Analysis of Consumer Behavioral Factors Between Online Shopping and Physical Store Experience in the M-Commerce Era

Abstract

Consumer behavior has changed considerably over time. In recent decades, people have used resources at a rate that exceeds the total consumed throughout history. This paper aims to address the determinants of the smartphone purchase decision, emphasizing gender differences, price influence, and previous online shopping experience. The methodology used combines a bibliometric review of the literature to identify major trends in consumer behavior research and a quantitative research survey that provides insight into consumer behavior in the smartphone purchase process. The survey highlights brand preferences, purchase patterns, product selection criteria, and the influence of socioeconomic factors on the purchase decision, identifying the determinants of online versus physical store purchase decisions among young consumers in the northeastern, east, and southeastern regions of Romania. Thus, our analysis aims to identify the variables influencing consumer preferences and to assess the statistical significance of these differences using quantitative methods and relevant statistical tests. The collected data came from a valid sample of 456 respondents for the general analysis and 271 valid cases for the online shopping analysis. The analysis shows that gender is a significant predictor of online purchase decisions, with men being 2.65 times more likely to purchase a smartphone online than women. The collected data were analyzed using t-tests, Chi-square tests, and logistic regression to assess the influence of variables on online smartphone purchase intention.

1. Introduction

Looking back on its history, the consumer has evolved differently from the 1950s to the 2000s, reflecting a range of rational economic perspectives and more complex approaches, including psychology, sociology, and other social disciplines.

In the 1950s and 1960s, consumers were seen as “rational information processors” and their purchasing decisions were seen as logical and economic [1]. This period introduced models of buyer behavior based on cognitivism effectively to improve marketing strategies and address consumer preferences through rational analysis.

Since the 1980s, new perspectives, influenced by “behavioral economics” and experiential theory, have emerged, challenging the idea of pure rationality. Consumers were increasingly seen as individuals motivated by emotions and experiences rather than rational calculations [2]. This led to more varied research methods, including interpretative, ethnographic, and introspective approaches to explore the symbolic and emotional meanings of consumption.

By the 2000s, the scope of research had broadened to include not only managerial utility, but also the value of knowledge itself, by exploring consumer behavior from a multidisciplinary perspective [3]. This has opened the way to understanding consumers as complex beings whose behavior is influenced by psychological, social, and cultural factors, not just economic ones [4].

Consumer behavior has changed considerably in recent years as a result of today’s digital marketplace, which creates opportunities as well as challenges to develop a sustainable marketing strategy. How individuals, groups, or organizations satisfy their needs through the purchase, use, and disposal of products, services, or experiences that describe consumer behavior [5]. Most often, purchasing decisions are driven by emotional, social, or psychological processes.

In m-commerce, smartphones have started to play a crucial role as mobile internet penetration has increased and applications dedicated to online shopping have developed. In the beginning, users used their mobile phones for limited activities, such as checking emails, or accessing websites. However, as technology has advanced and mobile internet speeds have increased, smartphones have become essential tools for searching, comparing, and buying products. Smartphones have evolved from simple communication devices to advanced tools that have revolutionized many aspects of everyday life, including e-commerce. The first smartphone models appeared in the late 1990s and early 2000s, but their true popularity was triggered in 2007 with the launch of the iPhone by Apple, which redefined the concept of a mobile phone by integrating an advanced operating system, internet connectivity, and an easy-to-use interface [6]. Technology and affordability have turned smartphones into focal points for digital activities.

Today, smartphones are an essential pillar of m-commerce, allowing users to access global marketplaces, make instant payments, and receive real-time updates on products and services [7]. In addition, emerging technologies such as artificial intelligence and augmented reality are integrating smartphones even more deeply into the m-commerce ecosystem, offering personalized and interactive shopping experiences. Consumers’ willingness to buy a product is expressed by intention that represents a consumer’s preliminary plan to purchase specific products within a range, highlighting individual preferences for particular products and the prioritization of available options. The consumers’ decision-making process starts with identifying a product need, followed by searching for relevant information, evaluating the options, making the actual purchase, and finally providing feedback on the experience [8]. The diversity of brands, models, and designs available on the market makes choosing a smartphone, a complex process for consumers. When planning their purchase, customers consider factors such as brand reputation, price, functionality, functionality, durability, social influences, and comparative advantages of the product. These elements help shape the purchase decision and consumer preferences.

In this study, we analyzed the factors influencing the purchase of smartphones, with a focus on the mode of purchase and how consumer preferences have evolved both before and after the pandemic. Smart mobile devices, such as smartphones, have evolved to incorporate advanced processing and communication features. They offer a wide range of uses, including calling, messaging, social interaction, gaming, and access to applications that are useful in everyday life. By combining wireless telephony technology with internet connectivity, email applications, and social networking, smartphones stand out from traditional mobile phones by using a modern operating system. Since their debut in the 1990s, the popularity of these devices has grown exponentially, driven by advances in telecommunications and digital technologies [9].

Although gender has been analyzed in previous e-commerce studies, our study uniquely quantifies the extent to which gender influences brand preference and purchase channel choice through both statistical tests and predictive modeling, demonstrating how these factors are intertwined. By applying a dual-model approach, this research goes beyond simple demographic comparisons and provides a structured empirical validation of how gender influences smartphone purchase behavior in the post-pandemic era. The COVID-19 pandemic fundamentally altered the way consumers interact with retail environments [10], accelerating the transition to e-commerce and digital consumer engagement. While studies [11] since the beginning of the pandemic have documented a temporary shift toward online shopping, there is still limited research assessing whether these behaviors have been maintained in the post-pandemic period. This study addresses this gap by providing empirical evidence of long-term changes in consumer behavior and examining how these trends continue to shape the smartphone industry.

In addition, gender-specific purchase preferences have become increasingly relevant as marketing strategies now prioritize personalized digital experiences and targeted advertising [12]. This study introduces a comparative gender analysis to assess whether male and female consumers exhibit statistically significant differences in brand preference, price sensitivity, and purchase channels, a topic often neglected in recent smartphone adoption research. By combining statistical analysis with predictive modeling, this research goes beyond descriptive results and provides a more precise quantification of these differences.

The objectives of our study involve the following:

O1: To analyze changes in consumer behavior in smartphone purchasing, given the transition from physical stores to online shopping.

O2: To assess the influence of gender on brand and channel preference, providing a statistical and predictive analysis of this phenomenon.

O3: Determining the key factors influencing brand loyalty and price sensitivity, analyzing whether these variables are driven by demographic or economic factors.

Given the evolution of consumers’ digital behavior, companies can leverage the findings of this study to develop personalized marketing strategies based on artificial intelligence, tailoring promotional campaigns to consumers’ preferences for online and offline shopping. Companies can also help improve digital retail experiences by optimizing recommendation algorithms that take into account gender-based shopping behaviors. Last but not least, companies can optimize omnichannel strategies by integrating e-commerce platforms with physical store experiences to bridge the gap between traditional and digital retail.

1.1. Theoretical Perspectives on Consumer Smartphone Buying Behavior

Our study focuses on consumer decision-making in smartphone purchasing, examining key variables such as brand preference, price sensitivity, and online vs. offline purchasing behavior. To strengthen our research, we have incorporated a behavioral economic perspective within the study, linking our findings to established theories that align with consumer behavior in digital retail. To address this concern, we have included discussions on the following two guiding theoretical frameworks: Theory of Planned Behavior and the Consumer Decision-Making Process Model.

To understand the psychological factors influencing consumers’ smartphone purchase behavior, this study is based on the Theory of Planned Behavior (TPB) proposed by Ajzen (1991) [13]. TPB provides a sound theoretical framework for exploring the relationships between attitudes, social norms, and perceived behavioral control, highlighting how these variables influence intention and actual purchase behavior. This approach is essential in the context of e-commerce and consumer preferences in the m-commerce era.

Using the Theory of Planned Behavior, Dilek [14] studied smartphone purchasing behavior in the digital economy. Key factors identified in smartphone choice include warranty, camera resolution, quality, price, and battery life. Also, Rozenkowska [15] conducted a systematic review of 118 consumer behavior studies based on the Theory of Planned Behavior, its components, and extensions, identifying an increased interest in consumers’ environmental consumer behavior and food purchase intention. Also, Reference [16] investigated consumers’ online purchase intention using the theory of planned behavior through descriptive statistics and regression analysis, showing that perceived behavioral control has a stronger influence on online purchase intention than subjective attitudes and norms. Liao et al. [17] investigated the correlations between users’ attitudes, social norms, perceived behavioral control, and purchase behaviors based on the Theory of Planned Behavior (TPB).

This study applies the Theory of Planned Behavior (TPB) to investigate consumers’ intentions to purchase smartphones online. TPB assumes that behavioral intentions, determined by attitudes, subjective norms, and perceived behavioral control, underly actual behavior. To this end, we hypothesized and collected relevant data through a questionnaire administered to a sample of 456 respondents. Statistical analysis was conducted to determine the extent to which these components influence online purchasing decisions.

The Technology Acceptance Model (TAM) [14] also highlights how perceptions of usefulness and ease of use influence the adoption of new technologies. In our context, TAM helps to explain how consumers adopt e-commerce platforms for smartphone purchases, considering factors such as digital experience, user interface, and the influence of online reviews.

It also analyzed the Technology Acceptance Model and its extension TAM2 [18], which explains consumers’ acceptance of technology, including factors such as social norms and image. Originally applied in domains such as email and online learning, these models are recently being used to study smartphone user behavior. Another study [19] analyzed how TAM factors and social influences affect the purchase intention of Digikala app users, revealing that age influences the relationship between perceived usefulness and attitude.

The TAM was used to assess online purchase intention, taking into account factors such as perceived usefulness and perceived ease of use of e-commerce platforms. The hypotheses were tested based on a questionnaire administered to a sample of 456 respondents, and the analysis was performed using logistic regression and Chi-square tests.

1.2. The Study Variables and Proposed Hypotheses

In this study, the hypotheses have been constructed to explore how demographic factors and individual preferences influence consumers’ smartphone purchase behavior. The proposed hypotheses are based on the literature review and aim to identify relationships between the variables tested and consumers’ purchase intentions.

The formulation of the hypotheses reflects current trends in digital commerce, especially in the context of the accelerated growth of online shopping. The study investigates whether demographic factors, such as gender, influence purchase behavior in terms of maximum price accepted, and purchase channel preferences. It also examines whether the COVID-19 pandemic has led to a lasting change in online purchasing behavior.

The hypotheses developed in this research are based on theoretical and empirical premises aimed at understanding consumer behavior in the digital age. Each hypothesis is supported by previous research and contextualized concerning the objectives of the study, thus ensuring a logical and relevant link between theory and practice. This approach provides a comprehensive perspective on the factors that determine smartphone purchase decisions and contributes to the literature on e-commerce and consumer behavior.

1.2.1. Maximum Accepted Price for a Smartphone

The maximum price accepted is the maximum amount a consumer is willing to pay for a smartphone. The variable reflects consumers’ perceptions of the perceived value of the product, its features, and value for money.

According to the study conducted by Kim and Park [20], perceived price plays a critical role in the decision-making for purchasing electronic products. Research by Shankar et al. [21] revealed that economic factors influence price decisions more than demographic differences such as gender. Also, Wang and Chen [22] showed that value perception is influenced by characteristics such as technological innovation and smartphone brands.

In the current study, we explore whether demographic factors (gender) influence consumers’ perceptions of the maximum acceptable price in the context of increasing online shopping and digitization of consumer behavior. Previous studies [1] indicate that price decisions are more influenced by economic factors than gender differences.

H1. 

There are no significant differences between men and women in the maximum price they are willing to pay for a smartphone. The dependent variable is the maximum price of the next smartphone and the independent variable is the gender of the consumer.

1.2.2. Preferred Shopping Channel (Online Vs. Offline)

The preferred purchase channel presents how consumers choose to purchase their smartphones, either through online platforms or from physical stores, reflecting consumers’ purchasing preferences and behavior in the context of digital commerce.

The study by García-Salirrosas et al. [23] demonstrated that online purchase preferences are influenced by cultural, economic, and technological accessibility factors. According to Hamli and Sobaih [24], the COVID-19 pandemic has accelerated preferences for online purchases, but gender differences in the choice of purchase channel remain limited. Also, Helmi et al. [25] emphasized that factors such as convenience and varied options determine consumer preferences for online platforms.

Analyzing this variable contributes to understanding how preferences for online or offline purchases are influenced by demographic and contextual factors such as pandemics. The analysis is justified by studies [11] suggesting that preferences for online purchases may vary depending on cultural and technological factors.

H2. 

There is no difference in the proportion of online purchases between men and women. The dependent variable is the purchase channel (online vs. offline) and the independent variable is gender.

1.2.3. Online Smartphone Purchase Intention

Online purchase intention reflects the likelihood that a consumer will opt to purchase a smartphone via e-commerce platforms. This variable is influenced by factors such as previous experience, income, and brand of smartphone owned.

According to the study conducted by Davis [13] through the Technology Acceptance Model (TAM), the intention to use technology is determined by perceived usefulness and ease of use. Rozenkowska [15] conducted a systematic review of consumer behavior studies and identified an increased interest in green behavior and food purchase intention. Also, Liao et al. [17] revealed that perceived behavioral control influences online purchase intention.

In the context of this study, smartphone online purchase intention is analyzed concerning the changes brought about by the COVID-19 pandemic and emerging e-commerce trends. The COVID-19 pandemic has accelerated the digitization of purchases, and recent studies [12] indicate a possible persistence of this behavior.

H3. 

Consumer intentions to purchase smartphones online are increasing. The dependent variable is online purchase intention and the independent variables include previous experience, income, and current smartphone brand.

1.3. Evolution of Consumer Behavior Regarding Smartphone Purchases

The COVID-19 pandemic has changed the way consumers relate to buying products or services by accelerating online shopping. Through e-commerce, the need for a face-to-face meeting between the seller and the buyer has been eliminated, and transactions have largely been conducted online. In other words, through the COVID-19 pandemic, e-commerce grew considerably, so that, following the restrictions of that period, shoppers explored both essential and non-essential options, and online platforms and brands responded positively to such demand by delivering products or services directly to the doorstep [26]. E-commerce has grown considerably after the pandemic, so much so that, according to Romania’s largest marketplace, emag.ro sales increased in the pandemic year by 52% compared to 2019. Although globally, sales were forecast to fall by around 3% in 2020, sales increased by around 28% [27]. Figure 1 shows how sales have evolved in the largest marketplace in Romania over the last 10 years.

Over the last 10 years, e-commerce has been under pressure to reconfigure its sustainability performance in response to the demands of stakeholders, governments as well as society [28]. Thus, consumers have pointed to several advantages of this domain, highlighting practicality, convenience, utility, and comfort, which help to avoid the inconvenience caused by traffic, weather, or environmental problems. This pressure has led to the integration of environmental, social, and economic aspects into e-commerce processes, bringing sustainability to the forefront of discussions about consumer behavior in this area.

It also looks at consumer-to-business (B2C), business-to-business (B2B), and consumer-to-consumer (C2C) online transactions from the perspective of the three pillars of sustainability: economic, social, and environmental [29].

A thorough understanding of the interplay between consumer behavior and the way smartphones are purchased in physical format and m-commerce requires a multidisciplinary examination, which has attracted growing academic interest.

Technology has increasingly become part of everyday life, so technological devices have become both accessible and convenient for most consumers, regardless of age or skill. Technological evolution has also seen the development of the smartphone, which is considered an essential part of everyday life [30]. The smartphone is defined as a mobile phone with advanced computing and connectivity capabilities, going beyond the basic functions of a traditional phone. In recent years, smartphones have replaced traditional phones as a result of a huge increase in demand [31]. Depending on their preferences and affinity for certain brands, people of all ages around the world use smartphones, whose functionalities can be comparable to those of a minicomputer. These devices are perceived as a necessity rather than a luxury in everyday life, generating numerous changes in lifestyle and consumer status. From calls, messaging, apps, social networks, or games, smartphones, in the context of the COVID-19 pandemic, have been an indispensable commodity, so subsequently, education, health, or government services have become accessible online. During the pandemic, education went online due to the closure of schools, so pupils, students, and teachers had to use either laptops or smartphones.

Globally, the evolution of smartphone sales has been extremely high, starting as early as 2013 and up to 2018, when the evolution of cell phone sales was surprising. The 2019–2020 period was a turning point for smartphone sales [32], mainly due to the uncertain situation caused by the COVID-19 pandemic, but from 2021, when more and more activities became indispensable in the online environment, smartphone sales increased again [33]. Figure 2 shows how smartphone sales have evolved globally.

According to GSMA Intelligence, there were 28 million mobile connections in Romania, at the beginning of 2024. Many people around the world use multiple mobile connections, for example, one for personal use and one for work—so it is common for the total number of mobile connections to be considerably higher than the total population. The GSMA Intelligence report shows that mobile connections in Romania accounted for 141.9% of the country’s population in January 2024. Between the beginning of 2023 and the beginning of 2024, the number of mobile connections in Romania decreased by 830 thousand (a reduction of 2.9%) [34]. Figure 3 illustrates the total number of mobile connections in Romania and their percentage of the population for the beginning of 2023 and 2024. On the left-hand side is the total number of connections (in millions), and on the right-hand side is the percentage of mobile connections in the population.

Many people use their smartphones for taking phone calls and texting, and in the digital age, these aspects focus on communicating on online networks as well as searching for information or playing games. The main features for which users choose a smartphone are the packaging and features of the phone, which also influence brand perception. On the other hand, consumers also emphasize convenience, brand, recommendations, price, and psychographic factors.

In recent years, e-commerce has been increasingly driven to improve its sustainability, enabling the integration of economic, social, and environmental aspects into its processes. Consumers have become significantly more aware of their environmental impact, leading to adopting sustainable solutions [35]. In this context, several collaborative consumption models have developed, facilitated by modern technologies, which prioritize resource efficiency and waste reduction. Rita and Ramos developed a study in which they analyzed 104 relevant articles on consumer behavior and sustainability in e-commerce, providing insights into the annual scholarly output, journal impact, influential citations, most active authors, and keywords used [12]. Fu et al. highlighted the effects of CRM cause-related marketing messages, concluding that they improve consumer perceptions and purchase intentions by reducing perceptions of self-serving, regardless of individual self-construal style [36]. Dinesh and MuniRaju (2021) explored the factors that supported improved operations of e-commerce businesses during the pandemic, highlighting the significant migration of consumers to online shopping due to safety concerns [37].

2. Materials and Methods

This research aims to analyze the perception of consumers in the northeast region of Romania on smartphone purchasing. The study combines bibliometric analysis of the literature with empirical research conducted among young people to provide a comprehensive understanding of the dynamics of their preferences and behaviors.

2.1. Procedure of Bibliometric Analysis

This paper combines the two complementary approaches, bibliometric analysis, and an empirical, questionnaire-based empirical study, to provide a deeper understanding of consumer behavior in smartphone purchasing. In this context, bibliometric analysis is used to identify the main research trends in m-commerce and consumer behavior, focusing on smartphone purchases. This analysis serves to delimit the theoretical context by identifying existing gaps in the literature, justifying, in this case, the need for an empirical investigation, as well as establishing a basis for the delineation of hypotheses and study variables, ensuring the alignment of research with current trends.

The results of the bibliometric analysis played a fundamental role in the design and structuring of the empirical study, by identifying keywords in the literature, which guided the formulation of the variables and questions included in the questionnaire. Thus, gender differences in purchasing behavior were found to be a frequently studied topic, which justifies its inclusion as a central variable in our analysis. Thus, the bibliometric analysis provides an overview of the evolution of the field, identifying the main themes and factors influencing consumer behavior, while the questionnaire-based study brings an applied perspective on the preferences and behavior of Romanian consumers, validating or not the trends observed in the international literature.

The present study uses secondary sources (other articles) and primary sources (we conducted a survey with a questionnaire).

The first objective of the research is to conduct a bibliometric analysis focusing on consumer behavior in the smartphone purchase process. Using the Web of Science (WoS) database and VOSviewer 1.6.17.0 software, we analyzed relevant articles published between 2010 and 2024, using keywords such as “smartphone purchase”, “consumer behavior”, “pandemic”, and “online” for consumer behavior, and “brand”, “mobile phone”, “sales”, and “connections” for m-commerce, respectively. The analysis highlighted the qualitative and quantitative evolution of research in this field, identifying the main themes and concepts used in the literature.

2.2. Questionnaire and Data Collection Procedure

The second objective of the research is to assess the preferences of young university students in terms of smartphone purchases. To this end, online questionnaires were applied to collect data on technical specifications, the most attractive brands, and the price levels influencing purchasing decisions. We also investigated how the pandemic has altered student preferences, emphasizing the shift toward online purchasing and changing the product selection criteria.

Objective 3 aims to identify the main factors influencing brand loyalty and price sensitivity, assessing the extent to which these variables are driven by demographic characteristics or economic conditions.

2.3. Mathematical Models and Statistical Formulae Applied in Sample Analysis

Unlike previous research that relies solely on statistical software for hypothesis testing, this study employs a two-method approach, integrating mathematical modeling (Formulas (1)–(4)) to provide a structured theoretical representation of decision-making processes, empirical statistical analysis (IBM SPSS Statistics 27: t-tests, Chi-Square tests, regression models) to validate findings using observed consumer data, effect size measures (Cohen’s d, Cramer’s V) to strengthen interpretations beyond conventional significance tests (p-values). By combining these methodologies, this study increases analytical depth, reduces the reliance on purely empirical findings, and provides a more structured, theory-based framework for understanding consumer decision-making in the smartphone market.

The validity of the current research was obtained by the manner in which the study was conducted. The questions were made from similar examples from other research, and also the validity of the content was made by pretesting the questionnaire several times on students [38]. In terms of the reliability of the research, we conducted several pretests of the questionnaire with some students who completed the questionnaire several times, and we had the same answers. Pretesting was also used for the validity of the instrument of measurement. For opinions and perceptions, we used an interval scale and the semantic differential, with values from 1 to 5. Also, on SPSS, we conducted an analysis of the questions regarding the next phone, including some questions about online acquisition.

We only had 9 questions like this, and Cronbach’s Alpha was 0.659, an acceptable value for this number of questions [39].

To identify the factors that influence the likelihood that a young person will buy a smartphone online, we will apply regression analysis, and we will establish a series of hypotheses through these equations:

$$Y={\beta }_0+{\beta }_1\times Gender+{\beta }_2\times Income+{\beta }_3\times Favoritebrand+{\beta }_4\times Maximumprice+\epsilon$$

(1)

$Y$ is the probability of buying smartphones online (1 if bought online, 0 if not);

${\beta }_0$ is the intercept;

${\beta }_1$, ${\beta }_2$, ${\beta }_3$, ${\beta }_4$ represents the regression coefficients for the independent variables

$\epsilon$ is the residual error.

In this case, if ${\beta }_1$ > 0, then women are more likely to buy online than men.

Also, to understand why a respondent prefers a particular brand, we include a number of independent variables, such as gender, income and online shopping habits, using a multinomial logit model:

$$\ln ({\displaystyle \frac{P\left({Brand}_i\right)}{P\left({Brand}_{ref}\right)}})={\beta }_0+{\beta }_1\times Gender+{\beta }_2\times Income+{\beta }_3\times Onlinepurchase+{\beta }_4\times Maximumprice$$

(2)

$P\left({Brand}_i\right)$ represents the probability that a respondent prefers $i$ brand;

$P\left({Brand}_{ref}\right)$ is the probability for the reference brand, for example “Apple”;

In this case, if ${\beta }_3$ > 0, then those who buy online prefer $i$ brand more than Apple.

At the same time, to identify the regression for the maximum price that young buyers will pay for the next handset, we will use the simple regression model:

$$Y={\beta }_0+{\beta }_1\times Income+\epsilon$$

(3)

$Y$ represents the maximum price that respondents are willing to pay;

${\beta }_1$ is the price sensitivity to income;

$\epsilon$ is the residual error.

In this case, ${\beta }_1$ shows how much income influences the maximum price.

To analyze the likelihood that the next smartphone will be purchased online, we will use data on current purchases, preferred brand, gender, and income, using the formula:

$$Y={\beta }_0+{\beta }_1\times Currentpurchase+{\beta }_2\times Favoritebrand+{\beta }_3\times Gender+{\beta }_4\times Income+\epsilon$$

(4)

If ${\beta }_1$ > 0, then prior experience with online shopping increases the future probability of online shopping, and if ${\beta }_3$ > 0, then women are more likely to shop online.

For the research limits, we can mention that we conducted the study in two universities in Romania, and the sample was made only of students. Also, in the model of the logical regression, we have only a small part of the variance explained by the independent variables, so are other variables predictors who were not included in the study.

3. Results

3.1. Results Obtained Through Qualitative Analysis

As part of our analysis, to determine how research in the field of consumer behavior research has fared, focusing on online sales, consumer brand behavior, smartphone purchases, smartphone purchase preferences, and online or physical store gadget purchase preferences, we generated a keyword co-occurrence map with VOSviewer, which is an essential method for analyzing the relationships between commonly used concepts in the literature, providing a detailed insight into dominant research trends and emerging thematic structures. This map visualizes the intensity of relationships between keywords, reflected by the frequency with which they appear together in the same document. The value of each cell in the matrix indicates the number of co-occurrences, and the associated colors, ranging from light to dark blue, suggest the degree of connection: light colors indicate more frequent co-occurrence, while dark colors indicate weaker relationships. The analysis includes the top twenty most frequently used keywords, selected based on their presence in the dataset. The most intense relationships on the map reveal strong connections between central concepts, such as, for example, “e-commerce” and “consumer behavior.” These links indicate major themes and areas of interest frequently investigated in the literature. Emerging relationships, characterized by weaker connections, may signal new or little-explored topics that could represent opportunities for future research. Figure 4 shows the most used keywords extracted from research articles in the e-commerce field, consumer behavior, and online sales.

The keywords were collected from a total of 1603 articles, covering a variety of academic topics, from analyzing the performance of the smartphone market to exploring consumer behavior and the impact of e-commerce on the digital economy. The most frequent keywords include terms such as “e-commerce”, “consumer behavior”, “smartphone adoption”, “sustainability”, and “technology innovation”. The frequency of these terms indicates the prevailing trends in research and the topics with the greatest impact on the academic community. Preliminary analysis suggests that the articles included in the study were written by authors coming from diverse academic institutions and research organizations, contributing interdisciplinary perspectives. The distribution of authors reveals extensive collaboration, which is characteristic of large-scale studies. The articles analyzed cover a significant period, starting from 2000 and continuing to the present, with a higher concentration in the recent period. This temporal distribution reflects the growing interest in topics such as digitization, smartphone use, and consumer behavior in the post-pandemic era. Most recent studies explore the transition to online commerce and the technological innovations shaping consumer preferences. Figure 5 shows how the most used keywords mentioned above appear in the specialized literature.

This map represents the visualization of the frequency of keywords extracted from a dataset or academic articles, and the size of each word reflects the frequency with which it appears, with larger terms being used more often in the context analyzed: “e-commerce” and “consumer” appear to be the largest, indicating that these concepts are central to the data analyzed. These studies are focused on e-commerce and consumer behavior. Also, other keywords not reported are “international“ and “business”, “conference”, “science “ and “journal”, “marketing”, and “digital”, indicating that much of the data used comes from scientific publications, conferences, and academic journals, suggesting a strong focus on digital marketing strategies. On the other hand, the least frequent keywords are “management”, “economics”, “information”, and “proceedings”, terms that are visible but are smaller, indicating that they are mentioned less frequently but have contextual relevance. This map indicates widespread academic interest in topics such as e-commerce, consumer behavior, and the international business environment, all in the context of digital transformation. The data show a strong connection between scientific and applied aspects such as marketing and economic impact. Figure 6 shows the connection between scientific and applied aspects, such as marketing and economic impact.

In this analysis, three main keyword clusters were identified, each representing significant aspects. These clusters were refined from an extensive set of over 300 terms, with relevance determined based on keyword frequency and co-occurrence within the analyzed sources. Cluster 1 is derived from Figure 4, which reflects the general distribution of core terms used in e-commerce and consumer research. The final selection includes 10 core terms from an initial set of over 300. Terms such as “e-commerce”, “international”, and “consumer” dominate this cluster, indicating general concerns for international trade and consumer behavior. The rarest terms are ‘digital’, ‘conference’, and ‘marketing’, suggesting related but less central topics. Cluster 2 is based on Figure 5, focusing on more specific concepts related to ‘smartphones’ and ‘online sales’. In total, 10 relevant terms were selected from an initial set of over 300. Terms such as “smartphone”, “sales” and “methods” dominate the cluster, indicating interest in technological innovation and sales strategies. On the other hand, terms such as “price”, “shopping”, and “market” appear less frequently, but are still important indicators of the market and consumer behavior. Cluster 3 is derived from the co-occurrence matrix—Leaf 6 and highlights the thematic relationships between the relevant terms. Eight terms were refined from an initially expanded set of over 90. “E-commerce”, “consumer behavior”, and “online shopping” are the most prominent terms, suggesting the dominant interest in consumer behavior in the digital environment. “Supply chain management”, “evaluation”, and “simulation” appear less frequently, but indicate related operational or technical issues.

Table 1. Complementary perspectives of the three clusters, ranging from general and interdisciplinary to technological.

Cluster	Number of Terms	Most Occurrences	Fewest Occurrences
Cluster 1		e-commerce	digital
	323	international	conference
		consumer	marketing
Cluster 2	301	smartphone	price
		sales methods	shopping market
Cluster 3		m-commerce	supply chain
	94	consumer	management
		behavior	evaluation
		online shopping	simulation

presents the three clusters, which offer complementary perspectives, ranging from general and interdisciplinary (Cluster 1) to more applied and technology (Clusters 2 and 3).

In our analysis, we have identified the most cited authors to highlight their contribution to the development of the field of consumer behavior in terms of the experience in online and physical stores when purchasing smartphones. These authors have published a significant number of research articles, playing an important role in future research directions through the citations received. The analysis of the productive authors revealed that author Boardman, R. leads the ranking with a total of two published articles, followed by Fedorko, R. and Mccormick, H., who are in the same position with two published articles each. In terms of academic impact, the most cited author is Flanagin, AJ., who ranks first with a total of 165 citations, followed by other authors such as Boardman, R. and Mccormick, H., who also received a significant number of citations (25 each), reflecting the relevance of their research. The concentration of scholarly activity around a small number of authors indicates the formation of a nucleus of influential researchers contributing significantly to the sustainability field and consumer behavior in m-commerce.

Table 2. The most productive and cited authors.

Author	No. of Articles	Total Citations	First Publication Year
Fedorko, R	2	2	2022
Boardman, R	2	25	2022
Mccormick, H	2	25	2022
Becker, K	2	4	2010
Lee, JW	1	4	2010
Flanagin, AJ	1	165	2014

presents the most productive and cited authors in this field.

In this study, we administered a questionnaire to analyze consumer behavior related to smartphone purchases, focusing on gender differences and online purchasing trends. The main objective of the research was to identify factors influencing consumer preferences and to assess the statistical significance of these differences. The proportion of male students was 34.4% (157) and the others 65.6% (300) females. The structure of the sample is presented in

Table 3. Structure of the sample by studies.

	Number of Cases	Percentage	Percentage in the Romanian University Population [40]
Bachelor	310	68.2%	76.6%
Master	112	24.6%	19.2%
PhD	28	6.1%	4.1%
Post PhD	5	1.1%

.

For our research, the percentage of smartphones purchased online is important, in

Table 4. Structure of the sample by type of acquisition.

Categories (Purchased Actual Phone)	Number	Percentage
from a physical store	226	49.6%
from an online store	116	25.4%
from the free market (friends, black market)	32	7%
I don’t know (I got it as a gift)	82	18%

we have the sample structure in terms of the place where they bought the phone:

In terms of age, the sample has the following structure, as in Figure 7:

3.2. The Results Obtained Through Quantitative Analysis

Young people who have used or currently use a smartphone, regardless of model or brand, were selected for the questionnaire. The convenient sampling method was chosen due to its accessibility, low cost, and high frequency of use in research of this type.

The survey was conducted in December 2024, and we had 456 cases. The questionnaire was applied online to students at Stefan cel Mare University of Suceava and Dunarea de Jos University of Galati. The questionnaire did not demand any identification data. We did not do sampling; we sent the questionnaire to all students from the University of Suceava by email to their professional email box, and for the University of Galati, the questionnaire was posted on some faculty’s social media accounts.

Table 5. A summary of the most common answers provided by the respondent.

No.	Questions Addressed to Respondents	Frequent Answers	%	Less Frequent Answers	%
1.	What brand is the smart phone?	Apple	37.9	Realme	0.4
		Samsung	36.2
2.	Where did you buy your last mobile phone from?	Physical store	64	I don’t know (I received it as a gift)	18
3.	The brand of the next mobile phone I will buy will be:	Apple	41.4	OnePlus	0.4
		Samsung	24.5
4.	The telephone is a symbol of social status.	Strong disagreement	30	Strong agreement	7.2
		Neutral	28.2
5.	The price-quality ratio is the most important when purchasing a phone.	Strong agreement	45.6	Strong disagreement	4.2
6.	Regardless of the brand, the most important thing is the product’s technical characteristics.	Strong agreement	61.5	Strong disagreement	1.8
7.	Before purchasing a phone online, I will test the phone in a physical store:	Neutral Strong agreement	26.8 25.3	Disagreement	14.9
8.	Before purchasing a phone online, I will read reviews about it.	Strong agreement	73.2	Disagreement	1.1
9.	I think I can get a better price for a phone online.	Strong agreement	32.7	Strong disagreement	3.3
10.	Gender	Feminine	65.6	Masculine	34.4
11.	Total monthly income	3500–4999	21.2	20,000+	3.1

provides insight into consumer behavior in the smartphone purchase process, highlighting brand preferences, purchase patterns, product selection criteria, and the influence of socioeconomic factors on the purchase decision.

The first section of the table examines brand preference among respondents. Apple emerges as the most preferred brand, with 37.9% of participants indicating that they own an Apple smartphone. Close behind, Samsung holds a significant market share, with 36.2% of respondents selecting it as their brand of choice. The dominance of these two brands suggests strong consumer loyalty and brand positioning in the high-end smartphone segment. In contrast, Realme, a relatively new competitor in the global market, was selected by only 0.4% of respondents, indicating a minimal penetration rate within this sample group. This distribution of preferences highlights a market inclination toward well-established brands known for their ecosystem integration, reliability, and perceived prestige.

The second section explores the channels through which consumers acquire their smartphones. The majority of respondents (64%) reported purchasing their last mobile device from a physical store, indicating a preference for in-person shopping experiences where customers can physically test the device before committing to a purchase. Meanwhile, 18% of respondents indicated that they did not make the purchase themselves but rather received their phone as a gift. This finding suggests that gift purchases constitute a notable portion of the smartphone market, potentially influenced by family decisions, holiday promotions, or brand loyalty within households.

When asked about the brand of the next smartphone they intend to purchase, Apple remains the leading choice, with 41.4% of respondents planning to buy an Apple device in the future. Samsung remains the second most preferred brand, with 24.5% of respondents expressing interest in purchasing a Samsung smartphone. The preference for Apple in future purchases suggests high brand loyalty, reinforcing its strong presence in the premium smartphone market. Notably, OnePlus is the least preferred brand, selected by only 0.4% of respondents, which indicates limited consumer awareness or demand for this particular manufacturer within the given sample.

The perception of smartphones as a symbol of social status was also assessed in the survey. The results show that 30% of respondents strongly disagree with the idea that smartphones serve as a status symbol, while 28% remain neutral on the subject. Only 7.2% strongly agree with this statement, suggesting that, within this sample, smartphones are viewed more as functional tools rather than as indicators of social hierarchy. This finding aligns with contemporary trends where practicality, technological features, and brand reputation tend to outweigh luxury perceptions in consumer decision-making.

Another critical factor influencing purchasing decisions is the price-to-quality ratio. The majority of respondents (45.6%) strongly agree that the price-to-quality ratio is the most crucial factor when purchasing a smartphone, while only 4.2% strongly disagree. This indicates that consumers prioritize value for money, expecting a balance between the cost of the device and the technological advancements it offers. Furthermore, the results reinforce the importance of technical specifications in consumer decision-making. A significant 61.5% of respondents strongly agree that, regardless of brand, the most crucial factor in choosing a smartphone is its technical characteristics. Only 1.8% of respondents strongly disagree with this statement, confirming that consumers place significant importance on specifications such as processing power, battery life, camera quality, and software capabilities over branding alone.

The survey also investigates consumer behavior regarding online shopping habits. When asked whether they would test a smartphone in a physical store before purchasing it online, responses were mixed. The results indicate that 26.8% of respondents strongly agree with this approach, while 25.3% remain neutral, and 14.9% express disagreement. This suggests that a considerable proportion of consumers engage in “showrooming”, a phenomenon where individuals explore products in physical stores before finalizing purchases online, often to secure better prices or deals.

Furthermore, the significance of online reviews in consumer decision-making is evident in the responses. An overwhelming 73.2% of respondents strongly agree that they will read online reviews before purchasing a smartphone, whereas only 1.1% disagree. This highlights the crucial role of customer feedback and expert reviews in shaping purchasing decisions, reinforcing the importance of digital reputation management for smartphone brands and retailers.

Regarding price perceptions, 32.7% of respondents strongly agree that they can find a better price for a smartphone online, whereas only 3.3% strongly disagree. This finding suggests that many consumers associate online shopping with cost savings, likely due to promotional discounts, e-commerce-exclusive deals, and greater price transparency compared to traditional stores.

Demographic information about respondents provides further context for these findings. The survey sample consists of 65.6% female participants and 34.4% male participants, indicating that women were more heavily represented in the study. Additionally, the total monthly income distribution of respondents reveals that 21.2% fall within the 3500–4999 RON income range, while a smaller percentage (3.1%) report earning 20,000 RON or more per month.

3.3. Testing the Study Hypotheses

The quantitative research with a questionnaire had several hypotheses. We used SPSS [26] to test the hypothesis.

H1. 

In terms of the maximum price of the next smartphone there is no difference between males and females. The average maximum price for the group of males is 3250 RON (approx. USD 679), and for the females, it is (approx. USD 742). We use the Independent-Sample T Test. In

Table 6. Descriptive statistics of intended maximum price for mobile phones by gender.

Group Statistics
Gender		N	Mean	Std. Deviation	Std. Error Mean
The maximum price I intend to pay for my future mobile phone is (price in RON)	Male	157	3230.62	1859.212	148.381
	Female	299	3550.67	2383.396	137.835

we can see the descriptive statistics developed in SPSS of the maximum expected price for mobile phones by gender.

Table 7. Independent samples t-Test for maximum intended price for mobile phones by gender.

Independent Samples Test
		Levene’s Test for Equality of Variances		t-test for Equality of Means
		F	Sig.	t	df	Sig. (2-Tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
The maximum price I intend to pay for my future mobile phone is (price in RON)	Equal variances assumed	0.277	0.599	−1.464	454	0.144	320.041	218.535	749.507	109.425
	Equal variances not assumed			−1.580	389.543	0.115	320.041	202.523	718.216	78.134

presents the results of the independent samples t-test, which examines gender differences in the maximum price respondents intend to pay for their future mobile phones in RON. The table includes both the results of Levene’s test for equality of variances and the results of the t-test for equality of means, providing a detailed insight into the statistical significance of the differences.

Levene’s test for equality of variances indicates that the variances between gender groups are similar (F = 0.277, Sig. = 0.599), which allows the assumption of equality of variances in the t-test interpretation. The results of the t-test for equality of means show that the mean difference between males and females in terms of the maximum intended price for the purchase of a cell phone is not statistically significant (t = −1.464, df = 454, Sig. = 0.144 for assumed equal variances).

The table also shows the estimated mean difference between the two groups (mean difference = 320.041 RON) and the 95% confidence interval for this difference (Lower = −749.507; Upper = 109.425), indicating significant overlap between the groups.

The mean price between the two groups is not significantly different. With a probability of 95%, we can assume that the maximum price a young male will pay for the next smartphone will be between (USD −156; USD 23), then the maximum of a young female. In this case, the hypothesis H1 is accepted. Also, according to H2, there is no difference in the proportion of using Apple by gender.

H2. 

There is no difference in the proportion of online purchases between men and women. The dependent variable is the purchase channel (online vs. offline), and the independent variable is gender.

We will test if the brand of the actual smartphone is independent of gender with the chi-squared Test. The males comprise 24.20% of Apple users, and the females are users of the same brand at a proportion of 45.15%. In total, 33% of the expected count is less than five. This assumption is violated, and we analyze the Likelihood Ratio. The Likelihood Ratio is 40.223 for eight liberty grades, and p is lower than 0.01. We can determine that there is a dependence between the two variables with a medium effect (the value of Cramer’s V is 0.300) according to the categories of Cohen [41]. We conducted the same by creating categories for the “other brands” mentioned in the following question, and we had similar results: sig value 0.000 and a Likelihood Ratio 52.829 for 14 liberty grades, and a magnitude relationship medium (0.335). In

Table 8. Chi-square test results for association between variables.

Chi-Square Tests
	Value	df	Asymptotic Significance (2-Sided)
Pearson Chi-Square	41.006 a	8	0.000
Likelihood Ratio	40.223	8	0.000
N of Valid Cases	456

^a. Six cells (33.3%) have expected count less than five. The minimum expected count is 69.

, we can observe the results of the Chi-Square test used to evaluate the existence of a significant association between the analyzed variables. Also,

Table 9. Magnitude of the relationship between gender and brand phone preference.

Symmetric Measures
		Value	Approximate Significance
Nominal by Nominal	Phi	0.300	0.000
	Cramer’s V	0.300	0.000
N of Valid Cases		456

presents the symmetric measures for association strength between variables.

We conducted the same test for the next brand of phone they wanted to buy (of course, we had some cases where they did yet not know the brand for the next smartphone –24.6%). Even for the next phone, we can say that we have a relation between the gender of the brand of the phone: Chi-square is 37,840, with 11 degrees of liberty, and p = 0.000. The relationship remains medium (the value of Cramer’s V is 0.332). We can conclude that women prefer Apple. Therefore, H2 confirms that there is no difference in the proportion of buying online by gender.

We applied the Chi-square test to see if there is a significant difference between genders in the proportion of smartphones bought online.

H3. 

Consumer intentions to purchase smartphones online are increasing. The dependent variable is online purchase intention, and the independent variables include previous experience, income, and current smartphone brand.

We will test the H3 hypothesis in two situations: the actual smartphone and the next smartphone. The Chi-Square is 15.565 for three liberty grades, and p is lower than 0.01. We could tell that there was dependence between the two variables with a low to moderate effect (the value of Cramer’s V is 0.182), as we can see in

Table 10. Chi-Square test results for variable association analysis.

Chi-Square Tests
	Value	df	Asymptotic Significance (2-Sided)
Pearson Chi-Square	15.024 a	3	0.002
Likelihood Ratio	15.576	3	0.001
N of Valid Cases	456

^a. Zero cells (0.0%) have an expected count of less than five. The minimum expected count is 11.02.

and

Table 11. Symmetric measures of association strength between nominal variables.

Symmetric Measures
		Value	Approximate Significance
Nominal by Nominal	Phi	0.182	0.002
	Cramer’s V	0.182	0.002
N of Valid Cases		456

.

We conducted the same analysis for the intentions of the sample to determine the next smartphone they will buy. In this case, the Chi-square is 17.438 for three liberty grades, and p is lower than 0.01. Cramer’s V value is almost the same at 0.196, meaning we have an association between the two variables, but it is a low to moderate influence from the independent variable gender. Males are buying smartphones online more than females. In the case of H3, there are increasing customer intentions to purchase online smartphones.

We will use the Paired Proportions Test (McNemar’s Test). We have two questions regarding the acquisition of online smartphones: the first is regarding the actual phone, and the second one is about the next phone. In the two questions, we derived some responses, which are not dichotomic if the phone was bought online (they received the phone like a present and they did not know how it was bought), and for the second question, they are undecided yet regarding where from they will buy the next phone. The database, in this case, will have 241 valid cases. Afterward, we created two dichotomic variables in function if they bought/will buy online. Of the 70 cases who bought online 65 will buy the same phone as the next phone and five will buy it on site. Of the 171 cases of people who did not buy the phone online, 12 will change for the next acquisition. The sig value is 0.143.

Table 12. McNemar test results for paired nominal data analysis.

Chi-Square Tests
	Value	Exact Sig. (2-Sided)
McNemar Test		0.143 a
N of Valid Cases	241

^a. Binomial distribution used.

presents this in more detail.

In this context, according to the analysis carried out, the ratio of online smartphone purchases will not change significantly.

In the regression analysis, several variables were entered, including gender, current brand, continuous variable age, maximum price, and income, but the regression closest to a normal regression, which we cannot validate, is by income category, current brand, and gender. There are likely other independent variables that influence the decision to purchase a phone online, but these have not yet been identified or included in this study. As a limitation of the research, we could mention that the regression that we tried to perform cannot be validated with the independent variables considered in the survey.

Even in step 0, some independent variables are considered statistically significant predictors; we can see in the regression model they are not significant predictors of the outcome at the 5% level.

Gender is a very strong predictor in the initial step, and it is also in the final equation of the regression (in both cases, p is lower than 0.01). In our case, according to the positive coefficient, men are more likely to shop online than women. Thus, since B = 0.977, p = 0.001, and Exp (B) = 2.658, men are 2.65 times more likely to buy online than women. The sex variable is the most important in the model, indicating a clear behavioral difference between men and women in terms of online purchases.

In terms of the current brand, p = 0.017 represents a significant predictor variable, indicating a preference for a particular brand, which influences online purchase decisions. In the regression model, the brand relationship is marginally significant, indicating a slight preference for certain brands in online purchase decisions. Thus, as B = 0.051, p = 0.077, and Exp (B) = 1.052, it shows that there is a preference for certain brands, which slightly contributes to the likelihood of an online purchase. Although the current brand, shows marginal trends, the variable indicates a weak tendency for preferences for certain brands to influence online purchase decisions.

Categorized income, with a score of 4.360 and p = 0.037, initially demonstrates that this factor plays an important role in online purchase decisions. Income is not significant in this model, but the Exp (B) value indicates a slight increase in likelihood with income. Thus, since B = 0.075, p = 0.234, and Exp (B) = 1.078, this demonstrates that although there is a slight tendency for higher income to increase the likelihood of online purchases (Exp (B) = 1.078), this is not statistically confirmed. Although income is not so significant in this model, it may have theoretical significance in other contexts.

To interpret the logistic model, we constructed a plot reflecting the predicted probabilities of online shopping, according to the three relevant variables: gender, brand actual, and IncomeCat. Figure 8 illustrates the probability differences between males and females, the impact of brand preference on the decision, as well as how income influences the likelihood of online purchases.

Men are 2.65 times more likely to shop online than women. The orange line (trend line) connects the tops of the bars to visually highlight the trend in the scores, helping us to see the variation between variables. The model is statistically significant, but it does not explain a large portion of the variability in the outcome. In the next research, we might add other variable predictors for the intentions of buying a smartphone online.

4. Discussion

Data from the questionnaire were analyzed using SPSS, and statistical methods appropriate for each hypothesis. The first hypothesis of the study (H1) assumed that there is no significant difference between men and women in terms of the maximum price they are willing to pay for their next smartphone. The mean maximum expected price for men was 3250 RON (approximately USD 679), while the mean maximum expected price for women was 3550 RON (approximately USD 742). The t-test for independent samples tested whether there was a statistically significant difference between the means of the maximum estimated price for men and women. Thus, the results of the test showed that the 95% confidence interval for the difference between the maximum prices is between USD −156 and USD 23. Since the confidence interval includes zero, this result suggests that the difference between the two groups is not statistically significant.

The H1 hypothesis analysis showed that there is no statistically significant difference between men and women in the maximum amount they are willing to pay to purchase a smartphone, which means that price sensitivity is mainly determined by individual financial situation and perceived value of the product rather than gender. This finding aligns with previous research [28], suggesting that price sensitivity is influenced more by individual financial situations and the perceived value of the product rather than gender. Therefore, this issue emphasizes that pricing strategies in smartphone marketing should focus on consumer segments defined by disposable income and perceived value, rather than adopting gender-differentiated approaches.

The second hypothesis (H2) investigated whether there is a significant relationship between gender and using an Apple smartphone. To test this hypothesis, the Chi-Square Test of Independence (Chi-square test) was used, which is suitable to assess the association between two categorical variables. The results showed that 24.20% of males use an Apple phone compared to 45.15% of females. The Chi-square test indicates a Likelihood Ratio value of 40.223 with eight degrees of freedom and p < 0.01, suggesting that there is a statistically significant relationship between gender and Apple brand usage. Cramer’s coefficient V = 0.300 indicates a medium size relationship as categorized by Cohen [41]. Given these results, hypothesis H2 is rejected, which means that there is an association between gender and Apple brand usage. This result suggests that women are more likely to use an iPhone compared to men. The same association was also tested for other smartphone brands. The results showed a Likelihood Ratio of 52.829 to 14 degrees of freedom and p < 0.01, confirming a medium-sized relationship between gender and preferences for various brands.

Regarding hypothesis H2, the results indicate that gender significantly influences the preference for smartphone brands, with women more likely to use Apple devices as a premium brand associated with high standards of quality, reliability, and social status [1]. From a marketing strategy perspective, campaigns should also be tailored to gender-based preferences, emphasizing aspects such as design, ergonomics, and brand perception, so that they are targeted to different consumer segments effectively.

Hypothesis H3 hypothesized that there is no significant difference between men and women in terms of online purchases of smartphones. This hypothesis was tested in two scenarios: current smartphone purchase and intention to purchase the next smartphone. The Chi-square test of independence was used to test the hypothesis, which allows us to assess the relationship between gender and online purchase preference. The Chi-square test results indicated that Chi-square = 15.565, with 3 degrees of freedom, and p < 0.01, suggesting that there is a statistically significant relationship between gender and online smartphone purchase. Cramer’s V value = 0.182 indicates a relationship of low to moderate intensity.

The findings for Hypothesis H3 suggest that men are more likely to purchase smartphones online compared to women, contradicting the assumption that online shopping behavior is independent of gender. This result is consistent with previous research [3], which highlights that men are generally more confident in m-commerce environments, often perceiving online shopping as more efficient and convenient. On the other hand, women may still prefer in-store purchases due to the ability to physically test the product before purchase. This finding has practical implications for m-commerce platforms, which could improve their online shopping experience for female consumers by offering more detailed product images, interactive features, and augmented reality testing options.

This analysis indicates that gender influences the online purchase of a smartphone, and men are more likely to buy online compared to women. Thus, Hypothesis H3 is rejected as there is statistical evidence suggesting a significant relationship between gender and online purchasing behavior.

Therefore. hypothesis H1 regarding gender differences in the maximum price of a smartphone was accepted, indicating that there is no significant difference between men and women in this regard.

Hypothesis H2 on the independence between gender and Apple brand usage was rejected, showing that there is a significant relationship and females are more likely to use Apple.

Hypothesis H3 on the independence between gender and preference for purchasing a smartphone online was rejected, indicating that men are more likely to purchase a smartphone online than women.

These results contribute to a better understanding of the dynamics of consumer behavior and can be used in the marketing strategies of technology and m-commerce companies.

Table 13. Hypotheses results and their comparison with previous research.

Hypothesis	Result	Comparison with Previous Research
H1: No difference in maximum smartphone price availability by gender.	Accepted—No significant difference in maximum price availability was found between men and women.	Similarly to studies indicating that price sensitivity does not differ significantly by gender [39].
H2: No difference in Apple brand preference by gender.	Rejected—A significant relationship was found; females are more likely to use Apple than males.	Unlike some studies that suggest that brand preference is independent of gender, this study aligns with research showing that women prefer Apple due to perceived quality and status [1].
H3: No difference in online smartphone purchases by gender.	Rejected—A significant relationship was found; men are more likely to buy smartphones online than women.	Consistent with previous research indicating that men are more likely to engage in online shopping for technology products due to the perceived ease and confidence of m-commerce [3].

summarizes the final results of the hypotheses and their comparison with previous research.

The results of this study emphasize that consumer buying behavior in the smartphone market is influenced by several factors beyond gender, such as brand perception, trust in technology, and shopping habits.

Future research could explore additional factors influencing smartphone purchase behavior, such as trust in online platforms, social influence, and technological literacy, to develop a more comprehensive understanding of consumer preferences in the digital age.

5. Conclusions

The analysis of 1603 articles reveals a thematic diversity that reflects current trends in academic research. The most frequent keywords, such as ‘e-commerce’, ‘consumer behavior’, ‘smartphone adoption’, ‘sustainability’, and ‘technological innovation’, highlight areas of major interest, characterized by the intersection between digitalization, consumer behavior, and innovation. The frequency of these terms suggests that digitization and the transformations of the global economy are central concerns for the academic community, especially in the post-pandemic period.

Analysis of the temporal distribution of articles shows an increase in recent research focusing on issues such as m-commerce and consumer behavior. Studies published in recent decades explore the transition to digital commerce, consumer preferences, and the impact of technological innovation on the economy.

The data highlight an interdependence between academic research and its practical applications. Topics such as digital marketing, technological innovation, and the economic impact of m-commerce are addressed in an applied way, suggesting a strong connection between theory and practice. The distribution of articles over more than two decades emphasizes a steady evolution of research in these areas, adapted to global economic and technological transformations.

Regarding the hypothesis we tested, we can say that in terms of the price of the next phone, there are no differences by gender, but males are buying significantly more online smartphones than females. The papers show we are not expecting an increase in the online acquisitions of smartphones by students from Romania.

In the quantitative analysis, the results of the logistic regression show that although men and women differ on average on the maximum price for the next smartphone (3230 lei for men and 3550 lei for women), these differences are not statistically significant (p > 0.05). This suggests that general factors such as income or specification preferences have a greater impact than gender. The analysis shows a slight tendency for females to prefer Apple, and the moderate value of Cramer’s V (V = 0.30) supports a relationship between gender and brand preference.

The logistic model shows that most of those who purchase smartphones online retain this behavior for future purchases (p < 0.01). In addition, a small proportion of those who purchased offline indicate an intention to switch to online shopping. This reflects a growing acceptance of m-commerce, likely spurred by its convenience and accessibility.

Figure 9 illustrates comparatively the current behavior and future intentions of purchasing smartphones, highlighting the transitions between online and offline shopping. The data presented is extracted from a quantitative survey-based research, with 241 valid cases for this analysis. Figure 8 illustrates current smartphone behavior and future intentions. It clearly shows the differences between online and offline purchases, highlighting consumer preference trends. The graph suggests high loyalty in purchasing behavior, with most consumers maintaining their preferred method of purchase (online or offline) for future purchases. However, there is a slight trend towards an increase in preference for online shopping, which could reflect an adaptation to digital trends and the convenience offered by m-commerce.

Although men and women differ on average in the maximum price they are willing to pay, these differences are not statistically significant. This suggests that price is influenced by general factors rather than gender.

The relationship between gender and brand indicates a slight tendency for women to prefer Apple, and Cramer’s V supports this moderate relationship.

The majority of those who bought online continued this behavior, while a small proportion of those who did not buy online made the transition. These results suggest a growing acceptance of m-commerce for smartphone purchases.