Exploring the Impact of Customized Products on Consumer Purchase Intention: An Empirical Study
 
Karuna1*, Dr. Himanshu Kumar Pandey2
1 Research Scholar, CCSU, Meerut, U.P. India
karunadknmu@gmail.com
2 Assistant Professor, M.M.(PG) College, Modi Nagar, U.P. India
Abstract: Product customization has emerged as an important strategy for responding to changing consumer preferences and increasing demand for personalized products. With a focus on personalised product features, perceived product value, perceived uniqueness, consumer traits, and social influence, this research experimentally investigates how personalised goods affect consumers' desire to buy. Three hundred and eighty-five customers in Ghaziabad, Uttar Pradesh, were surveyed utilising a five-point Likert scale questionnaire as the main data source for this descriptive and empirical study. Statistical methods such as descriptive statistics, multiple regression, Pearson correlation, Cronbach's Alpha, and percentage and frequency analysis were used to examine the data. With mean ratings between 3.92 to 4.16, the results show that respondents had positive impressions of personalised items. From 0.789 to 0.858, the measurement scales showed good internal consistency according to Cronbach's Alpha. Consumers' desire to buy was positively and significantly correlated with all research variables. Statistical analysis revealed that the regression model was meaningful (F = 107.024, p < 0.001) and that it accounted for 58.4% of the variation in purchase intention (R² = 0.584). Consumer traits (β = 0.198), perceived product originality (β = 0.245), perceived value (β = 0.286), social influence (β = 0.154), and customised product aspects (β = 0.312) were the highest predictors of purchase intention. All five hypotheses were supported. The findings highlight the importance of personalization, perceived value, uniqueness, consumer characteristics, and social influence in shaping consumers' intention to purchase customized products.
Keywords: Customized Products, Consumer Purchase Intention, Product Uniqueness, Perceived Value, Consumer Characteristics, Social Influence, Product Customization, Consumer Behaviour, Personalization.
1. INTRODUCTION
There has been a major reorganisation of the world market throughout the last 20 years due to rapid technological advancement, digitisation, and changing customer expectations. Traditional mass manufacturing tactics focused on offering uniform commodities to a large customer base are being supplanted by client-centric approaches that stress personalisation and individual preferences. Product customisation has become an important competitive strategy in today's economy, allowing companies to better satisfy the specific demands, preferences, and ways of life of their customers. A variety of sectors have seen a surge in customised products due to the proliferation of e-commerce, artificial intelligence, big data analytics, & advanced manufacturing technology. These include the fashion, footwear, automotive, electronics, cosmetics, furniture, and consumer goods sectors.
Modern consumers aren't satisfied with mass-produced items that only meet functional requirements. The truth is that most individuals shop for things that mirror their values, interests, and identity. Mass customisation, which merges the adaptability of bespoke services with the efficacy of industrial scale, has supplanted mass production as the method of choice for many businesses in response to this shift. Product customisation allows customers to pick features, size, colour, design, material, packaging, and functionality, allowing items to better suit their individual demands. These personalised experiences not only make customers happier, but they also make them feel more connected to the brands and products they purchase, which influences their purchasing decisions.
The notion of consumer purchase intention, which refers to the probability or propensity of consumers to buy a particular service or good, is fundamental to the study of consumer behaviour. A consumer's propensity to buy is affected by a myriad of variables, including as their familiarity with the product, its pricing, advertisements, brand perception, trust, perceived risk, past experiences, and the ease of use of the product or service. Product customisation has garnered significant attention among these factors because it allows consumers to have a greater say in product design and promotes a sense of uniqueness and ownership. Customers are more likely to feel invested in the buying process, have a more positive impression of the product overall, and form stronger bonds with the company when they can personalise their purchases. Internet shopping's meteoric rise has only served to heighten the importance of personalisation in modern business. Thanks to digital platforms, shoppers can now examine and edit products before making a purchase, making customisation more accessible than ever. Some examples of successful marketing strategies that use customisation features are Nike, Adidas, Dell, Apple, and several online businesses. This helps these brands stand out from competitors and boost customer involvement. Thanks to advancements in technology like 3D printing, AR/VR, AI, ML, and efficiency increases in product modification, businesses can now provide tailored experiences to a wider audience.
Customers' lifestyle, personality, attitudes, interests, and beliefs are all part of their psychographics, which also significantly impacts their selections for personalised items. Customised items tend to sell better to customers that value uniqueness, self-expression, and originality than to those who are more concerned with price or conformity. Just like this, factors including perceived risk, product quality, packaging, advertising effectiveness, and brand reputation substantially impact buyers' intentions to purchase customised things. Consequently, businesses who want to make their customers happier and develop effective marketing strategies need to understand how these factors interact. While several studies have examined consumer purchase intention and other marketing determinants, very few have conducted comprehensive evaluations of the impact of tailored items on purchase intention, particularly in developing country contexts. Given that customers' tastes are always shifting as a result of developments in product awareness, changes in lifestyle, and the pervasiveness of digital platforms, it is critical to analyse how personalisation affects consumer behaviour in the current market. It would be helpful to do empirical study to better understand the aspects that either enhance or diminish the association between customer desire to purchase and customised goods.
The current research takes this into account and uses an empirical method to investigate how customised goods affect consumers' inclination to purchase. The results will have real-world implications for producers, advertisers, merchants, and policymakers in addition to adding to the body of knowledge on customer behaviour and product customisation. Understanding the factors that influence consumers' acceptance of customised products can help organisations develop more effective personalisation strategies, improve customer experiences, increase brand loyalty, and gain a lasting competitive edge in a market that is increasingly customer-driven and dynamic.
2. OBJECTIVES
3. RESEARCH METHODOLOGY
Using a descriptive & empirical research strategy, this study looks at how personalised goods affect consumers' propensity to buy. Descriptive statistics profile the sample and its perceptions, while empirical/statistical testing (correlation and regression) validates the proposed hypotheses.
Population and Sampling Frame
The universe comprises male and female consumers of Ghaziabad, Uttar Pradesh (Census 2011 population: 4,681,645).
Sample Size
Using the Krejcie-Morgan algorithm, a total of 385 respondents were selected for the study.
at a 95% confidence level (X² = 3.84) and 5% margin of error.
Sampling Technique
Respondents who were either familiar with or have bought personalised items were chosen using a purposeful (judgemental) sample technique.
Data Collection:
Using a 5-point Likert scale, a structured questionnaire was developed to gather primary data. The questionnaire was based on six constructs: personalised product qualities, social impact, consumer characteristics, perceived product uniqueness, perceived value, and purchase intention.
Statistical Tools:
An analysis was conducted on the data using SPSS (IBM v.22), utilising:
4. RESULT
The study's findings give light on what elements are affecting consumers' intentions to buy personalised goods. With 385 respondents' data in hand, we ran the numbers to see how factors like personalised product features, perceived product value, consumer traits, and social impact affected desire to buy. Analyses of demographics, descriptive statistics, reliability, correlation, multiple regression, and hypothesis testing are used to display the findings. Although there was a little bias toward younger and middle-aged consumers, the demographic results show that the sample did include customers from a variety of age, gender, educational, occupational, and economic brackets. In addition to the positive impressions of personalised items shown by the measurement findings, the reliability analysis also reveals that all constructions have adequate internal consistency. Regression analysis finds the individual and collective predictive contributions of the independent variables, whereas correlation analysis finds substantial positive connections between the independent variables and purchase intention. Thus, the following findings provide an empirical foundation for testing the research hypotheses and a methodical evaluation of the suggested links.
Table 1: Demographic Profile of Respondents (N = 385)
Variable
Category
Frequency
Percentage (%)
Gender
Male
210
54.55
 
Female
175
45.45
Age
Below 20 Years
42
10.91
 
21–30 Years
168
43.64
 
31–40 Years
96
24.94
 
41–50 Years
53
13.77
 
Above 50 Years
26
6.75
Education
Higher Secondary
38
9.87
 
Graduate
154
40.00
 
Post Graduate
137
35.58
 
Professional Degree
56
14.55
Occupation
Student
92
23.90
 
Private Sector Employee
126
32.73
 
Government Employee
48
12.47
 
Business/Self-employed
71
18.44
 
Others
48
12.47
Monthly Income
Below ₹25,000
89
23.12
 
₹25,001–₹50,000
142
36.88
 
₹50,001–₹75,000
91
23.64
 
Above ₹75,000
63
16.36
Source: Field survey data (n = 385).
Male and female consumers participated in the study as revealed by the gender-wise distribution of the respondents. Total 385 respondents with 210 male respondents (54.55%) and 175 female (45.45%) respondents. There were somewhat more male responses than female respondents, according to the distribution. Customised items are relevant and desired by customers regardless of gender since the disparity between the sexes is not huge. In order to have a more complete picture of the customer's desire to buy personalised items, it's helpful to have both representations. A diverse range of customers from various age groups participated in the survey, as seen by the age distribution of the responses.
There is greater proportion of the respondents aged 21-30 years, 168 (43.64%). The next age group is aged 31-40 years with 96 respondents OR 24.94%.The next age group is the 31-40 age group with 24.94% respondents. Respondents aged between 41 and 50 years make up 13.77% of the total sample, the young below 20 years make up 10.91% of the sample. The youngest group of respondents (above 50 years) have the lowest representation at 6.75%. The distribution shows that young and middle age consumers constitute the primary sample population for analysing the customized product purchase behaviour.
The educational qualification analysis indicates that the respondents had been from the various educational backgrounds and were involved in the research. Graduates had the maximum number of respondents, 154 (40%) of the total respondents. Postgraduate felt 137 (35.58%) while professional degree holders felt 56 (14.55%). This was the lowest among the respondents who had higher secondary qualifications which was 9.87%. The distribution shows that most of the respondents have higher educational qualifications that may lead to better awareness and understanding of customized products and their purchasing attributes.
Occupational classification classification means that the respondents were from different professional groups. The largest number is those who work in the private sector with 126 respondents (32.73%). Students were the second largest group at 92 respondents (23.90%) followed by business/self-employed persons at 71 respondents (18.44%). The government employees and other occupational categories had 12.47% each. The distribution shows that there are individuals who are consumers of customized products across different occupational groups, which enabled the study to reflect different opinions on purchasing behaviour.
When respondents were divided into income groups, there was variation in the purchasing capacity of the consumers. The highest number of respondents, 142 (36.88%) are in the income group of ₹25,001 to ₹50,000. Respondents earning ₹50,001–₹75,000 accounted for 23.64%, while 23.12% respondents had income below ₹25,000. The highest income group (more than ₹75000) constituted 16.36% of the sample size. The distribution shows that customised product consumers are from various income groups and hence allows for analysis of purchase intention by different economic groups.
Table 2: Study Constructs' Descriptive Statistics
Construct
Overall Mean
Std. Deviation
Customized Product Attributes
4.14
0.81
Perceived Product Uniqueness
4.13
0.82
Perceived Value
4.16
0.80
Consumer Characteristics
4.16
0.82
Social Influence
3.92
0.90
Consumer Purchase Intention
4.05
0.85
 
Across all dimensions measured, respondents consistently hold favourable attitudes toward customised products, as all construct means lie between 3.9 and 4.2 on a 5-point scale. Social Influence has the lowest mean (3.92) and highest SD (0.90), suggesting peer/social factors matter but are less uniformly influential than product- or value-related factors.
Table 3: Cronbach's Alpha Reliability Statistics
Variable
Number of Items
Cronbach's Alpha
Customized Products
5
0.842
Perceived Product Uniqueness
4
0.817
Perceived Value
4
0.836
Consumer Characteristics
4
0.821
Social Influence
4
0.789
Purchase Intention
4
0.858
 
This instrument has been validated for use in further statistical analyses (regression and correlation) because all values are higher than the acceptable threshold of 0.70 and Cronbach's Alpha for each construct is between 0.789 to 0.858, which is significantly higher than the benchmark of 0.70 for acceptable internal consistency.
Table 4: Correlation Matrix of Study Variables
Variable
CP
PPU
PV
CC
SI
PI
Customized Products (CP)
1
 
 
 
 
 
Product Uniqueness (PPU)
.612**
1
 
 
 
 
Perceived Value (PV)
.654**
.581**
1
 
 
 
Consumer Characteristics (CC)
.573**
.622**
.546**
1
 
 
Social Influence (SI)
.489**
.461**
.502**
.437**
1
 
Purchase Intention (PI)
.721**
.634**
.687**
.596**
.521**
1
 
There is no substantial multicollinearity since all variables have a positive and significant correlation with each other (p < 0.01) and no coefficient is more than 0.80. Among the research variables, the three that most strongly correlate with purchase intention are perceived product uniqueness (0.634), perceived value (0.687), and customised products (r = 0.721).
Table 5: Summary of the Regression Model
Model
R
R Square
Adjusted R Square
Std. Error
1
0.764
0.584
0.578
0.492
 
With an R-value of 0.764, we can see that the predictors and purchasing intention are strongly related. A behavioural model in social science research has considerable explanatory power; the five independent variables together explain 58.4% of the variation in consumer purchase intention (R Square = 0.584).
Table 6: Regression Model ANOVA
Source
Sum of Squares
df
Mean Square
F
Sig.
Regression
129.542
5
25.908
107.024
0.000
Residual
91.594
379
0.242
 
 
Total
221.136
384
 
 
 
 
The combined statistical significance of the regression model (F(5, 379) = 107.024, p = 0.000 (< 0.05)) proves that the independent variables collectively outperform chance in predicting purchase intention, hence verifying the model's overall appropriateness and fit.
Table 7: Coefficients of Regression
Predictor Variable
Beta (β)
t-value
Sig.
Customized Products
0.312
6.842
0.000
Perceived Product Uniqueness
0.245
5.921
0.000
Perceived Value
0.286
6.413
0.000
Consumer Characteristics
0.198
4.826
0.000
Social Influence
0.154
3.774
0.001
Dependent Variable: Consumer Purchase Intention.
The statistical significance of each predictor is less than 0.05. The individual impact on purchase intention is most strongly influenced by customised products (β = 0.312), next by perceived value (β = 0.286), and finally by perceived product uniqueness (β = 0.245). Social Influence has the weakest, though still significant, effect (β = 0.154), meaning it is a real but comparatively minor driver of purchase intention.
Table 8: Summary of Hypotheses Testing
Hyp.
Relationship
r
β
t
Sig.
Decision
H1
Customized Products → Purchase Intention
0.721
0.312
6.842
0.000
Supported
H2
Perceived Uniqueness → Purchase Intention
0.634
0.245
5.921
0.000
Supported
H3
Perceived Value → Purchase Intention
0.687
0.286
6.413
0.000
Supported
H4
Consumer Characteristics → Purchase Intention
0.596
0.198
4.826
0.000
Supported
H5
Social Influence → Purchase Intention
0.521
0.154
3.774
0.001
Supported
 
Consistent with all five hypotheses (H1–H5), there is a positive and statistically significant impact of personalised product traits, perceived uniqueness, perceived value, consumer attributes, and social influence on consumer purchase intention. All of the study's conceptual models are supported by this.
Table 9: Consolidated Summary of Key Findings
Construct
Mean (SD)
r with PI
Std. β
Hypothesis
Decision
Customized Product Attributes
4.14 (0.81)
0.721
0.312
H1
Supported
Perceived Product Uniqueness
4.13 (0.82)
0.634
0.245
H2
Supported
Perceived Value
4.16 (0.80)
0.687
0.286
H3
Supported
Consumer Characteristics
4.16 (0.82)
0.596
0.198
H4
Supported
Social Influence
3.92 (0.90)
0.521
0.154
H5
Supported
Consumer Purchase Intention (DV)
4.05 (0.85)
—
—
DV
—
 
Every construct in this table is associated with an above-midpoint mean, a positive association with purchase intention, & a validated hypothesis, as seen in the consistent pattern. Firms aiming to impact a purchase intention should prioritise Customised Product Attributes as the biggest predictor overall. Perceived Value & Perceived Product Uniqueness are the next most significant levers.
5. CONCLUSION
Consumers' propensity to buy is positively and significantly impacted by product customisation, according to the research. The relatively high mean scores reported across the research variables show that respondents generally had good sentiments about customised items. All of the measuring instruments met the criteria for appropriate internal consistency, as shown by reliability assessments; their Cronbach's Alpha values were higher than the threshold of 0.70. There were statistically significant positive correlations between all of the independent variables and the desire to buy. Following perceived value (r=0.687), perceived product originality (r=0.634), consumer characteristics (r=0.596), and social influence (r=0.521), the most strongly correlated product feature with purchase intention was customised product attributes (r = 0.721). With a statistically significant overall regression model, the findings of the multiple regression analysis showed that the combined variables explained 58.4% of the variance in consumers' purchase intention. Perceived value (β = 0.286), perceived product originality (β = 0.245), consumer characteristics (β = 0.198), social influence (β = 0.154), and customised product traits (β = 0.312) were the predictors that had the most impact. All of the assumptions were confirmed since each of the five predictors had a substantial statistical impact. Taken together, the results indicate that individualisation, perceived value, distinctiveness, consumer traits, and social impact all play a role in determining the purchase intention toward personalised items. Given the significance of personalised product qualities, it's crucial to provide customisation choices that are relevant, meaningful, and focused on the user, all while ensuring that the product remains distinctive and has significant value.
 
 
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