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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
An Empirical Study of Consumer Challenges in Rural Areas of
Kumbakonam
S. Gayathri
1
, Dr. V. Buvaneswaran
2
1
Research Scholar, Department of Commerce, Rajah Serfoji Government College (Autonomous),
(Affiliated to Bharathidasan University), Thanjavur, Tamil Nadu, India.
2
Assistant Professor, Department of Commerce, Rajah Serfoji Government College (Autonomous),
Thanjavur, Tamil Nadu, India.
DOI: https://doi.org/10.51583/IJLTEMAS.2026.15020000113
Received: 23 February 2026; Accepted: 03 March 2026; Published: 20 March 2026
ABSTRACT
This study examines the complex issues faced by rural consumers in the Kumbakonam area of Tamil Nadu
through an empirical investigation. By gathering primary data from rural households using a structured
questionnaire, the research examines the economic, informational, infrastructural, digital, and market-related
barriers that influence consumer behavior.
Descriptive and inferential statistical methods, such as mean, standard deviation, ANOVA, Multivariate analysis,
and Structural Equation Modeling (SEM), are utilized to examine the relationships among key constructs. The
findings reveal that information asymmetry, price sensitivity, restricted market access, and digital exclusion have
a significant impact on the purchasing choices of rural consumers. This study provides region-specific insights
into the rural marketing literature and offers practical recommendations for policymakers, marketers, and
educational institutions to enhance rural consumer empowerment.
Keywords: Rural consumers, Consumer challenges, Buying behavior, Kumbakonam, Rural markets, Structural
Equation Modeling, policymakers
INTRODUCTION
India's rural market is crucial to the national economy, contributing significantly to both consumption and
demand. Although infrastructure and connectivity have improved, rural consumers still face a variety of
challenges that influence their purchasing choices and overall quality of life.
These challenges include limited access to branded products, insufficient information, low purchasing power,
exploitation by middlemen, and a lack of digital access. Kumbakonam, a notable area in Tamil Nadu with a
strong rural presence, offers an excellent context for examining these issues. This study aims to empirically
explore the various challenges rural consumers face and how these affect their consumer behavior.
REVIEW OF LITERATURE
Sharma and Gupta (2021) looked into marketing communication challenges in rural India. They noted that
traditional advertising methods have limited reach in rural areas. Consequently, rural consumers often lack
sufficient product knowledge and depend on recommendations from retailers, which may not always be
impartial.
Joseph and Varghese (2020) researched financial literacy among rural consumers. They identified that a poor
understanding of pricing, credit terms, and digital financial services hinders rural consumers. The study stressed
that financial illiteracy increases the risk of over-borrowing and exploitation in rural markets.
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Nair (2020) examined digital inclusion and how it affects rural consumers. The study found that digital illiteracy
and weak internet access prevent rural consumers from finding online information, making digital payments,
and using e-commerce platforms. This situation widens the gap between rural and urban consumption.
Kumar and Bishnoi (2019) looked into the awareness of consumer rights among rural households. They
discovered that most rural consumers do not know about grievance redressal systems or consumer protection
laws. The study pointed out that low literacy rates and a lack of outreach from institutions worsen consumer
vulnerability in rural areas.
Rao and Srivastava (2019) conducted a study on consumer satisfaction in rural markets. They discovered that
rural consumers frequently face issues such as a lack of quality assurance, no after-sales service, and counterfeit
products. The study concluded that enhancing consumer awareness and enforcing regulations is vital for
improving rural consumer welfare.
Chandrashekar (2018) studied how socio-economic factors shape rural consumer behavior. He found that
income, education, and occupation significantly affect awareness, brand perception, and purchasing decisions.
The study suggested that education is important for bridging the information gap among rural consumers.
Singh and Pandey (2018) studied rural consumer behavior in India. They found that poor infrastructure,
inconsistent supply chains, and reliance on middlemen heavily impact rural consumers' access to quality
products. Their research showed that limited competition in rural markets often leads to higher prices and fewer
product options.
Reddy and Ramesh (2017) analyzed purchasing challenges in rural South India. They found that rural
consumers often rely on local traders for credit-based purchases. This dependence can lead to unfair pricing,
limited bargaining power, and less choice, especially for low-income households.
According to Kotler and Keller (2016), there are notable differences between rural and urban consumers,
particularly in terms of income, purchasing power, access to information, and brand awareness. Their research
pointed out that rural consumers are very sensitive to prices and depend heavily on word-of-mouth and local
retailers, which often restricts their ability to make informed choices and makes them vulnerable to exploitation.
THEORETICAL FRAMEWORK
To ensure the study has a robust theoretical foundation, the study has been guided by the Consumer Behaviour
Theory and the Information Asymmetry Theory.
The Consumer Behaviour Theory posits that the decisions made by consumers to buy certain products are
influenced by the economic environment, the availability of information, socio-cultural factors, and the
accessibility of the market.
The study has been guided by the theoretical perspective that rural consumers make their decisions in an
environment that is characterized by limited infrastructural facilities, restricted market access, and low levels of
education.
The study has also been guided by the Information Asymmetry Theory, which posits that the chances of market
exploitation and market inefficiency are higher for those consumers who do not have adequate information
compared to the sellers.
The study has been guided by the theoretical perspective that rural consumers face challenges in the market, and
based on the theoretical perspectives, the challenges facing rural consumers have been grouped into four major
dimensions.
The four major dimensions of the challenges facing rural consumers include:
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• Economic Challenges
• Infrastructural Challenges
• Behavioral and Digital Challenges
• Market Service Challenges
Need for the Study
To comprehend the challenges faced by rural consumers on a regional basis
To evaluate the influence of socio-economic and digital factors on consumer behavior
To aid in formulating policies for protecting rural consumers
To aid marketers in designing marketing strategies for rural markets
Objectives of the Study
1. To identify the major challenges faced by rural consumers in the Kumbakonam region.
2. To analyze the relationship between socio-economic factors and consumer challenges.
3. To examine the differences in consumer challenges faced by different segments of consumers.
4. To assess the impact of awareness and access to technology on purchasing.
Scope of the Study
The study area of this research includes selected rural areas in the Kumbakonam region. The research focuses
on the issues faced by consumers in pricing, availability, information, and access in the digital world. The
research is limited to household consumers.
Limitations of the Study
1. The study is conducted in a specific geographical region.
2. The responses may be biased, as they are based on self-reported data.
3. Limited time and resources were available to conduct the study.
Research Design and Methodology
This research study has employed a descriptive and analytical research design to assess the challenges faced by
rural consumers in the Kumbakonam region.
Sampling Design
This research study has employed a convenience sampling method, and the rural consumers have been selected
from the rural areas surrounding the Kumbakonam region. The respondents have been selected based on the
following criteria:
The respondents should be from the rural areas surrounding the Kumbakonam region.
The respondents should be actively involved in the purchase decisions of the household.
The respondents should be willing to participate in the research study.
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Sample Size Justification
A total of 150 respondents have been considered for the research study. The sample size was considered sufficient
for conducting statistical tests such as ANOVA, MANOVA, and Structural Equation Modeling (SEM) for data
analysis, as prescribed by the research methodologies in the social sciences discipline.
Data Collection Procedure
This research study has employed a primary data collection method, and the data was collected by interacting
with the respondents. The questionnaire had three sections: demographic information, consumer challenges in
rural markets, and awareness and digital access variables. The secondary data was collected from the published
research articles and government reports on rural consumers.
Data Analysis Techniques
Descriptive Statistics, such as Mean and Standard Deviation, to identify major consumer challenges. ANOVA to
examine the difference in consumer challenges based on income and education levels, MANOVA to examine
the impact of socio-economic factors on consumer challenges, Structural Equation Modeling (SEM) to examine
relationships between socio-economic status, awareness, and purchase behavior, All these techniques are applied
to provide descriptive as well as inferential results about rural consumer behavior.
Operationalization of Variables
For better understanding, the major variables were operationalized based on the previous studies conducted in
the field of rural marketing and consumer behavior.
Construct
Description
Economic Challenges
Issues like price fluctuations, purchasing power, and affordability
Infrastructural Challenges
Issues like transportation, product availability, and infrastructure
Digital Literacy
Ability to use digital payment systems and digital information platforms
Market Service Challenges
Issues like consumer rights, grievance redressal, and product quality
Data Analysis and Interpretation
Reliability Analysis (Cronbach’s Alpha)
TABLE 8.1: RELIABILITY
Construct
Number Of Items
Cronbach’s Alpha
Economic Challenges
2
0.812
Infrastructural Challenges
2
0.785
Digital Challenges
1
0.764
Market Service Challenges
1
0.731
Overall Scale
6
0.842
Table 8.1:Item-wise Reliability
Item
Scale Mean
Cronbach’s Alpha
Limited Product Availability
3.79
0.821
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Price Fluctuations
3.82
0.833
Poor Transportation
3.90
0.824
Lack of Digital Knowledge
3.95
0.836
Limited Banking Facilities
3.88
0.829
Poor After-Sales Service
3.93
0.832
Interpretation: To assess the reliability of the results obtained from the study, reliability analysis was carried
out. Cronbach’s Alpha was used to evaluate the reliability of the results obtained from measuring rural consumer
challenges. From the results, the reliability of the results obtained from measuring rural consumer challenges, as
indicated by Cronbach’s Alpha, is 0.842.
This indicates that the results obtained from measuring rural consumer challenges are reliable. In addition, the
reliability of each of the constructs, as indicated by Cronbach’s Alpha, is within the acceptable limit. For
instance, the reliability of the results obtained from measuring economic challenges, infrastructural challenges,
and digital challenges are 0.812, 0.785, and 0.764, respectively. Therefore, all the results obtained from
measuring rural consumer challenges are reliable. Moreover, reliability analysis indicates that removal of any
variable does not affect the reliability of the results, thus all variables are reliable.
Overall, the reliability analysis confirms that the questionnaire items used in this study provide consistent and
dependable measurements of rural consumer challenges, and therefore the data is suitable for further statistical
analyses such as factor analysis, ANOVA, and Structural Equation Modeling (SEM).
Factor Analysis of Rural Consumer Challenges
Table 8.3: KMO and Bartlett’s Test
Test
Value
Kaiser–Meyer–Olkin Measure (KMO)
0.781
Bartlett’s Test Chi-Square
312.64
df
15
Interpretation: The KMO value of 0.781 indicates that the sampling adequacy is good and suitable for factor
analysis. Bartlett’s Test of Sphericity is significant (p < 0.001), confirming that correlations among variables are
sufficiently large to proceed with factor extraction.
Table 8.4: Total Variance Explained
Factor
Eigenvalue
% of Variance
Cumulative %
Market and Price Constraints
2.48
41.32
41.32
Infrastructure and Accessibility Issues
1.63
27.14
68.46
Digital Literacy and Financial Access Challenges
1.12
18.70
87.16
Interpretation: Only factors with Eigenvalue greater than 1 were retained. Thus three factors explaining 87.16%
of total variance were extracted.
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Table 8.5: Rotated Factor Loadings
Variables
Market & Price Challenges
Infrastructure Challenges
Digital & Financial
Challenges
Limited product availability
0.842
0.214
0.106
Price fluctuations
0.801
0.173
0.192
Poor after-sales service
0.734
0.241
0.185
Poor transportation
0.182
0.857
0.146
Limited banking facilities
0.244
0.698
0.322
Lack of digital knowledge
0.132
0.248
0.881
Limited product availability
0.842
0.214
0.106
Interpretation: Among these factors, market and price constraints explain the highest proportion of variance
(41.32%), indicating that product availability and price fluctuations are the most dominant issues influencing
rural consumer behaviour. These results align with the theoretical framework based on Consumer Behaviour
Theory and Information Asymmetry Theory, which suggest that limited information, poor infrastructure, and
market inefficiencies significantly affect rural purchasing decisions.
Table 8.6 Age Distribution
Age Group
Frequency
Percentage
Below 25 years
22
14.7%
26 – 35 years
38
25.3%
36 – 45 years
46
30.7%
46 – 55 years
28
18.7%
Above 55 years
16
10.6%
Age Distribution
Interpretation: It is also clear that when it comes to age distribution, most of the participants fall in the age
range of 36 to 45 years (30.7%). This is closely followed by those who fall in the age range of 26 to 35 years
(25.3%). This also indicates that most of the participants are in their early to mid-career stages of their lives. In
addition, those who fall in the age range of 46 to 55 years account for 18.7%, while those who fall below 25
years account for 14.7%. Finally, those who are above 55 years have the least (10.6%). More than half of the
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participants (56%) fall in the age range of 26 to 45 years, indicating that this study mainly represents the views
of the active and professionally experienced workforce.
Age Group
Frequency
Percentage
Below 25 years
22
14.7%
26 – 35 years
38
25.3%
36 – 45 years
46
30.7%
46 – 55 years
28
18.7%
Above 55 years
16
10.6%
Age Distribution
Interpretation: The educational qualification of the population distribution indicates that most of the population
has attained Secondary education, as shown by 32.0%. This indicates that the population has an average level of
education. Additionally, the population with Primary education stands at 21.3%, while those who have attained
Higher Secondary education are 20.0%. This indicates that most of the population has a basic education.
Furthermore, those who have attained Graduate and above stand at 14.7%. This indicates that few people are
highly educated. However, 12.0% of the population is illiterate, indicating that they do not have any educational
qualifications. From the educational qualifications of the population, it is clear that most of the population has
attained basic education.
Major Challenges Faced by Rural Consumers
Table 8.8 Ranking of Consumer Challenges
Challenges
Mean
Std. Deviation
Rank
Limited product availability
4.18
0.82
I
Price fluctuations
4.02
0.91
II
Poor transportation
3.88
1.05
III
Lack of digital knowledge
3.74
1.12
IV
Limited banking facilities
3.51
0.98
V
Poor after-sales service
3.46
0.89
VI
Interpretation: The analysis of the challenges based on mean scores reveals that Limited Product Availability
(Mean = 4.18) is the most significant challenge faced by the respondents. It ranks in first place and implies a
14.70%
25.30%
30.70%
18.70%
10.60%
BELOW 25 YEARS 26 – 35 YEARS 36 – 45 YEARS 46 – 55 YEARS ABOVE 55 YEARS
Age Distribution
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strong level of agreement on the impact of the challenge. Price fluctuations rank in second place with a mean
score of 4.02. Poor Transportation ranks in third place with a mean score of 3.88.
Lack of Digital Knowledge ranks in fourth place with a mean score of 3.74. Limited Banking Facilities and Poor
After-Sales Service rank the fifth and sixth places with mean scores of 3.51 and 3.46, respectively. Overall, the
results reveal that supply and price-related challenges are the most significant challenges faced by the
respondents.
Relationship between Socio-Economic Factors and Consumer Challenges
Table 8.9 Multivariate Test Results (MANOVA)
Independent Variable
Dependent Variable
Wilks’ Lambda
F Value
Sig. (p)
Income
Price challenges
0.842
4.215
0.003*
Education
Digital challenges
0.781
6.104
0.001
Occupation
Availability issues
0.865
3.476
0.012*
*Significant at 5% level, Significant at 1% level
Interpretation: From the results of the MANOVA test, it is evident that demographic factors have a statistically
significant impact on various types of challenges. Income is found to be significant for price challenges (Wilks'
Lambda = 0.842, F = 4.215, p = 0.003), which means that differences in income levels are highly influential in
determining the types of price challenges faced by individuals. Since the probability value is below 0.05, it is
highly significant at 5%. Education is found to be highly significant for digital challenges (Wilks' Lambda =
0.781, F = 6.104, p = 0.001), which means that differences in education levels are highly influential in
determining digital challenges faced by individuals. Since the probability value is below 0.01, it is highly
significant at 1%. Occupation is found to be highly significant for availability challenges (Wilks' Lambda =
0.865, F = 3.476, p = 0.012), which means that differences in occupation are highly influential in determining
availability challenges faced by individuals. Since the probability value is below 0.05, it is highly significant at
5%. Income, education, and occupation play a crucial role in determining the types of challenges faced by
individuals.
Differences in Consumer Challenges across Demographic Groups.
Table 8.10 ANOVA – Income vs Price Perception
Income Group
Mean Score
Standard Deviation
Below ₹10,000
4.35
0.68
₹10,001–₹20,000
4.05
0.82
Above ₹20,000
3.60
0.91
ANOVA Result
Source
F Value
Sig. (p)
Between Groups
5.827
0.004*
*Significant at 5%
Interpretation: From the mean comparison, it is clear that the lowest income group, i.e., those who earn below
₹10,000, face the highest level of challenges, as the mean value for this group is 4.35, with an SD of 0.68.
Similarly, those who earn between ₹10,001 and ₹20,000 face the next level of challenges, as the mean value for
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this group is 4.05, with an SD of 0.82. On the other hand, those who earn above ₹20,000 face a low level of
challenges, as the mean value for this group is 3.60, with an SD of 0.91.
This shows that the lowest income groups face a higher level of challenges than the highest income groups. From
the ANOVA result, it is clear that the value of F, i.e., 5.827, is significant at 5% level, as the value of p is 0.004.
This shows that income level has an impact on the level of challenges faced.
Table 8.11 ANOVA – Education vs Digital Challenges
Education Level
Mean Score
Illiterate
4.20
Primary
3.95
Secondary
3.70
Graduate
3.10
F = 7.942, p = 0.001
Interpretation: The mean scores show that the level of challenge is the highest for the illiterates (Mean - 4.20),
followed by the primary-educated (Mean-3.95), then the secondary-educated (Mean-3.70), and the least level of
challenge is reported by the graduates (Mean-3.10). It is clear from the mean scores that the level of challenge
decreases with the increase in the level of educational qualification. The result of the ANOVA is F = 7.942 and
p = 0.001. The result is statistically significant at the 1% level of significance. Therefore, the hypothesis is
accepted, which states that the level of educational qualification is highly significant in affecting the level of
challenge.
Table 8.12: Model Fit Indices
Fit Index
Value
Acceptable Level
Chi-square/df
2.18
< 3
CFI
0.93
> 0.90
GFI
0.91
> 0.90
RMSEA
0.067
< 0.08
Interpretation: The model fit indices show that the proposed model has a good model fit overall. This is
supported by the fact that the value of Chi-square/df is 2.18, which is below the acceptable limit of 3. In addition,
the value of CFI, which is 0.93, is above the acceptable level of 0.90, indicating a strong fit for this model.
Furthermore, the GFI value of 0.91 is also above the acceptable level of 0.90, indicating a strong fit for this
model as well. Moreover, the value of RMSEA, which is 0.067, is below the acceptable cut-off value of 0.08,
indicating a reasonable error of approximation. All model fit indices are above the acceptable level, indicating
that the structural model is well-fitted for further interpretation of relationships between variables.
Table 8.13 SEM Path Coefficients
Path
Standardized Beta (β)
CR Value
p Value
Result
Socio-economic Status → Consumer
Challenges
0.61
5.84
0.000
Significant
Education → Digital Awareness
0.72
6.92
0.000
Significant
Digital Awareness → Purchasing
Behaviour
0.57
4.76
0.000
Significant
Awareness → Purchasing Behaviour
0.49
3.98
0.001*
Significant
*Significant at 5%,,Significant at 1%
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Interpretation: The structural path analysis indicates that all hypothesized relationships are positive and
statistically significant. Socio-economic Status has a strong positive influence on Consumer Challenges (β =
0.61, CR = 5.84, p = 0.000), significant at the 1% level, suggesting that variations in socio-economic status
substantially affect the level of challenges faced by consumers. Education shows a very strong positive impact
on Digital Awareness (β = 0.72, CR = 6.92, p = 0.000), also significant at the 1% level, indicating that higher
education significantly enhances digital awareness. Further, Digital Awareness positively influences Purchasing
Behaviour (β = 0.57, CR = 4.76, p = 0.000), confirming that digitally aware consumers demonstrate more
informed purchasing decisions. Similarly, Awareness has a positive and significant effect on Purchasing
Behaviour (β = 0.49, CR = 3.98, p = 0.001), significant at the 5% level. Overall, the findings confirm that socio-
economic and educational factors indirectly and directly influence purchasing behaviour through awareness and
digital competence.
Findings of the Study
1. The findings of the study indicate that rural consumers in Kumbakonam face multiple structural
challenges related to product availability, pricing fluctuations, and limited digital literacy. The results
support previous studies which highlight the role of information asymmetry and infrastructural
constraints in shaping rural consumption patterns.
2. The demographic analysis revealed that the majority of the respondents belonged to the age group of 36-
45 years, which constitutes 30.7%. Further, 32% of the respondents had completed secondary education.
Nearly 48% of the respondents belonged to the monthly income group below ₹20,000.
3. Among the major challenges, the problem of limited product availability had the highest mean score
(Mean = 4.18, SD = 0.82). It was ranked first in the list of major challenges.
4. Price fluctuation was identified as the second major issue with a mean score of 4.02 (SD = 0.91).
5. Poor transportation facilities were found to have a mean value of 3.88 (SD = 1.05), which suggests that
there is a moderate to high level of concern about transportation facilities.
6. Challenges faced due to digital knowledge were found to have a mean score of 3.74 (SD = 1.12),
indicating that digital illiteracy is a major issue. The standard deviation of 1.12 suggests that there is a
lack of digital knowledge among the people.
7. ANOVA results show a significant difference in terms of income groups and price-related challenges (F
= 5.827, p = 0.004). The result shows that the mean score for those who earn less than 10,000 is higher
(Mean = 4.35) than for those who earn more than 20,000 (Mean = 3.60).
8. A significant difference was also found in terms of education level and digital challenges (F = 7.942, p =
0.001). The result shows that the mean score for illiterates is higher (Mean = 4.20) than the mean score
for graduates (Mean = 3.10).
9. Results of the multivariate analysis show that the variable of income level is a significant influence on
price perception (Wilks’ Lambda = 0.842, p = 0.003). Education level is also a significant influence on
digital challenges (Wilks’ Lambda = 0.781, p = 0.001).
10. Results from the Structural Equation Modeling confirm that consumer challenges are significantly
affected by socio-economic status (β = 0.61, p < 0.001). Education has a strong positive effect on digital
awareness (β = 0.72, p < 0.001), and digital awareness has a strong effect on purchasing behaviour (β =
0.57, p < 0.001).
11. The model fit indices are good (CFI = 0.93, GFI = 0.91, RMSEA = 0.067), thus confirming that the model
is reliable.
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12. The SEM model has confirmed that socio-economic status indirectly affects purchasing behaviour
through digital awareness.
Suggestions of the Study
1. As the limited product availability had the highest mean value of 4.18, companies should focus on
strengthening the rural distribution network and opening more retail outlets in rural areas.
2. As the price fluctuation had a high mean value of 4.02 and a significant difference in the mean values
across the income groups (F = 5.827, p = 0.004), the government authorities should focus on
strengthening the price control and monitoring.
3. As the mean value for the transportation challenges was 3.88, the authorities should focus on
strengthening the rural transport facilities.
4. As the digital challenges had a significant difference in the mean values across the education groups, the
digital literacy programs should focus on the illiterate and primary-level educated consumers separately.
5. As the role of education is significant in the creation of digital awareness, the effectiveness of awareness
campaigns and adult education should be increased in the context of improving purchasing behavior.
6. As digital awareness is found to significantly influence purchasing behavior, financial organizations
should focus on creating digital payment options and should provide hands-on experience for rural
consumers.
7. As the socio-economic status of the consumer is found to significantly influence the challenges faced by
the consumer, the effectiveness of poverty alleviation and employment generation should be increased in
the context of improving the purchasing power of the consumer.
8. Special focus should be accorded to those income groups who earned below ₹10,000, as they faced
maximum price-related stress.
9. Government agencies and policymakers should use reliable consumer challenge indicators such as price
instability, product availability, and digital awareness while designing rural consumer protection policies.
10. Marketing organizations should conduct regular consumer perception surveys using reliable scales to
better understand rural market dynamics and improve service delivery.
CONCLUSION
The conclusion drawn from the study is that rural consumers in the Kumbakonam region face a number of
challenges that affect their purchasing habits. The major challenges faced by consumers are the availability of
products, price variations, transportation facilities, and digital knowledge. The study also revealed that rural
consumers are affected by socio-economic factors such as income and education, as these factors influence the
number of challenges faced by consumers. The role of education is also significant in the development of digital
knowledge, as it helps consumers make better decisions regarding purchases.
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue II, February 2026
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