hirose, 2009). Understanding these differences can help businesses tailor their offerings and marketing strategies to meet the needs of diverse customer segments more effectively.

Given the highly competitive nature of the consumer electronics market, exploring the dynamics of purchase intent and customer satisfaction is essential for businesses to develop strategies that not only attract new customers but also retain existing ones. This study seeks to build on existing literature by analyzing the relationship between purchase intent, customer satisfaction, and demographic factors such as gender within the context of consumer electronics sales. Specifically, it aims to assess how these variables interact and influence overall sales performance using real-world data.

Review of literature

Understanding consumer behaviour in the context of electronics sales is vital for identifying the factors that influence purchase intent across product categories, price distributions, and other variables. Over the past decade, numerous studies have examined these elements, providing insights into the patterns that govern consumer decisions. This review focuses on recent literature from the last ten years, particularly studies investigating the role of various variables—such as product attributes, price sensitivity, brand loyalty, technological advancements, and consumer reviews—on purchase intent.

According to Nizam et al. (2022), consumers often associate certain product categories with specific needs, influencing their decision-making process. In their study on mobile phone purchases, the researchers found that consumers prioritize features such as battery life, camera quality, and processing speed. Product categories like smartphones and laptops often drive higher purchase intent due to their status as necessities in modern life (Filieri et al., 2017; Jiménez-Parra et al., 2014).

Kulshreshtha & Tripathi (2017) highlighted the role of product functionality in consumer choices. The study examined Indian consumers' decision-making regarding consumer durable goods, specifically split air-conditioners, based on several factors including brand equity, price, advertisement type, celebrity endorsement, and country-of-origin and found that consumers' purchase intentions for electronic goods were significantly influenced by factors such as brand equity, moral appeal in advertisements, credible celebrity endorsements, and the country of origin. In addition,, they were willing to pay more for products that align with their ethical values and were endorsed by trustworthy celebrities. Price was found to be less critical compared to these other attributes. Nonetheless, price is a critical variable influencing purchase decisions, particularly in the electronics market where items can range from affordable to premium products. In addition, customers may have a specific range of prices they find acceptable for a product or service. Some customers are willing to pay higher prices within their range, while others might be more restrictive. The way customers view and react to different prices helps determine how sensitive a market is to price changes (Abdullah-Al-Mamun et al., 2014). Additionally, Gorji & Siami (2020) examined the role of price promotions in driving consumer purchase intent. Their study revealed that promotional discounts and bundle deals could enhance the perceived value of a product, especially when applied to mid-range and high-end electronics (Lauren & Grewal, 2001). However, the researchers also noted that over-reliance on price reductions could negatively impact brand perception, leading consumers to question the quality of the products (Palazon & Delgado‐Ballester, 2009). Still, loyalty to their favourite brand may influence their purchase intent.  A study on Bokomo brands in Namibia found that effective branding strategies enhanced brand equity, which in turn increased consumer purchase intentions (Nyathi & Tafirenyika, 2024). Brand loyalty an also extends beyond the physical product to encompass customer service and brand reputation (Lama, 2017). The study involving electroniic purchases highlighted that consumers who have previously had positive experiences with a brand are more likely to exhibit repeat purchase behavior, even if competitors offer lower prices or better features (Al-Khayyal et al. 2020).

Technological Advancements and Innovation

The pace of technological advancements may influence consumer purchase decisions in the electronics market and innovation can be a key driver of consumer behaviour, particularly in categories like smartphones, tablets, and smartwatches. Vieira et al. (2022)’s study which focussed on how the perceived quality of technical assistance services affects customer satisfaction and repurchase intentions implies that quality service is essential for maintaining customer loyalty and satisfaction.  The research highlighted that a positive brand image, influenced by service quality, can lead to higher repurchase intentions suggesting that if technological advancements enhance service quality, they could indirectly affect consumer behavior.

Influence of Online Reviews and Social Media

Online reviews and social media have emerged as powerful factors influencing consumer purchase intent, particularly within the electronics industry. Studies have shown that user-generated content, including product reviews and ratings on e-commerce platforms, plays a critical role in shaping purchase decisions. For instance, positive reviews can significantly increase purchase intent, especially for high-priced electronics, as consumers often rely on feedback from other users to validate their choices before making substantial purchases (Park & Nicolau, 2015; Mudambi & Schuff, 2017). Similarly, the influence of social media personalities has garnered considerable attention. Research indicates that credible and knowledgeable social media influencers can notably impact consumer behavior in electronics sales, as their recommendations hold considerable sway for high-involvement products such as smartphones and gaming consoles (Lou & Yuan, 2019; Erkan & Evans, 2016). This effect is particularly pronounced when consumers perceive influencers as trustworthy and authentic, thereby increasing the likelihood of purchase based on influencer endorsements.

METHODOLOGY

Data Collection

This study analyzed a dataset from Kaggle on consumer electronics sales, focusing on variables such as product category, price, customer demographics, satisfaction levels, and purchase intent.

Statistical Analysis

Using SPSS, descriptive statistics were calculated for key variables to summarize data trends. Pearson’s correlation was conducted to examine relationships between customer age, purchase frequency, price, gender, satisfaction, and purchase intent, providing insight into these factors' interactions.

Binary Logistic Regression

A binary logistic regression model predicted purchase intent based on customer satisfaction and gender. Model accuracy was evaluated using a confusion matrix and metrics like precision and recall, indicating how well the model predicted purchase behavior.

Visualization

Visual tools (histograms, box plots) illustrated key data trends, such as price distributions across product categories and age-related purchase intent patterns, to effectively convey customer behavior insights.

RESULTS AND DISCUSSION

Table 1 presents an average product price of 1527.43 iin the present dataset with high variability (SD = 829.90), ranging from 100.38 to 2999.85. Customer ages averaged 43.35 years, and purchase frequency averaged 10.05, with substantial variation (SD = 5.46) and a range of 1 to 19 purchases. Customer satisfaction had a mean score of 2.996, with scores spread across 1 to 5. Overall, the data indicated variability in prices and customer behavior, with balanced satisfaction scores across the range.

Table 1: Descriptive statistics

The bar charts in Figure 1 illustrates the frequency distribution across different product categories i.e Headphones, Laptops, Smart Watches, Smartphones, and Tablets. Each category had a similar percentage, roughly between 19-22%, indicating a fairly even distribution across product types.Laptops had the highest frequency, while Tablets have the lowest, though the difference is minimal. Samsung leads as the most frequent brand in the dataset, with other brands, Sony, and Apple also having significant representation.

Figure 2: Cox plot showng the price distribution across product categories

The box plot (Figure 2) illustrates the price distribution across product categories. Each category demonstrated a similar price range, with values primarily spanning from approximately 500 to 2500. Median prices are consistent across categories, suggesting a comparable central tendency. The interquartile ranges (IQRs) for each category show limited variation, indicating that most prices are clustered closely around the median. Additionally, the absence of outliers and the uniform spread suggest stable pricing structures across these product lines.

Figure 3: Age Distribution of customers

The histogram presented in Figure 3 illustrates the age distribution of customers in the dataset, spanning from 20 to 70 years. The frequency of customers remains relatively consistent across most age groups indicating a balanced distribution. Minor decreases in customer numbers were observed in the early 20s and among those in their 60s. Conversely, customer frequencies are higher among individuals in their 30s to 50s. A notable decline is evident at age 70, which suggests a lower representation of customers within this oldest age group.

Correlational analysis

Table 2 shows that the Customer Age had a significant positive correlation with Purchase Intent (r = 0.290, p < 0.01), suggesting that their intent to purchase tends to increase as customers age. This study showed that younger customers, particularly those under 30, exhibited lower purchase intent compared to older age groups (Figure 4). This trend sharply shifted at around age 30, where purchase intent became significantly higher and stabilized until approximately age 70. Age has been found to significantly affect consumer attitudes towards products by several studies, with younger individuals showing a preference for luxury items and financial value, while older consumers prioritize usability and trust (Srinivasan et al., 2014). Also, it is observed that customer relationship proneness, which varies by age, can positively impact purchase intent, suggesting that marketers should tailor strategies based on age demographics (Menidjel & Bilgihan, 2022).

Table 2: Correlation Matrix

 Each cell shows the correlation coefficient followed by the significance value (p-value) in parentheses.

** indicates that the correlation is significant at the 0.01 level (2-tailed).

Figure 4: Relationship between Average purchase intention and customer age  

Beneke (2013) when examining the influence of demography on the perceived risk and thereby purchase intention found that age and gender influenced purchase intention. Specifically, different age groups exhibit varying sensitivities to perceived risks, with the 26-35 age group being more affected by social risk, while time risk significantly impacted female consumers. However, the weak correlation between Customer Age and other variables, such as Purchase Frequency (r = 0.006, p > 0.05) and Product Price (r = -0.009, p > 0.05), suggests that age alone may not be a strong predictor of purchasing behaviour across all product categories. The correlation between Customer Gender and Purchase Intent is significant and positive (r = 0.504, p < 0.01), suggesting that gender may influence buying intentions. This aligns with the study by Stefko et al. (2021), which found that gender affects purchase intention due to variations in priorities and sensitivities. Behavioural differences, which may arise from upbringing and socialization, can influence attitudes and decision-making processes during shopping. Figure 5 illustrates the differences in purchase intentions, purchase frequency, and customer satisfaction between males and females, highlighting the most notable disparities in purchase intention while showing no significant differences in customer satisfaction or purchase frequency.

Figure 5: Difference of Purchase intention in males and females

Gender has been observed to influence purchase intention by influencing brand engagement, meaning that through gendered branding engagement could be enhanced thereby affecting purchase intentions (Yeganeh et al., 2020). Additionally, Customer Gender is weakly but significantly associated with Customer Satisfaction (r = 0.008, p > 0.05), hinting at potential gender-based variations in satisfaction levels, though further research is needed to confirm these patterns.

Customer Satisfaction was found to be positively correlated with Purchase Intent (r = 0.391, p < 0.01), reinforcing the idea that satisfaction is a key driver of repeat purchases. The relationship is depicted in Figure 6. The purchase intent significantly increased with customer satisfaction scores above 3.0, peaking at satisfaction levels of 4.0 and 5.0. This aligns with the findings of Khatoon et al. (2020) where customer satisfaction served as a significant mediator between e-banking service quality and customer purchasing behaviour. Higher levels of customer satisfaction lead to increased customer loyalty and stronger relationships, which in turn enhance business performance and purchasing intentions. The study indicated that satisfied customers were more likely to engage in repeat purchases and maintain long-term relationships with the bank. However, Customer Satisfaction exhibited weak correlations with Purchase Frequency (r = 0.021, p > 0.05) and Product Price (r = 0.002, p > 0.05), in the present study suggesting that satisfaction may not directly influence how often or at what price point purchases are made.

Figure 6: Relationship between Average Purchase Intention and Customer Satisfaction

Finally, Purchase Frequency showed no significant correlation with Purchase Intent (r = -0.001, p > 0.05), implying that frequent purchases may not necessarily indicate a high intent to buy.

Prediction of Purchase Intention using Binary Logistic regression model

The binary logistic regression analysis was conducted to predict purchase intent based on customer gender and customer satisfaction. The model achieved an overall accuracy of 79%, indicating that the model correctly predicts purchase intent in approximately 79% of cases. The confusion matrix (Table 3) illustrates the model's performance in classifying purchase and non-purchase events. Specifically, there were 850 true negatives (correctly predicted non-purchase cases) and 1279 true positives (correctly predicted purchase cases), alongside 338 false positives and 233 false negatives.

Table 3. Confusion Matrix for Purchase Intent Prediction

Table 4 presents the classification reports of the model. The precision for predicting a purchase  was 79%, indicating that 79% of predicted purchases were actual purchases. The recall (sensitivity) for purchase prediction was 85%, reflecting the model's effectiveness in identifying true purchase cases. The F1-score, which provides a balance between precision and recall, was 0.82 for predicting purchases.

Table 4. Classification Metrics for Purchase Intent Prediction

Table 5 gives the correlation coefficients revealing that customer gender had a strong positive influence on purchase intent, with a coefficient of 3.03. This suggests that males (Gender = 1) are significantly more likely to show purchase intent compared to females. Customer satisfaction also positively influenced purchase intent, with a coefficient of 0.94, indicating that higher satisfaction increases purchase likelihood, although its impact is less substantial than that of gender.

Table 5. Logistic Regression Coefficients for Predictive Features

The odds ratio analysis in this logistic regression model revealed the effect of various factors on purchase intent. The odds ratio for customer age is 1.088, meaning that with each additional year, the likelihood of purchase intent increases by 8.8%, holding other factors constant. Gender has the largest effect, with an odds ratio of 45.894, suggesting that males are substantially more likely to exhibit purchase intent than females. Customer satisfaction also significantly impacted purchase intent, with an odds ratio of 3.172, indicating that higher satisfaction triples the likelihood of purchase. Product price and purchase frequency show negligible effects on purchase intent, as their odds ratios are close to 1 and are not statistically significant.

Conclusion

This study highlighted key factors influencing purchase intent in the electronics market, focusing on customer satisfaction, age, and gender. Results showed that higher customer satisfaction (especially above a score of 3.0) and male gender are strongly linked to increased purchase intent, while older age groups (30+) demonstrate stable purchasing patterns. Although satisfaction significantly enhanced intent, it did not impact purchase frequency or price preferences. The predictive model achieved 79% accuracy, emphasizing satisfaction and gender as primary predictors. These findings suggest that improving customer satisfaction, targeting marketing by age and gender, and competitive pricing can effectively enhance purchase intent and loyalty in the electronics sector.

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