E-ISSN:2250-0758
P-ISSN:2394-6962

Research Article

Consumer Buying Behaviour

International Journal of Engineering and Management Research

2026 Volume 16 Number 4 August
Publisherwww.vandanapublications.com

Factors Influencing Consumer Buying Behaviour in Live Commerce and Traditional E-commerce Platforms: A Study of Indian Consumers

Chawla G1*
DOI:10.31033/IJEMR/16.4.2026.1935

1* Govind Chawla, Assistant Professor and Head, Department of Commerce, Government Degree College, Mathura, Uttar Pradesh, India.

The rapid growth of online shopping has transformed consumer buying behaviour. Traditional e-commerce platforms provide convenience and customer reviews, while live shopping introduces real-time interaction between sellers and consumers. The present study examines the influence of trust, live interaction and convenience on consumer buying behaviour in online shopping. Primary data were collected from 150 respondents through interviews using pre-defined questions based on the study objectives. The study used descriptive statistics, reliability analysis, correlation analysis and multiple linear regression. The reliability analysis demonstrated high internal consistency of the measurement scales, with Cronbach’s alpha values ranging from 0.933 to 0.954. The regression model produced R = 0.811 and R² = 0.658, indicating that trust, live interaction and convenience collectively explain 65.8% of the variation in consumer buying behaviour. Trust and convenience were found to have significant positive effects on consumer buying behaviour, whereas live interaction did not show a statistically significant independent effect. The findings highlight the importance of building consumer trust and providing convenient online shopping experiences.

Keywords: Trust, Live Shopping, Convenience, Consumer Buying Behaviour, E-commerce, Online Shopping

Corresponding Author How to Cite this Article To Browse
Govind Chawla, Assistant Professor and Head, Department of Commerce, Government Degree College, Mathura, Uttar Pradesh, India.
Email: 		Chawla G, Factors Influencing Consumer Buying Behaviour in Live Commerce and Traditional E-commerce Platforms: A Study of Indian Consumers. Int J Engg Mgmt Res. 2026;16(4):1-6.
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https://ijemr.vandanapublications.com/index.php/j/article/view/1935

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2026-07-02 2026-07-17 2026-08-03
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 5.37

© 2026 by Chawla G and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

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Download PDFBack To Article1. Introduction2. Literature
Review3. Research
Methodology4. Measurement
Items5. Results and
Analysis6. Discussion7. Conclusion8. LimitationsReferences
Chawla G. Factors Influencing Consumer Buying Behaviour

1. Introduction

The rapid development of digital technology has significantly changed the way consumers search for information, evaluate products and make purchasing decisions. Online shopping has become an important component of modern retailing because consumers can compare products, read reviews, make payments and purchase products without visiting a physical store.

Traditional e-commerce platforms mainly depend on product information, customer reviews, ratings, secure payment systems and convenient purchasing procedures. At the same time, the emergence of live shopping has introduced a more interactive form of online shopping in which consumers can communicate with sellers, ask questions and receive product information in real time.

Consumer buying behaviour in online shopping is influenced by several psychological and technological factors. Among these, trust is particularly important because consumers cannot physically inspect products before purchasing them. Consumers therefore depend on seller information, customer reviews, payment security and other signals of reliability.

Another important factor is convenience. Consumers generally prefer platforms that are easy to use, provide quick information, simplify payment and offer convenient return and refund procedures.

Live interaction represents another emerging dimension of online shopping. Interaction with sellers can allow consumers to clarify their doubts and obtain additional product information. However, the actual influence of live interaction may vary depending on consumer preferences and the quality of the interaction.

Therefore, this study examines whether trust, live interaction and convenience significantly influence consumer buying behaviour.

2. Literature Review

Rastogi and Avantika (2010) examined the buying behaviour of Indian online consumers by considering different socio-economic variables. The study focused on understanding the factors influencing consumers’ attitudes and goals towards online shopping, along with perceptions regarding the ease of use and usefulness of online shopping.

The findings suggest that analysing consumer buying behaviour can provide useful insights for understanding online shoppers and can serve as a guide for market-oriented decisions.

Fernandes and Tharakan (2021) examined the factors influencing consumer buying behaviour towards e-commerce websites in the Indian context. The study investigated factors such as hedonic motivation, price-saving orientation, time-saving orientation, post-usage usefulness of services, and attitude towards online websites, with the objective of understanding consumers’ adoption of e-commerce platforms. Using primary data collected from respondents in India, the study highlighted the role of various consumer-oriented factors in the adoption of online shopping. The findings are relevant to the present study because they demonstrate that consumers’ perceptions of time saving, usefulness and attitudes towards online platforms can influence their online shopping behaviour.

Rakesh (2024) examined the factors influencing online shopping decisions in the context of e-commerce. The study identified price sensitivity, convenience, product variety and trust in online platforms as important determinants of consumer behaviour. It also considered demographic factors such as age, gender and income, along with psychological factors including perceived risk, customer reviews and brand loyalty. In addition, technological factors such as website design, user interface and mobile accessibility were highlighted as important elements shaping online purchasing behaviour. The study suggests that understanding these factors can help e-commerce platforms improve the user experience and develop long-term customer engagement.

Rao and Ansari (2023) examined the factors influencing consumers’ online buying behaviour in Bihar, India. The study focused on understanding consumers’ attitudes, perceptions and behaviour towards online shopping and identifying the factors that influence online purchasing behaviour. Primary data were collected from 200 respondents in Bihar using a self-administered questionnaire and Google Forms. The study highlights the importance of understanding consumer behaviour in online shopping to help businesses attract and retain online buyers. The findings provide relevant background for examining the determinants of online buying behaviour in the present study.


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Chawla G. Factors Influencing Consumer Buying Behaviour

Das (2018) examined the factors influencing consumers’ behaviour in e-shopping, with evidence from Guwahati City, Assam. The study explored consumer buying behaviour in the context of the growing e-retailing market using primary data. The findings indicated that time saving was one of the major reasons for the growing popularity of online shopping. In addition, family income, trust and perceived risk were identified as significant factors affecting online shopping behaviour. The study highlights the importance of convenience-related benefits and consumer trust in understanding online shopping behaviour.

Research Gap

Although previous studies have established the importance of trust, interactivity, customer reviews and other platform characteristics in online and live-shopping environments, much of the existing research focuses on purchase intention rather than broader consumer buying behaviour. In addition, many studies examine individual factors separately or use structural equation modelling to investigate complex mediating mechanisms. Therefore, the present study examines the influence of Trust, Live Interaction and Convenience on Consumer Buying Behaviour and assesses the individual contribution of each factor through multiple regression analysis. In particular, the study also examines whether Live Interaction has a significant independent effect on Consumer Buying Behaviour after accounting for Trust and Convenience.

3. Research Methodology

3.1 Research Design

The study adopted a quantitative research design to examine the influence of Trust, Live Interaction and Convenience on Consumer Buying Behaviour in online shopping.

3.2 Data Collection

Primary data were collected from 150 respondents through interviews using pre-defined questions based on the objectives of the study. The responses were recorded using a five-point Likert scale ranging from 1 = Strongly Disagree to 5 = Strongly Agree.

3.3 Measurement of Variables

The study examined four major constructs: Trust, Live Interaction, Convenience and Consumer Buying Behaviour.

Each construct was measured using three items, resulting in a total of 12 measurement items.

3.4 Data Analysis

The collected data were analysed using jamovi. Descriptive statistics (mean and standard deviation) were used to summarise the responses. Cronbach’s alpha was used to assess the reliability of the measurement scales. Pearson correlation was examined to assess the relationships among the constructs. Multiple linear regression was used to determine the individual and combined effects of Trust, Live Interaction and Convenience on Consumer Buying Behaviour. Variance Inflation Factor (VIF) and Tolerance were examined to assess multicollinearity among the predictors.

3.5 Hypotheses

H1: Trust has a significant positive effect on Consumer Buying Behaviour.
H2: Live Interaction has a significant positive effect on Consumer Buying Behaviour.
H3: Convenience has a significant positive effect on Consumer Buying Behaviour.
H4: Trust, Live Interaction and Convenience collectively have a significant effect on Consumer Buying Behaviour.

The study adopted a quantitative research design. Primary data were collected from 150 respondents through a structured questionnaire.

A five-point Likert scale was used:

ResponseScore
Strongly Disagree1
Disagree2
Neutral3
Agree4
Strongly Agree5

The questionnaire consisted of four constructs:

Trust (TR) – 3 items

Live Interaction (LI) – 3 items

Convenience (CV) – 3 items

Consumer Buying Behaviour (CBB) – 3 items

Thus, the instrument contained 12 measurement items. The overall scores for each construct were computed by taking the mean of its three respective items.


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Chawla G. Factors Influencing Consumer Buying Behaviour

Data analysis was performed using jamovi. Descriptive statistics, Cronbach’s alpha, Pearson correlation and multiple linear regression were employed.

4. Measurement Items

Trust

TR1: Information provided during live shopping sessions appears trustworthy.
TR2: I trust customer reviews available on traditional e-commerce platforms.
TR3: Secure payment methods increase my confidence in online shopping.

Live Interaction

LI1: Interaction with sellers during live shopping helps consumers make informed purchase decisions.
LI2: Real-time interaction with sellers enhances the overall shopping experience.
LI3: The opportunity to ask questions during live shopping helps consumers make informed purchase decisions.

Convenience

CV1: Online platforms provide immediate responses to my queries regarding products.
CV2: Traditional e-commerce platforms are easy to use.
CV3: Return and refund processes are easy when shopping online.

Consumer Buying Behaviour

CBB1: Live shopping influences consumer’s buying behaviour.
CBB2: Customer reviews influence my buying behaviour.
CBB3: Discounts and promotional offers encourage me to purchase products.

5. Results and Analysis

5.1 Descriptive Statistics

The study was based on 150 respondents, with no missing observations reported for the individual variables.

The mean scores of the individual items were generally above the neutral point of 3, indicating a generally positive response towards trust, live interaction and convenience.

VariableNMeanSDMinimumMaximum
TR11503.600.88215
TR21503.491.01015
TR31503.780.91915
LI11503.391.13015
LI21503.351.07015
LI31503.271.08015
CV11503.531.16015
CV21503.361.08015
CV31503.660.88915
CBB11503.501.03515
CBB21503.830.86215
CBB31503.870.92415

The results indicate that respondents generally agreed with the statements relating to online shopping.

5.2 Reliability Analysis

The reliability of the questionnaire was assessed using Cronbach’s alpha.

The overall Cronbach’s alpha obtained was:

Cronbach’s α = 0.935

This indicates excellent internal consistency among the measurement items. Therefore, the questionnaire can be considered reliable for further statistical analysis.

5.3 Correlation Analysis

Pearson’s correlation analysis was conducted to examine the relationships among the major constructs.

The correlation results were:

VariablesPearson's rp-value
Live Interaction – Convenience0.753< .001
Live Interaction – Trust0.773< .001
Convenience – Trust0.758< .001

All correlations were positive and statistically significant at the 1% level.

These findings indicate that trust, live interaction and convenience are positively associated with one another. However, correlation analysis alone does not establish causal influence; therefore, multiple regression was conducted.


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Chawla G. Factors Influencing Consumer Buying Behaviour

5.4 Multiple Linear Regression

Multiple linear regression was used to examine the influence of Trust, Live Interaction and Convenience on Consumer Buying Behaviour.

Model Summary

RR²
0.8110.658

The value of R² = 0.658 indicates that trust, live interaction and convenience collectively explain approximately 65.8% of the variance in consumer buying behaviour.

The remaining 34.2% may be explained by other factors not included in the present model.

PredictorEstimate (B)SEtp
Trust0.5040.0965.26< .001
Live Interaction-0.0230.082-0.28.780
Convenience0.4860.0915.35< .001

Interpretation

Trust: Trust has a positive and statistically significant effect on consumer buying behaviour (B = 0.504, p < .001). Therefore, H1 is supported.

Live Interaction: Live interaction has a very small negative coefficient and is statistically insignificant (B = −0.023, p = .780). Therefore, H2 is not supported.

Convenience: Convenience has a positive and statistically significant effect on consumer buying behaviour (B = 0.486, p < .001). Therefore, H3 is supported.

The model-level F statistic can be derived from the reported R², sample size and three predictors as approximately F(3,146) = 93.4, indicating a highly significant overall model (p < .001). Thus, H4 is supported.

5.5 Multicollinearity

Multicollinearity was assessed using VIF and tolerance values.

PredictorVIFTolerance
Live Interaction2.960.337
Convenience2.810.356
Trust3.020.331

All VIF values are below the commonly used threshold of 5. Therefore, serious Multicollinearity is not indicated in the regression model.

5.6 Hypothesis Testing

HypothesisResult
H1: Trust significantly affects CBBSupported
H2: Live Interaction significantly affects CBBNot Supported
H3: Convenience significantly affects CBBSupported
H4: Trust, Live Interaction and Convenience collectively affect CBBSupported

6. Discussion

The findings demonstrate that trust is one of the most important factors influencing consumer buying behaviour in online shopping. The significant positive coefficient suggests that consumers who perceive online shopping information, customer reviews and payment systems as trustworthy are more likely to exhibit favourable buying behaviour.

The study also finds that convenience significantly influences consumer buying behaviour. Easy-to-use platforms, quick access to information and convenient return and refund procedures can encourage consumers to purchase products online.

Interestingly, live interaction does not have a significant independent effect after controlling for trust and convenience. Although live interaction is positively correlated with the other constructs, its regression coefficient is insignificant. This suggests that merely providing interaction during live shopping may not automatically translate into stronger buying behaviour. The effectiveness of live interaction may depend on the quality and usefulness of the interaction.

7. Conclusion

The study concludes that trust and convenience are significant determinants of consumer buying behaviour in online shopping. The overall regression model explains 65.8% of the variation in consumer buying behaviour, demonstrating substantial explanatory power.

Trust has the strongest positive coefficient (B = 0.504), followed closely by convenience (B = 0.486). In contrast, live interaction does not show a significant independent effect (p = .780).


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Chawla G. Factors Influencing Consumer Buying Behaviour

The findings suggest that online shopping platforms should focus not only on interactive features but also on building consumer trust, ensuring secure transactions, providing reliable information, simplifying the shopping process and improving convenience.

8. Limitations

The study has certain limitations. First, the sample size was limited to 150 respondents, which may restrict generalisation to the wider population. Second, the study relied on self-reported responses. Third, only three major explanatory variables—trust, live interaction and convenience—were included in the regression model. Other factors such as price, perceived usefulness, product quality, social influence and promotional activities may also influence consumer buying behaviour.

References

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[2] Das, M. (2018). A study on influencing factors of consumers’ behaviour in e-shopping: Evidence from Guwahati City, Assam. DIMENSIONS International Journal of Management.

[3] Fernandes, C. R., & Tharakan, A. I. (2021). Factors influencing consumer buying behaviour towards e-commerce websites. International Journal of Emerging Technologies and Innovative Research, 8(7), f805–f826. ISSN 2349-5162. Paper ID: JETIR2107716.

[4] Handoyo, S. (2024). Purchasing in the digital age: A meta-analytical perspective on trust, risk, security, and e-WOM in e-commerce. Heliyon, 10(8), e29714.

[5] Lestari, K. P. K., Rafiah, K. K., & Arviansyah, M. R. (2025). Live shopping and consumers’ purchase intention in e-commerce: A systematic literature review. Airlangga Journal of Innovation Management, 6(1).

[6] Liu, X., & Zhang, L. (2024). Impacts of different interactive elements on consumers’ purchase intention in live streaming e-commerce. PLOS ONE, 19(12), e0315731.

[7] Phamthi, V. A., Nagy, Á., & Ngo, T. M. (2024). The influence of perceived risk on purchase intention in e-commerce—Systematic review and research agenda. International Journal of Consumer Studies, 48(4), e13067.

[8] Rakesh, K. R. (2024). Consumer behavior in e-commerce: Factors influencing online shopping decisions. In: Research Integration: Multidisciplinary Insights and Methodologies, pp. 33–44. https://doi.org/10.25215/8198133194.04⁠

[9] Rao, M. V. K. S., & Ansari, M. K. (2023). Study on factors influencing consumer’s online buying behavior: A study in Bihar, India. International Journal for Research in Applied Science & Engineering Technology (IJRASET). https://doi.org/10.22214/ijraset.2023.53301⁠

[10] Rastogi, A. K. (2010). Study of Indian online consumers & their buying behaviour. Avantika University.

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