1. Faculty of Management Purbanchal University, Nepal.
2. Lecturer, Faculty of Management, Shanker Dev Campus, Tribhuvan University, Kathmandu, Nepal.

* Correspondence: kcsanzay@gmail.com

Keywords: impulse, buying, cognitive dissonance

1. INTRODUCTION

An author [1] defined impulse buying as an unplanned and spontaneous purchase, devoid of any prior shopping intentions or specific buying objectives. It typically involves a sense of excitement, pleasure, or a strong compulsion to make the purchase. This behavior is driven by irrational thinking. Impulse buying occurs when customers make impulsive decisions influenced by their internal emotional states or by persuasive promotional messages that draw their attention to the product. According to [2], impulse buying is a pervasive aspect of consumer behavior and a crucial consideration for marketing activities. It is a complex phenomenon that occurs across various product types, making it impossible to categorize under a single category. Examples of impulse purchases include chocolates, clothing, cell phones, vehicles, and jewelry. Individuals who frequently make such spontaneous purchases are referred to as impulse purchasers or impulse buyers. Marketers and retailers aim to capitalize on this behavior in order to increase profits, as reported by [3]. An author [4] emphasized that impulse buying has received significant research attention in developed nations. It has been observed that up to 80% of all purchases are made impulsively, and these impulsive purchases are more likely to involve new products compared to planned purchases. The concept of impulse buying has been a subject of theoretical debate for many years. However, consumer behavior and retailing researchers have been studying this phenomenon for over fifty years, as noted by Clover in 1950. An impulse buyer is more likely to make unplanned purchases while visiting a hypermarket without prior intentions to do so. Mobile phone manufacturers often leverage this characteristic of their users by introducing additional devices, such as exercise bands and watches, which can be used as accessories for their phones. These products are strategically placed in checkout aisles to catch the attention of impulse shoppers and encourage them to make purchases they may not have otherwise noticed or considered, as highlighted by [3]. An author [5] claimed Cognitive Dissonance is the final stage in the consumer decision making process. Cognitive Dissonance is defined as any contradictory thought in the human mind that develops from a discrepancy between what the consumers think and any evidence that contradicts that belief. A consumer is bound to be unhappy if there is a discrepanc between the levels of satisfaction he expected from a product and the levels of satisfaction that he actually experienced. It has also been suggested that cognitive dissonance includes not only cognitive but also emotional elements [6].After an impulse purchase is made, it is typically accompanied by an array of emotions. Consumers may experience a sense of satisfaction and pleasure from buying something spontaneously, or they may feel regret for their impulsive decision. When a product meets customers’ expectations and performs as perceived, they feel satisfied. Conversely, when their expectations are not fulfilled, consumers experience dissatisfaction. In the face of cognitive dissonance, consumers seek ways to alleviate it. One approach is to seek reassurance by gathering information, such as consulting friends and family, to validate whether the purchase they made was the right choice. Consumers tend to recall their experiences, regardless of the outcome, and these recollections significantly influence their future decision-making. Moreover, through word of mouth, consumers can also influence the purchasing decisions of others [7]. Various researchers have suggested that cognitive dissonance is not only present in the post- purchase stage but also in the pre-purchase stage. During the pre-purchase stage, consumers can experience cognitive dissonance when faced with numerous product options and are unsure about which one to choose. This confusion arises from the extensive advertising tools and promotional strategies used to promote different products [8]. For marketers worldwide, cognitive dissonance poses a significant marketing challenge. They struggle to address the changing levels of dissonance experienced by customers as they transition from the pre-purchase to the post-purchase stages. Marketers recognize the importance of monitoring customer dissonance levels because they understand that high levels of dissonance can lead to the loss of loyal customers. To mitigate the possibility of customers questioning their judgments, businesses must periodically provide reassurance to customers that they have made the right purchase decision. While impulse buying behavior has been extensively studied globally, with observations highlighting its prevalence in retail stores, there has been relatively less research conducted on consumer cognitive dissonance in the post-purchase behavior context. In Nepal, despite the popularity of impulse purchases, there is a lack of sufficient research on this phenomenon. Although certain aspects may have been explored, further research is needed to deepen our understanding of impulse buying and its related cognitive dissonance in the Nepalese context. Therefore, this study aims to examine the level of cognitive dissonance and explore any significant differences in the mean scale of impulse buying behavior between male and female consumers in Kathmandu, Nepal. Additionally, it seeks to investigate the relationship and impact of impulse buying on cognitive dissonance in the post-purchase behavior of Nepalese consumers. The findings of this study can provide valuable insights to marketers, enabling them to better comprehend and stimulate such consumer behavior. It can also help marketers understand and address consumers’ cognitive dissonance in the post-purchase phase. Moreover, this research can assist consumers in gaining an understanding of their own impulse buying tendencies and the reasons behind their purchases exceeding their initial expectations.

2. MATERIAL AND METHODS

The study’s structure is linked to research inquiries that seek to assess how impulse buying impacts customers’ behavior after making a purchase in the Kathmandu Valley. These inquiries focus on Product Category, Product Promotion, Availability of Money, Mood of the Consumer, Price, Reference Group and Store Layout as factors. The study took a quantitative and cross- sectional method, using descriptive research to outline the elements of impulsive and post- purchase buying actions. This approach has assisted in presenting dependable and systematic details regarding the population, circumstance, or occurrence to the readers. The relationship between the constructs of impulse buying behavior and cognitive dissonance has also been investigated using causal research. It gives the reader information about the precise relationship between the variables, as well as an equation or model that describes the dependent variable as an additive combination of the independent variables. The research focuses on a population comprising students ranging from SLC to Masters Level and employed individuals residing in the Kathmandu Valley. This population falls within the age bracket of 16 to 35, which is commonly referred to as the Young Adults category. This specific group was chosen due to findings from the literature review indicating a notable presence of impulsive buying and post-purchase behavior among young adults, as opposed to middle-aged and older individuals. All the customer of Kathmandu valley of age group of 16 to 35 represent the population of study. Since the exact number of this group is unknown hence the above age group who visit supermarkets or make any kind of impulse purchases. Therefore, 400 respondents were randomly selected as a sample however, only 70 %, i.e. 283 respondents are considered a representative sample size for the proposed study. The sample size consists of respondents with variation in genders, ages, income levels, occupations, and marital statuses are the ones who have the ability and capacity to make any kind of purchases. Determination of this sample size is also backed up by past researches done in different parts of the world where 200 is an average sample size considered for their respective studies. The sampling units are both, male and female, as well as those who go to supermarkets, departmental stores, shopping mall or others for shopping. The sample consists of respondents with variation in gender, age, income level, occupation and marital status. The primary approach for gathering data in this study involved utilizing a questionnaire. This questionnaire was administered online to 283 selected participants. Among these, 183 responses were obtained through an online platform utilizing Google Forms, while the remaining 100 responses were collected using physical, printed questionnaires. The questionnaire included seven categories of the impulse buying value scale, encompassing product category, product promotion, availability of cash, mood of the consumer, price, reference group and store layout. Specifically, the Product category consisted of the goods of basic needs, quality product, in good looks, festive products and product popularity. Product promotion indicates urge to buy, sales/clearance signs, point of sales events, demonstration, coupons or voucher reward, and memory of advertisements. The Availability of Cash category comprised the ability to afford unplanned purchases, managing a tight shopping budget, having extra money for discretionary spending, and a tendency to exceed unnecessary purchases when cash is available and availability of ATM or debit card nearby. The Mood of the consumer section contained mood, excitement, contentment, happiness, and moments of pleasure. The Price category encompassed Price sense, attractive sales amount offers, discount and display of products. The Reference Group consisted of happy to go shopping trip with company, tend to buy more with company, family, skilled staff, friends’ behavior and choices. The store layout has five items i.e., attractively arranged stores, convenient store location, directional signage, shelf arrangement, and attention-grabbing, and intriguing window displays. These items were employed to gauge the levels of the impulse buying value scale. The initial step in the data analysis process involved assessing the normality of the collected data. This assessment was carried out using both the Shapiro-Wilk Test and the Histogram method to determine whether the data followed a normal distribution. As the data did not demonstrate a normal distribution, a non-parametric statistical test, specifically the Mann- Whitney Test as described in Glen’s work in 2015, was employed. This test aimed to ascertain whether there existed a significant difference in impulse buying tendencies between male and female participants. Subsequent to the normality test, a descriptive analysis of the data was performed. This analysis was utilized to provide a comprehensive presentation of the data pertaining to respondents’ characteristics, item responses, and demographic variables. In the subsequent phase, the data analysis process involved employing Correlation Analysis to gauge the extent of the relationship between variables such as product category, product promotion, availability of cash, mood of the consumer, price, reference group and store layout, and their influence on post-purchase behavior. Additionally, a multiple regression analysis was undertaken to evaluate the collective impact of these variables’ product category, product promotion, availability of cash, mood of the consumer, price, reference group and store layout on post-purchase behavior. This analytical approach aimed to uncover the interplay and significance of these factors in shaping post-purchase behaviors that due credit is given where it is deserved and upholds the principles of intellectual honesty.

3. RESULTS AND DISCUSSION

Table 01: Demographic Profile of Respondents

Demographic Variable		No of Respondents	Percentage%
Gender	Male	179	63.3%
	Female	104	36.7%
Education Level	SEE	5	1.8%
	2	17	6.0%
	Bachelors	158	55.8%
	Masters & above	103	36.4%
Age	16-22	59	20.8%
	23-27	89	31.4%
	28-34	96	33.9%
	35 and above	39	13.8%
Total		283	100

Table 02: Descriptive Statistics of Product Category

Product Category	N	Mean	Std. Deviation
I buy more if the goods are in basic need category.	283	1.75	.776
I buy more if the product quality is best.	283	2.04	.816
I am tempted to buy product if it looks good.	283	2.62	1.060
I bought requirements of product in festival season without much hesitation.	283	2.20	.962
Product popularity is frequently a reminder for unplanned purchase.	283	2.59	1.036

Table 03: Descriptive Statistics of Product Promotion

Product Promotion	N	Mean	Std. Deviation
Buy- one-get-one-free, limited stock, or limited stock time offers can be a reason to buy.	283	2.48	.994
Sales/ clearance signs are a reason to buy things on impulse.	283	2.63	1.035
Point-of-sale events (demonstrations, degustation, etc.) can induce my unplanned purchase.	283	2.72	.893
Getting coupons or rewards motivates me to shop more.	283	2.26	.990
Product ads, fliers, point-of-sale 0tices induce my unplanned purchase.	283	2.79	.984

Table 04: Descriptive Statistics of Availability of Cash

Availability of Cash	N	Mean	Std. Deviation
I feel financially capable of making impulsive purchases.	283	2.66	.992
I don’t feel restricted by a tight shopping budget.	283	2.96	1.081
I have extra money for shopping and am willing to spend it on something I really like.	283	2.61	1.067
When I have cash on hand, I tend to overspend on shopping.	283	2.89	1.189
I tend to shop impulsively when a debit card or an ATM is readily available nearby.	283	2.95	1.136

Table 05: Descriptive Statistics of Mood of the Consumer

Mood of the Consumer	N	Mean	Std. Deviation
I shop as a way to improve my mood.	283	3.18	1.172
I feel a sense of excitement when I make an impulse buying.	283	2.80	1.050
I don’t always feel regret after making an impulse purchase.	283	3.10	1.115
I spend more when I am happy.	283	2.72	1.131
I purchase products based on my current emotions.	283	2.84	1.059

Table 06: Descriptive Statistics of Price

Price	N	Mean	Std. Deviation
I am a price sensitive person.	283	2.07	.926
When I see attractive amount (like 199, 248, 599), I have urge to buy the product.	283	2.98	1.014
Price discount influence me to make unplanned purchase.	283	2.36	.853
I often purchase products displayed in stores.	283	2.85	1.003
I used to buy product which are in offer.	283	2.62	.900

Table 07: Descriptive Statistics of Reference Group

Reference Group	N	Mean	Std. Deviation
I feel more delighted to have companions on the shopping trip.	283	2.35	1.029
When shopping with companions, I buy more products.	283	2.84	1.074
I often buy more than needed when I go shopping with my family members.	283	2.69	1.147
Friendly and skilled staff often talks me into buying a product I didn’t plan to buy.	283	2.78	1.135
My companions affect my buying behavior and choice.	283	2.61	1.110

Table 08: Descriptive Statistics Store Layout

Store Layout	N	Mean	Std. Deviation
Attractively arranged store stimulates me to buy more than planned.	276	2.41	.916
Store location affects my unplanned buying.	277	2.56	1.022
I find it comfortable to shop in a 3 with directional signage.	281	2.33	.961
Shelf arrangement (e.g. products within hand reach) affects my unplanned purchasing.	275	2.75	.966
The store had attractive displays.	276	2.43	.922

Table 09: Descriptive Statistics of Post-Purchase Behavior- Cognitive Dissonance

Post Purchase Behavior- Cognitive Dissonance	N	Mean	Std. Deviation
I get anxious when I buy unplanned items from the store.	283	2.66	.885
I think my impulse buys are not beneficial.	283	2.83	.912
I try to convince myself that my impulse buys may have future benefits.	283	2.58	1.016
Planned purchases make me happier than unplanned ones.	283	2.16	.981
I tend to buy many unplanned items when I go shopping.	283	2.85	1.025
I buy things without thinking if I need them when I see them in the store.	283	2.93	1.133
I tell myself to buy first and think later when I’m unsure about buying something in the store.	283	3.07	1.172
Sometimes I lose interest in items that I have purchased.	283	2.33	1.042
I frequently purchase items that I never end up using.	283	2.88	1.039
I often buy things and regret my purchase later.	283	2.78	1.153

Table 10: Normality Test

Shapiro-Wilk
Latent Variable	Statistic	Df	Sig.
Product Category	.980	283	<.001
Product Promotion	.985	283	.004
Availability of Cash	.978	283	<.001
Mood of the Consumer	.981	283	<.001
Price	.986	283	.007
Reference Group	.950	283	<.001
Store Layout	.982	283	.001
Post Purchase Behavior-Cognitive Dissonance	.974	283	<.001

Figure 01: Histogram of Product Category

Figure 02: Histogram of Product Promotion

Figure 03: Histogram of Availability of Cash

Figure 04: Histogram of Mood of the Consumer

Figure 05: Histogram of Price

Figure 06: Histogram of Reference Group

Figure 07: Histogram of Store Layout

Figure 08: Histogram of Post Purchase Behavior-Cognitive Dissonance

Table 11: Reliability Statistics

Latent Variable	Cronbach Alpha
Product Category	.791
Product Promotion	.741
Availability of Cash	.751
Mood of the Consumer	.755
Price	.747
Reference Group	.771
Store Layout	.775
Post Purchase Behavior-Cognitive Dissonance	.797

Table 12: Communalities Statistics

Initial	Extraction
PC1	1.000	.758
PC2	1.000	.564
PC3	1.000	.401
PP1	1.000	.656
PP2	1.000	.598
PP3	1.000	.537
AOC1	1.000	.503
AOC2	1.000	.642
AOC3	1.000	.586
MOC1	1.000	.725
MOC2	1.000	.732
MOC3	1.000	.647
P1	1.000	.449
P2	1.000	.721
P3	1.000	.687
RG1	1.000	.607
RG2	1.000	.619
RG3	1.000	.497
SL1	1.000	.578
SL2	1.000	.456
SL3	1.000	.679
PPB-CD1	1.000	.711
PPB-CD1	1.000	.683
PPB-CD3	1.000	.711

Table 13: KMO and Barlettt’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.		.695
Bartlett’s Test of Sphericity	Approx. Chi-Square	1665.436
	df	276
	Sig.	<.001

Table 14: Rotated Component Matrix

Component
	1	2	3	4	5	6	7	8
PPB3	.801
PPB1	.776
PPB2	.672
RG1		.689
RG2		.664
RG3		.531
AOC3			.655
AOC2			.630
AOC1			.625
PP1				.694
PP2				.656
MOC2					.819
MOC1					.795
MOC3						.737
P1						.533
P2							.824
P3							.650
PC1								.813
PC2								.632

Table 15: Mann Whitney U Test between gender and post purchase behavior

Hypothesis Test Summary
	Null Hypothesis	Test	Sig.a,b	Decision
1	The distribution of PPB is the same across categories of Gender.	Independent-Samples Mann-Whitney U Test	.482	Retain the null hypothesis.

Table 16: Kurskal- Wallis Test between age group and post purchase behavior

Hypothesis Testing Summary
	Null Hypothesis	Test	Sig.a,b	Decision
1	The distribution of PPB is the same across categories of Age.	Independent-Samples Kruskal-Wallis Test	.737	Retain the null hypothesis.

Table17: Kurskal- Wallis Test between occupation and post purchase behavior

Null Hypothesis	Test	Sig.a,b	Decision
The distribution of PPB is the same across categories of Occupation.	Independent-Samples Kruskal-Wallis Test	.120	Retain the null hypothesis.

Table 18: Kurskal- Wallis Test between income and post purchase behavior

Null Hypothesis	Test	Sig.a,b	Decision
The distribution of PPB is the same across categories of Monthly Income.	Independent-Samples Kruskal-Wallis Test	.969	Retain   the         null hypothesis.

Table 19: Kurskal- Wallis Test between marital status and post purchase behavior

Null Hypothesis	Test	Sig.a,b	Decision
The distribution of PPB is the same across Categories of Marital Status.	Independent-Samples Kruskal-Wallis Test	.396	Retain   the         null hypothesis.

Table 20: Correlation Matrix

	Post Purchase Behavior	Product Category	Product Promotion	Availability of Cash	Mood  of the Consumer	Price	Reference Group	Store Layout
Post Purchase Behavior	1
Product Category	-0.067	1
Product Promotion	.182**	.396**	1
Availability of Cash	.195**	.317**	.539**	1
Mood    of the Consumer	.254**	.236**	.494**	.448**	1
Price	.233**	.255**	.528**	.413**	.461**	1
Reference Group	.144**	.194**	.362**	.366**	.313**	.458**	1
Store Layout	.370**	.119*	.315**	.306**	.298**	.339**	.299**	1
N	283	283	283	283	283	283	283	283

The correlation coefficient (r) value of -0.067 between product category and post-purchase behavior suggests a very weak negative association between these two variables. In practical terms, this means that there is a minimal, almost negligible, relationship between the degree or level of product category and post-purchase behavior. The negative sign of the correlation indicates that as the product category level increases, post-purchase behavior may slightly decrease, although this relationship is too weak to be practically meaningful. Essentially, changes in product category are not strongly related to changes in post-purchase behavior based on this correlation coefficient, and other factors likely play a more influential role in determining post-purchase behavior. The correlation coefficient (r) value of 0.182 between product promotion and post-purchase behavior indicates a positive and relatively weak association between these two variables. In practical terms, this suggests that there is a modest, but not very strong, relationship between the effectiveness of product promotion efforts and post-purchase behavior. A positive correlation means that as product promotion becomes more effective or impactful, post-purchase behavior tends to increase to some extent. However, the value of 0.182 indicates that this relationship is not particularly strong, implying that other factors beyond product promotion likely also influence post-purchase behavior. Therefore, while there is a positive connection between the two variables, the strength of this relationship is relatively modest, and it is important to consider other factors that might contribute to post-purchase behavior as well. The correlation coefficient (r) value of 0.195 between the availability of cash and post-purchase behavior signifies a positive but relatively weak relationship between these variables. In practical terms, this suggests that there is a modest connection between having more cash readily available and an increase in post-purchase behavior. However, the correlation is not particularly strong, indicating that other factors beyond cash availability significantly influence post-purchase behavior. Therefore, while having more cash on hand may contribute to increased post-purchase activity, it is just one of several factors at play in shaping consumers’ post-purchase behaviors. The correlation coefficient (r) value of 0.254 between mood of the consumer and post-purchase behavior suggests a moderate positive association between these two variables. In practical terms, this means that there is a noticeable and moderately strong relationship between a consumer’s mood and their post-purchase behavior. A positive correlation indicates that when a consumer’s mood improves or becomes more positive, their post-purchase behavior tends to increase as well. The value of 0.254 further indicates that this relationship is moderately strong, implying that changes in a consumer’s mood can have a meaningful impact on their post-purchase actions. However, it’s important to note that while mood plays a role, other factors may also influence post-purchase behavior, and individual variations can exist. The correlation coefficient (r) value of 0.233 between cash and post-purchase behavior suggests a positive but relatively moderate association between these two variables. In practical terms, this means that there is a noticeable and moderately strong relationship between the availability of cash and post-purchase behavior. A positive correlation indicates that as the availability of cash increases, post-purchase behavior tends to increase as well to some extent. The value of 0.233 suggests that this relationship is moderately strong, implying that changes in the availability of cash can have a meaningful impact on post-purchase actions. However, it’s important to consider that other factors may also influence post-purchase behavior, and individual differences can play a role. Nevertheless, this correlation indicates that cash availability is a relevant factor in understanding post-purchase behavior. The correlation coefficient (r) value of 0.144 between reference group and post-purchase behavior indicates a positive but relatively weak association between these two variables. In practical terms, this suggests that there is a slight and relatively weak relationship between a consumer’s reference group (influences from peers or social circles) and their post-purchase behavior. A positive correlation implies that as the influence or impact of the reference group increases, post-purchase behavior tends to increase slightly as well. However, the value of 0.144 suggests that this relationship is not particularly strong, indicating that while reference groups may have some influence on post-purchase actions, other factors likely play a more significant role in shaping consumer behavior following a purchase. It’s important to consider that individual differences and context may also affect this relationship. The correlation coefficient (r) value of 0.370 between store layout and post-purchase behavior indicates a moderate to strong positive association between these two variables. In practical terms, this suggests that there is a noticeable and relatively robust relationship between the layout and arrangement of a store and a consumer’s post-purchase behavior. A positive correlation indicates that when the store layout is more attractive or stimulating, post-purchase behavior tends to increase significantly. The value of 0.370 signifies that this relationship is not only statistically significant but also has a meaningful impact, implying that store layout can play a substantial role in shaping consumer actions after making a purchase. However, it’s important to note that while store layout is influential, other factors may also contribute to post-purchase behavior, and individual variations can exist.

Table 21: Model Summary of Regression Analysis

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.438a	.192	.171	2.36197
a. Predictors: (Constant), SL, PC, RG, MC, AOC, Price, PP

Table 21 exhibits the results of a statistical model, which appears to be a regression analysis. The model reports an R value of 0.438, indicating a moderate positive correlation between the predictors (independent variables) and the unspecified dependent variable related to consumer behavior. The R Square value of 0.192 suggests that approximately 19.2% of the variance in the dependent variable is explained by the predictors. The Adjusted R Square value of 0.171, adjusted for the number of predictors, indicates that the model’s explanatory power is somewhat reduced. The Std. Error of the Estimate is 2.36197, reflecting the standard deviation of the residuals. Overall, the model provides a modest level of explanation for the dependent variable, but it may benefit from further refinement or consideration of additional factors for a more comprehensive understanding of the phenomenon under study.

Table 22: ANOVA Table

Model	Sum of Squares	df	Mean Square	F	Sig.
Regression	363.748	7	51.964	9.314	.001
Residual	1534.196	275	5.579
Total	1897.943	282

Table 23: Coefficient Table

	Unstandardized Coefficients		Standardized Coefficients
Model	B	Std. Error		T	Sig.
(Constant)	2.870	.950		3.021	.003
PC	-.168	.055	-.180	-3.022	.003
PP	.017	.062	.020	.274	.785
AOC	.041	.047	.060	.874	.383
MC	.111	.054	.136	2.034	.043
PRICE	.072	.056	.089	1.268	.206
RG	-.019	.049	-.025	-.394	.694
SL	.303	.060	.304	5.076	.001

Table 23 exhibits the coefficients table represents the results of a multiple regression analysis, assessing the impact of various independent variables (predictors) on a dependent variable. Among the predictors, Mood of the Consumer (MC) and Store Layout (SL) stand out as statistically significant factors. A one-unit increase in Mood of the Consumer is associated with an approximately 0.111 unit increase in the dependent variable. Similarly, a one-unit increase in Store Layout corresponds to a significant increase of approximately 0.303 units in the dependent variable. These findings suggest that both consumer mood and store layout have meaningful and positive impacts on the dependent variable. However, it’s crucial to acknowledge that other predictors, such as Product Category (PC), Product Promotion (PP), Availability of Cash (AOC), Price (PRICE), and Reference Group (RG), do not appear to have statistically significant effects in this analysis. The practical implications of these relationships should be considered within the specific context of the study to draw meaningful conclusions. Furthermore, store layout is the most dominant factor followed by product promotion, mood of the consumer, price, store, availability of cash, and price in the study with the value of Beta 0.303, 0.17, 0.111, 0.072 and 0.041 respectively.

Hence, Regression Equation that can be determined is: Y= b0+b1x1+b2x2

= 2.870 + (-0.168) x 1+0.303×2

Table 24: Hypothesis Testing Results

Hypothesis	P-Value	Decision
Product categories have a notable impact on cognitive dissonance (Post Purchase). (H1)	.003	Accepted
Product promotion has a substantial influence on cognitive dissonance (Post Purchase). (H2)	.785	Rejected
The availability of cash has a noteworthy impact on cognitive dissonance (Post Purchase). (H3)	.383	Rejected
The mood of the consumer has a significant impact on cognitive dissonance (Post Purchase). (H4)	.043	Accepted
Price has a notable influence on cognitive dissonance (Post Purchase). (H5)	.206	Rejected
The influence of a reference group has a significant impact on cognitive dissonance (Post Purchase). (H6)	.694	Rejected
Store layout has a significant influence on cognitive dissonance (Post Purchase). (H7)	.001	Accepted

Table 24 exhibits the hypothesis testing result summary. It can be seen that H1, H4 and H7, have been accepted and H2, H3, H5 and H6 have been rejected as per the analysis of the result. (H1): “Product categories have a notable impact on cognitive dissonance (post-purchase)” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of Product category. The (H1) has been accepted because the p-value which is 0.03 is less than the 0.05 level of significance which states that there is significant impact of product category on cognitive dissonance. (H2): “Product promotion has a substantial influence on cognitive dissonance (post-purchase)” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of Product promotion. The (H2) has been rejected because the p-value is 0.785 which is above than 0.05 which states that there is no significant impact of product promotion on cognitive dissonance. (H3): “The availability of cash has a noteworthy impact on cognitive dissonance (post- purchase)” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of Availability of Cash. The (H3) has been rejected because the p-value is 0.383 which is more than 0.05 level of significance which states that there is no significant relation and impact of availability of cash on cognitive dissonance. (H4): “The mood of the consumer has a significant impact on cognitive dissonance (Post Purchase)” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of mood of the consumer. The (H4) has been accepted because the p- value which is 0.043 is less than 0.05 level of significance which states that there is a significant relation and impact of mood of the consumer on cognitive dissonance. (H5): “Price has a notable influence on cognitive dissonance (Post Purchase)” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of price. The (H5) has been rejected because the p-value which is 0.206 is more than0.05 level of significance which states that there is no significant relation and impact of store layout on cognitive dissonance. (H6): “The influence of a reference group has a significant impact on cognitive dissonance” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of reference group. The (H6) has been rejected because the p-value which is 0.694 is more than 0.05 level of significance which states that there is no significant relation and impact of reference group on cognitive dissonance. (H7): “Store layout has a significant influence on cognitive dissonance” where Correlation and Multiple Regression Analysis have been used to understand the degree and impact of store layout. The (H7) has been accepted because the p-value which is 0.001 is less than 0.05 level of significance which states that there is significant relation and impact of store layout on cognitive dissonance.

4. DISCUSSION

The primary goal was to test how post purchase behavior is impacted by impulse buying value scale and the mean difference in cognitive dissonance i.e. post purchase behavior with product category, product promotion, availability of cash, mood of the consumer, price, reference group and store layout. To that end, it was found that impulse value scale, i.e., product category is negatively correlated whereas product promotion, availability of cash, mood of consumer, price and store layout are positively correlated. The research conducted by [9] revealed a modest connection between impulse buying and the perception of having extra money. It suggested that individuals with larger budgets tend to engage in more impulsive purchases compared to those with smaller budgets. This perception of having surplus funds available for a specific occasion is termed “money available.” When people believe they have more readily available money, it has a more pronounced positive influence. In line with [9], having more money on hand tends to encourage consumers to make impulsive purchases and also leads to greater satisfaction with the products they buy. The research conducted by [10] found that women tend to engage in more intense impulse buying compared to men. When comparing these findings to the present study, similar results were observed in relation to factors like store window displays, free product offers, and price discounts provided by businesses. However, there is a distinction in terms of the mean difference in impulse buying between genders. In [11], females exhibited a higher inclination for impulse buying, whereas in the current study, both males and females showed a high propensity for impulse buying. Additionally, the current study included participants who hold positions in organizations below the managerial level, which implies that the sample had greater purchasing power. Consequently, no significant differences were detected in impulse buying based on gender in the current study. The research conducted by [12] revealed that two significant factors strongly predict impulse purchases: in-store browsing and consumer happiness. When consumers are in a positive mood, they are more inclined to explore various sections and aisles within a store. Moreover, they are more likely to accompany friends, peers, or family members on their shopping trips to enhance their excitement and enjoyment. This positive mood also leads them to respond more positively to the store environment and stimuli related to evaluating products. The research carried out by [13] found that the extent of post-purchase regret among consumers does not vary based on factors such as gender, age, education level, or race. This study suggests that making an unplanned purchase does not necessarily result in post-purchase regret. Therefore, both this study and the one mentioned in Armstrong’s research indicate that there is no gender-based difference in impulse buying and subsequent post- purchase behavior. It’s worth noting that both studies were conducted in developing countries, with Armstrong’s study taking place in Ghana and the current study being conducted in Nepal, and they have yielded similar findings. The results of the current study support the previous findings of [14], which indicated a positive connection between impulse buying and post-purchase regret. They also reinforce the link between sales promotions and bankcard payments with impulse buying behavior. The study’s results indicate that individuals with lower incomes and male consumers tend to exhibit a positive relationship between unplanned purchases and post-purchase regret. Additionally, unplanned purchases are linked to bank card payments but not to sales promotions. However, in the present study, there is no disparity in the impulse buying behavior and post-purchase behavior of the respondents based on their gender. The research conducted by [15] indicates that a significant proportion of individuals who visit supermarkets tend to make impulse purchases. The study’s findings highlighted several key factors that play a significant role in triggering impulse buying behavior. These factors include the availability of cash, the consumer’s mood, the presence of POS terminals or ATMs, pricing, store layout, time availability, product promotions, store ambiance, and the influence of reference groups. Likewise, the current study has also yielded similar results, showing that the consumer’s mood and the availability of cash have a notably positive influence on their impulse buying behavior. The research conducted by [16] suggests a connection between post-purchase regret and impulse buying. Their findings indicate that the level of post-purchase regret can vary based on an individual’s income. Additionally, the study suggests that individuals who tend to be more impulsive are likely to experience higher levels of post-purchase regret compared to those who are less impulsive. The research conducted by [17] indicates a significant connection between consumers and post-purchase dissonance, as well as between the income of consumers and post-purchase dissonance. In comparison to planned purchases, impulse purchases were found to generate more dissonance. This is because consumers dedicate more time to planning their purchases, resulting in a greater sense of confidence in their decisions and, consequently, a reduced experience of dissonance. When consumers have the final say in decision-making and are held accountable for a product’s poor performance, they are likely to experience greater dissatisfaction. The current study has produced similar findings to support these conclusions.

5. CONCLUSIONS

Findings of the study revealed that there is significant impact of the constructs of impulse buying value scale, i.e. product category, product promotion, mood of consumers, price, reference group and store layout on the post purchase behavior of the consumers in Kathmandu Valley whereas, product promotion, availability of cash, reference group and Availability of Cash does not have significant impact on Post Purchase dissatisfaction. The findings have presented logical facts. When people are in good moods or when their moods are influenced by someone such as family, friends, relatives, celebrities and so on, they tend to believe in the words or experience regarding the use of products and services, which tends to develop favorable mood and make people buy the products even though there is no need for it or not much valuable to the consumer which ultimately lead towards dissatisfaction at the later stage. Similarly, product category influences the way people perceive a product or category can have a significant impact on their post-purchase behavior, as it plays a role in influencing their preferences and choices. When attitudes vary among consumers, it underscores the importance for businesses to address a wide range of consumer preferences. This diversity in preferences can ultimately affect post-purchase decisions and the level of loyalty consumers have towards a particular brand. Moreover, the way consumers perceive the store layout can have a substantial impact on their post-purchase behavior, influencing elements like their overall satisfaction and the probability of making repeat purchases. Nevertheless, the diversity in how people perceive store layouts emphasizes the importance for businesses to tailor their store designs to cater to a wide range of consumer preferences. By optimizing the layout to align with these diverse perceptions, businesses can improve the overall post-purchase experience, potentially resulting in increased customer loyalty and positive word-of-mouth referrals. Therefore, as a marketer, must adopt a strategic approach that considers the unique characteristics of each element. The nature of the product category should guide marketing strategies, as some categories are more conducive to impulse buying, while others require more thoughtful consideration. Understanding the influence of consumer mood is crucial, as happy or excited consumers may be more inclined toward impulsive purchases, whereas marketers should adapt strategies for consumers in a negative or neutral mood. Moreover, the store layout plays a pivotal role in shaping consumer behavior; a well-designed layout can stimulate impulse buying by strategically placing products. Customizing store layouts to cater to diverse consumer preferences can enhance the overall shopping experience, potentially fostering brand loyalty. It is vital, however, to avoid deceptive marketing practices, prioritize long-term customer satisfaction, and ensure that promotional activities do not lead to cognitive dissonance among consumers, thereby safeguarding brand equity and market share.

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