Effect of demographic factors and apparel product categories on online impulse buying behaviour of apparel consumers

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INTRODUCTION https://doi.org/10.31881/TLR.2022.91 evaluate the impact of demographic factors in an online purchasing where apparel product categories account for the majority of purchases [22]. Therefore, the aim of this study is to evaluate the role of demographic factors and various apparel product categories on the online impulse purchasing behaviour of apparel consumers. The current study is to investigate the impact of demographic factors on the online impulsive purchase behaviour of apparel consumers in Delhi NCR, India. Accordingly, this research has the following objectives: a) To identify the simultaneous effect of gender, education, income, age, and occupation on the online impulse-buying behaviour of apparel consumers. b) To identify the association between gender and online impulse buying. c) To identify the association between age and online impulse buying. d) To identify the association between family income and online impulse buying. e) To identify the association between education and online impulse buying. f) To identify the association between occupation and online impulse buying.

Online impulse buying behaviour for apparel
Impulse purchases are those that are done without any prior intention of looking for a certain kind of thing to meet a particular need [23]. Physical stores have historically been the focus of the majority of popular impulsive purchase research [24]. Due to a large number of businesses moving to online platforms, traditional enterprises have faced a considerable downturn. Numerous researchers have examined consumers' impulsive online purchase behaviour, finding the virtual environment and demographic characteristics as key factors in luring buyers [25]. According to Rook, when a person makes an impulsive purchase, it's usually a sudden, self-gratifying urge [19].

SOR model
Most research on online consumer behaviour uses the Stimulus, Organism, and Response Model (SOR), which was first developed in 1974 by Mehrabian and Russell [26]. The SOR model postulates that Stimulus (S) is a trigger that arouses individual shoppers [27]. Organism (O) is an inner assessment of customers, and Response (R), is a result of customers' reactions toward online impulse buying stimuli and their inner assessment. Consumers' demographic factors play a crucial role in how they react to the revealing atmospheric cues of stores during shopping [28]. These demographic factors are also influenced by sensible and insensible perceptions and the shopping website design [29]. So, several other numerous factors related to products/items are also included in the SOR model of consumer behaviour, for example, product type, price, promotional offers, brands, quality, etc. Peng and Kim https://doi.org/10.31881/TLR.2022.91 used the SOR model to examine consumer online shopping behaviour and discovered that the web environment is crucial to online purchasing [30]. The findings of Shen and Khalifa highlighted the significance of the interactivity and vividness of the website as an external stimulus for an individual's impulsive buying behaviour online [31]. Lim and Dubinsky [32] outlined the SOR model's function and investigated product type and online store layout as two major factors and internal emotional cues that influence consumers' inclination to make impulsive purchases. Perboteeah, Velacich, and Wells identify the influence of website characteristics on consumers' urge to buy impulsively [33]. They further identify the navigation, visual appeal, perceived enjoyment, and perceived usefulness as external website cues that affect online impulse buying behaviour. Further, Zhou, Yang, and Chen discussed that website environment stimuli influence consumers' internal state (perceived value, emotion, and attitude) and their behavioural response (purchase behaviour or intention) [34].
The main focus of this study was to evaluate the effect of demographic factors on online impulsebuying behaviour for apparel consumers. These demographic factors (age, gender, income level, education, and occupation) of OIBB apparel consumers will be affected by the external cues of the websites while doing apparel shopping online. Therefore, this research aimed to evaluate the role of demographic factors and apparel product categories on online impulse buying behaviour for apparel consumers.

Demographic factors
Numerous studies show a connection between impulse buying behaviour and demographic factors.
Age and impulsive buying are inversely connected, according to Mai et al. Younger and wealthier consumers are more prone to engage in impulse purchases [35]. Ghani and Jan examined how urban customers' impulsive purchasing behaviour was influenced by demographic parameters like age, gender, and income in Pakistan [36]. The results of their investigation demonstrate that there was an inverse association between urban customers' age and impulsive purchase behaviour. Furthermore, they found no connection between gender (females) and impulsive purchasing behaviour and found no correlation between customer impulsive purchasing behaviour and income. Coley and Burgess examined how impulsive buying behaviours of consumers in metropolitan areas were influenced by demographic parameters like as gender, income, and education [37]. They did not find any link between consumer impulse buying and education and consumers' impulse purchase behaviour and gender. They observed a connection between consumer impulsive purchases and income. Foroughi et al. investigated how customer demographics affected impulsive purchasing patterns for different product categories among Iranian consumers [38]. They found a correlation between consumer age and impulse buying behaviour finding an inverse link between the two. They also discovered a significant correlation between consumer education and impulsive purchasing. From the literature https://doi.org/10.31881/TLR.2022.91 survey, it was observed that most of the studies are related to the effect of demographic factors on offline impulse shopping. No specific study has been found related to demographic factors in online impulse buying and specifically for apparel consumers in Delhi NCR India. The main purpose of this research is to identify the effect of demographic factors and various apparel product categories on the online impulse purchasing behaviour of apparel consumers and provided suggestions to marketers to help them in making strategies. To fulfil the objectives of this study, online impulse buying behaviour (OIBB) for apparel consumers is a dependent variable, and five demographic factors; gender, age, income, education, and occupation, are considered independent variables. To investigate the relationships between these demographic factors and the OIBB of apparel consumers, the following hypotheses were developed. H1: Age, gender, income, education, and occupation all have a simultaneous impact on online impulsive purchasing of apparel consumers.
H2: Gender has a significant relationship with the online impulse-buying behaviour of apparel consumers.
H3: Age has a significant relationship with the online impulse-buying behaviour of apparel consumers.
H4: Income has a significant relationship with the online impulse-buying behaviour of apparel consumers.
H5: Education has a significant relationship with the online impulse-buying behaviour of apparel consumers.
H6: Occupation has a significant relationship with the online impulse-buying behaviour of apparel consumers.

METHODOLOGY
The impact of demographic characteristics (gender, age, income, occupation, and education) on consumers' impulse purchase behaviour in Delhi (NCR) was investigated using a descriptive crosssectional approach. This research used a quantitative study approach.
The questionnaire for the online survey was structured and used for this research as an instrument to collect primary data. Non-probability (convenience) sampling techniques were used for the research.
A sample group was selected from Delhi (NCR) to participate in the online survey. Target respondents were Indian (Delhi-NCR) consumers who shop for clothing online. Based on the sample size formula shown, the current study aimed to obtain 385 respondents [40]. This study developed and employed a scale on which factors were tested to determine online impulsive buying behaviour (OIBB), age, gender, income, education, and occupation. For the questionnaire survey, closed-ended questions and 5-point Likert scales were used [39]. Each attribute in the questionnaire was evaluated using a 5-point Likert scale with a range of 1 to 5 with 5 representing strongly agree and 1 representing strongly disagree. Details about each measurement item and scale can be found in Appendix 1. The survey questionnaire was divided into three parts to evaluate the impact of online impulse buying behaviour of apparel consumers as follows: Part 1: Demographic variables of customers, such as gender, age, income level, occupation, and education.
Part 2: To measure the online impulse buying behaviour of apparel consumers using a 5 -point Likert scale for the five statements used in the questionnaire [41,42].
Part: 3 was designed to evaluate the frequency of most purchase apparel product categories impulsively.

DATA ANALYSIS
Regression analysis, percentage analysis, and chi-square test were analyzed with the help of SPSS-23 software. The association between the independent variables (demographic factors) and the dependent variable of OIBB for apparel was examined using multiple regression analysis. For the regression analysis purpose, all five demographic factors were used as predictor variables by coding them as dummy variables. The rule specifies a categorical variable with K categories, (K-1) dummy variables are required [43]. Table 1   The proposed regression model is as follows for the study: Y= β0 + β1X1 + β2X2+ β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 + β9X9 + β10X10 + β11X11 + β12X12 + β13X13  Tables 3 and 4 summarize the results of regression analysis and analysis of variance (ANOVA).  Although, the t-value is greater than 1.96 and the p-value is less than 0.05 indicating a significant influence between gender and OIBB.  Additionally, Table 6 represents a Chi-square value of 81.796, a df (12), and a significance level of 0.001, or p < 0.05, indicating that there was a very strong correlation between age and OIBB for apparel consumers. It also indicates that consumers' ages increase when OIBB decreases. Therefore, hypothesis (H3) is accepted. This finding is consistent with the study of Mai et al. and Ghani and Jan [35,36].  or p < 0.05, indicating that there was a very strong association between income level and OIBB for apparel consumers. It also reveals that as the income level of apparel consumers increases then OIBB also increases. So, hypothesis (H4) is accepted. This finding is consistent with the study of Mai et al.
On the other hand, according to table 8, the majority of the respondents 191 (47.28%) and 114 (28.22%) are from graduation and post-graduation backgrounds. In their responses, 94 (23.27%) and 53 (13.12%) agreed and strongly agreed towards OIBB for graduation and post-graduation education background. It shows that educational backgrounds are directly linked with OIBB for apparel consumers. Additionally, Table 8 represents a Pearson Chi-square value of 76.804, a df (12), and a significance level of 0.000, or p < 0.05, indicating that there was a very strong association between education level and OIBB for apparel consumers. It also reveals that as the education level of apparel consumers increases then OIBB also decreases. So, hypothesis (H5) is accepted. This finding is not consistent with the study of Coley and Burgess [37] but consistent with the study of Foroughi et al. [38]. Chi-square = 76.804, df = 12, p-value = 0.000 * S. DA = Strongly Agree, D.A = Disagree, Ne = Neutral, AG = Agree, S.A. = Strongly Agree According to Table 9, the majority of the respondents 244 (60.40%) and 112 (27.72%) belonged to the students and employed occupation backgrounds respectively. In their responses, 123 (30.45%) and 52 (12.87%) agreed and strongly agreed towards OIBB for students and employed occupation backgrounds. It showed that the occupation of the respondents was directly associated with OIBB for apparel consumers. Additionally, Table 9 represents a Pearson Chi-square value of 62.74, degree of freedom of 12, and significance level of 0.001, or p < 0.05, indicating that there was a very strong association between the occupation of the respondents and OIBB for apparel consumers. So, hypothesis (H6) is accepted.
Furthermore, online apparel shoppers were asked to choose any apparel items that they had previously purchased impulsively from an e-commerce apparel website. The total number of apparel product categories was 11, which is listed in the survey questionnaire. Seven product categories (jacket, underwear, socks, skirt, sweater, swimsuit, and tie) had less than ten purchases, indicating that online shoppers of apparel were not frequently purchased these product categories impulsively. The category that showed the highest frequency was T-shirts 131 (32.43%), followed by jeans 49 (12.13%) and shirts 22 (5.45%). Swimwear, sweater, and tie 2 (0.495%) were the items that were least frequently purchased online impulsively.

Limitations and future scope
The results presented in the study have a few limitations which can be used for future studies.  [44,45].

Recommendations
Based on the above-mentioned research investigation, the following recommendations were made: 1. The e-commerce companies in Delhi (NCR), must consider the importance of online impulse buying behaviour for identifying the buying pattern of apparel consumers.
2. Online retailers also need to consider many other factors (like e-store atmosphere, visual appeal of the product, navigation of the website, promotional offers, cashback, review, payment choices, and product delivery, etc.) that can affect the individual impulsive shopping behaviour of apparel consumers [33,48].
3. E-commerce companies need to focus on serviceability (free, fast, and convenient delivery) and payment features (secure payment, feedback system, and usability) of the website [49].
4. E-tailers must implement the latest shopping experiences to create maximum satisfaction among impulsive shoppers [33,50].

CONCLUSION
A relationship between various demographic factors (gender, age, income level, education, and occupation), apparel product categories, and OIBB of apparel consumers was investigated. This research helps e-commerce retailers and researchers to investigate the strong links between e-store attributes and online impulse buying behaviour of apparel consumers. According to the findings, demographic factors including gender, age, education, income level, and occupation have a significant and simultaneous impact on the online impulsive purchase of apparel consumers. Additionally, this research demonstrates that gender is significant and inversely connected to online impulse buying.
Further, it was observed that the consumers' ages have a significant and inverse impact on their impulse buying patterns. Family income of apparel consumers has a significant association with OIBB of apparel consumers and as the creditability of money increases, the impulse buying phenomenon also increases. The education of apparel consumers has a strong influence on the OIBB of apparel consumers, as the education level increases, this buying behaviour decreases. The occupation of consumers also has a significant and inverse relationship with the impulsive purchase of apparel consumers.
It has been observed that young female consumers age group up to 35 years having education level up to graduation are having more impulsive purchases as compared to other groups. These findings can be utilized by e-stores to create effective promotional offers and eye-catching merchandise. Senior e-commerce managers may use these insights to boost the company's profitability and offer more convenient shopping experiences to attract clients. E-commerce businesses must take into account a wide range of additional aspects (such as the visual appeal of an e-store, product display, shopping enjoyment, secure payment, discounts, cashback offer, and advertisements) that may also influence customers' impulsive behaviour.

Author Contributions
Conceptualization -Trivedi V and Joshi P; methodology -Trivedi V and Chatterjee KN; formal analysis Appendix: 1