Journal of International Trade, Logistics and Law, Vol. 9, Num. 1, 2023, 241-250 241 IMPACT OF GENDER AND MONTHLY INCOME ON CONSUMER BUYING BEHAVIOR Hafsa DAOUDI Istanbul Ticaret University, Turkey Evrim İldem DEVELI Istanbul Ticaret University, Turkey Received: May 02, 2023 Accepted: May 29, 2023 Published: June 01, 2023 Abstract: This paper discovers the variables that influence consumer purchasing decisions in Morocco and Turkey as well as their relationships. The study put forth the hypothesis that gender and monthly income had an effect on stress levels and shopping choices for both online and offline stores. A 21-question survey from the Journal of the Academy of Marketing Science that was broken down into three main components was used as the research methodology. To better define the target market for a particular brand, the questionnaire asked demographic questions like gender, age range, country of residence, and monthly pay. A straightforward random sampling method was used to gather information from 280 respondents in Morocco and Turkey. In SPSS, descriptive statistics, correlation analysis, and regression analysis were all carried out on the data. According to the study, shopping choices for both online and offline stores as well as stress levels are influenced by gender and monthly income. The study also discovered a link between a preference for online shopping and the stress level associated with shopping in brick and mortar stores. The bulk of answers were from Morocco, with Turkey coming in second. There were five different salary ranges for the respondents. The survey offers helpful information about consumer shopping habits and preferences, which businesses may find beneficial in identifying their target market and creating efficient marketing plans. Keywords: Consumer Behavior, Online Shopping, Retail Shopping, Gender, Income 1. Introduction Consumer buying behavior is a critical aspect of marketing research and is of interest to businesses looking to develop effective marketing strategies. In today's digital age, consumers have more choices than ever, and how they shop has undergone significant changes. Online shopping has become increasingly popular, and it has become important to understand the factors that influence online shopping preferences. 2. Literature Review Consumer purchasing patterns affect online sales revenue. Because it is more convenient and there is more money available, people with higher monthly salaries prefer to shop online (Li and Su, 2021). Due to financial limitations, those with lower incomes tend to shop at physical stores. Higher-income consumers are more likely to buy high-end things online because they have more faith in their quality and authenticity (Liu and Lin, 2021). Quality, pricing, and brand reputation are influencing factors that have an impact on consumer purchasing decisions (Hsiao and Chen, 2021). Online purchases priorities these variables. Different demographic groups prefer internet shopping differently, with women favoring it more because of the time demands of childcare and employment (Mittal and Kamakura, 2021). Hence, the impact of monthly income on consumer buying behavior in online shopping is significant. Higher- income consumers tend to have a higher preference for online shopping and are more likely to purchase high-end products online. Though, it is important to see that other factors which include product quality, price, as well as brand reputation also influence consumer behavior in online shopping. Additionally, demographic factors such as Hafsa DAOUDI & Evrim İldem DEVELI 242 gender also play a role in determining online shopping preferences. Studies have also examined the impact of gender on online shopping preferences. It has been found that females are more likely to shop online than males (Kumar and Dash, 2021). This is because females generally have less time for shopping due to their busy lifestyles and responsibilities, making online shopping a more convenient option. Moreover, females are more likely to engage in online social activities, such as online shopping, than males. In addition, Ahsen et al. (2020) have found that females tend to shop for different products online compared to males. While men are more likely to make purchases of electronics and gadgets online, women are more likely to invest in clothing, accessories, and beauty items there. Additional research that Jin et al. (2021) have revealed that gender influences internet shoppers' choice-making. Males are far more inclined to make quick decisions, whereas females are more likely to make informed choices according to product information and reviews. This highlights the importance of targeting marketing strategies to specific genders, as it can significantly affect their online shopping behavior. Due to financial limitations and perceived social standing, lower-income customers are more stressed when shopping in physical businesses (Bostic et al., 2014). They might experience more tension and anxiety as a result of feeling stigmatised or judged by store personnel and other customers (Kim et al., 2020). In contrast, higher-income consumers may experience lower levels of stress when shopping in physical stores. This is because they have more financial resources to purchase products and may have a higher sense of confidence in their ability to navigate the shopping experience (Kim et al., 2020). Additionally, higher-income consumers may have access to more high-end retail stores with better customer service, leading to a more positive shopping experience. In physical stores, females typically suffer higher levels of stress than males do owing to things like crowding and societal pressure to appear nice. Males may feel less stress at physical stores since they may not feel as pressured to meet cultural standards for looks and fashion. Shopping stress levels are significantly influenced by personal preferences and individual differences. (Murray et al., 2010; Yeh et al., 2020). 2.1. Monthly income impact on Online Shopping One of the important factors that affect online shopping is the monthly income of consumers, as indicated by previous research showing varying levels of disposable income among consumers (Huang and Yen, 2010). Hence, a person’s income can have an impact on their decision to shop online or in physical stores, with higher-income consumers more likely to choose online shopping due to the convenience and greater product variety it offers. Online shopping is influenced by various factors, including consumers' income levels and gender. For instance, higher-income consumers may prefer the convenience and greater product variety of online shopping, while lower- income consumers may be more inclined to shop in physical stores to take advantage of in-store deals. Additionally, gender is another important factor that can impact online shopping preferences. Studies have shown that women tend to shop more frequently than men and spend more time on online shopping sites, suggesting a greater preference for online shopping. By understanding these factors, businesses can tailor their online shopping experiences to cater to the needs and preferences of their target customers (Park et al., 2016). 2.2. Gender impact on Online Shopping Gender is a significant factor that affects online shopping preferences. Research has consistently shown that men and women have different attitudes toward online shopping, with women being more likely to shop online than men. Women also tend to spend more time browsing online shopping sites and are more likely to make purchases on those sites. These differences in online shopping behavior may be attributed to various factors, including differences in decision-making styles, shopping motives, and perceptions of risk (Sinha and Banerjee, 2018). For instance, women are more likely to shop for personal and household items online, while men are more likely to shop for electronics and gadgets (Zhao et al., 2018). 2.3. Monthly income has an impact on retail store shopping stress level Several studies have investigated the relationship between monthly income and retail store shopping stress level. One study found that consumers with higher incomes experience less stress while shopping in physical stores (Lin and Chen, 2015). Another study suggested that income level may affect a consumer's perception of shopping stress, with those with lower incomes reporting higher levels of stress in retail stores (Thamizhvanan and Xavier, 2014). Impact of Gender and Monthly Income on Consumer Buying Behavior 243 2.4. Gender impact on retail Stores shopping stress level Gender is an important factor that can impact consumer behavior in both online shopping and retail store shopping. For instance, studies have shown that women may have different shopping behaviors and experiences compared to men. Research indicates that women tend to spend more time on online shopping sites than men, suggesting a greater preference for online shopping (Park et al., 2016). However, women are also more likely to experience shopping stress compared to men (Dittmar et al., 1996; Vedhahari and Vijayalakshmi, 2015). 2.5. Relation between retail store shopping stress level and online shopping preference Online buying is often preferred by customers who find physical store shopping stressful due to variables including crowded stores, long lineups, and the need to make selections quickly (Khan and Bhatnagar, 2008). According to studies, customers who are under a lot of stress in physical stores are more inclined to favor online purchasing, which is primarily motivated by simplicity and ease (Dholakia and Rego, 2016). Hence, the relationship between retail store shopping stress levels and online shopping preference is complex and influenced by various individual and situational factors, more study is required to examine this connection and comprehend the underlying factors that influence consumers to prefer online purchasing over conventional physical stores (Moshrefjavadi et al., 2019). Hypothesis H1: Monthly income has an impact on online shopping preference. H2: Gender has an impact on online shopping preference. H3: Monthly income has an impact on retail store shopping stress level. H4: Gender has an impact on retail store shopping stress level H5: There is a correlation between "retail store shopping stress level" and "online shopping preference". 3. Research Methodology This section explains the research procedure to find out factors of consumer buying behavior in population of Morocco and Turkey. This section includes the population of the study, sample and sample size, data collection instrument, and data analysis procedures. 3.1. Questionnaire Design The questionnaire used in the research was from Journal of the Academy of Marketing Science (Albrecht et al., 2017). The questionnaire consists of 21 questions, with the first two questions being multiple-choice questions about shopping habits and preferences. Question 3 asks participants to choose their preferred brand, and the remaining 18 questions are related to the chosen brand. The questions are mostly in the Likert scale format with responses from strongly agree to strongly disagree as there would be just these five options. The questionnaire also includes four demographic questions related to gender, age range, country of residence, and monthly salary. The purpose of the questionnaire is to gather insights into shopping behavior and preferences, as well as to understand the target market of a specific brand. 3.2. Research Design This study examined factors affecting consumer buying behavior with a major focus on economic factors, cultural and social factors, psychological factors, and personal factors. Economic factors may include personal assets and the income levels of respondents. Age, nationality, and gender reflected the personal factors of consumers. Motivation, involvement, learning, attitude, and lifestyle included psychological factors while social classes and roles reflected the cultural and social factors of respondents. Data collected using questionnaires were processed and analyzed in SPSS. To understand how variables behave, descriptive statistics were examined. Correlation analysis was then performed to determine whether the variables included in the study models were related. To verify the hypothesis, one-way ANOVA and T-tests were used. 3.3. Sampling Method Sample size of 280 respondents was selected for the study based on a simple random sampling technique and results were generalized to the whole population of the study. Hafsa DAOUDI & Evrim İldem DEVELI 244 3.4. Data Collection Tool Data was collected using questioner tool. Questioner was divided into three major sections. Section one was based on questions about buying behavior and preferred brands for shopping. The second section included the question regarding the factors including economic factors, psychological factors, cultural and social factors while the third section included demographic factors including age, gender, nationality, and salary that reflected personal factors of consumer buying behavior. The reliability of questioner was analyzed using 40 responses and after proving the scale valid and reliable, it was used to collect 280 responses from Morocco and Turkey. The data was collected in the month of January and February in 2023. 4. Data Analysis and Interpretation of Results Data collected using question questionnaires were analyzed in SPSS. The following section explains descriptive statistics along with frequency distribution, correlation analysis, and regression analysis. 4.1. Descriptive Statistics In this chapter, descriptive statistics are presented to provide an overview of the data collected in the table 01. A distribution of gender is also presented in the figure. 4.1.1. Gender The distribution of gender among respondents is presented in the following figure: Table 1: Descriptive of gender Variable Frequency Mean Median Standard Deviation Variance Female 133 1.5250 2.0000 0.50027 0.500 Male 147 Out of the total respondents of the study there were 133 female respondents and 147 male respondents. The mean value of gender was 1.5250, the median was 2.0000. Standard deviation is termed as a measure of dispersion round mean value and standard deviation for gender were found as 0.50027 and variance was found as 0.500. Distribution is indicated in the following figure: 4.1.2. Age In this section, the age distribution of the respondents is presented. The age groups were divided into three categories. The distribution of age groups is presented in the following figure. Table 2: Descriptive of Age Age group Frequency Mean Median Standard Deviation Variance 18-24 years 115 1.878 2.0000 0.82928 0.688 25-30 years 84 Above 30 81 Age of respondents was divided into three groups as respondents from age of 18 years to 24 years, the second group contained respondents from age of 25 years to 30 years and 81 were such respondents that belonged to age group above 81 years. 115 individuals, according to frequency distribution, were between the ages of 18 and 24. 84 individuals were between the ages of 25 and 30 and 81 respondents were there who were having age above 30 years Impact of Gender and Monthly Income on Consumer Buying Behavior 245 as indicated in the following figure. The mean value of age was found as 1.878, the median was found as 2.000, the standard deviation was found as 0.82928, and the variance was found as 0.688. 4.1.3. Nationality To provide further context, this table shows the frequency of respondents by country and their corresponding mean, median, standard deviation, and variance values. As per the table, the majority of respondents were from Morocco, followed by Turkey. Table 3: Descriptive of nationality Country Frequency Mean Median Standard Deviation Variance Turkey 121 1.579 2.0000 0.4926 0.246 Morocco 159 Descriptive statistics of nationality indicated that 121 respondents belonged to Turkey and 159 respondents were from Morocco. Moreover, the mean value of nationality was found as 1.579, the median was found as 2.0000, the standard deviation was found as 0.4926, and the variance was found as 0.246. 4.1.4. Salary In this section, the salary of respondents is presented in 5 different salary ranges. Salary range (₺) Frequency Mean Median Standard Deviation Variance 0-199 139 1.7571 2.0000 0.98280 0.966 200-399 99 400-699 22 700-999 11 +1000 9 Salary ranges for respondents were broken down into five categories: 0–199 ($139 respondents), 200–399 ($99 respondents), 400–699 ($22 respondents), 700–999 ($11 respondents), and over $1000 ($9). The median wage was $2,000, the standard deviation was $98280, and the variance was $96. The mean pay was $1.7571. Reliability Statistics Cronbach's Alpha N of Items .906 21 Item-Total Statistics Hafsa DAOUDI & Evrim İldem DEVELI 246 Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted BB1 45.8000 248.985 .031 .917 BB2 47.3500 255.926 -.178 .912 BB3 47.3750 254.394 -.091 .911 BB4 46.2500 215.321 .845 .894 BB5 46.1500 226.131 .560 .902 BB6 46.3500 214.131 .854 .893 BB7 47.2000 255.292 -.165 .911 BB8 47.3000 255.651 -.186 .911 BB9 47.3250 254.943 -.143 .911 BB10 46.1500 245.874 .198 .909 BB11 46.2750 214.512 .854 .893 BB12 46.1250 226.933 .550 .902 BB13 46.3750 213.317 .864 .893 BB14 46.2500 215.321 .845 .894 BB15 46.1000 226.656 .558 .902 BB16 46.2500 215.423 .799 .895 BB17 46.2250 215.051 .853 .893 BB18 46.0250 228.179 .511 .903 BB19 46.2750 215.538 .828 .894 BB20 46.0250 228.179 .511 .903 BB21 46.3250 213.866 .860 .893 These results appear to be related to reliability analysis of a scale consisting of 21 items. The Cronbach's alpha coefficient was computed and found to be .906, which suggests high internal consistency among the items in the scale. The Item-Total Statistics table provides information on how each item contributes to the reliability of the overall scale. The corrected item-total correlation values range from -.186 to .864, with most values being positive and indicating a positive relationship between the item and the overall scale. While the Scale variation if Item Deleted column displays the variation without a particular item, the Scale Mean if Item Deleted column displays mean scores without that item. The Cronbach's Alpha if the Item is DELETED column displays how the alpha coefficient is affected by the deletion of an item. The scale has strong internal consistency overall, but a few items need more investigation to make sure they accurately measure the desired construct without lowering the scale's reliability because they exhibit negative correlations and lower alpha values. 4.2: Analysis for Hypothesis Testing The research performed to evaluate the hypothesis H1, which asserts that monthly income has an effect on preference for online shopping, is shown in section 4.2 of the report. The data were examined using one-way ANOVA, and the outcomes are shown in the ANOVA table. Impact of Gender and Monthly Income on Consumer Buying Behavior 247 H1: Monthly income has an impact on online shopping preference. One-way ANOVA was run to test H1 and results are shown in the following table: ANOVA Sum of Squares df Mean Square F Sig. Between Groups 9.633 4 2.408 3.063 .017 Within Groups 216.204 275 .786 Total 225.837 279 The significance value was found as 0.17 was less than 0.05 this means that Monthly income has a positive impact on online shopping preference such that online shopping preference is enhanced with increase in income level thus H1 of study is accepted. H2: Gender has an impact on online shopping preference. T-test was used to analyze H2 of the study and results are shown in the following table: One-Sample Test Test Value = 2 t df Sig. (2- tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Gende r -15.888 279 .000 -.47500 -.5339 -.4161 The significance value was found as 0.000 which is less than 0.05 this means that results were found in the acceptable range such that gender has a significant impact on online shopping preference thus H2 of the study is accepted. H3: Monthly income has an impact on retail store shopping stress level. One-way ANOVA was run to test H3 and results are shown in the following table: ANOVA Sum of Squares df Mean Square F Sig. Between Groups 4.291 4 1.073 .938 .443 Within Groups 314.625 275 1.144 Total 318.916 279 The significance value for the impact of income on retail shopping stress level was found as 0.443 is higher than the acceptable value 0.05 this means that monthly income has no impact on retail store shopping stress level thus H3 of study is not accepted. Hafsa DAOUDI & Evrim İldem DEVELI 248 H4: Gender has an impact on retail store shopping stress level T-test was used to analyze H4 of the study and results are shown in the following table: One-Sample Test Test Value = 1 t df Sig. (2- tailed) Mean Difference 95% Confidence Interval of the Difference Lower Upper Gende r 17.560 279 .000 .52500 .4661 .5839 The significance value was found as 0.000 which is less than 0.05 this means that results were found in the acceptable range such that gender has a significant impact on online shopping stress level thus H4 of the study is accepted. H5: There is a correlation between "retail store shopping stress level" and "online shopping preference". Correlation analysis was done to find out the relationship between retail store shopping stress level and online shopping preference as shown below: Correlation Analysis 01 02 03 04 05 01: Gender -- 02: Age .267** -- 03: Income -.381** -.023 -- 04: Nationality .109 .203** .063 -- 05: Online Shopping Preference -.143* .039 .166** .028 -- 06: Retail Store Shopping Stress Level -.035 -.007 .099 .030 .681** -- The association between the factors was determined using correlation analysis. According to the analysis's findings, consumers' preferences for online shopping and their degree of shopping stress is positively correlated. With a significance level of <0.05, the correlation coefficient was determined to be 0.681. This indicates that the stress level will rise with every one-point increase in the value of preference for online purchasing, and H5 of the study is accepted as true. 5. Conclusion The process of consumer purchasing is complicated as well as is affected by a various other variables. These elements can be both internal and external and may differ from one person to the next. To create successful marketing strategies, it is essential to have a thorough awareness of the various aspects that affect consumer behavior. Businesses and marketers can create targeted and personalized marketing tactics that connect with their customers by analyzing the effects of income, gender, and the stress level in retail stores on consumer buying behavior. Income and gender have an impact on consumer behavior. Businesses may build effective marketing strategies, including pricing strategies and advertising campaigns, by understanding how income influences consumer purchase behavior. Another significant variable that may have an impact on consumer purchase decisions and the stress levels Impact of Gender and Monthly Income on Consumer Buying Behavior 249 related to in-person versus online shopping is gender. When constructing marketing tactics, such as campaigns catered to particular genders, businesses and marketers must take gender into consideration. The correlation between in-store shopping stress and preference for internet shopping has an impact on consumer shopping behavior. Customers are more likely to favor internet shopping if they are under higher stress while making in-store purchases. Businesses must take into account elements that affect the stress levels in retail stores in order to deliver a great in-store experience. In today's cutthroat industry, understanding client purchase behavior is essential for success. According to Chen et al. (2012), firms can create efficient marketing strategies that appeal to their target audience by taking into account elements like income, gender, and the stress levels in retail stores. Businesses can develop tailored marketing strategies that cater to customer preferences and raise their chances of success by analyzing these aspects. The study has a number of limitations, including a small sample size that may limit the generalizability of the results, underrepresentation of certain demographic groups, potential bias or inaccuracies in self-reported data, a limited scope in terms of the factors considered or the geographic region covered, and the study's partial relevance to current consumer behavior due to changes over time. Sample size: There were limitations in terms of sample size. The results might not be generalizable because of the possibility that a smaller sample size is not representative of the larger population. Demographic bias: Certain demographic groups, like those with low income, minorities, or people with disabilities, were underrepresented. The findings may not be as applicable to these groups as a result. Self-reported data: Some of the research mentioned relies on participant self-reported data, which could be biased or inaccurate. Limited scope: There are factors considered or the geographic region covered in the studies listed may be limited. The findings' applicability to other contexts or groups may be constrained as a result. Time frame: Due to changes in consumer behavior over time, the research listed may only be partially relevant now. Therefore, the results might not be relevant to how consumers behave today. 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