# Examining Internet Behavior of Young Technology-Literate Consumers in India

**Authors:** Narasimha Rao Vajjhala (School of IT & Computing, American University of Nigeria, Nigeria) — ORCID 0000-0002-8260-2392; Kenneth David Strang (School of Business & Economics, State University of New York, USA) — ORCID 0000-0002-4333-4399
**Type:** Conference paper
**Source:** Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans, Association for Information Systems
**Published:** 2018
**Canonical page:** https://www.narasimharao.net/research/online-purchase-intention-young-consumers-india/
**Indexing:** Scopus · Web of Science
**Keywords:** Consumer; Internet; behavior; risk-avoidance; culture; power; regression; purchase

## Summary

**Question.** Which demographic factors (gender, age, income) and psychological factors (happiness, satisfaction, excitement, positive and pleasant feelings) predict the Internet purchase intention of young technology-literate consumers in India?

**Finding.** Surveying 63 Internet shoppers in Andhra Pradesh and Telangana, the study found that age (rho = −0.410, p = .001) and income level were related to purchase intention, and that happiness, satisfaction, positive feelings and pleasant feelings—but not excitement—predicted it. Satisfied consumers were almost 28 times more likely to plan a purchase, and a discriminant analysis using satisfaction alone correctly classified 90.5% (57 of 63) of respondents.

**Why it matters.** Because most online consumer behavior studies come from Western countries, the findings add evidence from India’s distinct culture of high power distance and low uncertainty avoidance, where consumers are likely to be willing to adopt new technology. The authors argue the results can be generalized to the upcoming generation of young consumers in India, while recommending replication with larger samples and other cultures.

## Key findings

1. The study surveyed 63 technology-literate consumers in the Indian states of Andhra Pradesh and Telangana who had shopped online at least once; 61.3% were male and the mean age was 33.6 years (SD = 9.5).
2. Among the Indian online consumers, age was negatively correlated with Internet purchase intention (Spearman rho = −0.410, p = .001, r² = 0.16), meaning younger participants were more likely to intend to buy online.
3. Income level was related to Internet purchase intention among the Indian consumers (Pearson chi-square = 4.281, p = 0.04), with about 50% of intending purchasers in the ₹400,000–₹599,999 income bands.
4. In the India study, satisfaction (rho = 0.67, r² = 45.9%) and happiness (rho = 0.621, r² = 38.6%) with the Internet environment were strongly correlated with purchase intention, and satisfied consumers were almost 28 times more likely to plan a purchase (odds ratio 27.9, 95% CI 4.43–175.89).
5. Positive feelings about the Internet predicted purchase intention among Indian consumers (r² = 52.5%), with an odds ratio of 94, while excitement did not reliably predict purchase intention (r² = 9.5%).
6. A discriminant analysis using happiness and satisfaction correctly classified 87.3% (55 of 63) of the Indian respondents as likely or unlikely to purchase online; using satisfaction alone, 90.5% (57 of 63) were correctly classified, including 94.2% (49 of 52) of those willing to purchase.
7. In a combined binary logistic model with demographic controls, only happiness and satisfaction remained significant predictors of Internet purchase intention in the India sample (r² = 47.9%, adjusted r² = 46.1%).

## Study at a glance

| Item | Detail |
|---|---|
| Research question | Can demographic and emotional/motivational factors predict Internet purchase intention of young technology-literate consumers in India? |
| Design | Within-group correlational survey study testing eight hypotheses (H1a–c, H2a–b, H3a–c). |
| Sample | 63 respondents from a random sample frame of 110 in Andhra Pradesh and Telangana who had shopped online at least once (61.3% male, mean age 33.6). |
| Methods | Chi-square tests, Spearman correlation, simple and binary logistic regression with goodness-of-fit tests, and discriminant analysis in Minitab 18. |
| Main result | Satisfaction was the strongest predictor: odds ratio 27.9, and a one-factor discriminant model correctly classified 90.5% of respondents. |
| Implication | Consumer satisfaction and happiness with the Internet environment are reliable levers for online purchase intention among young Indian consumers. |

## Abstract

In this study, we analyzed consumer Internet behavior in India since there were several unique cultural dimensions of interest. After reviewing the literature, we tested hypotheses that demographic and psychological factors such as happiness, excitement, satisfaction, positive feelings, pleasant feelings, gender, age, and income level could predict consumer Internet purchase behavior. We used Spearman correlation, binary logistic regression, and discriminant analysis techniques, which resulted in effect sizes ranging from 9.5% to 59.5%. Spearman correlation confirmed that gender, age, and income level were related to consumer Internet purchase behavior. Several binary logistic regression models with goodness-of-fit-tests revealed that all satisfaction, happiness, positive feelings and pleasant feelings, but not excitement, could predict consumer Internet purchase intention. A Discriminant Analysis model was able to correctly classify 87.3% of the sample respondents using two factors, and a second model with only one factor correctly categorized 90.5% of the consumers as willing to purchase on the Internet.

## Key terms

- **Purchase intention:** A consumer’s stated likelihood of buying a product or service in the near future; here measured as a yes/no ‘probable’ versus ‘improbable’ response.
- **Discriminant analysis:** A statistical technique that uses predictor variables to classify cases into predefined groups, such as likely versus unlikely purchasers.
- **Uncertainty avoidance:** A Hofstede national-culture dimension describing how far a society tolerates ambiguity and risk; India scores a medium-low 40.

## Limitations

- The sample size was small (N = 63), so the authors recommend replication with much larger samples in India.
- The study is limited by the honesty of participants’ survey responses and the amount of time available to conduct the study.
- Replication in other cultures is recommended for comparison and contrast.

## How to cite

Vajjhala, N. R., & Strang, K. D. (2018). Examining Internet Behavior of Young Technology-Literate Consumers in India. In Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans. Association for Information Systems. https://aisel.aisnet.org/amcis2018/

```bibtex
@inproceedings{vajjhala2018online,
  title = {Examining Internet Behavior of Young Technology-Literate Consumers in India},
  author = {Vajjhala, Narasimha Rao and Strang, Kenneth David},
  booktitle = {Twenty-fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans},
  year = {2018},
  publisher = {Association for Information Systems},
  url = {https://aisel.aisnet.org/amcis2018/}
}
```